Days Sales Outstanding
The average number of days it takes to collect payment after a sale.
455 KPIs in 30 areas of a business, each with its formula, DAX, SQL and Excel, a benchmark, the mistakes to avoid, and the charts that show it best.
The average number of days it takes to collect payment after a sale.
The average number of days the business takes to pay its suppliers.
The share of receivables that are more than 90 days past the invoice date (or due date).
The share of credit sales that had to be written off as never to be paid.
How much of the money that could be collected in a period actually was.
How many working days it takes to close the books after the month ends.
What it costs the accounts payable team to process one supplier invoice.
The average days from receiving a supplier invoice to approving it for payment.
The share of supplier invoices processed with no manual work.
The share of supplier invoices paid by their due date.
The share of customer invoices sent without errors that need a credit note or correction.
The share of journal entries that had to be reversed or corrected.
The share of balance sheet accounts reconciled by the close deadline.
The share of offered early-payment discounts the business actually took.
Interest earned minus interest paid, as a share of the assets that earn interest.
Operating costs as a share of operating income.
The share of loans 90+ days past due or unlikely to be repaid.
The share of loan balances at least 30 days late.
Loan loss provisions as a share of non-performing loans.
Loan loss provisions charged in the period as a share of average loans, yearly.
The bank’s highest-quality capital as a share of its risk-weighted assets.
Tier 1 capital as a share of total exposure, without risk weights.
Total loans as a share of total deposits.
High-quality liquid assets compared with the cash that could flow out in 30 days of stress.
Net income as a share of average total assets.
The share of loan applications approved.
How long it takes from a complete application to a credit decision.
How much customer deposits grew against the same point last year.
The average number of bank products each customer holds.
How long customers wait for the first human reply after contacting support.
How long it takes, from first contact, to solve a customer’s problem.
The share of issues solved in the first contact, with no follow-up needed.
The share of surveyed customers who rated their support experience as good (4 or 5 out of 5).
The average length of an interaction, from start to finish, including hold time and after-call work.
The share of calls or chats answered within a target time, such as 80% within 20 seconds.
The average time callers wait in the queue before an agent answers.
The share of callers who hung up before reaching an agent.
The number of open tickets waiting for work, often with how many are older than the target.
The share of tickets that met their service level agreement for response and resolution.
How many tickets each agent solves in a period.
The share of an agent’s logged-in time spent handling contacts.
The share of customers who contact support again about the same issue within a set number of days.
The share of support needs solved by help articles, bots or portals without an agent.
What it costs on average to handle one customer contact.
How likely customers are to recommend you, scored as the share of promoters (9–10) minus the share of detractors (0–6).
How easy customers found it to get what they needed, usually agreement on a 1–7 scale.
A combined score per account from usage, support, payments and sentiment, used to predict renewal or churn.
The share of contracts up for renewal in a period that renewed.
How long a new customer takes to reach the first meaningful result from the product.
The share of new customers who complete onboarding within the planned time.
The share of purchased seats or licences that are actively used.
The share of key accounts that had a business review with the customer in the quarter.
How long customers stay on average before leaving.
Formal complaints for every thousand customers or orders.
The average star rating customers give in public reviews.
The share of existing accounts that grew their spend in the period.
The average time between an attack or breach starting and the security team detecting it.
The average time from detecting a security incident to containing it.
The number of confirmed security incidents in a period, by severity.
How long critical vulnerabilities stay open from discovery (or patch release) to fix.
The number of open critical and high vulnerabilities, often with how many are past their deadline.
The share of staff who clicked a link in a simulated phishing email.
The share of staff who reported a simulated phishing email to security.
The share of user accounts (and admin accounts) protected by multi-factor authentication.
The number of accounts with administrator rights, often per 100 employees.
The share of devices with an active, up-to-date endpoint detection agent.
The share of staff who completed required security awareness training on time.
The share of security alerts that turned out to be harmless.
The share of critical vendors with a current security assessment.
The share of shop visits that end in an order.
The average amount spent per order.
The share of shopping carts that were created but never turned into an order.
The share of people who start checkout and finish the order.
The share of sessions (or product views) in which something was added to the cart.
The share of customers who have ordered more than once.
How many orders a customer places on average in a period, usually a year.
The gross profit a customer brings over the whole time they buy from you.
The share of items sold that customers sent back.
The average number of items in an order.
The revenue the shop earns for every visit.
The total value of goods sold through the shop or marketplace, before returns, fees and discounts.
The share of GMV the marketplace keeps as revenue, from commissions and fees.
The average cost to ship one order to the customer.
The share of product page views that ended in that product being bought.
How much the number of enrolled students changed against the same term last year.
The share of first-year students who return for their second year.
The share of students in an entry group who complete their programme within a set time (often 150% of normal length).
The share of enrolled students who withdraw during a year or term.
The share of scheduled sessions that students attended.
The share of students who missed 10% or more of sessions.
The share of students who passed an exam or course.
The average mark or grade point across students.
The number of students for every teacher (full-time equivalent).
The yearly operating cost divided by the number of students.
The share of admitted applicants who actually enrol.
The share of graduates in work or further study a set time (often 6–15 months) after finishing.
The share of learners who start an online course and finish it.
The share of students who agree they are satisfied with their course or school.
The average minutes of power outage each customer experienced in a year.
The average number of outages each customer experienced in a year.
The average time to restore power for customers who had an outage.
The energy a plant actually produced as a share of what it could produce running at full power all the time.
The share of time a plant was able to generate, whether or not it was asked to.
The share of energy put into the network that never reaches a paying customer.
The highest power draw in a period, usually the top hour.
Average demand as a share of peak demand.
The share of electricity generated (or bought) from renewable sources.
The CO₂ emitted for each kilowatt-hour generated.
Energy used for each unit of output, area or revenue.
The share of bills based on actual meter readings rather than estimates.
Billing complaints for every 10,000 customers.
Greenhouse gases emitted directly by sources the company owns or controls: fuel burned, company vehicles, refrigerant leaks.
Emissions from generating the electricity, heat and steam the company buys.
All other indirect emissions in the value chain: purchased goods, transport, business travel, use of sold products and more (15 categories).
Emissions for each unit of revenue (or output).
How far emissions have fallen from the baseline year, against the target.
The share of the electricity the company uses that comes from renewable sources.
The share of waste kept out of landfill and incineration, through reuse, recycling or composting.
Water taken from all sources for each unit of output or revenue.
Injuries that caused time off work, per million hours worked (or per 200,000 hours in the US).
The share of senior management (and board) positions held by women.
The share of board members who are independent of management.
The share of spend (or of suppliers) with suppliers that have their own science-based climate targets.
Money, time and goods given to communities, often as a share of pre-tax profit.
The share of revenue left after paying for the goods or services sold.
The share of revenue left after the costs of running the business, before interest and taxes.
Earnings before interest, taxes, depreciation and amortisation, as a share of revenue.
The share of revenue left as profit after every cost, interest and tax.
The cash the business generated from its normal operations in a period.
The cash left after running the business and investing in it, free to pay debt, dividends or grow.
How many times current assets cover current liabilities.
How many times the most liquid assets (cash, receivables, short-term investments) cover current liabilities.
Current assets minus current liabilities: the money tied up in day-to-day operations.
The days between paying suppliers and collecting from customers.
The profit the business made on the money its owners have in it.
The profit the business made for every unit of assets it holds.
The after-tax operating profit earned on all the capital invested by owners and lenders.
How much debt the business carries for every unit of owners’ equity.
How many years of EBITDA it would take to pay back net debt.
How many times operating profit covers the interest on debt.
How far actual results are from the budget, in money and as a percentage.
How close the financial forecast was to actual results.
How many months the business can keep running at its current burn before cash runs out.
Operating expenses as a share of revenue.
Capital spending as a share of revenue.
How much revenue the business makes for every unit of assets.
The share of available beds filled by patients, on average over a period.
The average number of days inpatients stay in hospital.
The share of discharged patients admitted again, unplanned, within 30 days.
How long patients wait from arriving at the emergency department to being seen by a clinician.
The share of emergency patients admitted, transferred or discharged within four hours of arrival.
The share of emergency patients who leave before a clinician sees them.
The share of patients who rate their care at the top of the scale (for example 9–10 out of 10).
The share of insurance claims denied by payers on first submission.
The share of claims paid on first submission with no edits or rework.
The average number of days it takes to collect payment for services.
The share of booked appointments patients missed without cancelling.
Infections patients caught in hospital, per 1,000 patient-days (or device-days).
The average cost of one day of inpatient care.
The number of patients each nurse looks after on a shift.
Observed deaths in hospital compared with the deaths expected for the patients treated.
The share of available rooms that were sold on average.
The average price paid per room sold.
Room revenue divided by all rooms available, sold or not.
Your RevPAR compared with your competitive set’s, as an index where 100 is fair share.
Gross operating profit of the hotel divided by rooms available.
All hotel revenue (rooms, food, spa, events) divided by rooms available.
How many days before arrival guests book, on average.
The share of bookings cancelled before arrival.
The share of room nights booked directly (website, phone) rather than through agencies.
The average rating guests give in online reviews, on a common scale.
The cost of food used as a share of food sales.
Cost of goods sold plus total labour cost, as a share of sales.
How many times each table is used by a new party during a service.
The average amount each guest spends.
Restaurant revenue divided by the number of seats times the hours open.
The share of employees who left the organisation in a period.
The share of employees who chose to leave in a period.
The share of employees who left that the company wanted to keep, usually strong performers.
The share of employees at the start of a period who are still employed at its end.
The share of new hires who leave within their first year.
The average number of days from opening a role (or from a candidate applying) to the offer being accepted.
What it costs on average to recruit one new employee.
The share of job offers that candidates accept.
A score of how well new hires perform and stay, usually from first-year ratings, retention and manager feedback.
The share of scheduled working time lost to unplanned absence.
How likely employees are to recommend the company as a place to work: promoters minus detractors.
The average score on the engagement questions of the employee survey, as a percentage of the top score.
The share of invited employees who answered the survey.
The average hours of training each employee received in a period.
What the organisation spends on learning per employee.
The share of open roles filled by people already in the company.
How much less, in percent, women earn than men on average (or at the median) across the organisation.
The share of a group (for example women, or an under-represented group) in the workforce or in leadership.
An employee’s pay compared with the midpoint of the pay range for their role.
The revenue the company earns for each employee.
Overtime hours as a share of regular hours worked.
The number of approved roles not yet filled.
Open positions as a share of all planned positions.
The profit returned for every unit spent on people.
Claims and claim handling costs as a share of premiums earned.
Costs of acquiring and running policies as a share of premium.
Loss ratio plus expense ratio: what the insurer pays out for every unit of premium.
How much premium written grew against the same period last year.
The share of policies up for renewal that renewed.
The number of claims for every 100 policies (or exposure units) in a year.
The average cost of a claim.
How long it takes from a claim being reported to it being settled.
The share of claimants satisfied with how their claim was handled.
The share of quotes that become policies.
The share of claims referred for fraud review that turned out to be fraudulent.
Eligible capital as a share of the capital the regulator requires.
How many times the average inventory is sold and replaced in a year.
How many days the current inventory would last at the current rate of sales.
The share of items (or item-days) with no stock available when customers wanted them.
The share of ordered units (or lines) shipped from stock in the first shipment.
The share of counted locations where the system quantity matched the physical count.
The share of order lines picked with the right item and quantity.
The share of orders shipped with no picking, packing or labelling error.
How long received goods take from arriving at the dock to being available to sell.
How long the warehouse takes from receiving an order to shipping it.
How many units (or order lines) a picker handles per hour worked.
The warehouse cost (labour, space, equipment) to fulfil one order.
The yearly cost of holding inventory (capital, storage, insurance, shrink and obsolescence) as a share of its value.
The share of inventory value that has not moved for a long time (e.g. 12 months).
The share of inventory lost to theft, damage, errors or fraud.
The share of items whose stock is below their safety stock level.
The share of storage locations (or cubic space) in use.
The share of time a service was up and usable.
The average time from an incident starting to the service working again.
The average time between a problem starting and the team knowing about it.
The number of high-priority incidents in a period.
The share of tickets and incidents resolved within their agreed time.
The number of open service desk tickets, with how many are past their target.
The share of service desk tickets solved by the first person who handled them.
The share of IT changes made without causing an incident or needing a rollback.
What the organisation pays its cloud providers in a period.
Cloud cost divided by a business unit of work: per customer, order, request or active user.
The share of cloud spend on resources barely used: idle servers, unattached disks, oversized databases.
The share of devices with all critical security patches installed within the target window.
Total IT spending as a share of company revenue.
The share of devices past their planned life or out of vendor support.
The share of users who rated their IT support experience as good.
The share of planned production time that was truly productive: running, at full speed, making good parts.
The share of planned production time the equipment was actually running.
How fast the equipment ran compared with its ideal speed, while it was running.
The share of parts made that were good, first time.
The share of units that pass every step without rework or scrap.
The share of material or units thrown away because they could not be used or fixed.
Hours of production lost to unplanned stops.
The average running time between one breakdown and the next.
The average time to fix equipment after a failure and get it running again.
The share of maintenance hours spent on planned work rather than emergency repairs.
The time from the last good part of one product to the first good part of the next.
The share of planned production completed as scheduled (right product, right quantity, right time).
What quality failures cost: scrap, rework, warranty claims, returns and inspections, often as a share of sales.
Defective parts found by customers for every million delivered.
The time from releasing a production order to finishing it.
The energy used to make one unit of product.
What it costs, on average, to win one new customer: all sales and marketing spend divided by the new customers it brought.
The revenue earned for every unit of money spent on advertising.
The profit marketing produced compared with what it cost.
What marketing spends to generate one new lead.
The share of marketing-qualified leads that sales accepts as sales-qualified.
Revenue from deals whose first touch was a marketing channel.
The average price paid for one click on an ad.
The share of people who saw an ad and clicked it.
The price of showing an ad one thousand times.
The share of delivered emails that were opened.
The share of delivered emails in which someone clicked a link.
The share of delivered emails that led someone to unsubscribe.
The share of your target market that knows your brand, from surveys (aided: they recognise it; unaided: they name it).
How many searches include your brand name in a period.
The share of people reached by a campaign who did what it asked: sign up, download, buy.
Marketing spend compared with the company’s revenue.
Your share of all the attention in your market: ads, mentions or search visibility, against competitors.
How many units of work the process finishes in a period: orders, cases, parts, claims.
How long one unit of work takes from start to finish.
The number of units started but not yet finished.
How much of the available capacity was used.
The share of paid hours spent on productive (or billable) work.
The average cost of producing or handling one unit of output.
The share of orders or jobs finished by the date promised.
The share of units that had an error and needed rework or correction.
How many days of work are waiting, at the current pace.
The share of total cycle time spent actually working on the item, rather than waiting.
Recordable work injuries for every 200,000 hours worked (about 100 full-time employees for a year).
How many near misses (events that could have caused harm) are reported, per 100 employees.
Money saved through procurement work: lower prices, better terms or avoided costs, against a baseline.
The share of total spend that procurement actively manages through sourcing, contracts or catalogues.
The share of spend that bypassed agreed suppliers, contracts or purchasing rules.
The share of spend in contracted categories that went through the contract at the contracted price.
The share of invoices (or spend) backed by a purchase order raised before the purchase.
How long it takes from a purchase request to sending the order to the supplier.
The share of received goods from a supplier that failed inspection or were returned.
The difference between what was paid and the standard or expected price, across purchases.
The share of spend going to the top few suppliers (or a single supplier in a category).
The number of suppliers paid in the last twelve months.
The savings procurement delivers for every unit of its own cost.
The share of purchase orders placed as urgent or expedited.
The number of different users who did something meaningful in the product on a given day.
The number of different users active at least once in the last 30 days.
The share of monthly users who use the product on a typical day.
The share of new sign-ups who reach the action that shows they got value (for example, created their first project).
The share of a sign-up cohort still active 30 days later (also D1 and D7).
The share of active users who used a given feature in a period.
How much time an active user spends in the product per day or week.
The share of free users who upgrade to a paid plan.
The share of new users who leave at each onboarding step.
NPS asked inside the product, to users who have used it for a while.
The share of A/B tests where the change beat the control with statistical confidence.
The share of users (or sessions) that had no app crash in a period.
Users who came back after being inactive for a set time (for example 30 days).
How much work was done compared with how much was planned by now, in money terms.
How much work was done for each unit of money spent.
The value of work done minus the value of work planned by now.
The value of work done minus what it actually cost.
The expected total cost of the project when finished, at its current cost efficiency.
The share of milestones completed by their baseline date.
The share of finished projects that met their planned end date.
The share of finished projects whose final cost stayed within budget (often with a tolerance like 5%).
How fast the project spends its budget, per week or month, compared with the plan.
The share of available team hours booked to project work.
The number of approved scope changes, often per project or per month.
The number of open risks rated high (by likelihood and impact).
The share of planned tasks completed so far.
The net benefit of a project compared with what it cost.
The share of rentable units (or area) that are leased and occupied.
The rent actually collected as a share of the rent the property could earn if fully let at market rent.
A property’s income after operating expenses, before debt payments, depreciation and taxes.
The yearly NOI a property earns as a share of its value.
The yearly cash an investor receives after debt payments, as a share of the cash they put in.
How many times NOI covers the yearly loan payments.
The share of rent billed that was collected on time.
Overdue rent as a share of the yearly rent roll.
The share of units whose tenants moved out in a year.
The share of leases ending in the period that the tenant renewed.
The average time left on leases, weighted by rent.
How many days a unit stays empty between tenants.
Operating expenses as a share of a property’s gross income.
How long a listed property takes to go under contract.
The final sale price as a share of the asking price.
Sales growth counting only stores open in both periods, so new and closed stores do not distort it.
How much revenue each unit of selling space produces in a year.
The share of store visitors who buy something.
The number of people entering a store.
The average amount spent per receipt.
The average number of items in a receipt.
The share of stock received that has been sold in a period.
The gross margin earned for every unit of money invested in stock.
The value of price reductions as a share of sales at full price.
Stock lost to theft, damage and errors, as a share of sales.
Revenue earned for every hour worked by store staff.
Store staff costs as a share of store sales.
How many weeks current stock would last at the recent rate of sales.
The share of sales made by loyalty programme members.
The predictable revenue from all active subscriptions, normalised to one month.
The yearly value of the recurring subscriptions active today.
How much ARR grew over the last twelve months, as a percentage.
How much MRR grew from last month, as a percentage.
The share of customers at the start of a period who cancelled during it.
The share of last month’s MRR lost to cancellations and downgrades, before counting any expansion.
MRR lost to churn and downgrades minus MRR gained from existing customers expanding, as a share of starting MRR.
The revenue a group of customers brings today compared with a year ago, counting expansion, downgrades and churn.
The share of last year’s revenue kept from the same customers, counting losses but not expansion.
The gross profit an average customer brings before they cancel.
How many times the profit from a customer covers what it cost to win them.
How many months of a new customer’s gross profit it takes to earn back what it cost to win them.
The average MRR each paying customer brings.
How much MRR the business gains for every unit it loses in a month.
Revenue growth rate plus profit margin, which together should reach 40%.
How much cash the company burns for each unit of net new ARR it adds.
How much new annual recurring revenue each unit of last quarter’s sales and marketing spend produced.
The share of trials (or free users) who become paying customers.
The share of customers at the start of a period who are still customers at the end.
The money earned from selling products or services in a period, after returns, discounts and allowances.
How much revenue grew compared with the same period last year (or the previous period), as a percentage.
The share of closed opportunities that were won.
The average value of the deals won in a period.
The average number of days from creating an opportunity to winning it.
How much of their sales target a rep or team reached in a period.
How many times the open pipeline for a period covers the quota still to be reached.
The open pipeline with each deal multiplied by its chance of closing.
How much revenue the pipeline produces per day, from four levers: number of deals, deal size, win rate and cycle length.
The share of leads that become a qualified sales opportunity.
How close the sales forecast made at the start of a period was to what was actually sold.
The number of customers who bought for the first time in a period.
How much below list price deals were closed, on average.
The revenue each quota-carrying rep brings in, on average.
How long a new rep takes to reach full productivity, usually their first month at or near full quota.
The number of calls, emails and meetings each rep logs in a period.
Revenue from existing customers buying more, a higher tier, or other products.
How much revenue is still missing to reach the target for the period.
The share of people who saw a post and interacted with it: liked, commented, shared, saved or clicked.
The number of different people who saw your content in a period.
How many times your content was shown, counting repeat views by the same person.
How many times, on average, each person reached saw your content.
How fast the number of followers grows in a period, as a percentage.
The share of people who saw a post and clicked its link.
Sign-ups, leads or purchases that came from social media.
The share of video plays that were watched to the end.
How long, on average, people watched a video.
Your share of all social mentions in your market, against named competitors.
How positive the talk about your brand is: positive mentions minus negative, as a share of all mentions that carry a feeling.
How long it takes to reply to comments and messages that need an answer.
How many posts you publish per week on each network.
What paid social spends for one interaction with an ad.
How often the team ships to production.
How long it takes a code change to go from commit to running in production.
The share of production deployments that cause a failure needing a fix, rollback or hotfix.
How long it takes to restore service after a failed deployment or production incident.
How long a work item takes from starting work to done.
The number of work items finished per week (or sprint).
How long a pull request waits from opening to its first review.
The median number of lines changed in a pull request.
Bugs found in production (by users or monitoring) rather than before release.
The number of open bugs, usually split by severity and age.
The share of code lines (or branches) run by automated tests.
The share of CI pipeline runs that pass.
How long the CI pipeline takes to run.
The time within which 95% of requests to a service are answered.
The share of requests to a service that failed with a server error.
How much of the allowed unreliability under the service level objective is left this period.
The share of engineering time spent on bugs, incidents and rework instead of planned work.
The share of orders delivered on the requested date with the full quantity ordered.
The share of orders that arrive on time, complete, undamaged, and with the right paperwork.
How close the demand forecast was to actual demand, weighted by volume.
Whether the forecast is too high or too low on average.
How long it takes from a customer placing an order to receiving it.
What it costs to move one unit (or one kilo, or one pallet) to the customer.
Total logistics cost (transport, warehousing, handling) as a share of revenue.
The share of purchase orders suppliers delivered on the agreed date (and in full).
The average days from sending a purchase order to receiving the goods.
The days between paying suppliers for materials and receiving cash from customers for finished goods.
The share of shipments that arrived damaged.
How full trucks or containers are, by volume or weight, when they leave.
The share of orders that could not be filled from stock when placed.
The number of visits to the website in a period. One person can make several sessions.
The number of different people (in practice, browsers or devices) who visited in a period.
The share of sessions in which the visitor did the thing the site is for: bought, signed up, asked for a demo.
The share of sessions that were not engaged. In GA4: under 10 seconds, one page, and no conversion.
The share of sessions that were engaged: lasted over 10 seconds, saw two or more pages, or converted.
How long, on average, the page was in focus on the visitor’s screen during a session.
The average number of pages viewed in a visit.
The share of users who had visited before.
Visits that came from unpaid search results.
The share of times a page shown in search results was clicked.
The average place of your pages in search results, weighted by how often they were shown. Lower is better.
How many target keywords rank on the first page of search results.
How long the biggest piece of content takes to appear, at the 75th percentile of real visits.
How long the page takes to respond visibly after a click, tap or key press, at the 75th percentile.
How much the page jumps around while it loads, as a unitless score at the 75th percentile.
How long pages take to load fully for real visitors.
The number of different websites that link to yours.
The share of people who start a form and submit it.