In this guide
- Use RFM (recency, frequency, monetary value) to segment customers with a simple spreadsheet.
- Cohorts and lifetime value show whether acquisition is getting better and what a customer is worth.
- Feed what you learn back into ads and e-mail: exclude, upsell and win back.
Customer data analytics sounds like a project for a data team. A small store can do the useful parts in a spreadsheet, with the orders it already has. This page explains five simple analyses, what each tells you and what to do with it. It continues from customer data collection and customer data management.
1. RFM segments
Score each customer by how recently they bought (recency), how often (frequency) and how much they spent (monetary). Group them into segments: champions, loyal, at risk, lost. A short list of rules is enough: for example, "bought in the last 30 days and at least three orders" is a champion.
| Segment | Typical rule | What to do |
|---|---|---|
| Champions | Recent, frequent, high spend | Reward, ask for reviews, early access |
| Loyal | Frequent | Cross-sell, loyalty offers |
| At risk | Used to buy often, quiet now | Win-back offer |
| New | First order recently | Welcome series, second-order nudge |
| Lost | No order for a long time | One last offer, then suppress |
Check your tracking in one pass
This is an example. Run the check on your own site to see where yours stands.
2. Cohorts
Group customers by the month of their first order and track their revenue over the following months. A table of cohorts shows whether new customers are repeating more or less than last year's, and when most repeat orders arrive. It tells you when to send the next offer.
3. Lifetime value
Add up the gross profit a customer brings over time, on average. Compare it with the cost of acquiring them. If a customer brings $140 of gross profit and costs $50 to acquire, you have room to spend more. Use gross profit, not revenue. See profit on ad spend.
3 things to remember
Scroll and the points stack up.
Use RFM (recency, frequency, monetary value) to segment customers with a simple spreadsheet.
Cohorts and lifetime value show whether acquisition is getting better and what a customer is worth.
Feed what you learn back into ads and e-mail: exclude, upsell and win back.
4. Churn
For subscriptions, churn is the share of customers who cancel each month. For shops, define "lapsed" as no order in a period that fits your products, and watch the share who lapse. A rising rate is an early warning.
5. Product affinity
Which products are bought together, and which first products lead to repeat buyers? Use it for bundles, recommendations and first-order offers. A simple count of pairs in orders is enough to start.
Use the results
In ads
Upload champions as a seed for similar audiences and exclude recent buyers from acquisition. See customer lists in Google Ads.
In e-mail
Send segment-specific messages.
In offers
Use affinity to design bundles.
In budgets
Set acquisition targets from lifetime value.
Make the data trustworthy
- Match orders to customers with a stable ID.
- Merge duplicate customers.
- Include refunds and returns.
- Keep consent status with each customer.
A realistic example
A coffee subscription shop exports its orders and builds a simple RFM table in a spreadsheet. It finds that its top 8 percent of customers produce a third of its gross profit, and that about a fifth of customers who bought three times in the past year have gone quiet for over ninety days. It sends the quiet group a small win-back offer, uploads the top group as a seed for similar audiences, and excludes everyone who bought in the last thirty days from its acquisition campaigns. In the following quarter the win-back offer brings back a meaningful share of the quiet group at almost no cost, and the acquisition campaigns stop wasting spend on existing buyers. None of it needed more than the orders the shop already had.
Orders from your store, back to the ad that earned them
Your store sends the order once. Open Tracked passes it on to each ad platform, cleaned and deduplicated.
Frequently asked questions
What is customer data analytics?
Using customer and order data to understand segments, behaviour, value and risk, so you can act.
What is RFM?
A way to segment customers by recency, frequency and monetary value of purchases.
Do I need a data warehouse?
Not to start. A spreadsheet and your shop's export are enough for the first analyses.
How do I use segments in ads?
Upload lists to ad platforms as seeds or exclusions, with consent.
What should I calculate first?
RFM segments and average gross profit per customer.
Do I need spreadsheets to read my results?
No. Once tracking is live, the Open Tracked analytics shows spend, revenue, ROAS and profit, and you can ask Ari, the built-in AI assistant, questions about your own numbers in plain words. Ari's allowance depends on your analytics plan.
Is Open Tracked different from Open Track or Open Space Track?
No. Open Tracked is the official name of our server-side tracking platform, and the website is opentracked.io. Many people search for it as Open Track, OpenTrack or Open Space Track. All of them lead to the same product: server-side Google Tag Manager hosting, first-party analytics and a CRM pipeline.
Fix your tracking at the source
Open Tracked hosts your server-side Tag Manager, keeps first-party cookies alive and shows what each ad is really worth.
The Open Tracked team
We build Open Tracked: server-side tracking hosting, analytics and the tools around them, for stores that spend on ads. Questions about this guide, or an idea for the next one?
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