> For the complete documentation index, see [llms.txt](https://docs.roivenue.com/roivenue-resources/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roivenue.com/roivenue-resources/roivenue-training/roivenue-features/cohort-analysis.md).

# Cohort Analysis

{% hint style="warning" %}
This feature is currently available only on **BETA**.
{% endhint %}

**Cohort Analysis** groups your customers by when — or through which channel — they first bought from you, and then follows each group over time. It answers the question most reports can't: *not* "how did this channel perform last month", but "are the customers we're winning actually worth it, and are they coming back?"

## When to use it

Reach for Cohort Analysis when you want to know:

* Are newer customers more or less valuable than the ones we acquired six months ago?
* Which campaign brings one-off buyers vs. loyal repeat customers?
* Did a specific launch, price change, or promo produce a better or worse cohort than usual?
* Do customers from one country, device, or product line retain better than others?

## The 6 KPIs

The cards at the top give you the headline numbers for the customers you've selected. The date picker decides **who counts**: everyone whose *first* purchase falls in the chosen period. Those customers are then tracked **all the way up to today**.

| KPI                                    | What it tells you                                                     | In plain terms                           |
| -------------------------------------- | --------------------------------------------------------------------- | ---------------------------------------- |
| **Customer Acquisition Cost**          | What you paid, on average, to win one new customer.                   | Total spend ÷ new customers              |
| **Avg. Order Value**                   | How much a typical order from these customers is worth.               | Total revenue ÷ total orders             |
| **Avg. Time to Second Purchase**       | How quickly new customers come back for order number two.             | Average days between 1st and 2nd order   |
| **Returning Customer Rate**            | The share of new customers who bought again at least once.            | Customers with 2+ orders ÷ all customers |
| **Avg. Number of Orders per Customer** | How often an average customer buys from you.                          | Total orders ÷ customers                 |
| **Avg. Lifetime Value (LTV)**          | The total revenue an average customer brings across all their orders. | Total revenue ÷ customers                |

## How to read the report

### The cohort table

Each **row** is a group of customers — one acquisition period (a week, a month…) or one value of the selected dimension (Platform Name, Campaign…). Each **column** shows how that group is doing as it ages: the first column is their starting point, and every column after it is one step later in their life as customers. Colour shading makes it easy to spot at a glance which groups grow and which fade. Hover any cell for the full detail.

### The performance chart

The line chart below the table puts every cohort on the same starting line, so you can compare their shape regardless of when they started. Shows the first 5 cohorts by default; you can pick up to 10.

### Cumulative vs. Absolute

A switch in the table header changes what each column means:

* **Cumulative** — a running total. Good for "how much has this cohort brought in *so far*".
* **Absolute** — just that single period. Good for spotting *when* activity happens or drops off.

## Settings

| Setting               | What it does                                                                                                                                                |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Metric**            | What the table and chart measure — e.g. Net Revenue, Conversions.                                                                                           |
| **Dimension**         | How customers are grouped. **Date** = group by when they were acquired; a channel dimension (e.g. Platform Name, Campaign) = group by where they came from. |
| **Granularity**       | The size of each period: Daily, Weekly, Monthly, Quarterly or Yearly.                                                                                       |
| **Number of periods** | How far into each cohort's life you track it — i.e. how many columns you see.                                                                               |

### How customers are counted <a href="#how-customers-are-counted" id="how-customers-are-counted"></a>

A "customer" is a unique person identified by their customer ID, with all their orders linked to them over time. Their **first order** makes them a **new** customer in a cohort; any **later orders** count as **repeat**. Importantly, **a customer is not the same as an order** — someone with 3 orders in January still counts as **1 customer** in the January cohort.

How a customer is assigned to a cohort depends on the dimension:

* **Date dimension — one customer, one cohort.** A customer belongs to exactly one cohort, decided by the date of their first order. All their future revenue and orders are tracked under that single cohort.
* **Other dimensions (e.g. Platform Name) — a customer can be split across cohorts.** Because a customer's journey often touches several channels, they are shared *fractionally* across those channels according to the **selected attribution model's** credit split. How the credit is divided depends on which model you pick.

## Filters

Filters respect where a customer *started*. A customer stays credited to the cohort and channel of their **first** purchase — so if you filter to a channel, you see the customers that channel *acquired*, including any revenue they later brought through other channels. (A handful of **order-level** filters, such as Conversion Type, Delivery Type or Order Status, **apply to all orders** instead.)

<details>

<summary>Full list</summary>

* Conversion Type
* Coupon
* Customer Birth At
* Customer City
* Customer Country Code
* Customer Email Domain
* Customer Email Hash
* Customer Gender
* Customer Is Company
* Customer Latitude
* Customer Longitude
* Customer Phone Hash
* Customer Postal Code
* Customer Segment
* Customer Street
* Delivery Type
* Destination
* Item Names
* Loan Attribution
* Order Status
* Payment Type
* Processing Type
* Product Type
* Sales Channel
* Source System

</details>

## Exporting

Both the table (XLSX) and the chart (PNG) can be exported. All of them — plus KPIs — can be added to a board.

## When is data available?

Cohort Analysis needs at least one site with a transaction feed. If your account doesn't have one yet, [**contact us**](https://roivenue.com/help) and we'll help you set it up.