▶ Customer analytics
Your sales problem is not always a sales problem.
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.
There is no average customer. A report that looks at averages hides the differences that matter most.
Atalay Deveci · Updated 2026-10-08
What this service actually is
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.
It is not a CRM implementation or campaign management. I don't run the campaigns; I show with data which customer group to reach, when, and with which offer.
The uncomfortable part
Falling sales are usually read as a need for more new customers. Most of the time the problem starts with existing ones.
Averages hide the difference that matters most.
Average basket or average order frequency blends a few very valuable customers and many one-time buyers into one number. When growth happens in one group and loss in another, the total shows neither.
New customers are counted, returning ones aren't.
The number of customers won each month gets reported; how many of them came back a second time doesn't. Without cohort analysis, losing old customers while winning new ones stays invisible.
By the time churn is noticed, it's too late.
In non-contract businesses customers don't announce they're leaving; they just stop coming. The loss shows up in a report months later, when winning them back is harder than finding new ones.
I will tell you early which of these applies. If I see your data isn't enough for customer-level analysis, I will tell you that too.
▶ 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.
▶ When you should not hire me for this
If any of these is true for you, let's not start this engagement:
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.
Nobody will act on the results.
A list of at-risk customers changes nothing if no one is going to reach out to them.
You're looking for someone to run your campaigns.
That is an agency or a marketing hire. I build the customer understanding and decision side.
▶ Engagement shape
If your customer data can be brought together we start with a fixed-scope pilot of 4–6 weeks. If the sources are scattered or unclear, a ~2-week AI & Data Opportunity Assessment first makes clear what is possible.
The scope is agreed in writing up front. At the end of the pilot you have segments, a cohort view and a list of customers at risk of leaving.
- 30-day exit
- If it isn't working, you can end the engagement with 30 days' notice.
- Invoiced from Türkiye
- I invoice through my company registered in Türkiye.
- Reply within 24 hours
- I reply to every message within 24 hours.
▶ Questions and answers
What data is needed?
At minimum, sales history per customer. CRM and e-commerce data make the analysis much stronger.
What is cohort analysis for?
It follows customers won in the same period together, showing what happens after the first purchase: how many come back, when they stop, and how their value changes over time.
Is it suitable for B2B companies?
Yes, if the number of customers and their purchase history are enough for analysis. Companies with a few large customers usually need a different approach.
Is customer data a KVKK problem?
The work is done within the rules your company sets and within KVKK. How personal data will be processed is agreed up front.
▶ Fastest route
Start with one email.
The link below opens an email with the questions that make the first conversation useful already written in. No form, no account. I reply within 24 hours.
hi@klarvoanalytics.com
▶ Other services
- Corporate trainingsThe team can't work with data.
- In-house AI assistantsCompany knowledge gets lost in documents.
- Demand and sales forecastingNobody can predict what will sell.
- Reporting and data infrastructureReports are built by hand and take days.
- Custom dashboardsOff-the-shelf BI tools can't show what we need.
- Fractional data science leadNobody owns data.