What Is Cohort Analysis?
Cohort analysis involves grouping your customers by a shared characteristic, most commonly their first order date, then tracking their behavior over time. Each group forms a cohort.
For example, the January 2025 cohort includes all customers who placed their first order in January 2025. You then track how many of these customers reordered in month 2, month 3, month 6, and so on. You also measure how much they spent cumulatively at each period.
The fundamental value of cohort analysis is separating the behavior of old and new customers. Without cohorts, your global metrics blend everything: a sales spike could come from loyal customers reordering or from new customers acquired through a campaign. Cohort analysis lets you distinguish these two dynamics.
It's also the best tool for measuring retention. Your store's overall retention rate is a misleading average. By looking cohort by cohort, you discover whether your customer quality is improving or declining over time, whether a specific acquisition campaign attracted lasting customers or one-time buyers, and whether your loyalty efforts have a measurable impact.
Why It's Impossible Natively on PrestaShop
PrestaShop stores all the information needed for cohort analysis: each customer's first order date, complete order history, and associated amounts. But the back office provides no tool to exploit this data in cohort form.
The native dashboard displays aggregated statistics: total revenue, order count, average order value. Customer reports show top customers by amount spent or number of orders. But no report groups customers by acquisition month to track their evolution over time.
The statistics modules available on PrestaShop Addons don't offer cohort analysis either. The reason is technical: building a cohort table requires complex SQL queries that cross-reference customer and order tables with dynamic temporal aggregations. This type of calculation is too heavy to run on-the-fly in PrestaShop's PHP back office.
To perform cohort analysis on PrestaShop, the only native option is to manually export your orders to CSV, import them into a spreadsheet or BI tool (Google Sheets, Excel, Looker Studio), build cohorts with formulas or pivot tables, then update this report regularly. It's a process that takes several hours and requires data manipulation skills.
Reading a Cohort Table: A Concrete Example
A cohort table is presented as a matrix. Rows represent cohorts (acquisition month) and columns represent subsequent periods (month 0, month 1, month 2, etc.). Each cell contains a metric: number of active customers, retention rate, or cumulative revenue.
Let's take a concrete example. The March 2025 cohort has 200 new customers. In month 0 (the acquisition month), these 200 customers generated total revenue of $12,000. In month 1 (April), 40 of these customers reordered, a 20% retention rate, for additional revenue of $3,200. In month 2, 25 customers reordered (12.5%), adding $2,100. In month 3, 18 customers (9%) for an additional $1,500.
Cumulatively, the March cohort generated $18,800 over 4 months, or an average LTV of $94 per customer. If you compare this with the February cohort, which has an LTV of $72 over the same period, you know that your March actions (campaign, new product, site improvement) attracted higher-quality customers.
The power of the cohort table lies in this comparison. You can immediately spot months where retention drops, cohorts acquired through aggressive promotions that never repurchase, and periods where your loyalty efforts bear fruit.
Key Metrics to Track in Your Cohorts
The first essential metric is retention rate by period. This is the percentage of customers in a cohort who place at least one new order in each subsequent period. A retention rate that declines slowly (from 20% in month 1 to 15% in month 3 then 12% in month 6) is a sign of a healthy customer base. A sharp drop (20% in month 1, 5% in month 2) indicates a satisfaction or product relevance problem.
The second metric is cumulative repurchase rate. Across an entire cohort, what percentage of customers placed at least 2 orders? At least 3? This metric is the most direct signal of customer loyalty. A 30% repurchase rate at 12 months is a good indicator for most e-commerce stores.
The third metric is cumulative revenue per cohort, which lets you calculate LTV at different time horizons. By plotting the cumulative revenue curve, you can see when the curve flattens, i.e., when customers stop repurchasing. This inflection point tells you the optimal window for your retention actions.
Finally, cumulative margin per cohort is the most advanced metric. It combines cohort analysis with net margin calculation to show not how much revenue a cohort generated, but how much actual profit it produced. This is the ultimate metric for evaluating the profitability of your acquisition channels.
How Fullmetrix Generates Cohorts Automatically
Fullmetrix syncs all your PrestaShop data (orders, customers, products) via the module compatible with 1.7 through 8.x, then automatically generates cohort analyses without any manual work on your part.
Cohorts are available in monthly and weekly granularity. The monthly view is ideal for long-term trends and strategic planning. The weekly view lets you measure the precise impact of a campaign or site change. You switch between views in one click.
Each cell in the cohort table displays revenue, number of active customers, and retention rate. A visual color code lets you instantly identify high-performing cohorts and those that are dropping off. You don't need data analysis skills to read the table: good and bad trends are immediately visible.
Fullmetrix goes beyond classic cohorts by integrating RFM segmentation (Recency, Frequency, Monetary) and lifecycle stages. These tools automatically categorize each customer into actionable segments: new customer, active customer, VIP customer, at-risk customer, lost customer. Combined with cohorts, they let you understand not only when customers drop off, but why and how to act. Data is hosted in the European Union, in compliance with GDPR.
3 Concrete Actions Based on Your Cohort Results
The first action is to identify and fix low-retention cohorts. If you observe that cohorts from certain months drop off faster than others, analyze what changed during that period: an aggressive acquisition campaign with heavy discounts often attracts bargain hunters who never return. The solution is to rebalance your acquisition mix by reducing aggressive promotions and investing more in channels that generate high-retention cohorts.
The second action is to trigger your retention campaigns at the right time. The cohort table shows you exactly when retention drops most. If most of your cohorts go from 20% to 8% between month 2 and month 3, that's the critical moment. Schedule a re-engagement email sequence at day 60 after the first order, with a personalized offer based on the customer's purchase history.
The third action is to compare cohort value by acquisition channel. Do customers acquired via Google Ads have better retention than those from Facebook? Do organic customers repurchase more than those from promotions? By crossing cohorts with acquisition sources, you can reallocate your ad budget toward channels that generate the most profitable long-term customers, not just the most numerous. This LTV-per-channel optimization rather than CPA optimization is what separates sustainably growing stores from those that stagnate.