Customer segmentation
Splitting customers into groups that differ, then treating each group differently.
What this course covers
One message for every customer rarely suits anyone. This course teaches you to build segments that are distinct, sizeable and actionable: starting with simple rules and RFM in a spreadsheet, then clustering when rules are not enough. You will turn segments into actions, measure their effect with a holdout group, and check that the segments are stable. Examples come from Indonesian online shops, banks and telcos, with figures in rupiah.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data analystData scientist
What you will be able to do
- Judge a segment against three tests: distinct, sizeable, actionable
- Build rules-based and RFM segments from an orders table
- Turn segments into actions and measure their effect with a holdout
- Check that segments are stable before building a strategy on them
Units and bites
- Unit 15 bitesAbout 15 min
Segmentation basics
Good segments, simple rules, RFM, turning segments into actions, and checking they are stable.
- 3 min
Why segment customers
One message for everyone wastes money. A good segment is distinct, sizeable and actionable.
- 3 min
Rules-based segments
The most useful first segmentation is often a few IF rules, written carefully.
- 3 min
RFM: a worked example
Recency, frequency, monetary: three numbers per customer that become segments in one spreadsheet.
- 3 min
From segments to actions
Every segment needs an action, a cost and a measure of success — including a comparison group.
- 3 min
Checking that segments are stable
A segment whose members change every week cannot carry a strategy.
- Unit 23 bitesAbout 9 min
Clustering for segmentation
When rules are not enough: clustering on customer data, from features to segment names. How k-means works is covered in the Supervised and unsupervised learning course.
- 3 min
Customer features for clustering
Choosing and preparing columns that describe behaviour, not just identity.
- 3 min
Naming clusters
From "cluster 3" to "double-date sale hunters" — and how to keep the name honest.
- 3 min
Rules or clusters?
When simple rules win, and when clustering finds what you could not see.
- Unit 33 bitesAbout 9 min
Behavioural and demographic data
What customers do versus who they are, and building one customer table from both.
- 3 min
Behavioural vs demographic
Why purchase history usually predicts better than age and city.
- 3 min
Building a customer table
One row per customer from orders, app events and customer service.
- 3 min
First-party data sources
The data you collect yourself, and what is missing without third-party data.
- Unit 43 bitesAbout 9 min
Measuring a segment strategy
Holdouts per segment, the right metric for each segment, and customers moving between segments.
- 3 min
Holdout groups for segments
How big a holdout needs to be, and how to pick it at random.
- 3 min
Segment-level metrics
Repeat rate for new customers, spend for active ones, cost per return for lapsed ones.
- 3 min
Customers moving between segments
Reading movement between segments as a measure of the strategy working.
- Unit 53 bitesAbout 9 min
Privacy and fairness in segmentation
Indonesia's personal data protection law, sensitive data, and making sure segments do not treat groups of people unfairly.
- 3 min
Personal data and consent
What may be used for segmentation under Indonesia's PDP law, and the lawful basis for it.
- 3 min
Sensitive attributes in segments
Religion, health and their proxies: columns that must not quietly decide a segment.
- 3 min
Fair offers across segments
When prices or credit limits differ by segment: checking who loses out.
Questions about this course
Learning
How long does Customer segmentation take?
About 51 minutes in all: 5 units made of 17 bites of about three minutes each, and every bite ends with a quick check. You can do one bite a day or several in a row.
Do I need to know how to code?
Not to start. The foundations explain every idea in plain words first; where a course uses SQL or Python, it teaches it from the beginning, with a playground in the lesson.
Where should I start?
If you are new to AI, begin with AI & data fundamentals. If you know the job you want, follow its career path; Customer segmentation has a place in it.
When can I start learning?
Datamew is coming to iOS and Android soon. There is no release date or price announced yet: join the waitlist and we will tell you when the app is out.
Is it available in Bahasa Indonesia?
Yes. Datamew is written in English and Bahasa Indonesia.
More courses
- AI & data fundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
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- Data engineerBuilding pipelines that deliver data on time and correct: SQL, Python, orchestration and warehouses.
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- SQL for analysisAsking the database directly: filtering, summarising, joining tables and window functions.
- StatisticsThe statistics every data role uses: summarising, reading distributions and measuring uncertainty.
- Supervised and unsupervised learningThe two ways machines learn from data: with answers, and without them.
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- A/B testingMeasuring whether a change really worked, with two random groups and an honestly read result.
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Read while you wait
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