200+ KPI Examples by Function & Industry

200+ KPI examples by function and industry, each with a definition, formula, and worked example — plus ClearPoint data on which KPIs get owned and completed.

Key Takeaways

Name one person as owner of every KPI. Projects with a named owner are completed about 2.5 times as often (21.5% vs 8.7%).
Keep five to nine KPIs per team. If a change in the number would not change a decision, it is a metric; leave it off.
Pair each lagging KPI with a leading one. The most-tracked KPIs are nearly all lagging, so results arrive too late to act on.
Give each KPI a formula, a target and a fixed review cadence. Review operational KPIs weekly and strategic ones monthly or quarterly.
Pick your KPIs in the free KPI Library. Filter the examples by function and industry, assign owners, and export.

Google AI answers

Get our answers first in Google.

Add ClearPoint as a preferred source →

A key performance indicator (KPI) is a quantifiable measure of progress toward a specific goal. A good KPI example is specific, tied to a target, owned by one person, and reviewed on a set cadence. The best ones are few. That is the whole definition.

Here is the part nobody tells you.

We pulled the numbers from over 20,000 strategic plans across 200+ organizations on the ClearPoint platform. Finding KPI examples was never the hard part. Choosing the few that someone actually runs is.

So this page does both jobs. Below you will find more than 200 KPI examples, organized by business function and by industry. Each one comes with a plain definition, the formula, and a worked example. Then we show you what the data says about the handful worth keeping. Jump to your function or your industry, or read straight through.

Good KPIs come in two flavors: leading and lagging. A lagging KPI tells you what already happened — revenue, churn, graduation rate. A leading KPI hints at what is coming — pipeline coverage, response time, training hours. Lagging KPIs are easier to trust. Leading KPIs are easier to act on. Strong scorecards pair them, so each result has an early-warning partner. Every example below is tagged so you can see which is which.

A good KPI example has five parts. A clear name. A formula you can compute. A target. A review cadence. And an owner — one person, by name. Drop any one of these and the KPI quietly stops working. The fifth part is the one almost everyone skips, and it is the one that decides whether the KPI survives.

Our own data backs that last point (see the data section). So as you browse these examples, do not just collect names. For each KPI you keep, write down who owns it. That single move separates a scorecard that runs from a list that rots.

KPI examples by function

Start here if you are building a scorecard for a team. Each function below lists its core KPIs with a definition, a formula, and a worked example. Where we run a deeper library, you will see a link to it.

Sales KPIs

Sales KPIs connect day-to-day rep activity to revenue outcomes, exposing whether the pipeline is healthy enough to hit the number and where deals stall on the way to closed-won. The strongest sales scorecards pair leading indicators you can still influence (coverage, conversion, cycle time) with the lagging results they ultimately produce (win rate, recurring revenue, retention).

↓ Go deeper: See the full Sales KPIs guide →

Win Rate

LaggingBetter when higherThe share of qualified opportunities that close as won over a period.

Formula: (Deals won ÷ total qualified opportunities) × 100.
Example: 45 won ÷ 180 qualified = 25%.
What good looks like: B2B win rates commonly run 15–30% of qualified opportunities; clearing ~30% consistently is strong
Weak → strong: "We close a decent amount of our deals" → "Lift mid-market win rate from 25% to 32% by Q4, owned by the VP of Sales."

Sales Cycle Length

LaggingBetter when lowerThe average elapsed time from opportunity creation to closed-won.

Formula: Total days to close across won deals ÷ number of won deals.
Example: 5,400 days ÷ 90 deals = 60 days.
What good looks like: a widely cited SaaS sales benchmark found a median sales cycle of 60 days; compare against deals of similar size and segment (lower is better)
Weak → strong: "Close deals faster" → "Cut the average sales cycle from 60 to 50 days within two quarters, owned by the VP of Sales via mutual close plans".

Quota Attainment

LaggingBetter when higherHow much of an assigned sales target a rep or team has achieved.

Formula: (Actual bookings ÷ quota) × 100.
Example: $480,000 ÷ $600,000 = 80%.
What good looks like: in a 2024 global survey, 84% of sales reps missed quota the year before and 67% did not expect to hit it, so a team where most reps reach quota is a strong result
Weak → strong: "Hit our numbers" → "Lift average quota attainment from 80% to 95% this fiscal year, owned by the VP of Sales via territory rebalancing".

Show all 15 Sales KPIs

Pipeline Coverage Ratio

LeadingThe amount of open pipeline relative to the revenue target for a period, signaling whether enough opportunities exist to hit goal.

Formula: Open pipeline value ÷ revenue target.
Example: $3,000,000 ÷ $1,000,000 = 3.0×.
What good looks like: set coverage from your own win rate rather than a fixed rule of thumb — a rep who usually closes 40% of pipeline needs about 2.5× coverage
Weak → strong: "Build more pipeline" → "Hold pipeline coverage at 3.5× or more of next quarter's target, owned by the VP of Sales and reviewed weekly".

Average Deal Size

LaggingThe mean revenue value of a closed-won deal in a period.

Formula: Total closed-won revenue ÷ number of won deals.
Example: $900,000 ÷ 90 = $10,000.
What good looks like: a widely cited SaaS sales benchmark found a median annual contract value of $25K, but deal size depends on market and price point, so track it against your own baseline
Weak → strong: "Win bigger deals" → "Grow average deal size from $10,000 to $12,000 within one year, owned by the VP of Sales via multi-product packaging".

Lead-to-Opportunity Conversion Rate

LeadingBetter when higherThe share of qualified leads that advance into sales opportunities.

Formula: (Opportunities created ÷ qualified leads) × 100.
Example: 300 opportunities ÷ 1,200 leads = 25%.
What good looks like: there is no universal number — it swings with how strictly leads are qualified and where they come from, so judge it against your own baseline by source
Weak → strong: "Convert more leads" → "Raise lead-to-opportunity conversion from 25% to 30% by Q3, owned by the sales development manager via faster lead follow-up".

Sales Velocity

LeadingBetter when higherThe rate at which revenue moves through the pipeline, combining opportunity volume, deal size, win rate, and cycle length.

Formula: (Number of opportunities × average deal size × win rate) ÷ sales cycle length in days.
Example: (200 × $10,000 × 0.25) ÷ 50 = $10,000/day.
What good looks like: there is no universal number — it blends four inputs, so compare it against your own trailing quarters and act on whichever input is lagging
Weak → strong: "Close revenue faster" → "Raise sales velocity from $10,000/day to $12,000/day within two quarters, owned by the VP of Sales by shortening the cycle".

Monthly Recurring Revenue (MRR)

LaggingBetter when higherThe predictable subscription revenue normalized to a single month.

Formula: Sum of all active monthly subscription fees.
Example: 500 customers × $200/month = $100,000 MRR.
What good looks like: there is no universal number — the healthy signal is steady month-over-month growth after churn and downgrades, so track new, expansion, and churned MRR separately
Weak → strong: "Grow monthly revenue" → "Grow MRR from $100,000 to $110,000 within two quarters, owned by the Head of Sales via new and expansion bookings".

Annual Recurring Revenue (ARR)

LaggingBetter when higherThe predictable subscription revenue normalized to a full year.

Formula: MRR × 12.
Example: $100,000 MRR × 12 = $1,200,000 ARR.
What good looks like: judge ARR by its growth rate rather than its size — median ARR growth in a 2022 survey of private SaaS companies was 31%
Weak → strong: "Grow recurring revenue" → "Grow ARR from $1,200,000 to $1,500,000 by year-end, owned by the Chief Revenue Officer".

Customer Acquisition Cost (CAC)

LaggingBetter when lowerThe fully loaded sales and marketing cost to acquire one new customer.

Formula: Total sales and marketing spend ÷ new customers acquired.
Example: $300,000 ÷ 100 = $3,000.
What good looks like: there is no universal dollar figure — judge CAC against customer value; many of the best SaaS businesses recover CAC in 5–7 months, and past 12 months profitability is anemic (lower is better)
Weak → strong: "Spend less to win customers" → "Cut CAC from $3,000 to $2,500 within two quarters, owned by the CMO via channel mix shift".

Customer Lifetime Value (LTV)

LaggingBetter when higherThe total gross profit expected from a customer over the entire relationship.

Formula: Average revenue per account × gross margin × average customer lifespan.
Example: $6,000/year × 0.75 × 4 years = $18,000.
What good looks like: judge LTV against acquisition cost — the best SaaS businesses keep LTV higher than 3× CAC, sometimes as high as 7 or 8×
Weak → strong: "Make customers more valuable" → "Grow average customer LTV from $18,000 to $21,000 within one year, owned by the Head of Customer Success via longer retention".

LTV:CAC Ratio

LaggingBetter when in target rangeThe ratio of customer lifetime value to the cost of acquiring that customer, measuring acquisition efficiency.

Formula: Customer lifetime value ÷ customer acquisition cost.
Example: $18,000 ÷ $3,000 = 6.0:1.
What good looks like: 3:1 is the widely cited healthy target — below 1:1 you lose money on every customer, while above ~5:1 can mean you're under-investing in growth
Weak → strong: "Our customers are worth more than they cost to win" → "Hold blended LTV:CAC at or above 3:1 while scaling spend 20%, reviewed monthly by RevOps."

Net Revenue Retention (NRR)

LaggingBetter when higherThe percentage of recurring revenue retained from existing customers including expansion, net of churn and contraction.

Formula: ((Starting recurring revenue + expansion − contraction − churn) ÷ starting recurring revenue) × 100.
Example: (($1,000,000 + $150,000 − $50,000 − $100,000) ÷ $1,000,000) × 100 = 110%.
What good looks like: above 100% means expansion from existing customers outweighs churn and contraction
Weak → strong: "Grow existing accounts" → "Lift NRR from 110% to 115% this fiscal year, owned by the Head of Customer Success via an expansion playbook".

Upsell/Cross-sell Rate

LaggingBetter when higherThe share of existing customers who purchase an upgrade or an additional product in a period. It can also be measured as additional products sold or additional revenue gained.

Formula: (Customers with an upsell or cross-sell ÷ total existing customers) × 100.
Example: 120 ÷ 1,000 = 12%.
What good looks like: there is no universal number — it depends on how many products you sell, so grow it against your own baseline and watch that expanded customers stay and renew
Weak → strong: "Sell more to existing customers" → "Raise the upsell/cross-sell rate from 12% to 15% this fiscal year, owned by the account management lead via quarterly business reviews".

New Logos Acquired

LaggingBetter when higherThe count of brand-new customer accounts won in a period, excluding expansion of existing accounts.

Formula: Sum of net-new customer accounts closed in the period.
Example: 28 net-new accounts in the quarter = 28 new logos.
What good looks like: there is no universal number — set it backward from your growth plan and track it alongside average deal size so new logos are the right-fit customers
Weak → strong: "Win more new customers" → "Win 35 net-new accounts per quarter, up from 28, owned by the new-business sales director".

Marketing KPIs

Marketing KPIs trace the journey from spend to pipeline, showing which channels generate demand efficiently and how cleanly that demand converts into sales-ready opportunities. A balanced set watches leading signals of demand creation (traffic, leads, engagement) alongside the lagging proof that marketing influenced revenue (sourced pipeline, ROMI, conversion to SQL).

Marketing Qualified Leads (MQLs)

LeadingBetter when higherThe count of leads that meet the agreed marketing-qualification threshold and are passed toward sales.

Formula: Sum of leads reaching the defined MQL score or criteria in a period.
Example: 600 leads crossed the MQL threshold last month = 600 MQLs.
What good looks like: there is no universal number — set the target from the SQLs you need and your own MQL-to-SQL rate, and grow volume without lowering the qualification threshold
Weak → strong: "Generate more qualified leads" → "Grow MQLs from 600 to 720 a month within two quarters, owned by the demand-gen manager".

Sales Qualified Leads (SQLs)

LeadingBetter when higherThe count of MQLs that sales accepts as genuine opportunities worth active pursuit.

Formula: Sum of leads accepted by sales as qualified in a period.
Example: 180 of the month's MQLs were accepted = 180 SQLs.
What good looks like: there is no universal number — set the target backward from revenue goals using your own win rates, and grow SQLs without loosening the qualification bar
Weak → strong: "Give sales more good leads" → "Grow SQLs from 180 to 220 a month by Q4, owned by the marketing ops manager with sales leadership".

Cost Per Lead (CPL)

LaggingBetter when lowerThe average marketing cost to generate a single lead.

Formula: Total campaign spend ÷ number of leads generated.
Example: $60,000 ÷ 1,200 = $50.
What good looks like: search ads averaged ~$66.69 per lead across industries in 2026; lower is better, but only while lead quality holds, so check it alongside lead-to-customer conversion
Weak → strong: "Generate cheaper leads" → "Cut cost per lead from $50 to $45 within two quarters, owned by the demand-gen manager via landing-page testing".

Show all 15 Marketing KPIs

Lead Conversion Rate

LaggingBetter when higherThe share of leads that complete the desired next step, such as becoming an opportunity or a customer.

Formula: (Conversions ÷ total leads) × 100.
Example: 120 conversions ÷ 1,200 leads = 10%.
What good looks like: this varies enormously by source — low single digits for cold traffic, double digits for high-intent demo requests — so judge it against your own baseline, not a universal number
Weak → strong: "Our leads convert reasonably well" → "Raise demo-request lead conversion from 10% to 14% by improving form UX, owned by the demand-gen lead this quarter."

Return on Marketing Investment (ROMI)

LaggingBetter when higherThe profit generated for every dollar invested in marketing, net of the marketing cost itself.

Formula: ((Revenue attributed to marketing − marketing cost) ÷ marketing cost) × 100.
Example: (($500,000 − $100,000) ÷ $100,000) × 100 = 400%.
What good looks like: a 5:1 revenue-to-cost ratio (~400% ROMI) is a common 'good' bar; roughly 2:1 is break-even once gross margin is counted
Weak → strong: "Marketing drives a lot of revenue" → "Deliver a 4:1 ROMI on the paid-search program in H2, reported monthly against attributed pipeline by the marketing ops manager."

Return on Ad Spend (ROAS)

LaggingBetter when higherThe revenue generated for every dollar spent on advertising.

Formula: Revenue from ads ÷ ad spend.
Example: $250,000 ÷ $50,000 = 5.0× (or 500%).
What good looks like: there is no universal number — break-even depends on your gross margin, so set the target from margin and judge it alongside total profit, not the ratio alone
Weak → strong: "Get more from our ad budget" → "Raise ROAS from 5.0× to 5.5× on paid search by Q4, owned by the performance marketing lead via budget shifts to top campaigns".

Organic Traffic

LeadingBetter when higherThe number of website visits arriving from unpaid search and other non-paid channels.

Formula: Sum of non-paid sessions to the site in a period.
Example: 80,000 unpaid sessions last month = 80,000 organic visits.
What good looks like: unpaid search alone drives ~53% of trackable website traffic on average across industries, so organic visits are usually the single biggest channel worth growing
Weak → strong: "Get more organic traffic" → "Grow organic visits from 80,000 to 95,000 a month within two quarters, owned by the SEO lead via a content refresh".

Click-Through Rate (CTR)

LeadingBetter when higherThe share of people who click an ad, link, or result after seeing it.

Formula: (Clicks ÷ impressions) × 100.
Example: 2,000 clicks ÷ 100,000 impressions = 2%.
What good looks like: search ads averaged ~6.64% CTR across industries in 2026, while the #1 unpaid search result earns ~27.6%, so compare like with like by channel and placement
Weak → strong: "Make our ads more clickable" → "Raise CTR from 2% to 2.5% on the main campaign by Q3, owned by the paid media manager via ad-copy testing".

Email Open Rate

LeadingBetter when higherThe share of delivered emails that recipients open.

Formula: (Emails opened ÷ emails delivered) × 100.
Example: 4,000 opens ÷ 20,000 delivered = 20%.
What good looks like: the all-industry average open rate is ~35.63%, but privacy features in some mail apps inflate opens, so treat open rate as a directional signal and lean on clicks
Weak → strong: "Get more people opening our emails" → "Lift open rate from 20% to 26% within two quarters, owned by the email marketing manager via subject-line testing and list cleanup".

Email Click Rate

LeadingBetter when higherThe share of delivered emails in which a recipient clicks at least one link.

Formula: (Emails with a click ÷ emails delivered) × 100.
Example: 600 clicks ÷ 20,000 delivered = 3%.
What good looks like: the all-industry average click rate is ~2.62% of delivered emails, so 3% is above typical; judge it by list and campaign type against your own baseline
Weak → strong: "Get more email clicks" → "Lift email click rate from 3% to 3.5% within two quarters, owned by the email marketing manager via segmented content".

Website Bounce Rate

LaggingBetter when lowerThe share of sessions in which a visitor leaves after viewing only one page without further interaction.

Formula: (Single-page sessions ÷ total sessions) × 100.
Example: 4,500 single-page sessions ÷ 10,000 sessions = 45%.
What good looks like: lower is better — cross-industry benchmark data put average bounce around ~43% on desktop and ~51% on mobile, with e-commerce averaging ~47%
Weak → strong: "Keep visitors on the site" → "Cut bounce rate from 45% to 40% on top landing pages within one quarter, owned by the web manager via faster load times".

Marketing-Sourced Pipeline

LaggingBetter when higherThe total value of sales pipeline originated by marketing-generated leads.

Formula: Sum of opportunity value where marketing created the originating lead.
Example: 50 opportunities × $20,000 average value = $1,000,000.
What good looks like: there is no universal number — set the target backward from the bookings plan and your own win rates, and judge it on pipeline that converts, not opportunities created
Weak → strong: "Marketing should drive more pipeline" → "Grow marketing-sourced pipeline from $1.0M to $1.3M a quarter by year-end, owned by the VP of Marketing".

Brand Search Volume

LeadingBetter when higherThe number of searches that include the company or product name, indicating brand demand.

Formula: Sum of monthly search queries containing the brand term.
Example: 12,000 branded queries last month = 12,000 searches.
What good looks like: there is no universal number — track it month over month against your own baseline and versus competitors' branded searches, as a sign that awareness work is building demand
Weak → strong: "Get more people searching for us" → "Grow branded searches from 12,000 to 14,000 a month by year-end, owned by the brand marketing manager".

Cost Per Acquisition (CPA)

LaggingBetter when lowerThe average marketing cost to acquire one customer or completed conversion.

Formula: Total campaign spend ÷ number of acquisitions.
Example: $100,000 ÷ 250 = $400.
What good looks like: there is no universal number — it depends on what an acquisition is worth, so judge it against customer lifetime value or order margin; lower is better only while volume holds
Weak → strong: "Acquire customers more cheaply" → "Cut CPA from $400 to $340 within two quarters, owned by the performance marketing lead via audience and bid optimization".

Marketing Qualified Lead-to-SQL Rate

LaggingBetter when higherThe share of MQLs that sales accepts and qualifies as SQLs, measuring lead quality and handoff alignment.

Formula: (SQLs ÷ MQLs) × 100.
Example: 180 SQLs ÷ 600 MQLs = 30%.
What good looks like: there is no reliable universal number — published figures disagree widely because each company defines an MQL differently, so agree the definition with sales and improve against your own baseline
Weak → strong: "Send sales better leads" → "Raise the MQL-to-SQL rate from 30% to 36% by Q4, owned by the marketing ops manager via a revised lead-scoring model".

Financial KPIs

Financial KPIs measure whether the business turns revenue into profit, collects cash efficiently, and stays solvent enough to fund its plans. Most are lagging indicators read from closed financials, so they are best paired with operational leading metrics that explain why the numbers moved.

↓ Go deeper: Browse the Financial KPI Library — 68 measures →

Gross Profit Margin

LaggingBetter when higherThe share of revenue left after the direct cost of goods sold.

Formula: ((Revenue − cost of goods sold) ÷ revenue) × 100.
Example: (($1,000,000 − $400,000) ÷ $1,000,000) × 100 = 60%.
What good looks like: U.S. listed firms averaged ~38% gross margin in January 2026, but it varies widely by industry, so benchmark against your sector
Weak → strong: "Improve product margins" → "Raise gross margin from 60% to 63% within a year, owned by the VP of Operations via supplier renegotiation".

Net Profit Margin

LaggingBetter when higherThe share of revenue remaining as profit after all expenses, interest, and taxes.

Formula: (Net income ÷ revenue) × 100.
Example: $120,000 ÷ $1,000,000 = 12%.
What good looks like: ~10% net margin is broadly 'good' and 20%+ is excellent — but it is highly sector-dependent (software runs far higher than grocery)
Weak → strong: "We're profitable overall" → "Expand net profit margin from 12% to 15% within the fiscal year by reducing overhead, owned by the CFO."

Operating Margin

LaggingBetter when higherThe share of revenue left as operating profit after operating expenses but before interest and taxes.

Formula: (Operating income ÷ revenue) × 100.
Example: $180,000 ÷ $1,000,000 = 18%.
What good looks like: U.S. listed firms averaged a ~13% pre-tax operating margin in January 2026, but margins vary widely by industry, so compare with sector peers
Weak → strong: "Be more profitable" → "Lift operating margin from 18% to 20% by fiscal year-end, owned by the COO via process automation".

Show all 16 Financial KPIs

EBITDA

LaggingBetter when higherEarnings before interest, taxes, depreciation, and amortization, used as a proxy for core operating cash generation.

Formula: Net income + interest + taxes + depreciation + amortization.
Example: $120,000 + $30,000 + $40,000 + $50,000 + $10,000 = $250,000.
What good looks like: read it as a share of revenue — EBITDA averaged ~17% of sales across U.S. listed firms in January 2026, with wide swings by industry
Weak → strong: "Improve earnings" → "Grow EBITDA from $250,000 to $300,000 this fiscal year, owned by the CFO via pricing and cost discipline".

Revenue Growth Rate

LaggingBetter when higherThe percentage change in revenue from one period to the next.

Formula: ((Current period revenue − prior period revenue) ÷ prior period revenue) × 100.
Example: (($1,200,000 − $1,000,000) ÷ $1,000,000) × 100 = 20%.
What good looks like: there is no universal number — compare growth with your market's growth and with inflation, since growth below either means losing share or shrinking in real terms
Weak → strong: "Grow faster" → "Sustain revenue growth of 20% or more for the next two fiscal years, owned by the Chief Revenue Officer via new-market expansion".

Current Ratio

LaggingA liquidity measure of whether current assets can cover current liabilities.

Formula: Current assets ÷ current liabilities.
Example: $600,000 ÷ $300,000 = 2.0.
What good looks like: there is no universal number — below 1.0 means current liabilities exceed current assets, while a very high ratio can mean idle cash, so compare with industry peers
Weak → strong: "Keep the balance sheet healthy" → "Maintain a current ratio between 1.8 and 2.2 at every quarter-end this year, owned by the Controller".

Quick Ratio

LaggingA stricter liquidity measure that excludes inventory from current assets.

Formula: (Current assets − inventory) ÷ current liabilities.
Example: ($600,000 − $150,000) ÷ $300,000 = 1.5.
What good looks like: there is no universal number — a ratio above 1.0 means you can cover current liabilities without selling inventory, but compare with industry peers since some sectors run leaner
Weak → strong: "Stay liquid" → "Keep the quick ratio at or above 1.5 at every quarter-end this year, owned by the Treasurer".

Days Sales Outstanding (DSO)

LaggingBetter when lowerThe average number of days it takes to collect payment after a sale.

Formula: (Accounts receivable ÷ total credit sales) × number of days in the period.
Example: ($300,000 ÷ $1,800,000) × 90 = 15 days.
What good looks like: lower is better; APQC benchmarks put top performers at 30 days or less, the cross-industry median at 38 days, and the bottom quartile at 48 days or longer
Weak → strong: "Get paid faster" → "Hold DSO at or below 15 days every quarter this year, owned by the AR Manager via e-invoicing and automated reminders".

Accounts Payable Turnover

LaggingHow many times a company pays off its average accounts payable during a period.

Formula: Total supplier purchases ÷ average accounts payable.
Example: $1,200,000 ÷ $200,000 = 6.0×.
What good looks like: lower turnover means paying suppliers more slowly and holding cash longer; the 1,000 largest U.S. public nonfinancial companies averaged 59 days payable outstanding in 2025, about 6 turns a year, so balance it against supplier terms
Weak → strong: "Manage supplier payments better" → "Move AP turnover from 6.0× to 5.5× within a year by paying on agreed terms rather than early, owned by the AP Manager".

Cash Conversion Cycle

LaggingBetter when lowerThe number of days it takes to convert investments in inventory and receivables back into cash, net of payables.

Formula: Days inventory outstanding + days sales outstanding − days payable outstanding.
Example: 40 + 15 − 25 = 30 days.
What good looks like: lower is better; the 1,000 largest U.S. public nonfinancial companies had a cash conversion cycle of 37 days in The Hackett Group's 2025 working capital survey
Weak → strong: "Free up cash" → "Cut the cash conversion cycle from 30 to 25 days within a year, owned by the CFO via faster collections and leaner inventory".

Burn Rate

LeadingThe rate at which a company spends its cash reserves each month, net of revenue.

Formula: (Starting cash − ending cash) ÷ number of months.
Example: ($1,200,000 − $900,000) ÷ 3 = $100,000/month.
What good looks like: there is no universal number — what matters is burn relative to cash, so keep it low enough that runway covers the time to your next funding milestone or to breakeven
Weak → strong: "Spend less" → "Cut net burn from $100,000 to $75,000 a month within two quarters, owned by the CFO via vendor consolidation".

Cash Runway

LeadingBetter when higherThe number of months a company can operate before exhausting its cash at the current burn rate.

Formula: Current cash balance ÷ monthly net burn rate.
Example: $900,000 ÷ $100,000 = 9 months.
What good looks like: longer is safer; runway should cover the expected time to your next round plus the months it takes to raise it
Weak → strong: "Don't run out of cash" → "Extend cash runway from 9 to 30 months by Q4, owned by the CFO via cost reductions and a bridge round".

Working Capital

LaggingThe short-term capital available to fund daily operations.

Formula: Current assets − current liabilities.
Example: $600,000 − $300,000 = $300,000.
What good looks like: there is no universal dollar figure — positive working capital means short-term assets cover short-term bills, but too much ties up cash, so track it with the current ratio and cash conversion cycle
Weak → strong: "Keep enough cash on hand" → "Hold working capital at or above $300,000 at every quarter-end this year, owned by the Controller via weekly cash forecasting".

Return on Equity (ROE)

LaggingBetter when higherThe profit generated for every dollar of shareholders' equity.

Formula: (Net income ÷ shareholders' equity) × 100.
Example: $120,000 ÷ $800,000 = 15%.
What good looks like: U.S. listed firms averaged ~17% ROE in January 2026, but a high ROE driven by heavy debt is riskier than one driven by margins, so read it alongside the debt-to-equity ratio
Weak → strong: "Earn more for shareholders" → "Lift ROE from 15% to 17% within two years, owned by the CFO via margin improvement rather than added leverage".

Return on Assets (ROA)

LaggingBetter when higherThe profit generated for every dollar of total assets, measuring how efficiently assets produce earnings.

Formula: (Net income ÷ total assets) × 100.
Example: $120,000 ÷ $1,500,000 = 8%.
What good looks like: there is no universal number — asset-heavy industries such as utilities and banks earn far lower ROA than software or services firms, so compare with industry peers and your own trend
Weak → strong: "Use our assets better" → "Raise ROA from 8% to 9% within two years, owned by the CFO via selling idle assets".

Budget Variance

LaggingBetter when in target rangeThe difference between budgeted and actual figures, showing how closely spending or revenue tracked the plan. A smaller absolute difference means tighter control.

Formula: ((Actual − budgeted) ÷ budgeted) × 100.
Example: (($1,050,000 − $1,000,000) ÷ $1,000,000) × 100 = +5%.
What good looks like: mature finance teams hold variance within ±5% (often ±3% on operating budgets); the goal is small and explained, not zero
Weak → strong: "We came in close to budget" → "Keep departmental budget variance within ±3% each quarter, with monthly reviews owned by the finance business partner."

HR & Human Capital KPIs

HR KPIs measure how effectively an organization attracts, develops, and retains its people. They blend lagging outcomes like turnover with leading signals like engagement and time to productivity.

↓ Go deeper: See 50+ HR & human capital KPI examples →

Employee Turnover Rate

LaggingBetter when lowerThe rate at which employees leave the organization over a period, usually a year, counting both voluntary and involuntary separations.

Formula: (Separations ÷ average headcount) × 100.
Example: 18 leavers ÷ 200 avg headcount = 9%.
What good looks like: ~10% annual voluntary turnover is often considered healthy; sustained 20%+ usually signals a retention problem, though it varies sharply by sector
Weak → strong: "Keep turnover low" → "Hold voluntary turnover below 10% annually, owned by the People team, reviewed each quarter".

Employee Retention Rate

LaggingBetter when higherThe share of employees who stay with the organization over a period.

Formula: (Employees retained ÷ headcount at start) × 100.
Example: 184 retained ÷ 200 at start = 92%.
What good looks like: there is no universal number — read it alongside voluntary turnover (U.S. quits ran ~2.0% of employment per month in 2025) and break it out by team and tenure to find where people leave
Weak → strong: "Keep good people" → "Raise annual employee retention from 92% to 94%, owned by the HR Director, reviewed quarterly by department".

Time to Fill

LaggingBetter when lowerThe average number of days from opening a requisition to a candidate accepting the offer.

Formula: Total days to fill ÷ number of roles filled.
Example: 1,200 days ÷ 30 hires = 40 days.
What good looks like: HR recruiting benchmarks put the median time to fill at about a month and a half for both executive and nonexecutive roles in 2025, so set targets by role family and track the slowest stages (lower is better)
Weak → strong: "Fill jobs faster" → "Cut average time to fill from 40 to 35 days for non-executive roles by Q4, owned by the Talent Acquisition Manager".

Show all 15 HR & Human Capital KPIs

Cost Per Hire

LaggingBetter when lowerThe average total recruiting cost to fill one open role.

Formula: (Internal + external recruiting costs) ÷ number of hires.
Example: $120,000 ÷ 30 hires = $4,000.
What good looks like: HR benchmarking put the 2025 median cost per hire at ~$1,200 for nonexecutive roles and ~$10,625 for executive roles, so track it by role level and watch quality of hire alongside it (lower is better)
Weak → strong: "Hire more efficiently" → "Cut cost per hire from $4,000 to $3,500 within a year, owned by the Talent Acquisition Manager via more employee referrals".

Employee Net Promoter Score (eNPS)

LeadingBetter when higherA measure of how likely employees are to recommend the organization as a place to work.

Formula: % Promoters − % Detractors.
Example: 55% promoters − 15% detractors = +40.
What good looks like: scores run from −100 to +100; one employer benchmark puts the median near +26 with the middle half between +11 and +44, and anything below zero means detractors outnumber promoters
Weak → strong: "Make this a place people recommend" → "Raise eNPS from +40 to +45 at the next pulse survey, owned by the People team via manager action plans".

Absenteeism Rate

LaggingBetter when lowerThe proportion of scheduled workdays lost to unplanned absence.

Formula: (Absent days ÷ total scheduled workdays) × 100.
Example: 400 absent days ÷ 50,000 scheduled = 0.8%.
What good looks like: U.S. full-time workers lost ~1.7% of usual hours to absence in 2025 and ~3.2% were absent in a given week; use that as an outside reference and watch for teams trending up (lower is better)
Weak → strong: "Reduce absences" → "Hold the absenteeism rate at or below 0.8% in every department this year, owned by department managers via return-to-work check-ins".

Revenue Per Employee

LaggingBetter when higherThe average revenue generated per full-time employee, a proxy for workforce productivity.

Formula: Total revenue ÷ total headcount.
Example: $40,000,000 ÷ 200 employees = $200,000.
What good looks like: there is no universal number — it swings widely by industry and business model, so compare it with direct peers and your own trend, and watch it alongside margin and service quality
Weak → strong: "Be more productive" → "Grow revenue per employee from $200,000 to $220,000 by next fiscal year, owned by the COO via process automation".

Training Hours Per Employee

LeadingThe average hours of formal training delivered per employee over a period.

Formula: Total training hours ÷ total headcount.
Example: 4,000 hours ÷ 200 employees = 20 hours.
What good looks like: there is no universal number — set a target by role family and count formal training separately, since on-the-job learning comes on top
Weak → strong: "Invest in training" → "Deliver 24 hours of formal training per employee this year, up from 20, owned by the Learning and Development Manager".

Offer Acceptance Rate

LeadingBetter when higherThe share of job offers extended that candidates accept.

Formula: (Offers accepted ÷ offers extended) × 100.
Example: 27 accepted ÷ 30 extended = 90%.
What good looks like: a large recruiting-software benchmark put overall offer acceptance at ~73% in 2022 and 2023, with business roles about 20 points above technical roles, so a rate near 90% is well above typical
Weak → strong: "Close more candidates" → "Hold offer acceptance at 90%+ for all roles this year, owned by the Talent Acquisition Manager via pre-offer compensation alignment".

Quality of Hire

LaggingBetter when higherA composite score of how well new hires perform and stay after joining.

Formula: Average of new-hire performance, retention, and ramp scores (indexed to 100).
Example: (90 performance + 85 retention + 80 ramp) ÷ 3 = 85.
What good looks like: there is no universal number — define it as a composite of ramp time, performance, and one-year retention, then beat your own trailing baseline
Weak → strong: "Hire good people" → "Reach a 90-day quality-of-hire index of 85+, owned by hiring managers, scored at each 90-day review".

Time to Productivity

LeadingBetter when lowerThe average time for a new hire to reach full expected performance.

Formula: Total days to full productivity ÷ number of new hires.
Example: 2,700 days ÷ 30 hires = 90 days.
What good looks like: there is no universal number — set targets by role family, since routine and specialist roles ramp at very different speeds, and shorten them with structured onboarding (lower is better)
Weak → strong: "Get new hires up to speed" → "Cut time to productivity from 90 to 75 days for customer-facing roles within a year, owned by hiring managers via a 30-60-90 onboarding plan".

Internal Mobility Rate

LeadingThe share of roles filled by existing employees moving internally.

Formula: (Internal moves ÷ total roles filled) × 100.
Example: 12 internal moves ÷ 40 roles = 30%.
What good looks like: employees who made an internal move had a ~75% chance of staying two years versus ~56% for those who did not, so a rising internal-fill share usually pays off in retention
Weak → strong: "Promote from within" → "Fill 35% of open roles internally, up from 30%, by year-end, owned by the Talent Acquisition Manager via posting every role internally first".

Employee Engagement Score

LeadingBetter when higherThe average level of employee commitment and motivation from survey responses.

Formula: (Favorable responses ÷ total responses) × 100.
Example: 1,360 favorable ÷ 1,700 total = 80%.
What good looks like: national survey data find ~31% of U.S. employees engaged versus ~70% at best-practice organizations; that scale differs from a simple favorable share, so track your own trend by team
Weak → strong: "Boost engagement" → "Raise the engagement favorable score from 80% to 84% at the next annual survey, owned by department heads via team action plans".

Diversity Ratio

LaggingThe representation of a given group within the workforce.

Formula: (Employees in group ÷ total headcount) × 100.
Example: 90 ÷ 200 employees = 45%.
What good looks like: compare each group's share with the labor market you hire from; women were ~47% of employed people in the U.S. in 2025, and local census or labor data give a fairer benchmark for each role
Weak → strong: "Build a more diverse workforce" → "Raise the share of women in management from 45% to 48% within two years, owned by the HR Director via diverse candidate slates".

Voluntary vs Involuntary Turnover

LaggingThe split of departures initiated by employees versus by the organization.

Formula: (Voluntary separations ÷ total separations) × 100.
Example: 14 voluntary ÷ 18 total = 78% voluntary.
What good looks like: across the U.S. economy, quits made up ~61% of separations in 2025 and layoffs and discharges ~34%; a share far from that mix is worth explaining
Weak → strong: "Understand why people leave" → "Bring the voluntary share of separations from 78% to 70% within a year by reducing avoidable quits, owned by the HR Director via exit-interview follow-up".

Operations & Supply Chain KPIs

Operations and supply chain KPIs track how efficiently work flows from input to delivered output. They expose bottlenecks, quality losses, and reliability gaps across production and fulfillment.

Overall Equipment Effectiveness (OEE)

LaggingBetter when higherA composite measure of how productively equipment is used, combining availability, performance, and quality.

Formula: Availability × Performance × Quality.
Example: 90% × 95% × 99% = ~85%.
What good looks like: 85% OEE is the textbook gold-standard mark; ~60% is typical for discrete manufacturers, and under 40% signals major hidden losses
Weak → strong: "Run machines efficiently" → "Hold line OEE at 85%+, owned by the plant manager, reviewed weekly per production line".

Cycle Time

LaggingBetter when lowerThe average time to complete one unit of work from start to finish.

Formula: Total production time ÷ units produced. For a single task: end time − start time.
Example: 600 minutes ÷ 300 units = 2 minutes/unit.
What good looks like: lower is better — there is no universal number, so cut it against your own baseline for each process and watch for waiting time between steps, which is often larger than the work itself
Weak → strong: "Work faster" → "Cut cycle time from 2.0 to 1.8 minutes per unit on the packaging line by Q3, owned by the process engineer".

Throughput

LaggingBetter when higherThe number of units produced or processed per unit of time.

Formula: Units produced ÷ time period.
Example: 4,800 units ÷ 8 hours = 600 units/hour.
What good looks like: there is no universal number — it depends on the process, so raise it against your own baseline by finding and fixing the bottleneck step, and check that quality holds as speed rises
Weak → strong: "Produce more" → "Raise Line 1 throughput from 600 to 650 units per hour by Q4 without a rise in defects, owned by the production manager".

Show all 13 Operations & Supply Chain KPIs

On-Time Delivery Rate

LaggingBetter when higherThe share of orders delivered on or before the promised date.

Formula: (On-time deliveries ÷ total deliveries) × 100.
Example: 950 on-time ÷ 1,000 = 95%.
What good looks like: leading supply chains target 95%+ on-time-in-full; slipping below ~90% starts to erode customer trust
Weak → strong: "Ship orders on time" → "Hit 98% on-time delivery against promised dates, owned by the fulfillment lead, reviewed weekly".

Order Accuracy Rate

LaggingBetter when higherThe share of orders shipped complete and error-free.

Formula: (Accurate orders ÷ total orders) × 100.
Example: 990 accurate ÷ 1,000 = 99%.
What good looks like: higher is better; order accuracy is one part of a perfect order (on time, complete, undamaged, correct paperwork), so it should run higher than your overall perfect order rate
Weak → strong: "Ship orders correctly" → "Lift order accuracy from 99% to 99.5% by Q3, owned by the warehouse manager via scan-verified picking".

Inventory Turnover

LaggingHow many times inventory is sold and replaced over a period.

Formula: Cost of goods sold ÷ average inventory.
Example: $6,000,000 ÷ $1,000,000 = 6 turns.
What good looks like: U.S. manufacturers carry ~1.5 months of shipments in inventory (Census data), but healthy turns vary widely by sector, so benchmark within your industry and watch stockouts as turns rise
Weak → strong: "Carry less inventory" → "Raise inventory turnover from 6 to 7 turns this fiscal year without raising the backorder rate, owned by the supply chain director".

Capacity Utilization

LeadingThe share of available production capacity actually used.

Formula: (Actual output ÷ maximum possible output) × 100.
Example: 800 units ÷ 1,000 capacity = 80%.
What good looks like: U.S. manufacturing ran at 75.9% of capacity in September 2026 against a 1972–2025 average of 78.2%; far above that leaves little slack for demand spikes or maintenance, far below means idle assets
Weak → strong: "Use our plant better" → "Raise capacity utilization from 80% to 84% by year-end without adding overtime, owned by the operations director".

Defect Rate

LaggingBetter when lowerThe proportion of units produced that fail to meet quality standards.

Formula: (Defective units ÷ total units) × 100.
Example: 15 defects ÷ 1,000 units = 1.5%.
What good looks like: lower is better — there is no universal number across industries, so cut it against your own baseline and attack the few defect types that cause most of the failures
Weak → strong: "Reduce defects" → "Cut the defect rate from 1.5% to 1.0% within two quarters, owned by the quality manager via root-cause fixes on the top three defect types".

First Pass Yield

LaggingBetter when higherThe share of units that pass through production correctly the first time without rework.

Formula: (Good units first pass ÷ total units started) × 100.
Example: 940 ÷ 1,000 = 94%.
What good looks like: there is no universal number — compare it against your own baseline by product and process step, since the gap to 100% is the hidden cost of rework and scrap
Weak → strong: "Get it right the first time" → "Lift first pass yield from 94% to 96% on final assembly by Q3, owned by the quality engineer".

Lead Time

LaggingBetter when lowerThe total elapsed time from order placement to delivery.

Formula: Delivery date − order date (averaged).
Example: 140 total days ÷ 20 orders = 7 days.
What good looks like: there is no universal number — it varies by product and supply chain, so drive it down against your own baseline and keep it predictable, since a reliable promise often matters more to customers than the fastest one
Weak → strong: "Deliver faster" → "Cut average order-to-delivery lead time from 7 days to 5 days by Q2, owned by the supply chain manager".

Backorder Rate

LaggingBetter when lowerThe share of orders that cannot be fulfilled from current stock.

Formula: (Backordered items ÷ total ordered items) × 100.
Example: 30 backordered ÷ 1,000 = 3%.
What good looks like: lower is better — there is no universal number, so benchmark it against your own baseline by product line and fix your highest-volume items first, where each backorder costs the most sales
Weak → strong: "Keep things in stock" → "Cut the backorder rate from 3% to 2% on top-selling items by Q3, owned by the inventory planning manager".

Cost Per Unit

LaggingBetter when lowerThe average total cost to produce one unit of output.

Formula: Total production cost ÷ units produced.
Example: $300,000 ÷ 60,000 units = $5/unit.
What good looks like: there is no universal number — it depends on product and volume, so compare it against your own trend and standard cost, and check that falling unit cost is not coming from deferred maintenance or lower quality
Weak → strong: "Make products cheaper" → "Cut cost per unit from $5.00 to $4.70 by Q4, owned by the plant controller via scrap and changeover reductions".

Unplanned Downtime

LaggingBetter when lowerThe share of scheduled production time lost to unexpected stoppages.

Formula: (Unplanned downtime ÷ scheduled production time) × 100.
Example: 12 hours ÷ 160 hours = 7.5%.
What good looks like: lower is better — in an ARC survey most plants put production lost to unplanned downtime at 1–12%, and over half above 6%, so log every stoppage with its cause and shift lost time toward planned maintenance
Weak → strong: "Reduce breakdowns" → "Cut unplanned downtime from 7.5% to 5% of scheduled time on Line 2 by year-end, owned by the maintenance manager via preventive maintenance".

Customer Service & Success KPIs

Customer service and success KPIs measure how well an organization resolves issues and retains customers. They pair satisfaction signals with operational speed and loyalty outcomes.

Customer Satisfaction (CSAT)

LaggingBetter when higherThe share of customers who report being satisfied after an interaction.

Formula: (Satisfied responses ÷ total responses) × 100.
Example: 880 satisfied ÷ 1,000 = 88%.
What good looks like: 75–85% satisfied is a common 'good' band, but the trend and the reasons behind your detractors matter more than the absolute score
Weak → strong: "Keep customers happy" → "Maintain a 90%+ CSAT on post-resolution surveys, owned by the support manager, reviewed monthly".

Net Promoter Score (NPS)

LaggingBetter when higherA measure of customer loyalty based on likelihood to recommend, gathered with a single-question survey.

Formula: % Promoters − % Detractors.
Example: 60% promoters − 15% detractors = +45.
What good looks like: any positive score means more promoters than detractors; across 150,000+ organizations the 2025 median is +44 and the top quartile is +72 or higher, though norms vary widely by industry
Weak → strong: "Grow loyalty" → "Raise relationship NPS from +45 to +55 by year-end, owned by the VP of Customer Experience".

Customer Effort Score (CES)

LeadingHow much effort customers feel they expended to get an issue resolved.

Formula: Sum of effort ratings ÷ number of responses (typically 1–7).
Example: 1,500 ÷ 1,000 responses = 1.5.
What good looks like: there is no universal number — confirm your scale direction first (some surveys ask how easy, others how much effort), then track the trend against your own baseline
Weak → strong: "Make support easier" → "Improve the customer effort score from 1.5 to 1.3 (less effort) on post-resolution surveys by Q4, owned by the support operations manager".

Show all 13 Customer Service & Success KPIs

First Response Time

LeadingBetter when lowerThe average time between a customer raising a ticket and the first reply.

Formula: Total first-response time ÷ number of tickets.
Example: 2,000 minutes ÷ 1,000 tickets = 2 minutes.
What good looks like: lower is better, but expectations differ sharply by channel — set separate targets for phone, chat, and email, and measure the median so a few slow replies do not hide the typical experience
Weak → strong: "Answer customers faster" → "Keep chat first response at or under 2 minutes for 90% of contacts this quarter, owned by the support team lead".

Average Resolution Time

LaggingBetter when lowerThe average time taken to fully resolve a customer issue.

Formula: Total resolution time ÷ number of resolved tickets.
Example: 4,000 hours ÷ 1,000 tickets = 4 hours.
What good looks like: lower is better, but there is no universal number — it depends on channel and issue complexity, so set targets by priority level and drive the trend down without hurting first contact resolution
Weak → strong: "Solve issues faster" → "Cut average resolution time from 4 hours to 3 hours for standard tickets by Q3, owned by the support manager".

First Contact Resolution Rate

LaggingBetter when higherThe share of issues resolved in a single interaction without follow-up.

Formula: (Issues resolved on first contact ÷ total issues) × 100.
Example: 750 ÷ 1,000 = 75%.
What good looks like: ~70%+ FCR is widely considered strong; every point of FCR tends to cut repeat contacts and cost-to-serve
Weak → strong: "Resolve issues fast" → "Resolve 80% of tickets on first contact, owned by the support team lead, reviewed weekly by channel".

Ticket Volume

LeadingThe total number of support tickets received over a period.

Formula: Count of tickets created in the period.
Example: 5,000 tickets in a month = 5,000.
What good looks like: there is no universal number — volume tracks your customer base and releases, so normalize it per customer and aim to cut repeat questions through self-service
Weak → strong: "Handle ticket volume" → "Cut monthly ticket volume from 5,000 to 4,500 by Q4 through self-service articles, owned by the support knowledge manager".

Ticket Backlog

LaggingBetter when lowerThe number of unresolved tickets remaining open at a point in time.

Formula: Tickets opened − tickets closed (cumulative).
Example: 1,000 opened − 900 closed = 100 open.
What good looks like: lower is better, but there is no universal number — size the backlog against daily ticket volume and keep it stable or shrinking, with few tickets aging past your SLA
Weak → strong: "Clear the backlog" → "Cut the open backlog from 100 to under 60 tickets, with none older than 7 days, by Q2, owned by the support team lead".

Customer Churn Rate

LaggingBetter when lowerThe share of customers lost over a period.

Formula: (Customers lost ÷ customers at start) × 100.
Example: 50 lost ÷ 1,000 = 5%.
What good looks like: lower is better, but it varies sharply by industry and contract model — compare with sector peers, separate voluntary from failed-payment churn, and drive the trend down
Weak → strong: "Lose fewer customers" → "Cut annual churn from 5% to 4% by year-end, owned by the VP of Customer Success, reviewed monthly".

Customer Retention Rate

LaggingBetter when higherThe share of customers kept over a period, excluding new acquisitions.

Formula: ((Customers at end − new customers) ÷ customers at start) × 100.
Example: (1,050 − 100) ÷ 1,000 = 95%.
What good looks like: there is no universal number — retention varies sharply by industry and contract model, so benchmark against sector peers and track it by customer segment
Weak → strong: "Keep more customers" → "Lift customer retention from 95% to 96% this fiscal year, owned by the Head of Customer Success via an at-risk account program".

SLA Compliance Rate

LaggingBetter when higherThe share of tickets resolved within the agreed service-level target.

Formula: (Tickets meeting SLA ÷ total tickets) × 100.
Example: 970 ÷ 1,000 = 97%.
What good looks like: there is no universal number — the target is the commitment in your service-level agreement, and steady performance at or above it matters more than an occasional perfect month
Weak → strong: "Meet our SLAs" → "Hold SLA compliance at 98% or higher every month, up from 97%, with no priority-1 breaches, owned by the support operations manager".

Escalation Rate

LaggingBetter when lowerThe share of tickets escalated beyond the first support tier.

Formula: (Escalated tickets ÷ total tickets) × 100.
Example: 80 escalated ÷ 1,000 = 8%.
What good looks like: lower is better, but there is no universal number — it depends on product complexity, so give tier-1 agents better knowledge and authority and track it alongside resolution quality
Weak → strong: "Escalate less" → "Cut escalation rate from 8% to 6% by Q3, owned by the support team lead via a tier-1 knowledge base".

Customer Health Score

LeadingBetter when higherA composite index predicting how likely an account is to renew or churn.

Formula: Weighted average of usage, engagement, and support signals (indexed to 100).
Example: (80 usage + 70 engagement + 90 support) weighted = 80.
What good looks like: there is no universal number — validate the score by checking that low-scoring accounts actually churn more, then work to move accounts out of the at-risk band
Weak → strong: "Keep accounts healthy" → "Raise the average customer health score from 80 to 85 this year, owned by the Head of Customer Success".

IT KPIs

IT KPIs measure the reliability, responsiveness, and security of technology services. They focus on uptime, incident recovery speed, and how quickly teams detect and resolve problems.

System Uptime / Availability

LaggingBetter when higherThe share of time a system is operational and available to users.

Formula: (Uptime ÷ total scheduled time) × 100.
Example: 7,128 hours ÷ 7,200 = 99%.
What good looks like: 99.9% ('three nines') still leaves ~8.8 hours of downtime a year; critical systems target 99.99%
Weak → strong: "Keep systems up" → "Maintain 99.9% availability for tier-1 systems, owned by the infrastructure lead, reviewed monthly against SLA".

Mean Time to Repair (MTTR)

LaggingBetter when lowerThe average time to restore service after an incident occurs.

Formula: Total repair time ÷ number of incidents.
Example: 500 minutes ÷ 10 incidents = 50 minutes.
What good looks like: there is no universal target — set it against your SLA and drive the trend down; elite teams restore tier-1 service in under an hour
Weak → strong: "Fix outages quickly" → "Restore tier-1 service within 60 minutes (MTTR), owned by the on-call lead, reviewed per incident".

Mean Time Between Failures (MTBF)

LaggingBetter when higherThe average operating time between system failures, a measure of reliability.

Formula: Total operating time ÷ number of failures.
Example: 10,000 hours ÷ 10 failures = 1,000 hours.
What good looks like: higher is better, but there is no universal number — it depends on the equipment and how hard it runs, so compare against manufacturer ratings and your own history
Weak → strong: "Make systems more reliable" → "Raise MTBF from 1,000 hours to 1,500 hours by year-end, owned by the infrastructure manager via preventive maintenance".

Show all 12 IT KPIs

Mean Time to Detect (MTTD)

LaggingBetter when lowerThe average time between an issue arising and the team detecting it.

Formula: Total detection time ÷ number of incidents.
Example: 150 minutes ÷ 10 incidents = 15 minutes.
What good looks like: lower is better; for operational issues the aim is to detect before users report, while for security breaches a major 2025 study found identifying and containing them took 241 days on average
Weak → strong: "Spot problems sooner" → "Cut mean time to detect from 15 minutes to 5 minutes by Q3, owned by the monitoring lead via automated alerting".

Incident Volume

LeadingBetter when lowerThe total number of IT incidents logged over a period.

Formula: Count of incidents in the period.
Example: 120 incidents in a month = 120.
What good looks like: lower is better, but there is no universal number — volume grows with users and systems, so compare it per user or per device and trend it against your own baseline
Weak → strong: "Reduce IT incidents" → "Cut monthly incident volume from 120 to 95 by Q4, owned by the problem manager via root-cause fixes on the top repeat issues".

Average System Response Time

LeadingBetter when lowerThe average time a system takes to respond to a request.

Formula: Total response time ÷ number of requests.
Example: 2,000 ms ÷ 10,000 requests = 200 ms.
What good looks like: about 0.1 second feels instant to users and about 1 second keeps their flow of thought unbroken; past 10 seconds they lose focus, so lower is better
Weak → strong: "Make the system faster" → "Cut average system response time from 200 ms to 150 ms by Q2, owned by the application performance lead, reviewed weekly".

Ticket Resolution Time

LaggingBetter when lowerThe average time to close an IT support ticket.

Formula: Total resolution time ÷ number of tickets.
Example: 3,000 hours ÷ 1,000 tickets = 3 hours.
What good looks like: lower is better, but there is no universal number — it varies by ticket type and priority, so set targets per priority level against your service-level agreement
Weak → strong: "Close tickets faster" → "Cut average ticket resolution time from 3 hours to 2 hours by Q3, owned by the service desk manager, reviewed weekly by priority".

Cost Per Ticket

LaggingBetter when lowerThe average cost to handle one IT support ticket.

Formula: Total support cost ÷ number of tickets.
Example: $25,000 ÷ 1,000 tickets = $25.
What good looks like: one industry benchmark put the North American average service desk cost per ticket at ~$15.56; lower is better, but read it alongside satisfaction and resolution quality
Weak → strong: "Lower support costs" → "Cut cost per ticket from $25 to $20 by year-end, owned by the service desk manager via self-service deflection".

Security Incident Count

LaggingBetter when lowerThe number of confirmed security incidents over a period.

Formula: Count of verified security incidents in the period.
Example: 4 incidents in a quarter = 4.
What good looks like: lower is better, but there is no universal number — the count depends on how well you detect, so read it alongside detection time and severity, and trend it against your own baseline
Weak → strong: "Have fewer security incidents" → "Cut confirmed security incidents from 4 to 2 per quarter by year-end, owned by the security manager via phishing training and MFA".

Patch Compliance Rate

LeadingBetter when higherThe share of systems patched to the required level within policy timelines.

Formula: (Compliant systems ÷ total systems) × 100.
Example: 950 ÷ 1,000 = 95%.
What good looks like: aim for near-full compliance on critical patches; as a reference point, U.S. federal agencies were required to fix known exploited vulnerabilities within two weeks under CISA's BOD 22-01
Weak → strong: "Keep systems patched" → "Raise patch compliance from 95% to 98% within policy timelines by Q2, owned by the systems administrator, reviewed monthly".

Backup Success Rate

LaggingBetter when higherThe share of scheduled backups that complete successfully.

Formula: (Successful backups ÷ total scheduled backups) × 100.
Example: 990 ÷ 1,000 = 99%.
What good looks like: there is no universal number — every failed job should be fixed and rerun promptly, and regular restore tests matter as much as the success rate
Weak → strong: "Make backups reliable" → "Raise backup success from 99% to 99.8% by Q3, with a monthly restore test, owned by the infrastructure lead".

Sprint Velocity

LaggingThe average amount of work a development team completes per sprint.

Formula: Total story points completed ÷ number of sprints.
Example: 120 points ÷ 3 sprints = 40 points/sprint.
What good looks like: there is no universal number — velocity is a planning tool for one team, not a score to compare across teams, so look for a stable, predictable trend rather than a rising one
Weak → strong: "Deliver more each sprint" → "Hold sprint velocity within ±10% of 40 points for the next six sprints, owned by the scrum master, reviewed each retrospective".

Project Management KPIs

Project management KPIs track whether work is delivered on time, on budget, and within scope. Earned value metrics turn schedule and cost performance into early warning signals.

↓ Go deeper: See the 30 project management KPIs to track →

On-Time Completion Rate

LaggingBetter when higherThe share of projects or tasks completed by their planned due date.

Formula: (Completed on time ÷ total completed) × 100.
Example: 45 on time ÷ 50 = 90%.
What good looks like: owned initiatives complete far more often (see the data section below), so 'has an owner' is the first target
Weak → strong: "Deliver projects on time" → "Complete 90% of milestones by their baseline date, owned by the project manager, reviewed at each gate".

On-Budget Completion Rate

LaggingBetter when higherThe share of projects completed at or under their approved budget.

Formula: (Projects on/under budget ÷ total projects) × 100.
Example: 44 ÷ 50 = 88%.
What good looks like: in PMI's 2017 survey, top-performing organizations completed an average of 90% of projects within budget, while underperformers managed only 25%
Weak → strong: "Stay on budget" → "Raise on-budget completion from 88% to 92% of projects this fiscal year, owned by the PMO director, reviewed quarterly".

Schedule Variance (SV)

LaggingBetter when higherThe difference between work scheduled and work performed, expressed in cost terms. A negative SV means the project is behind schedule.

Formula: Earned Value − Planned Value.
Example: $90,000 EV − $100,000 PV = −$10,000.
What good looks like: a schedule variance of zero means you are exactly on schedule and a negative one means behind, so the goal is zero or positive, read alongside SPI to judge its size
Weak → strong: "Catch up on the schedule" → "Close schedule variance from −$10,000 to $0 by the next milestone, owned by the project manager, reviewed monthly".

Show all 12 Project Management KPIs

Cost Variance (CV)

LaggingBetter when higherThe difference between budgeted and actual cost of work performed.

Formula: Earned Value − Actual Cost.
Example: $90,000 EV − $95,000 AC = −$5,000.
What good looks like: a cost variance of zero means you are exactly on budget and a negative one means over budget, so the goal is zero or positive, read alongside CPI to judge its size
Weak → strong: "Stop overspending" → "Bring cost variance from −$5,000 to $0 or better by project close, owned by the project manager, reviewed monthly".

Cost Performance Index (CPI)

LeadingBetter when higherA measure of cost efficiency comparing earned value to actual cost. Below 1.0 means each dollar buys less work than planned.

Formula: Earned Value ÷ Actual Cost.
Example: $90,000 ÷ $95,000 = 0.95.
What good looks like: a CPI of 1.0 means you are exactly on budget; below 1.0 is over budget; sustained ≥1.0 across the portfolio is the goal
Weak → strong: "Stay on budget" → "Hold CPI at or above 1.0 across active projects, owned by the PMO, reviewed monthly".

Schedule Performance Index (SPI)

LeadingBetter when higherA measure of schedule efficiency comparing earned value to planned value. Below 1.0 means the project is behind schedule; above 1.0, ahead of it.

Formula: Earned Value ÷ Planned Value.
Example: $90,000 ÷ $100,000 = 0.90.
What good looks like: an SPI of 1.0 means you are exactly on schedule; below 1.0 is behind, and a sustained 1.0 or higher across the portfolio is the goal
Weak → strong: "Get back on schedule" → "Raise SPI from 0.90 to 1.0 by the next phase gate, owned by the project manager, reviewed monthly".

Resource Utilization Rate

LeadingThe share of available resource time spent on productive project work.

Formula: (Billable or project hours ÷ total available hours) × 100.
Example: 1,500 ÷ 2,000 hours = 75%.
What good looks like: there is no universal number — very high utilization leaves no slack for unplanned work, so set a target band against your own baseline and role mix
Weak → strong: "Use our people well" → "Hold project utilization between 75% and 80% for delivery staff this year, owned by the resource manager, reviewed monthly".

Scope Creep

LaggingBetter when lowerThe degree to which project scope expands beyond the original baseline.

Formula: ((Added scope items ÷ original scope items) × 100).
Example: 8 added ÷ 40 original = 20%.
What good looks like: lower is better; in PMI's 2017 survey, top-performing organizations saw scope creep on an average of 28% of their projects versus 68% for underperformers, so route every addition through change control
Weak → strong: "Control scope" → "Keep added scope under 10% of the original baseline, down from 20%, on all new projects, owned by the project manager via change control".

Milestone Hit Rate

LaggingBetter when higherThe share of project milestones met on or before their target date.

Formula: (Milestones hit on time ÷ total milestones) × 100.
Example: 18 ÷ 20 = 90%.
What good looks like: the closer to 100% the better; a falling rate is an early warning that the end date is at risk, well before the project is formally late
Weak → strong: "Hit our milestones" → "Raise the milestone hit rate from 90% to 95% across active projects by Q4, owned by the PMO director".

Earned Value (EV)

LaggingThe budgeted value of work actually completed at a point in time.

Formula: % work complete × budget at completion.
Example: 45% × $200,000 = $90,000.
What good looks like: earned value has no target on its own — compare it with planned value for schedule and actual cost for budget; earned value at or above both is the goal
Weak → strong: "Show real progress" → "Raise earned value from $90,000 to the $100,000 planned value by next month's review, owned by the project manager".

Planned vs Actual Hours

LaggingThe comparison of hours estimated for work against hours actually spent.

Formula: (Actual hours ÷ planned hours) × 100.
Example: 1,100 actual ÷ 1,000 planned = 110%.
What good looks like: 100% means the estimate was exact; results consistently above 100% mean work is being underestimated, so use the gap to improve future estimates
Weak → strong: "Estimate better" → "Bring actual hours from 110% to within 105% of plan on next quarter's projects, owned by the project manager".

Risk Count

LeadingThe number of active, identified risks tracked on a project at a point in time.

Formula: Count of open risks in the risk register.
Example: 12 open risks logged = 12.
What good looks like: there is no universal number — a count of zero usually means risks are not being logged; what matters is that every open risk has an owner and a response, and that high-severity risks trend down
Weak → strong: "Keep risks under control" → "Cut open risks from 12 to 8 by the next phase gate, each with a named owner, managed by the project manager".

Click through these KPIs live in ClearPoint — take the instant tour

KPI examples by industry

Different sectors live and die by different numbers. These are the KPIs that matter most in each industry — starting with the four where we see the most data: local government, healthcare, education, and utilities.

See these KPIs in the product

Switch industries in the live demo below to see real example KPIs on a ClearPoint scorecard — current value, status, owner, and the share with no owner. Sample data shown for illustration.

Local Government KPIs

Local government KPIs measure how efficiently a municipality delivers core services, manages public funds, and maintains infrastructure for its residents. They balance operational responsiveness with long-term fiscal and community-health outcomes.

↓ Go deeper: Browse all 143 local government KPIs & scorecard measures →

Emergency Response Time

LaggingBetter when lowerAverage elapsed time from a 911 call being received to the first responder arriving on scene.

Formula: Sum of all response times ÷ number of incidents.
Example: 5,400 minutes ÷ 720 incidents = 7.5 min average.
What good looks like: NFPA 1710 sets a 4-minute first-engine response standard for career fire departments and ~8 minutes for EMS; response time is one of the most-tracked KPIs across the governments on the ClearPoint platform
Weak → strong: "We respond to emergencies quickly" → "Median fire/EMS response under 7 minutes for 90% of priority-1 calls, owned by the Fire Chief, reviewed monthly".

311 Service Request Resolution Rate

LaggingBetter when higherShare of non-emergency 311 service requests closed within the published target window.

Formula: (Requests resolved on time ÷ total requests received) × 100.
Example: 8,500 ÷ 10,000 = 85% resolution rate.
What good looks like: leading cities publish on-time closure rates by request type and target 90%+ within the posted service-level window
Weak → strong: "We handle citizen requests" → "Close 90% of 311 requests within the service-level target by Q4, owned by the 311 Operations Manager".

Permit Processing Time

LaggingBetter when lowerAverage number of business days from permit application submission to final approval, issuance, or denial.

Formula: Sum of processing days ÷ number of permits decided (issued or denied).
Example: 4,200 days ÷ 600 permits = 7 days average.
What good looks like: there is no universal number — publish a target by permit type, report against it, and drive the average down (lower is better)
Weak → strong: "Approve permits faster" → "Cut average permit processing time from 7 to 5 business days by Q4, owned by the Permit Center Manager".

Show all 13 Local Government KPIs

Road/Pavement Condition Index

LaggingBetter when higherComposite score (typically 0–100, where 100 is excellent and 0 is failed) rating the surface condition of a road network from field inspections.

Formula: Weighted average of individual segment condition scores across the network.
Example: Network-wide weighted average = 72 PCI (good).
What good looks like: there is no universal target — keep the network out of the poor range, since roads cost far more to fix once they fall into poor condition, and drive the average up over time
Weak → strong: "Fix the roads" → "Raise the network PCI from 72 to 75 within three years, owned by the Public Works Director via preventive maintenance".

General Fund Balance

LaggingUnrestricted reserves held in the general fund, often expressed as months of operating expenditure.

Formula: Unassigned fund balance ÷ average monthly operating expenditure.
Example: $30M ÷ $5M per month = 6 months of reserves.
What good looks like: GFOA recommends unrestricted general fund balance of no less than two months of regular operating revenues or expenditures; governments with volatile revenue or disaster risk often hold more
Weak → strong: "Keep healthy reserves" → "Hold unassigned fund balance at or above 6 months of operating expenditure each year, owned by the Finance Director".

Budget Variance

LaggingBetter when in target rangeDifference between budgeted and actual spending, a measure of fiscal discipline.

Formula: ((Actual − budgeted) ÷ budgeted) × 100.
Example: (($52M − $50M) ÷ $50M) × 100 = +4% over budget.

Citizen Satisfaction Score

LaggingBetter when higherResident-rated satisfaction with municipal services, captured through community surveys.

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 1,560 ÷ 2,000 = 78% satisfaction.
What good looks like: there is no universal number — use the same survey each cycle, compare with peer communities on the same questions, and focus on the services residents rate lowest
Weak → strong: "Make residents happier" → "Raise resident satisfaction from 78% to 82% on the next community survey, owned by the City Manager".

Crime Rate / Clearance Rate

LaggingCrime rate is reported offenses per 100,000 residents; clearance rate is the share of those crimes solved or closed.

Formula: Clearance rate = (cases cleared ÷ reported cases) × 100.
Example: 1,800 cleared ÷ 6,000 reported = 30% clearance rate.
What good looks like: nationally police cleared 45.5% of violent crimes and 17.2% of property crimes in 2019, per the FBI, so compare by offense type rather than one blended rate
Weak → strong: "Solve more crimes" → "Raise the overall clearance rate from 30% to 34% within a year, owned by the Chief of Police, reviewed monthly by offense type".

Infrastructure Projects On-Time

LaggingBetter when higherShare of capital infrastructure projects completed on or before their scheduled deadline.

Formula: (Projects delivered on time ÷ total projects completed) × 100.
Example: 34 ÷ 40 = 85% on-time.
What good looks like: there is no universal number — set it against your own baseline, measure against the original schedule rather than a revised one, and report the reasons for each delay
Weak → strong: "Deliver projects on time" → "Complete 90% of capital projects by their baseline date this year, up from 85%, owned by the Capital Projects Director".

Water Quality Compliance Rate

LaggingBetter when higherPercentage of water quality tests that meet regulatory safety standards over a reporting period.

Formula: (Compliant samples ÷ total samples tested) × 100.
Example: 1,990 ÷ 2,000 = 99.5% compliance.
What good looks like: the goal is full compliance; nationally ~92% of people on community water systems were served by systems with no health-based violations in FY 2023, per EPA
Weak → strong: "Keep water safe" → "Reach 100% of samples meeting standards this year, up from 99.5%, owned by the Water Quality Manager".

Property Tax Collection Rate

LaggingBetter when higherShare of property taxes levied that are actually collected within the fiscal year.

Formula: (Taxes collected ÷ taxes levied) × 100.
Example: $96M ÷ $100M = 96% collection rate.
What good looks like: there is no universal number — compare with peers on the same tax calendar, and track the uncollected share by age so delinquencies do not build up
Weak → strong: "Collect more taxes" → "Raise the current-year collection rate from 96% to 97.5% by fiscal year-end, owned by the Tax Collector".

Average Time to Fill Potholes

LaggingBetter when lowerAverage elapsed time from a pothole being reported to it being repaired.

Formula: Sum of repair times ÷ number of potholes filled.
Example: 4,800 hours ÷ 1,600 potholes = 3 hours average.
What good looks like: there is no universal number — publish a repair target, report against it by season, and drive the trend down (lower is better)
Weak → strong: "Fill potholes faster" → "Cut average pothole repair time from 3 hours to 2.5 hours by spring, owned by the Street Maintenance Superintendent".

Public Transit On-Time Performance

LaggingBetter when higherPercentage of transit trips arriving within the agency's defined on-time window.

Formula: (On-time trips ÷ total scheduled trips) × 100.
Example: 18,400 ÷ 20,000 = 92% on-time.
What good looks like: on-time windows vary by agency — a common definition is no more than 1 minute early and 5 minutes late — so publish your window and trend the share against your own baseline
Weak → strong: "Run buses on time" → "Raise on-time performance from 92% to 94% within a year, owned by the Transit Operations Manager, reviewed monthly by route".

Healthcare KPIs

Healthcare KPIs track clinical quality, patient experience, and financial sustainability across a hospital or health system. They connect the safety and outcomes of care delivery to the operational and revenue metrics that keep the organization solvent.

↓ Go deeper: See 30 healthcare KPIs & metrics to track →

Hospital Readmission Rate

LaggingBetter when lowerShare of discharged patients readmitted to a hospital within a defined window, typically 30 days.

Formula: (Readmissions within 30 days ÷ total discharges) × 100.
Example: 240 ÷ 3,000 = 8% readmission rate.
What good looks like: U.S. 30-day all-cause readmission runs ~14–15%, and CMS financially penalizes excess readmissions, so beating the national rate is the goal
Weak → strong: "Reduce readmissions" → "Cut 30-day all-cause readmissions to under 10% for heart-failure patients by year-end, owned by the VP of Quality".

Average Length of Stay (ALOS)

LaggingAverage number of days an admitted patient stays in the hospital per inpatient episode.

Formula: Total inpatient days ÷ number of discharges (or admissions).
Example: 12,600 patient days ÷ 3,000 discharges = 4.2 days ALOS.
What good looks like: U.S. community hospital stays average ~4.5–4.6 days, but compare by diagnosis and case mix, since shorter is better only when readmissions hold steady
Weak → strong: "Shorten stays" → "Cut ALOS from 4.2 to 4.0 days by Q4 without raising readmissions, owned by the Director of Case Management".

Bed Occupancy Rate

LaggingProportion of available inpatient beds occupied over a period, indicating capacity utilization.

Formula: (Occupied bed days ÷ available bed days) × 100.
Example: 25,500 ÷ 30,000 = 85% occupancy.
What good looks like: U.S. hospital occupancy averaged ~75% after the pandemic, and ~85% is a widely used planning ceiling, above which surge room and patient flow suffer
Weak → strong: "Manage capacity better" → "Hold adult inpatient occupancy between 80% and 85% every month this year, owned by the Director of Patient Flow".

Show all 13 Healthcare KPIs

Patient Satisfaction (HCAHPS)

LaggingBetter when higherStandardized survey measuring patients' perspectives on their hospital care, often summarized as the share giving a top-box rating.

Formula: (Top-box "9 or 10" overall ratings ÷ total survey respondents) × 100.
Example: 1,440 ÷ 1,800 = 80% top-box score.
What good looks like: track the top-box (9–10) share against the national CMS percentile; the target is steady movement up the percentile, not a single fixed number
Weak → strong: "Improve patient experience" → "Raise HCAHPS overall top-box rating to 82% across med-surg units by Q3, owned by the Chief Nursing Officer".

Hospital-Acquired Infection (HAI) Rate

LaggingBetter when lowerRate of infections patients contract during their hospital stay, normalized to patient exposure.

Formula: (Number of HAIs ÷ patient days) × 1,000.
Example: (45 ÷ 30,000) × 1,000 = 1.5 infections per 1,000 patient days.
What good looks like: on any given day in 2023, ~1 in 38 U.S. hospital patients had at least one healthcare-associated infection, and patients were 27% less likely to have one than in 2015; the goal is a steady fall toward zero
Weak → strong: "Prevent infections" → "Cut HAIs from 1.5 to 1.2 per 1,000 patient days by year-end, owned by the Infection Prevention Manager".

Emergency Department Wait Time

LaggingBetter when lowerAverage time from a patient's ED arrival to being seen by a qualified clinical provider.

Formula: Sum of door-to-provider times ÷ number of ED patients.
Example: 50,000 minutes ÷ 2,000 patients = 25 min average.
What good looks like: the 2022 U.S. median ED wait to see a clinician was 16 minutes (mean 38.1), and only ~40.6% of visits were seen within 15 minutes; lower is better
Weak → strong: "Shorten ER waits" → "Cut average door-to-provider time from 25 to 18 minutes within two quarters, owned by the ED Medical Director via provider-in-triage".

Mortality Rate

LaggingBetter when lowerShare of admitted patients who die during their hospital stay, often risk-adjusted for case mix.

Formula: (In-hospital deaths ÷ total discharges) × 100.
Example: 60 ÷ 3,000 = 2% mortality rate.
What good looks like: roughly 2% of U.S. hospitalizations end in an in-hospital death, but compare risk-adjusted rates to expected, since case mix drives the raw number; lower is better
Weak → strong: "Save more lives" → "Cut risk-adjusted in-hospital mortality from 2% to 1.8% by year-end, owned by the VP of Quality via sepsis bundle compliance".

Medication Error Rate

LaggingBetter when lowerFrequency of medication errors (wrong drug, wrong patient, or wrong dose) relative to the volume of doses administered, across inpatient and outpatient services.

Formula: (Medication errors ÷ total doses administered) × 100.
Example: 150 ÷ 50,000 = 0.3% error rate.
What good looks like: direct-observation studies find errors in a median ~10.5% of dose opportunities, excluding wrong-time errors, so a very low reported rate often signals under-reporting; lower is better
Weak → strong: "Fewer medication errors" → "Cut medication errors from 0.3% to 0.2% of doses within a year, owned by the Director of Pharmacy via bedside barcode scanning".

Claim Denial Rate

LaggingBetter when lowerPercentage of submitted insurance claims denied by payers on first submission.

Formula: (Denied claims ÷ total claims submitted) × 100.
Example: 700 ÷ 10,000 = 7% denial rate.
What good looks like: there is no universal number: track initial denials as a share of both claims and claim dollars, compare by payer and denial reason against your own trend, and push it down; lower is better
Weak → strong: "Fewer denials" → "Cut the initial denial rate from 7% to 5% by year-end, owned by the Revenue Cycle Director via front-end eligibility checks".

Days in Accounts Receivable

LaggingBetter when lowerAverage number of days it takes to collect payment after a service is billed.

Formula: (Total accounts receivable ÷ average daily net patient revenue).
Example: $9M ÷ $200K per day = 45 days in A/R.
What good looks like: lower is better; compare your days in A/R against peer benchmarks for your specialty and practice size, and track the trend month over month
Weak → strong: "Get paid faster" → "Cut days in A/R from 45 to under 40 by Q3, owned by the Revenue Cycle Director via daily denial follow-up".

Patient No-Show Rate

LaggingBetter when lowerShare of scheduled appointments where the patient fails to attend without canceling.

Formula: (No-show appointments ÷ total scheduled appointments) × 100.
Example: 800 ÷ 8,000 = 10% no-show rate.
What good looks like: studies report no-show rates ranging from ~12% to 42%, so compare against your own specialty and baseline; lower is better
Weak → strong: "Reduce no-shows" → "Cut the no-show rate from 10% to 7% within two quarters, owned by the access manager via text reminders".

Staff-to-Patient Ratio

LeadingNumber of clinical staff (e.g., nurses) on duty per patient during a given shift, a leading indicator of care capacity and safety.

Formula: Number of nurses on shift ÷ number of patients.
Example: 25 nurses ÷ 100 patients = 1:4 ratio.
What good looks like: California law requires at least 1 licensed nurse per 5 medical-surgical patients and 1 per 2 in critical care; the right ratio depends on unit acuity
Weak → strong: "Staff units safely" → "Hold med-surg staffing at 1:4 or better on every shift this year, owned by the Chief Nursing Officer".

Operating Margin

LaggingBetter when higherProfitability of core operations as a share of total operating revenue.

Formula: ((Operating revenue − operating expenses) ÷ operating revenue) × 100.
Example: (($210M − $200M) ÷ $210M) × 100 = 4.8% margin.

Higher Education KPIs

Higher education KPIs gauge an institution's ability to attract, retain, and graduate students while sustaining its academic and financial health. They link the student-success funnel to the research and advancement metrics that fund the mission.

↓ Go deeper: See the full guide to KPIs in higher education →

Student Enrollment

LaggingTotal count of students registered at the institution for a given term, typically reported as headcount or full-time equivalent.

Formula: Sum of all registered students for the term.
Example: 9,200 undergraduates + 2,800 graduates = 12,000 enrolled.
What good looks like: there is no universal number — measure it against your enrollment plan and capacity, and track headcount and FTE by program so growth in one area does not hide declines in another
Weak → strong: "Grow enrollment" → "Grow total enrollment from 12,000 to 12,400 by next fall, owned by the VP of Enrollment Management".

Student Retention Rate

LeadingBetter when higherShare of first-year students who return to the same institution for their second year, an early signal of eventual graduation.

Formula: (Students returning year two ÷ first-year cohort) × 100.
Example: 2,550 ÷ 3,000 = 85% retention.
What good looks like: first-to-second-year retention averages ~75% at four-year institutions and swings widely by selectivity, so set the target against your peer group
Weak → strong: "Keep more students" → "Lift first-to-second-year retention to 87% for the incoming cohort by next fall, owned by the VP of Student Success".

Graduation Rate

LaggingBetter when higherShare of an entering cohort that completes a degree within a defined period, commonly 150% of normal time (6 years for a 4-year degree).

Formula: (Graduates within the window ÷ entering cohort) × 100.
Example: 1,950 ÷ 3,000 = 65% 6-year graduation rate.
What good looks like: the federal 6-year graduation rate averages ~60–64% at four-year schools; compare to peer institutions rather than a universal bar
Weak → strong: "Graduate more students" → "Raise the 6-year graduation rate to 68% for the 2020 entering cohort, owned by the Provost".

Show all 12 Higher Education KPIs

Student-to-Faculty Ratio

LaggingNumber of students per full-time-equivalent instructional faculty member, a proxy for class size and access.

Formula: FTE students ÷ FTE faculty.
Example: 12,000 ÷ 800 = 15:1 ratio.
What good looks like: lower means more faculty access but higher cost; degree-granting institutions averaged 13.1 FTE students per FTE faculty in fall 2022 — 14.3 at public and 10.0 at private nonprofit institutions
Weak → strong: "Keep classes small" → "Hold the student-to-faculty ratio at or below 15:1 through next fall's enrollment growth, owned by the Provost".

Cost Per Student

LaggingTotal educational expenditure divided by enrollment, measuring the cost of delivering instruction per student.

Formula: Total operating expenditure ÷ FTE students.
Example: $240M ÷ 12,000 = $20,000 per student.
What good looks like: there is no target on its own; total expenses ran $52,900 per full-time-equivalent student at public four-year institutions and $69,150 at private nonprofit four-year institutions in 2020–21, so compare with sector peers
Weak → strong: "Spend more wisely" → "Hold cost per student at or below $20,000 this fiscal year while keeping course completion steady, owned by the CFO".

Job Placement Rate

LaggingBetter when higherShare of graduates employed or in further education within a set period after graduation, typically six months.

Formula: (Graduates placed ÷ graduates seeking placement) × 100.
Example: 1,700 ÷ 2,000 = 85% placement.
What good looks like: there is no universal number — define the window and what counts as placed up front, survey every graduating class the same way, and compare by program
Weak → strong: "Get graduates jobs" → "Raise six-month placement from 85% to 88% for next year's graduates, owned by the Director of Career Services".

Time-to-Degree

LaggingBetter when lowerAverage elapsed time students take to complete their degree from initial enrollment.

Formula: Sum of years to completion ÷ number of graduates.
Example: 8,400 years ÷ 2,000 graduates = 4.2 years average.
What good looks like: lower is better; nationally 64% of first-time, full-time students at four-year institutions finish within six years (fall 2014 cohort), so track the share finishing on time as well as the average
Weak → strong: "Graduate students faster" → "Cut average time-to-degree from 4.2 to 4.0 years for the next graduating cohort, owned by the Provost via clearer degree maps".

Course Completion Rate

LeadingBetter when higherShare of enrolled course seats that students complete with a passing grade, an early indicator of progression.

Formula: (Courses completed successfully ÷ courses enrolled) × 100.
Example: 46,000 ÷ 50,000 = 92% completion.
What good looks like: there is no universal number — compare by course, delivery mode, and student group, and target the high-enrollment gateway courses with the lowest completion first
Weak → strong: "Help students pass" → "Raise course completion from 92% to 94% in gateway courses by next spring, owned by the Provost via early-alert advising".

Research Funding Secured

LaggingBetter when higherTotal value of external research grants and contracts awarded to the institution in a period.

Formula: Sum of all awarded grant and contract dollars.
Example: 180 awards totaling $45M secured.
What good looks like: there is no universal number — compare with peer institutions of similar size and mission, and track awards by funding source so a dip at one agency does not catch you off guard
Weak → strong: "Win more grants" → "Grow research awards from $45M to $50M next fiscal year, owned by the Vice President for Research via proposal support".

Alumni Giving Rate

LaggingBetter when higherShare of solicitable alumni who make a financial gift to the institution in a given year.

Formula: (Alumni donors ÷ total solicitable alumni) × 100.
Example: 12,000 ÷ 100,000 = 12% giving rate.
What good looks like: there is no universal number — compare with peer institutions and your own trend, and track donor counts alongside dollars raised, since a few large gifts can hide shrinking participation
Weak → strong: "Get more alumni to give" → "Lift the alumni giving rate from 12% to 14% this fiscal year, owned by the Director of Annual Giving".

Application Yield Rate

LeadingBetter when higherShare of admitted students who choose to enroll, signaling enrollment strength ahead of the term.

Formula: (Students who enroll ÷ students admitted) × 100.
Example: 3,000 ÷ 7,500 = 40% yield.
What good looks like: there is no universal number — yield varies widely by selectivity and sector, so compare with peer institutions and your own trend by admit group
Weak → strong: "Enroll more admits" → "Raise yield from 40% to 43% for next fall's class, owned by the Dean of Admissions via admitted-student outreach".

Endowment Growth

LaggingBetter when higherYear-over-year percentage change in the market value of the institution's endowment.

Formula: ((Ending value − beginning value) ÷ beginning value) × 100.
Example: (($840M − $800M) ÷ $800M) × 100 = 5% growth.
What good looks like: there is no universal number — growth depends on investment returns, spending, and new gifts, so compare it with peer endowment studies and check that it keeps pace with spending plus inflation
Weak → strong: "Grow the endowment" → "Grow the endowment from $840M to $880M this fiscal year, owned by the CFO via investment returns and a planned-giving push".

Utilities & Energy KPIs

Utilities and energy KPIs measure the reliability, safety, efficiency, and regulatory standing of an organization that delivers electricity, water, or gas. They quantify how consistently customers receive service and how well the utility manages risk and resource loss.

System Average Interruption Duration Index (SAIDI)

LaggingBetter when lowerAverage total duration of outages experienced by a customer over a period, measured in minutes per customer per year.

Formula: Sum of all customer interruption durations ÷ total number of customers served.
Example: 9,000,000 customer-minutes ÷ 100,000 customers = 90 minutes per customer.
What good looks like: U.S. utilities average roughly 100–150 minutes of interruption per customer per year excluding major events (which are reported separately); lower is better
Weak → strong: "Improve reliability" → "Cut SAIDI to under 90 minutes per customer this year (excluding major events), owned by the VP of Grid Operations".

System Average Interruption Frequency Index (SAIFI)

LaggingBetter when lowerAverage number of sustained interruptions a customer experiences over a period.

Formula: Total number of customer interruptions ÷ total number of customers served.
Example: 120,000 customer interruptions ÷ 100,000 customers = 1.2 interruptions per customer.
What good looks like: U.S. customers averaged ~1.08 interruptions in 2025 excluding major event days and ~1.34 including them, per EIA, so a SAIFI below that national figure is strong; lower is better
Weak → strong: "Have fewer outages" → "Cut SAIFI from 1.2 to 1.0 interruptions per customer within two years, owned by the reliability engineer via tree trimming and circuit hardening".

Customer Average Interruption Duration Index (CAIDI)

LaggingBetter when lowerAverage time to restore service per interruption, effectively SAIDI divided by SAIFI.

Formula: SAIDI ÷ SAIFI (sum of interruption durations ÷ total number of interruptions).
Example: 90 minutes ÷ 1.2 interruptions = 75 minutes per interruption.
What good looks like: U.S. CAIDI averaged ~116 minutes per interruption in 2025 excluding major event days and ~201 minutes including them, per EIA; lower is better
Weak → strong: "Shorten outages" → "Cut CAIDI from 75 to 65 minutes within 12 months, owned by the outage response manager via automated fault isolation".

Show all 11 Utilities & Energy KPIs

Percent Renewable Generation

LaggingShare of total energy generated or delivered that comes from renewable sources.

Formula: (Renewable generation ÷ total generation) × 100.
Example: 1,200 GWh ÷ 4,000 GWh = 30% renewable.
What good looks like: about 21% of U.S. utility-scale electricity came from renewable sources in 2023, per EIA; the right target depends on your resource mix and any state or corporate commitments
Weak → strong: "Go greener" → "Raise renewable generation from 30% to 40% of total by 2030, owned by the resource planning director via new solar and wind contracts".

Line Loss / Transmission Loss

LaggingBetter when lowerShare of generated electricity lost during transmission and distribution before reaching customers.

Formula: ((Energy generated − energy delivered) ÷ energy generated) × 100.
Example: ((4,000 − 3,760) ÷ 4,000) × 100 = 6% line loss.
What good looks like: U.S. transmission and distribution losses averaged about 5% of electricity delivered in 2018–2022, per EIA, so a rate above that suggests room to cut losses; lower is better
Weak → strong: "Lose less power on the grid" → "Cut line loss from 6% to 5% within two years, owned by the grid planning manager via conductor and transformer upgrades".

Customer Satisfaction

LaggingBetter when higherCustomer-rated satisfaction with utility service, reliability, and support, captured via survey.

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 4,200 ÷ 5,000 = 84% satisfaction.
What good looks like: there is no universal number — survey customers the same way each cycle and break results out by outage experience, billing, and contact channel, since reliability and service drive the score
Weak → strong: "Keep customers happy" → "Lift customer satisfaction from 84% to 88% within 18 months, owned by the customer service director via outage text alerts".

Safety Incident Rate (OSHA Recordables)

LaggingBetter when lowerOSHA-recordable injuries and illnesses normalized to 100 full-time workers per year (the Total Recordable Incident Rate).

Formula: (Number of recordable incidents × 200,000) ÷ total hours worked.
Example: (12 × 200,000) ÷ 1,000,000 = 2.4 TRIR.
What good looks like: a Total Recordable Incident Rate under ~3.0 is broadly considered good; the safest operations push toward zero
Weak → strong: "Work safely" → "Hold the Total Recordable Incident Rate below 2.0 across field crews this year, owned by the Director of Health & Safety".

Capacity Factor

LaggingRatio of actual energy output to the maximum possible output if a plant ran at full capacity the entire period.

Formula: (Actual energy output ÷ (rated capacity × hours in period)) × 100.
Example: (3,066 GWh ÷ 4,380 GWh) × 100 = 70% capacity factor.
What good looks like: it depends on the technology: U.S. nuclear plants ran at ~91% in 2025, coal ~49%, wind ~34%, and solar ~24%, so compare each plant with others of its type
Weak → strong: "Run the plant harder" → "Raise unit capacity factor from 70% to 75% next fiscal year, owned by the plant manager via shorter planned outages".

Non-Revenue Water (Water Loss)

LaggingBetter when lowerShare of water produced that is lost to leaks, theft, or metering errors before it is billed.

Formula: ((Water supplied − water billed) ÷ water supplied) × 100.
Example: ((10,000 − 8,500) ÷ 10,000) × 100 = 15% non-revenue water.
What good looks like: there is no universal number — run a standard water audit each year, set targets against your own baseline, and split real leakage from billing and meter losses; lower is better
Weak → strong: "Fix more leaks" → "Cut non-revenue water from 15% to 12% within two years, owned by the distribution manager via leak detection and meter replacement".

Regulatory Compliance Rate

LaggingBetter when higherShare of applicable regulatory requirements and reporting obligations met without violation in a period.

Formula: (Requirements met ÷ total applicable requirements) × 100.
Example: 245 ÷ 250 = 98% compliance.
What good looks like: the goal is full compliance; for drinking water, ~92% of people on community systems were served by systems with no health-based violations in FY 2023, per EPA, so any miss needs a corrective action
Weak → strong: "Stay compliant" → "Raise regulatory compliance from 98% to 100% of applicable requirements by year-end, owned by the compliance manager via a filing calendar".

Average Restoration Time

LaggingBetter when lowerAverage time taken to restore service to customers after an outage begins.

Formula: Sum of restoration times ÷ number of outage events.
Example: 4,500 minutes ÷ 60 outages = 75 min average.
What good looks like: U.S. utilities averaged ~116 minutes to restore each customer interruption in 2025 excluding major events, and ~201 minutes with them, so tracking both views matters; lower is better
Weak → strong: "Restore power faster" → "Cut average restoration time from 75 to 60 minutes within 12 months, owned by the distribution operations manager via crew pre-staging".

Banking & Financial Services KPIs

Banks balance profitability, risk, and capital adequacy under heavy regulation, so their KPIs blend financial returns with liquidity and credit-quality measures. The leading indicators below warn of margin and risk pressure before lagging profitability results land.

↓ Go deeper: See 17 KPIs every bank should track →

Net Interest Margin (NIM)

LaggingThe spread a bank earns between interest generated on assets and interest paid on funding, relative to its earning assets.

Formula: (Interest income − interest expense) ÷ average earning assets.
Example: ($120M − $45M) ÷ $2.5B = 3.0%.
What good looks like: U.S. bank net interest margins typically sit around ~3%; the right target depends on asset mix and the rate environment
Weak → strong: "Improve our margins" → "Lift NIM from 3.0% to 3.3% by Q4, owned by the Treasury lead through repricing the loan book".

Return on Assets (ROA)

LaggingBetter when higherHow efficiently a bank converts its total assets into net profit.

Formula: Net income ÷ average total assets.
Example: $90M ÷ $7.5B = 1.2%.

Return on Equity (ROE)

LaggingBetter when higherThe return generated on shareholders' invested capital.

Formula: Net income ÷ average shareholders' equity.
Example: $90M ÷ $750M = 12%.

Show all 12 Banking & Financial Services KPIs

Efficiency Ratio

LaggingBetter when lowerThe share of revenue consumed by operating expenses, where lower is better.

Formula: Non-interest expense ÷ (net interest income + non-interest income).
Example: $180M ÷ $300M = 60%.
What good looks like: below 50% is excellent for a bank, 50–60% is solid, and above ~70% signals cost pressure (lower is better)
Weak → strong: "Cut costs" → "Bring the efficiency ratio below 58% within two quarters, owned by the COO via branch consolidation".

Non-Performing Loan (NPL) Ratio

LaggingBetter when lowerThe proportion of the loan portfolio that is in or near default.

Formula: Non-performing loans ÷ total gross loans.
Example: $40M ÷ $2B = 2.0%.
What good looks like: noncurrent loans ran ~0.93% of total loans across U.S. banks in mid-2026; lower is better, and a ratio rising faster than peers is an early warning
Weak → strong: "Improve loan quality" → "Bring the NPL ratio from 2.0% to under 1.5% by year-end, owned by the Chief Credit Officer via early workout of troubled loans"

Common Equity Tier 1 (CET1) Capital Ratio

LaggingA bank's core equity capital measured against its risk-weighted assets, the central regulatory solvency gauge.

Formula: CET1 capital ÷ risk-weighted assets.
Example: $600M ÷ $5B = 12%.
What good looks like: the regulatory minimum is 4.5%, plus a 2.5% buffer to avoid payout limits (7% in all); U.S. banks' tier 1 risk-based ratio averaged ~13.8% in mid-2026, so most hold well above the floor
Weak → strong: "Stay well capitalized" → "Hold CET1 at or above 11.5% through the year while funding loan growth, owned by the CFO, reviewed quarterly against the capital plan"

Loan-to-Deposit Ratio

LeadingHow much of a bank's deposits are lent out, signaling liquidity and lending appetite.

Formula: Total loans ÷ total deposits.
Example: $2B ÷ $2.5B = 80%.
What good looks like: net loans ran ~66% of deposits across U.S. banks in mid-2026, but ~79–85% at banks with $100M to $10B in assets, so compare to your size group and balance lending against liquidity
Weak → strong: "Lend out more of our deposits" → "Hold the loan-to-deposit ratio between 80% and 85% through year-end, owned by the CFO, reviewed monthly at ALCO"

Cost of Funds

LeadingBetter when lowerThe average interest rate a bank pays to fund its assets through deposits and borrowings.

Formula: Total interest expense ÷ average interest-bearing liabilities.
Example: $45M ÷ $2.25B = 2.0%.
What good looks like: U.S. banks' cost of funding earning assets was ~2.03% in mid-2026 and the cost of interest-bearing deposits ~2.32%; lower is better, and the trend versus market rates matters most
Weak → strong: "Pay less for funding" → "Lower cost of funds from 2.0% to 1.8% by Q4 by growing noninterest-bearing deposits, owned by the Treasurer"

Deposit Growth Rate

LeadingBetter when higherThe pace at which a bank's deposit base is expanding over a period.

Formula: (Ending deposits − beginning deposits) ÷ beginning deposits.
Example: ($2.5B − $2.3B) ÷ $2.3B = 8.7%.
What good looks like: U.S. bank deposits grew ~5.4% in the year to mid-2026, and domestic deposits rose 0.8% in the second quarter alone, so compare your growth to that industry pace
Weak → strong: "Grow deposits" → "Grow total deposits from $2.5B to $2.7B (8%) by year-end, owned by the head of retail banking via a core-checking campaign"

Non-Interest (Fee) Income Ratio

LaggingThe share of total revenue coming from fees and services rather than lending spread.

Formula: Non-interest income ÷ total revenue.
Example: $90M ÷ $300M = 30%.
What good looks like: across U.S. banks, noninterest income ran ~1.46% of average assets against ~4.47% total net operating revenue in mid-2026, roughly a third of revenue; a steady fee share cushions swings in interest rates
Weak → strong: "Earn more from fees" → "Raise non-interest income from 30% to 33% of total revenue this fiscal year, owned by the head of treasury management services"

Customer Acquisition Cost

LeadingBetter when lowerThe average marketing and onboarding cost to win one new banking customer.

Formula: Total acquisition spend ÷ new customers acquired.
Example: $3M ÷ 15,000 = $200.
What good looks like: there is no universal number — lower is better, but judge it against the lifetime value of the relationship, since a deposit-plus-loan customer can justify far more spend than a single account
Weak → strong: "Spend less to win customers" → "Cut customer acquisition cost from $200 to $170 by Q4 while holding new-customer volume, owned by the head of marketing"

Digital Adoption Rate

LeadingBetter when higherThe percentage of customers actively using digital banking channels.

Formula: Active digital users ÷ total customers.
Example: 480,000 ÷ 600,000 = 80%.
What good looks like: in a national survey, 55% of U.S. consumers used a mobile app most often to manage their bank accounts and 22% used online banking, versus 8% using a branch
Weak → strong: "Get more customers online" → "Lift active digital users from 80% to 85% of customers by year-end, owned by the head of digital banking via in-branch enrollment"

Manufacturing KPIs

Manufacturing KPIs center on turning equipment, materials, and labor into quality output with minimal waste and downtime. Leading indicators on the line drive the lagging cost and delivery outcomes that customers feel.

Overall Equipment Effectiveness (OEE)

LaggingBetter when higherA composite score of how fully manufacturing equipment is utilized, combining availability, performance, and quality.

Formula: Availability × Performance × Quality.
Example: 90% × 95% × 98% = 83.8%.
What good looks like: 85% OEE is the textbook gold-standard mark; ~60% is typical for discrete manufacturers, and under 40% signals major hidden losses
Weak → strong: "Run the lines better" → "Raise Line 3 OEE from 83% to 88% by year-end, owned by the plant manager via downtime root-cause fixes".

Scrap Rate

LaggingBetter when lowerThe share of produced material discarded as unusable waste.

Formula: Scrapped units ÷ total units produced.
Example: 600 ÷ 30,000 = 2.0%.
What good looks like: there is no universal number — lower is better; it varies by process and material, so track it by line and defect cause and price it in dollars to rank fixes
Weak → strong: "Waste less material" → "Cut scrap from 2.0% to 1.5% on the stamping line within two quarters, owned by the process engineer".

First Pass Yield

LaggingBetter when higherThe percentage of units made correctly the first time without rework or scrap.

Formula: Good units (no rework) ÷ total units started.
Example: 28,500 ÷ 30,000 = 95%.
What good looks like: the best lines approach 99%+ first pass yield; the gap to 100% is your hidden cost of poor quality
Weak → strong: "Reduce defects" → "Lift first pass yield from 95% to 97% on the assembly cell within one quarter, owned by the quality engineer".

Show all 10 Manufacturing KPIs

On-Time Delivery

LaggingBetter when higherThe proportion of customer orders shipped by the promised date.

Formula: Orders delivered on time ÷ total orders shipped.
Example: 4,750 ÷ 5,000 = 95%.
What good looks like: there is no universal number — measure against the date promised to the customer, not a revised date, aim for a high and rising share, and report misses by cause
Weak → strong: "Ship on time" → "Lift on-time delivery from 95% to 98% by Q4, owned by the plant scheduler via daily order-risk reviews".

Inventory Turns

LaggingHow many times inventory is sold and replaced over a period, indicating working-capital efficiency.

Formula: Cost of goods sold ÷ average inventory.
Example: $48M ÷ $8M = 6.0 turns.
What good looks like: there is no universal number — turns vary widely by industry and product, so compare with peers and your own trend; higher turns free up cash, but too high can mean stockouts
Weak → strong: "Carry less inventory" → "Raise inventory turns from 6.0 to 7.0 by year-end without missing ship dates, owned by the supply chain manager".

Unplanned Downtime

LeadingBetter when lowerThe share of scheduled production time lost to unexpected equipment stoppages.

Formula: Unplanned downtime hours ÷ scheduled production hours.
Example: 60 ÷ 600 = 10%.

Throughput

LeadingBetter when higherThe volume of good units a process produces per unit of time.

Formula: Total good units produced ÷ time period.
Example: 24,000 units ÷ 20 hours = 1,200 units/hour.

Defects Per Million (PPM)

LaggingBetter when lowerThe number of defective parts expected per one million produced, a precision quality measure.

Formula: (Defective units ÷ total units) × 1,000,000.
Example: (15 ÷ 30,000) × 1,000,000 = 500 PPM.
What good looks like: there is no universal number — customer quality agreements often set the PPM limit, so stay below the contracted level and drive the trend down by part family (lower is better)
Weak → strong: "Ship fewer bad parts" → "Cut defects from 500 PPM to under 250 PPM on the top part family within two quarters, owned by the quality engineer".

Safety Incident Rate

LaggingBetter when lowerThe frequency of recordable workplace injuries normalized to hours worked (OSHA TRIR basis).

Formula: (Recordable incidents × 200,000) ÷ total hours worked.
Example: (5 × 200,000) ÷ 400,000 = 2.5.
What good looks like: U.S. private industry averaged 2.3 recordable cases per 100 full-time workers in 2024, down from 2.4 in 2023; beating that rate is a floor, and the safest plants push toward zero (lower is better)
Weak → strong: "Work more safely" → "Cut the recordable incident rate from 2.5 to below 2.0 by year-end, owned by the EHS manager via weekly hazard walks".

Cost Per Unit

LaggingBetter when lowerThe fully loaded production cost to make one unit of output.

Formula: Total production costs ÷ total units produced.
Example: $1.5M ÷ 30,000 = $50.

Retail & E-commerce KPIs

Retail and e-commerce KPIs track how efficiently traffic and inventory convert into profitable sales across stores and digital channels. Leading signals like conversion and traffic predict the lagging revenue and margin results.

Conversion Rate

LeadingBetter when higherThe share of visitors or shoppers who complete a purchase.

Formula: Transactions ÷ total visits (or visitors).
Example: 4,000 ÷ 160,000 = 2.5%.
What good looks like: e-commerce conversion typically runs 2–3%; well-optimized stores clear 4%+
Weak → strong: "Sell more online" → "Raise site conversion from 2.5% to 3.1% by Q3, owned by the ecommerce lead via checkout redesign".

Average Order Value (AOV)

LaggingBetter when higherThe average revenue collected per order placed.

Formula: Total revenue ÷ number of orders.
Example: $340,000 ÷ 4,000 = $85.
What good looks like: there is no universal number — grow it against your own baseline via bundling and free-ship thresholds, and always watch it alongside margin
Weak → strong: "Get bigger baskets" → "Grow AOV from $85 to $95 within two quarters, owned by merchandising via bundles and free-ship thresholds".

Sales Per Square Foot

LaggingBetter when higherRevenue generated per unit of selling floor space, a core store-productivity measure.

Formula: Net sales ÷ selling square footage.
Example: $2.5M ÷ 5,000 = $500/sq ft.
What good looks like: there is no universal number — it varies widely by format and category, so compare stores within your own chain and with the same period last year
Weak → strong: "Make stores more productive" → "Lift sales per square foot from $500 to $540 this fiscal year, owned by the regional store director via layout changes".

Show all 10 Retail & E-commerce KPIs

Inventory Turnover

LaggingHow many times stock is sold through and replenished over a period.

Formula: Cost of goods sold ÷ average inventory.
Example: $6M ÷ $1.2M = 5.0 turns.

Gross Margin Return on Investment (GMROI)

LaggingBetter when higherThe gross margin earned for every dollar invested in inventory.

Formula: Gross margin ÷ average inventory cost.
Example: $3M ÷ $1.2M = $2.50.
What good looks like: there is no universal number — above $1.00 means inventory earns back more in margin than it costs; compare by category and with your own trend to shift space and buying toward the strongest lines
Weak → strong: "Make inventory pay" → "Lift GMROI from $2.50 to $2.80 by year-end, owned by the merchandise planner via cutting slow-moving SKUs".

Cart Abandonment Rate

LeadingBetter when lowerThe share of shoppers who add items to a cart but leave without buying.

Formula: 1 − (completed purchases ÷ carts created).
Example: 1 − (4,000 ÷ 16,000) = 75%.
What good looks like: lower is better; the documented average across 50 studies is 70.22%, so a rate well under ~70% points to a smooth checkout
Weak → strong: "Fewer abandoned carts" → "Cut cart abandonment from 75% to 70% by Q3, owned by the ecommerce lead via guest checkout and upfront shipping costs".

Same-Store (Comparable) Sales Growth

LaggingBetter when higherRevenue growth from stores open at least a year, stripping out new-location effects.

Formula: (Current-period comp sales − prior-period comp sales) ÷ prior-period comp sales.
Example: ($10.5M − $10M) ÷ $10M = 5%.
What good looks like: there is no universal number — compare with inflation and peers, since comp growth below the rise in prices means fewer units sold
Weak → strong: "Grow existing stores" → "Grow same-store sales from 5% to 6% next fiscal year, owned by the VP of Store Operations".

Return Rate

LaggingBetter when lowerThe proportion of sold items that customers send back.

Formula: Units returned ÷ units sold.
Example: 400 ÷ 4,000 = 10%.
What good looks like: lower is better; U.S. retailers estimated 16.9% of their 2024 sales would be returned, so compare by category and channel and track the reason behind each return
Weak → strong: "Cut returns" → "Cut the return rate from 10% to 8% in apparel by Q4, owned by the merchandising lead via better size guides".

Customer Lifetime Value (CLV)

LaggingBetter when higherThe total gross profit expected from a customer across their entire relationship.

Formula: Average order value × purchase frequency × customer lifespan × gross margin.
Example: $85 × 4 orders/yr × 3 yrs × 50% = $510.
What good looks like: there is no universal number — read it against customer acquisition cost, and grow it through repeat purchases and retention rather than discounts that cut margin
Weak → strong: "Make customers worth more" → "Raise CLV from $510 to $560 within a year, owned by the CRM lead via a loyalty program that lifts orders from 4 to 4.4 a year".

Foot Traffic / Sessions

LeadingBetter when higherThe count of in-store visitors or online sessions reaching the channel, the top of the sales funnel.

Formula: Total store visits or website sessions in a period.
Example: 160,000 sessions in a month.
What good looks like: there is no universal number — compare with the same period last year and read it with conversion rate, since more visits only matter if they turn into sales
Weak → strong: "Get more visitors" → "Grow monthly sessions from 160,000 to 180,000 by Q3, owned by the ecommerce lead via search and email".

SaaS KPIs

SaaS KPIs measure recurring-revenue health, retention, and the economics of acquiring and keeping customers. Leading product and acquisition signals predict the lagging revenue and churn outcomes that determine growth efficiency.

Monthly Recurring Revenue (MRR)

LaggingBetter when higherThe predictable subscription revenue normalized to a monthly amount.

Formula: Sum of all active monthly subscription fees (annual plans ÷ 12).
Example: 1,000 customers × $200/mo = $200,000 MRR.

Annual Recurring Revenue (ARR)

LaggingBetter when higherThe annualized value of recurring subscription revenue.

Formula: MRR × 12.
Example: $200,000 × 12 = $2.4M ARR.

Customer Churn Rate

LaggingBetter when lowerThe share of customers who cancel within a period.

Formula: Customers lost in period ÷ customers at start of period.
Example: 20 ÷ 1,000 = 2% monthly.

Show all 10 SaaS KPIs

Net Revenue Retention (NRR)

LaggingBetter when higherRevenue retained from existing customers including expansion, net of downgrades and churn.

Formula: (Starting MRR + expansion − contraction − churn) ÷ starting MRR.
Example: ($200K + $30K − $8K − $12K) ÷ $200K = 105%.
What good looks like: 100% NRR means expansion fully offsets churn; the strongest SaaS companies sustain 120%+, the single strongest growth signal
Weak → strong: "Keep customers happy" → "Lift NRR from 105% to 115% this fiscal year, owned by the Head of CS via an expansion playbook".

Customer Acquisition Cost (CAC)

LeadingBetter when lowerThe fully loaded sales and marketing cost to acquire one new customer.

Formula: Total sales & marketing spend ÷ new customers acquired.
Example: $600,000 ÷ 100 = $6,000.

Customer Lifetime Value (LTV)

LaggingBetter when higherThe total gross profit a customer generates over their entire subscription life.

Formula: (Average revenue per account × gross margin) ÷ churn rate.
Example: ($2,400/yr × 80%) ÷ 0.20 = $9,600.

CAC Payback Period

LeadingBetter when lowerThe number of months of gross profit needed to recover the cost of acquiring a customer.

Formula: CAC ÷ (monthly revenue per customer × gross margin).
Example: $6,000 ÷ ($200 × 80%) = 37.5 months.
What good looks like: under 12 months is healthy for most SaaS; under ~6 months is exceptional
Weak → strong: "Make acquisition pay off faster" → "Cut CAC payback from 37 months to under 18 months by Q4, owned by the CMO via channel mix shift".

Activation Rate

LeadingBetter when higherThe share of new users who reach a defined first-value milestone.

Formula: Users reaching activation event ÷ total new signups.
Example: 650 ÷ 1,000 = 65%.
What good looks like: there is no universal number — define one clear first-value event, measure it against your own baseline, and work to shorten the time new users take to reach it
Weak → strong: "Onboard users better" → "Raise activation from 65% to 72% by Q3, owned by the growth product manager via a guided setup checklist".

DAU/MAU Ratio

LeadingBetter when higherA stickiness measure comparing daily to monthly active users.

Formula: Daily active users ÷ monthly active users.
Example: 12,000 ÷ 40,000 = 30%.
What good looks like: there is no universal number — it depends on how often the product is meant to be used, so set it against your own baseline and the usage pattern your customers need
Weak → strong: "Make the product stickier" → "Lift DAU/MAU from 30% to 35% within two quarters, owned by the product manager via a daily workflow feature".

Gross Margin

LaggingBetter when higherThe share of revenue left after the direct cost of delivering the software service.

Formula: (Revenue − cost of revenue) ÷ revenue.
Example: ($2.4M − $480K) ÷ $2.4M = 80%.
What good looks like: compare your subscription and blended gross margins with SaaS peers of similar size, and watch the trend as you scale
Weak → strong: "Improve margins" → "Raise gross margin from 80% to 82% this fiscal year, owned by the VP of Operations via hosting cost optimization".

Nonprofit KPIs

Nonprofit KPIs measure both financial stewardship and mission impact, proving to donors and boards that dollars convert into outcomes. Leading indicators on fundraising and retention drive the lagging efficiency and impact results.

Program Efficiency Ratio

LaggingThe share of total spending that goes directly to programs rather than overhead.

Formula: Program expenses ÷ total expenses.
Example: $850,000 ÷ $1,000,000 = 85%.
What good looks like: charity evaluators often look for ~65–75%+ of spend going to programs, though context matters and an extreme ratio can mean starved infrastructure
Weak → strong: "Spend more on the mission" → "Raise program efficiency from 85% to 88% this fiscal year, owned by the Finance Director via overhead reduction".

Fundraising ROI

LaggingBetter when higherThe dollars raised for every dollar invested in fundraising.

Formula: Funds raised ÷ fundraising expenses.
Example: $1.2M ÷ $300,000 = $4.00.
What good looks like: BBB Wise Giving Alliance standards cap fundraising costs at 35% of related contributions, which means raising at least ~$2.86 per dollar spent; higher is better
Weak → strong: "Get more from fundraising" → "Lift fundraising ROI from $4.00 to $5.00 per dollar spent within 18 months, owned by the development director via donor retention".

Donor Retention Rate

LaggingBetter when higherThe share of prior-period donors who give again in the current period.

Formula: Returning donors ÷ total donors from prior period.
Example: 2,700 ÷ 6,000 = 45%.
What good looks like: average donor retention hovers around ~45%, and first-time-donor retention is far lower (~20–25%), so even small gains compound fundraising dramatically
Weak → strong: "Keep donors giving" → "Lift donor retention from 45% to 55% within one year, owned by the Development Manager via a stewardship program".

Show all 8 Nonprofit KPIs

Cost Per Dollar Raised

LaggingBetter when lowerThe expense incurred to raise each dollar of contributions, the inverse of fundraising ROI.

Formula: Fundraising expenses ÷ funds raised.
Example: $300,000 ÷ $1.2M = $0.25.
What good looks like: BBB Wise Giving Alliance standards say fundraising should cost no more than 35% of related contributions, or $0.35 per dollar raised; lower is better
Weak → strong: "Fundraise more efficiently" → "Cut cost per dollar raised from $0.25 to $0.20 within 18 months, owned by the development director via more major-gift work".

Program Outcome Achievement

LaggingBetter when higherThe degree to which programs hit their stated impact targets.

Formula: Outcomes achieved ÷ outcomes targeted.
Example: 900 ÷ 1,000 people served = 90%.
What good looks like: there is no universal number — the target is the outcome level each program committed to, so set targets from evidence and your own baseline and report misses with their causes
Weak → strong: "Make more impact" → "Raise program outcome achievement from 90% to 93% this fiscal year, owned by the programs director via mid-year course corrections".

Volunteer Retention Rate

LeadingBetter when higherThe share of volunteers who continue serving from one period to the next.

Formula: Returning volunteers ÷ total volunteers from prior period.
Example: 480 ÷ 600 = 80%.
What good looks like: there is no universal number — track it against your own baseline by role and program, and ask departing volunteers why they left, since recognition and good matching drive returns
Weak → strong: "Keep more volunteers" → "Raise volunteer retention from 80% to 85% within 12 months, owned by the volunteer coordinator via a recognition program".

Administrative Cost Ratio

LaggingThe portion of spending consumed by management and general administration.

Formula: Administrative expenses ÷ total expenses.
Example: $100,000 ÷ $1,000,000 = 10%.
What good looks like: BBB Wise Giving Alliance standards ask charities to spend at least 65% of total expenses on programs, which leaves at most 35% for administration and fundraising combined; too low can starve core systems
Weak → strong: "Cut overhead" → "Hold administrative costs at 10% of total expenses through next fiscal year while funding a new finance system, owned by the CFO".

Grant Success Rate

LeadingBetter when higherThe share of submitted grant applications that are awarded funding.

Formula: Grants awarded ÷ grant applications submitted.
Example: 12 ÷ 40 = 30%.
What good looks like: there is no universal number — success rates vary by funder and program, so track it against your own baseline and favor well-matched applications over sheer volume
Weak → strong: "Win more grants" → "Raise grant success from 30% to 40% this fiscal year, owned by the development director via tighter funder fit screening".

What over 20,000 strategic plans reveal about the KPIs that work

You can find KPI examples anywhere. What you cannot find anywhere else is what happens to them after someone picks them. We can see that part. Across over 20,000 strategic plans and 200+ organizations on the ClearPoint platform, the same three patterns repeat. They explain what the scorecards that work do differently.

A named owner changes the result

Ownership shows up in the outcomes: initiatives with a named owner complete at 2.5× the rate (21.5% vs. 8.7%). A KPI with no name beside it is a KPI nobody updates, and the data backs that up. The pattern holds across every sector.

Ownership is not paperwork.

Project completion rate, by ownership

Has a named owner21.5%

No owner8.7%

Initiatives on the ClearPoint platform. Owned projects finish ~2.5× as often.

“In 25+ years of helping organizations execute strategy, I have never seen a KPI fail because the formula was wrong. They fail because no one owns them. Pick fewer measures, put a name beside each one, and review them like you mean it. That is the whole game.”
— Ted Jackson, Co-Founder, ClearPoint Strategy

Owned projects finish more often

Picking the right KPI is step one. Closing the work behind it is the part that breaks.

The KPIs almost everyone tracks

Strip out the system roll-ups and a clear pattern emerges in what organizations actually put on their scorecards. It leans hard toward money, safety, and people.

1. Revenue — tracked by 185 organizations

LaggingBetter when higherThe total income an organization receives.

Formula: Price of goods × number of goods sold.
Example: $50 × 20,000 = $1,000,000.
What good looks like: there is no universal number — judge it against plan, prior periods, and market growth, and read it alongside margin so growth stays profitable
Weak → strong: "Grow the business" → "Grow annual revenue from $1,000,000 to $1,200,000 by fiscal year-end, owned by the Chief Revenue Officer".

2. Expenses — 174

LaggingBetter when lowerThe total costs an organization incurs during a reporting period, including marketing, sales, and operations costs.

Formula: Cost of goods sold + operating expenses + other expenses.
Example: $5,000,000 + $3,200,000 + $300,000 = $8,500,000.
What good looks like: there is no universal number — judge expenses against revenue and output, since spending that grows slower than revenue is the real goal
Weak → strong: "Spend less" → "Hold total expenses at $8,500,000 this fiscal year while revenue grows 5%, owned by the CFO."

3. Costs — 114

LaggingBetter when lowerThe total costs an organization incurs during a reporting period, including marketing, sales, and operations costs.

Show all 10 KPIs

4. Budget surplus — 97

Better when higherThe amount by which revenue exceeds spending in a budget period.

Formula: Total revenue − total expenditures.
Example: $52,000,000 − $50,000,000 = $2,000,000.
What good looks like: a modest surplus is healthy when it builds reserves toward GFOA's recommended minimum general fund balance of no less than two months of regular operating revenues or expenditures; large recurring surpluses may mean rates are set too high
Weak → strong: "Balance the budget" → "Run a $2,000,000 general fund surplus this fiscal year to rebuild reserves toward two months of operating expenditures, owned by the Finance Director."

5. Average employee wage — 90

The average pay earned per employee over a period, usually shown as an annual figure.

Formula: Total wages paid ÷ number of employees.
Example: $28,000,000 ÷ 400 = $70,000.
What good looks like: BLS puts the U.S. all-occupations annual mean wage at $69,770 (May 2025); compare against occupation and local-area figures for your roles rather than the national number
Weak → strong: "Pay competitively" → "Bring average wages from $70,000 to within 5% of the local market median for each job family by next fiscal year, owned by the Compensation Manager".

6. EMS / emergency response time — 90

LaggingBetter when lowerAverage elapsed time from a 911 call being received to the first responder arriving on scene.

7. Employee satisfaction — 84

LeadingBetter when higherAn employee satisfaction index can be determined by conducting an employee survey and taking into account the number of positive versus negative responses.

Formula: (Positive responses ÷ (positive + negative responses)) × 100.
Example: (1,800 ÷ (1,800 + 700)) × 100 = 72%.
What good looks like: the Conference Board's 2025 survey found U.S. job satisfaction at its highest level since the series began in 1987, with ~72% of workers 55 and older satisfied versus ~57% under 25, so benchmark by group, not one number
Weak → strong: "Make employees happier" → "Raise the satisfaction index from 72% to 78% on the next annual survey, owned by the CHRO via action plans for the lowest-scoring teams".

8. Average response time, priority-one calls — 82

LaggingBetter when lowerThe time police take to respond to high-priority calls that need immediate dispatch.

Formula: Sum of priority-1 response times ÷ number of priority-1 calls.
Example: 4,200 minutes ÷ 700 calls = 6 minutes.
What good looks like: there is no national standard — define whether the clock starts at call receipt or dispatch, publish a local target, and track the 90th percentile as well as the average (lower is better)
Weak → strong: "Get to emergencies faster" → "Cut average priority-1 response time from 6 to 5 minutes by Q4, owned by the Patrol Operations Commander, reviewed monthly".

9. Net profit — 80

LaggingBetter when higherThe amount of money an organization makes after taking out all expenses and other costs.

Formula: Income − expenses
Example: $2,500,000 − $2,200,000 = $300,000.
What good looks like: U.S. public companies keep ~10% of revenue as net profit on average, but it is highly sector-dependent, so compare your margin with peers and your own trend
Weak → strong: "Make more profit" → "Increase net profit from $300,000 to $350,000 this fiscal year through pricing and cost reviews, owned by the CFO."

10. Overhead costs — 77

Better when lowerThe ongoing costs of running an organization that are not tied directly to making a product or delivering a service.

Formula: Rent + utilities + administrative salaries + insurance + other indirect costs.
Example: $120,000 + $30,000 + $250,000 + $50,000 + $50,000 = $500,000.
What good looks like: there is no universal number — track it as a share of total costs against your own trend, and remember that cutting it too far can starve the systems the work depends on (lower is better)
Weak → strong: "Cut overhead" → "Reduce overhead from $500,000 to $460,000 next fiscal year by consolidating office space, owned by the Director of Finance."

Useful, but notice what is missing: almost nothing that looks ahead. The most-tracked KPIs are nearly all lagging. That is the quiet reason so many scorecards feel like rear-view mirrors.

The scorecards that work track less

Put the three patterns together and the lesson writes itself. The organizations that finish things do not track more KPIs. They track fewer, they put a name beside each one, and they review them on a cadence. More measures do not mean more progress. Usually they mean more places to hide.

How to choose your KPIs

You now have 200-plus examples and the data on what happens to them. Here is how to turn that into a short list you will actually run.

Pick few. A team can steer by five to nine KPIs. Beyond that, attention thins and the dashboard becomes wallpaper. If a measure would not change a decision, it is a metric, not a KPI. Leave it off. (More on the right number of KPIs to track.)

Pair leading with lagging. For every result you care about, add one early signal you can act on before the result lands. Revenue pairs with pipeline coverage. Graduation rate pairs with first-year retention. Outage minutes pair with maintenance completed. (See how to set leading indicators.)

Give every KPI one owner. Not a team. A person, by name. This is the move our data supports. If you cannot name an owner, you are not ready to track it.

Avoid the four traps. The ghost owner — a KPI assigned to everyone, owned by no one. The vanity metric — a number that always goes up and never forces a decision. The unsegmented average — a citywide or company-wide mean that hides the segment that is failing. And the gamed target — a number people hit by bending behavior, like closing tickets fast by closing them unresolved. Name the owner, segment the data, and tie the KPI to a goal, and all four mostly disappear.

How to build and track your KPIs

Once you have your short list, these guides take you the rest of the way — from writing the measure to watching it move.

Set up and measure them: how to set up, measure, and track your KPIs
Set targets: How to set KPI targets in 9 steps
Report on them: What is a KPI report, and how do you create one
Show status at a glance: How to establish RAG statuses for KPIs
Build a dashboard: KPI dashboards in Excel · KPI dashboard best practices
KPIs vs OKRs: OKRs vs KPIs — how they differ and when to use each

When a spreadsheet stops keeping up, that is what ClearPoint's KPI software and strategy execution platform are built for: one owner per measure, automatic status, and reports that build themselves.

Free KPI libraries by function & industry

You have the examples. When you are ready to build a scorecard, start from a vetted list instead of a blank page. Each library below is a free, downloadable set of the KPIs that matter most for that function or sector — the same measures our customers put to work every day.

Finance 68 Financial KPIs Free eBook for sharper financial insight → Human Capital 48 Human Capital KPIs Free library for a sharper HR strategy → Healthcare 108 Healthcare KPIs Free library to lift performance → Local Government 142 Local Government KPIs Free eBook for public-sector teams → Customer 53 Customer KPIs Free eBook for stronger CSAT →

Prefer the how-to? Our full guide on setting up, measuring, and tracking KPIs walks through the five-step build and the one field — the owner — that decides whether any KPI moves.

Get the full KPI library

Want the examples above as a working file, plus the benchmarks behind them? Our 2026 Strategic Planning Report pulls together the platform data on what high performers actually track — and what they have learned to stop tracking.

And when your KPIs outgrow the spreadsheet, see how ClearPoint puts one owner on every measure, flags the ones drifting red, and turns your scorecard into the report your board has been asking for.

A KPI is not a number on a dashboard. It is a promise about what you will pay attention to. The hard part was never the list. It is choosing the few you will keep — and putting a name beside each one.

See how ClearPoint would run your plan — book a demo

References & sources

Benchmarks and definitions in this guide draw on the sources below. Platform figures come from ClearPoint's proprietary strategy-execution data; benchmark ranges come from the standards bodies that govern each metric.

ClearPoint platform data — proprietary analysis of over 20,000 strategic plans across 200+ organizations (KPI ownership, completion, and tracking patterns).
Kaplan, R. & Norton, D., The Balanced Scorecard (Harvard Business Review) — leading vs lagging indicators.
NFPA 1710 — fire and EMS response-time standards.
U.S. Centers for Medicare & Medicaid Services (CMS) — Hospital Readmissions Reduction Program and the HCAHPS patient-experience survey.
U.S. Department of Education / NCES — college retention and six-year graduation-rate benchmarks.
IEEE 1366 — electric-utility reliability indices (SAIDI, SAIFI, CAIDI).
U.S. OSHA — Total Recordable Incident Rate (TRIR) methodology.
Government Finance Officers Association (GFOA) — local-government budgeting and financial-reporting best practices.

Common questions

We already track dozens of KPIs. How do we cut the list down?

Start by deleting every measure that would not change a decision; those are metrics, not KPIs. Then keep one lagging result per goal, pair it with one leading signal, and drop anything you cannot name an owner for. Most teams land at five to nine KPIs. Move the rest to an operational report instead of the scorecard.

Who should own a KPI that several departments affect?

Name one person anyway, usually the leader closest to the result, and make them responsible for reporting and escalation, not for doing all the work. Contributing departments can own their own supporting measures underneath. Ownership pays off: projects with a named owner are completed about 2.5 times as often as projects with none.

How often should KPIs be reviewed?

Match the review cadence to how fast the number can move. Operational KPIs such as response time, ticket backlog, or uptime deserve a weekly look. Strategic KPIs such as retention, margin, or completion rate fit a monthly review with a quarterly executive recalibration. Whatever you choose, fix the schedule and keep it.

How do you set a target for a new KPI with no historical data?

Collect two or three periods of data first and treat that as the baseline, then set the target as a deliberate improvement on it. Where a recognized standard exists, such as NFPA 1710 for emergency response times or CMS readmission measures, use it as a reference point. Revisit the target once a full year of data exists.

Ted Jackson

Co-Founder & Alabama Native

Co-Founder and Managing Partner of ClearPoint. Former VP at Kaplan and Norton's Balanced Scorecard Collaborative.

Ted Jackson is the co-founder of ClearPoint Strategy, a B2B SaaS platform that empowers organizations to execute strategic plans with precision. Before ClearPoint, he was Vice President at the Balanced Scorecard Collaborative — later the Palladium Group — the firm Drs. Robert Kaplan and David Norton founded to put their framework into practice, where he led global sales and support for its strategy management software and wrote three articles for the Balanced Scorecard Report. A Duke and Harvard Business School alumnus, he brings over 30 years' experience in strategy execution, including 15 years implementing the Balanced Scorecard in the field. Ted works closely with customers to ensure the software meets unique challenges, continually refining the platform with his global expertise.