---
title: "Level 5. Business Metrics"
id: "1787"
type: "page"
slug: "business-metrics"
published_at: "2026-09-20T21:35:46+00:00"
modified_at: "2026-09-20T21:43:44+00:00"
url: "https://xedant.com/agents/analytics/docs/templates/business-metrics"
markdown_url: "https://xedant.com/agents/analytics/docs/templates/business-metrics.md"
excerpt: "The fifth level is about the numbers you run a business with. How a metric…"
---

# Level 5. Business Metrics

[https://xedant.com/agents/analytics/docs/templates/business-metrics.md](https://xedant.com/agents/analytics/docs/templates/business-metrics.md)

The fifth level is about the numbers you run a business with. How a metric differs from a plain number, how revenue grows, where visitors leave, who comes back, what a customer brings over their whole lifetime, and which products actually feed the store. Nine lessons — each ends with a decision you can make on Monday.

Every lesson is a template with demo data: ten minutes to complete, one skill that stays. Open it in the Templates section of Analytics Agent and press “Use this template” to take the lesson on your own data.

### Not Every Number Is a KPI

**Shows:** three tests for a metric: does it move from your actions, do you understand what moves it, do you trust the data; three to five metrics, each with a goal and one owner; “empty” numbers are correct and useless — the customer counter grows in the worst month.

**You get:** you stop steering by numbers that cannot fall and pick a few that actually work.

### Revenue and the Average Check

**Shows:** revenue = number of checks × average check; a two-factor breakdown to the cent shows what exactly moved — the checks, the check size, or both; the check splits again into “items × price”. One month grew while checks were falling — and the breakdown sees it before decisions.

**You get:** the question “why did revenue change” gets exact arithmetic instead of opinions.

### Conversion: The Steps of a Customer

**Shows:** step conversion is those who got through divided by those who entered; the overall number hides the address of the leak, while a site sliced by devices finds a 39-point gap on one step; a point on the thinnest step is worth more than ten on the healthiest one.

**You get:** you know which step to fix first and what it will give — not “improve the site in general”.

### Retention: Who Comes Back

**Shows:** the retention curve — the share of one group of customers month after month; the shape is always the same: a steep drop in the first months, then the plateau of the loyal core — the number to move; the acquisition channel decides who stays: organic holds 2.5× more than coupon.

**You get:** “will customers come back” stops being a guess — and you see which acquisition door feeds you for the long run.

### Cohorts: Customers Grouped by First Purchase

**Shows:** everyone who started in the same month is a cohort; the overall average can hit records while nothing changes for anyone: the share of repeat orders reached an all-time high in the store’s worst month. Compare cohorts at the same age — month 3 against month 3.

**You get:** you see what really changed — the audience or the behavior — not just “everything grows / everything falls”.

### Reading the Cohort Table

**Shows:** the triangle where “rows are lives, columns are peers”; the empty corner is youth, not missing data; small rows swing on their own — quote them only with a size; hot starts cool down: the year’s best start settled into the middle by month three.

**You get:** the cohort table is an argument of the “you cannot explain this by season” level — and you can read it.

### What a Customer Is Worth Over the Years

**Shows:** customer value = average check × frequency × lifetime; acquisition pays off when cost is below value: one door returns 2.3× its cost in the first month, another returns 33.5 cents per dollar; young customers are “not worse — they are not finished yet”.

**You get:** you know how much you can really spend to acquire one customer — instead of a rule-of-thumb guess that is usually three times more optimistic than the truth.

### Churn: Where Customers Go

**Shows:** “left” is a definition derived from your own data rhythm: three silent months mean churn if the longest gap in the store is 44 days; the two diseases — “left” and “gone quiet” — are broken down by revenue; wake the recently quiet first — bringing them back is worth 2.4× more.

**You get:** you catch churn earlier and know whom to win back and whom to let go.

### ABC Analysis: The Vital Few

**Shows:** a sorted product list with your own threshold: A — the top up to 80% of value, B — up to 95%, C — the tail; the flat curve is the finding itself: here 25 of 40 products bring 80.9% of revenue; slices by revenue and by margin disagree on 11 of 40 products — the disagreement is the most interesting part.

**You get:** attention goes where the money is, automation goes to the tail; which value to rank by is your decision, not arithmetic.

[← Templates library](/agents/analytics/docs/templates)
 · [Training course section](/agents/analytics/docs/templates#wp-block-themeisle-blocks-advanced-heading-aaat3)