Data Analytics for Marketing
Metrics, funnels, A/B tests, attribution and cohorts — the numbers behind marketing decisions.
What this track covers
The numbers that decide where a marketing budget goes, explained with arithmetic you can do on your phone. Decision metrics vs vanity metrics, CAC and LTV, funnels and where they leak, A/B tests you can trust, attribution and its limits, cohorts and retention, segmentation, and dashboards people actually read. No coding.
New to AI? Every track builds on AI & Data Fundamentals.
What you will be able to do
- Choose metrics that guide decisions, not ones that only look good
- Calculate conversion rate, CAC, LTV and ROAS correctly
- Design and read an A/B test without fooling yourself
- Read cohorts and retention to see whether customers come back
Units and bites
- Unit 16 bitesAbout 18 min
Metrics that matter
Which numbers help you decide, and which only look nice.
- 3 min
Decision metrics vs vanity metrics
A good metric changes what you do next.
- 3 min
Reach, clicks, conversions
The three layers of campaign numbers and what each one tells you.
- 3 min
Conversion rate
Calculating it, comparing it, and not being fooled by a changing denominator.
- 3 min
Customer acquisition cost (CAC)
What it costs to win one new customer, channel by channel.
- 3 min
Customer lifetime value (LTV)
What a customer is worth over time, and why it has to beat CAC.
- 3 min
ROAS vs ROI
Revenue per rupiah of ads is not the same as profit.
- Unit 25 bitesAbout 15 min
Funnels
Seeing the customer journey as a chain of rates, and finding the weakest link.
- 3 min
The funnel as a chain of rates
From visit to cart to payment: every step has its own rate.
- 3 min
Where people drop off
Finding the step that loses the most people.
- 3 min
Funnels by channel
Why traffic from ads, search and email behaves differently.
- 3 min
Micro-conversions
Small steps — saving a product, joining a newsletter — that predict a purchase.
- 3 min
Fix the narrowest step
Working out where a small improvement pays off most.
- Unit 36 bitesAbout 18 min
A/B tests
Comparing two versions fairly, and reading the result without fooling yourself.
- 3 min
What an A/B test answers
One change, two random groups, one question.
- 3 min
One metric, chosen up front
Why picking the metric after seeing results is a sure way to find a fake winner.
- 3 min
How many people you need
Small differences need big samples — an intuition for sample size.
- 3 min
Reading the result
The difference, the uncertainty, and what "significant" means in plain words.
- 3 min
Don't peek
Stopping a test when it looks good makes the result untrustworthy.
- 3 min
When not to test
Low traffic, obvious changes, or decisions that are easy to reverse.
- Unit 45 bitesAbout 15 min
Attribution
Which channel deserves credit for a sale — and why no answer is perfect.
- 3 min
Who gets the credit
A customer sees an ad, reads a review, then searches on Google. Who made the sale?
- 3 min
Last click
The most common attribution model, and the channels it always favours.
- 3 min
Multi-touch
Splitting credit across touches, with assumptions you should know.
- 3 min
Incrementality
The more honest question: how many sales wouldn't have happened without this campaign?
- 3 min
Living with uncertainty
Making budget decisions when attribution is never certain.
- Unit 55 bitesAbout 15 min
Cohorts and retention
Do customers come back? Following groups of customers over time.
- 3 min
What a cohort is
Grouping customers by when they started.
- 3 min
Retention curves
Reading the chart that shows how many customers stay.
- 3 min
Churn
The rate customers leave, and why a small monthly churn adds up over a year.
- 3 min
LTV from cohorts
Estimating customer value from real cohort data, not assumptions.
- 3 min
Repeat purchase
A simple metric that often matters more than winning new customers.
- Unit 65 bitesAbout 15 min
Segmentation and dashboards
Grouping customers usefully, and reporting numbers people actually read.
- 3 min
RFM segmentation
Recency, frequency, monetary: a customer segmentation you can build in a spreadsheet.
- 3 min
Segments that behave differently
A good segment calls for a different action; otherwise it's just a label.
- 3 min
A dashboard people use
Few numbers, one question per chart, and a clear comparison.
- 3 min
Weekly reporting
A reporting rhythm that catches problems early without causing panic.
- 3 min
Telling the story
From numbers to a recommendation: what happened, why, and what we do now.
Questions about this track
Learning
How long does Data Analytics for Marketing take?
About 96 minutes in all: 6 units made of 32 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?
No. Every Datamew track is written for working professionals without a technical background. Where there is arithmetic, it is the kind you can do on a phone or in a spreadsheet.
Where should I start?
Everyone begins with AI & Data Fundamentals, which covers how AI works, how to write a good prompt and how to read data honestly. Then take Data Analytics for Marketing for your role.
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 tracks
- AI & Data FundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
- AI for MarketingResearch, content, personalisation and campaign analysis with AI — with your judgement still in charge.
- Data Analytics for HRPeople analytics: turnover, hiring, engagement and pay fairness — with privacy protected.
- Forecasting for Retail & E-commerceForecast demand, plan stock, and be ready for Ramadan, 11.11 and 12.12.
- Forecasting for MarketingForecast traffic, leads and campaign results, and defend the numbers in the budget meeting.
- Forecasting for FinanceBudgets, revenue and cash forecasts, rolling forecasts and scenarios, with spreadsheet arithmetic.
- Product Recommendation for E-commerceHow "you might also like" works, the data behind it, bundles, and how to measure the impact.
- Product Recommendation for Retail & MediaLoyalty programmes, personal vouchers and "watch next": recommendations beyond the online shop.
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