Retail analytics consulting Know which customers carry your revenue, and which are slipping away.
In a typical online store a minority of customers brings most of the money. We find them in your order data, track whether they come back, and put the numbers that matter on one weekly dashboard.
What usually goes wrong
Revenue grows, but nobody knows from whom
Sales reports show totals by product and month. They do not show that a fifth of customers may bring three quarters of revenue, or that this group is shrinking.
Discounts go to everyone
Without segments, a retention offer reaches loyal buyers who would have bought anyway and misses those about to leave.
Repeat rate is a guess
Cohort retention is rarely tracked, so a drop in second purchases shows up months later as a revenue gap.
Ten dashboards, no answer
Platform reports, ad cabinets and spreadsheets disagree on revenue and customers. Meetings start with "which number is right?"
The numbers we put on your dashboard
| Metric | Formula | Why it matters |
|---|---|---|
| Revenue share by RFM segment | segment revenue ÷ total revenue | Shows how concentrated revenue is and which group to protect first. |
| Repeat purchase rate | customers with 2+ orders ÷ active customers | The base of predictable revenue. |
| Cohort retention, month N | buyers in month N ÷ cohort size | Catches a falling second purchase months before revenue does. |
| Average order value | revenue ÷ orders | Separates "more orders" from "bigger orders". |
Mini dashboard · public data
What this looks like on real orders
Built on 541,910 real transactions of a UK online retailer (UCI Online Retail II, 2010-12 to 2011-12). Your dashboard uses your data.
Source: UCI Online Retail II (Chen, 2019), sheet Year 2010-2011, CC BY 4.0; our calculation
Source: UCI Online Retail II (Chen, 2019), sheet Year 2010-2011, CC BY 4.0; our calculation
Case study
E-commerce retention model on 500,000+ transactions
Which customers come back? A retention model on a public online-retail dataset that beats the benchmark by 9 points of AUC.
From the blog
RFM segmentation on 500K real transactions: what drives repeat revenue
We scored 4,334 customers of a real online retailer by recency, frequency and monetary value. A third of them bring three quarters of revenue. Here is the method, the numbers and what to do with each segment.
Services that fit
Data health check
from $1,500
One definition of revenue, orders and customers across your sources.
Dashboard build
from $2,500
RFM segments, cohorts and weekly KPIs on one dashboard you keep.
Analytics retainer
from $2,000 / month
Monthly segment review and answers to new questions.
Prices are starting points; scope and price are fixed in a written quote. All prices →
Bring one question and a sample of your data.
On a free 20-minute call we tell you what the data can answer and what it would cost.