Customer analytics for manufacturing
In B2B manufacturing, a few large customers make up much of revenue. When a dealer or customer slowly stops ordering, it's often noticed too late.
▶ The solution
I turn your customer data into segments, cohort and retention analysis, churn risk and customer value, making visible who is valuable, who is about to leave and what brings customers back.
▶ Use case
Dealers and customers are segmented by order frequency, volume and product mix, and customers whose ordering pattern breaks are flagged early to sales.
▶ Data you need
Order history per customer, product and price data, and customer master data.
The industry's data landscape
In manufacturing, data usually lives in the ERP (orders, stock, purchasing), in production tracking (downtime, scrap, shifts) and in quality records. These systems rarely talk to each other; even the same product can carry different codes in each. So the first job is often to bring them together in one table.
Customer analytics draws answers from how your customers buy: who is valuable, who is growing, who is about to leave and what happens after the first purchase. Segmentation, cohort analysis, retention, churn prediction, customer lifetime value and purchase patterns are the tools for those questions.
The math behind it
Churn rate = customers lost in period / customers at start × 100 · Average customer lifetime ≈ 1 / churn rate
- Example: losing 50 of 500 customers in a month is 10% monthly churn [1].
- An annual churn rate of 25% implies an average customer life of about 4 years [1].
How I work
- Week 1
Mapping the customer data.
Can sales, CRM and e-commerce data be joined at customer level? Does the same customer appear with the same identity across systems? What the analysis will rest on becomes clear here.
- Weeks 2–3
Segments and cohorts.
Customers are grouped meaningfully by how they buy: how often, how much and since when. Cohorts track what customers won in the same period do over time.
- Weeks 4–5
Churn risk and customer value.
Customers at risk of leaving are flagged in advance. Customer lifetime value is calculated per segment, making clear which group is worth investing in.
- Final week
Handover.
Segments and risk scores are pushed into the tools your teams use, with documentation on how to refresh and read them.
▶ Questions and answers
Customer analytics for manufacturing — what data do you need?
Order history per customer, product and price data, and customer master data.
When is this the wrong choice?
You can't track sales per customer. If you don't know which sale belongs to which customer, that foundation has to be built first. You have few customers and know them all. With a few dozen corporate customers, good account management beats a segmentation model.
How does this work start?
With a short intro call. I listen to what you need and tell you plainly whether your data is enough for the job and what the first step should be. I reply to every message within 24 hours.
▶ Concepts
Linked to Wikipedia and Wikidata.
▶ Sources
- [1]Wikipedia, Churn rate, 2026-10. en.wikipedia.org/wiki/Churn_rate
- [2]Wikipedia, Customer lifetime value, 2026-10. en.wikipedia.org/wiki/Customer_lifetime_value
▶ Intro call
Let's talk about your situation.
In a 30-minute call I listen to what you need and tell you plainly how I can help.
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