KPI Catalog Seed — Common Business & Functional Metrics (Expanded)
A practical, ready-to-adopt starter catalog of common KPIs across functions. Each entry includes a clear definition, calculation formula, typical cadence, suggested owner, decisions the metric supports, common data sources, typical pitfalls, and a sample visualization idea.
Welcome — How to use this KPI catalog
This catalog is a practical starter pack: pick a metric, adapt the formula and tolerances to your context, assign an owner, and connect to the data source. Each entry is intentionally decision-oriented — we describe what the metric should help you decide, common pitfalls to avoid, and a simple visualization suggestion so teams can act faster and more consistently.
Catalog entries
Active Users
Definition: Number of distinct users who engaged with the product or service in a given period.
Formula: Count(distinct user_id) in period (e.g., daily, weekly, monthly). Specify whether counting unique logins, unique accounts, or unique devices.
Typical cadence: Daily for operational monitoring, weekly/monthly for growth analysis.
Suggested owner: Product manager or Growth lead.
Decisions driven: Feature prioritization, engagement experiments, capacity planning, retention campaigns.
Typical data sources: Authentication logs, event-tracking system, user database.
Common pitfalls: Counting duplicate accounts, including test/staff accounts, changing definition mid-period. Always version your definition.
Sample visualization: Time series (line) with rolling 7-day average and annotations for releases or campaigns.
Churn Rate
Definition: Share of customers or users who stopped using or paying for the service during a period.
Formula: (Customers at start of period - Customers at end of period who remain active) / Customers at start of period. Or: Cancellations / Active customers at period start.
Typical cadence: Monthly or quarterly.
Suggested owner: Customer Success or Subscription/Product owner.
Decisions driven: Retention program design, pricing changes, onboarding improvements, customer health interventions.
Typical data sources: Billing system, subscription records, CRM.
Common pitfalls: Mixing voluntary and involuntary churn without distinction, ignoring reactivations, incorrect cohort boundaries.
Sample visualization: Cohort churn curve (survival chart) plus stacked bar showing voluntary vs. involuntary churn.
Conversion Rate
Definition: Proportion of users who complete a desired action compared to those eligible to take it.
Formula: Conversions / Visitors (or eligible users). Example: signups / unique visitors in period.
Typical cadence: Weekly for experiments, monthly for trend analysis.
Suggested owner: Growth/Product manager or Marketing lead.
Decisions driven: A/B testing, funnel optimization, marketing targeting, landing-page changes.
Typical data sources: Web analytics, event tracking, marketing platforms.
Common pitfalls: Undefined funnel stages, mixing different traffic sources without segmentation, small-sample inference.
Sample visualization: Funnel chart showing drop-off at each stage, plus conversion rate trend line by cohort or channel.
ARPU (Average Revenue Per User)
Definition: Average revenue generated per active user or paying customer over a period.
Formula: Total revenue in period / Average number of users (or paying customers) in period.
Typical cadence: Monthly or quarterly.
Suggested owner: Finance, Revenue Operations, or Product Owner.
Decisions driven: Pricing experiments, monetization strategy, segmentation for upsell.
Typical data sources: Billing system, revenue recognition reports.
Common pitfalls: Confusing ARPU with ARPPU (average revenue per paying user), mixing metrics across regions/currencies without normalization.
Sample visualization: Bar chart by customer segment and time-series for ARPU trend; annotate promotions or pricing changes.
Average Handle Time (AHT)
Definition: Average total time to handle a customer interaction, including talk time, hold time, and after-call work.
Formula: (Total talk time + total hold time + total after-call work) / Number of handled contacts.
Typical cadence: Daily for operations, weekly/monthly for staffing and process improvements.
Suggested owner: Contact center manager or Operations lead.
Decisions driven: Staffing levels, training needs, workflow automation opportunities.
Typical data sources: Contact center system, ACD logs, workforce management tools.
Common pitfalls: Targeting lower AHT at the expense of quality, ignoring complexity mix, excluding transferred or blended interactions inconsistently.
Sample visualization: Histogram of handle times with median and percentile lines; time-series segmented by channel or issue type.
OEE (Overall Equipment Effectiveness)
Definition: A composite manufacturing metric that measures availability, performance, and quality of production equipment.
Formula: OEE = Availability × Performance × Quality, where Availability = Operating time / Planned production time; Performance = (Ideal cycle time × Total pieces) / Operating time; Quality = Good pieces / Total pieces.
Typical cadence: Shift-level daily reporting and weekly trend analysis.
Suggested owner: Plant Manager or Production/Manufacturing Engineer.
Decisions driven: Maintenance prioritization, bottleneck identification, process improvement efforts.
Typical data sources: MES, PLCs, production logs, quality inspection data.
Common pitfalls: Inconsistent definitions of planned downtime, failing to separate quality loss types, not capturing micro-stops.
Sample visualization: Stacked bar showing the three OEE components over time and Pareto of top downtime causes.
MTTR (Mean Time to Repair)
Definition: Average time required to repair a failed piece of equipment and return it to production.
Formula: Sum of repair times / Number of repairs in period.
Typical cadence: Weekly for maintenance teams, monthly for reliability reviews.
Suggested owner: Maintenance Manager or Reliability Engineer.
Decisions driven: Spare parts inventory, maintenance staffing, training, redesign for maintainability.
Typical data sources: Maintenance work-order system, downtime logs.
Common pitfalls: Excluding diagnostics time inconsistently, mixing planned maintenance with corrective repairs, not tagging root-cause across events.
Sample visualization: Box-and-whisker chart of repair durations by equipment type and time-series of MTTR trend.
On-Time Delivery
Definition: Share of orders delivered to customers by the agreed or promised date.
Formula: Number of orders delivered on or before promised date / Total orders delivered in period.
Typical cadence: Weekly operational and monthly performance review.
Suggested owner: Supply Chain Manager or Fulfillment Lead.
Decisions driven: Supplier selection, schedule buffers, process improvements in picking/packing/shipping.
Typical data sources: Order management system, carrier tracking, ERP.
Common pitfalls: Unclear definition of promised date (order date vs. requested date), excluding cancelled orders inconsistently.
Sample visualization: Trend line with on-time percentage and a breakdown by customer, region, or warehouse.
Inventory Turnover
Definition: How frequently inventory is sold and replaced over a period.
Formula: Cost of goods sold (COGS) in period / Average inventory value during period. Alternatively use sales for non-manufacturing contexts.
Typical cadence: Monthly or quarterly.
Suggested owner: Inventory or Supply Chain Manager.
Decisions driven: Replenishment policy, safety stock levels, SKU rationalization.
Typical data sources: ERP, inventory management system, accounting records.
Common pitfalls: Using period-end inventory instead of average, not accounting for seasonal seasonality, ignoring obsolete stock.
Sample visualization: Bar chart of turnover by product family plus trend line and days-of-inventory equivalent.
Forecast Accuracy
Definition: How close your forecasts are to actual results for demand, sales, or other measurable quantities.
Formula: Common choices include MAPE (Mean Absolute Percentage Error) = (1/n) × Σ(|Actual - Forecast| / Actual) or MAD (Mean Absolute Deviation). Choose and document one method.
Typical cadence: Monthly or aligned to planning cycle.
Suggested owner: Demand Planning or Forecasting Lead.
Decisions driven: Inventory policy, promotion planning, production scheduling, safety stock sizing.
Typical data sources: Sales history, POS, ERP, demand signals.
Common pitfalls: Reporting biased metrics (MAPE can be unstable with low-volume SKUs), not segmenting by product or lead time, ignoring bias vs. variance.
Sample visualization: Forecast vs. actual overlay with error band and histogram of percentage errors by SKU or region.
Final notes and adoption tips
- Version each KPI definition (formula, filters, exclusions) so teams can track changes.
- Always include a suggested data source and an assigned owner before publishing a metric.
- Start with a small set of decision-oriented KPIs rather than metric overload — each KPI should connect to a concrete decision or action.
- Document tolerances or target ranges and what actions to take when metrics move outside those ranges.
If you'd like, this catalog can be converted into an interactive template set where teams can copy selected KPIs into their domain, complete owner and data-source fields, and save a versioned KPI definition for governance and dashboarding.
Discussion
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