Product Recommendation for Retail & Media
Loyalty programmes, personal vouchers and "watch next": recommendations beyond the online shop.
What this track covers
For retail, CRM and media teams who use customer data to recommend products or content. Loyalty programme data and RFM segmentation, next best offer and personal vouchers, cross-merchandising from basket analysis, content recommendations that aim for satisfaction rather than just clicks, measuring impact with holdout groups, and consent, privacy and fairness. No coding.
New to AI? Every track builds on AI & Data Fundamentals.
What you will be able to do
- Use loyalty programme data and RFM segmentation for recommendations
- Design personal vouchers that drive new purchases
- Choose the right goal for content recommendations
- Measure uplift with a holdout group
Units and bites
- Unit 14 bitesAbout 12 min
Recommendations beyond the online shop
Physical stores, loyalty programmes and content platforms recommend too, with different data.
- 3 min
Recommendations in store and in media
Member vouchers, "watch next" and a cashier's suggestion: recommendations that don't look like an online shelf.
- 3 min
Online vs offline data
Physical stores have no "clicks": they have receipts, members and locations.
- 3 min
Omnichannel customers
Connecting shoppers who buy in store, in the app and on marketplaces.
- 3 min
What to optimise
Sales, repeat visits or satisfaction: the goal decides the recommendation.
- Unit 24 bitesAbout 12 min
Loyalty and member data
Loyalty programmes, RFM segmentation and basket analysis in physical retail.
- 3 min
What a loyalty programme collects
Receipts, points and member profiles as recommendation data.
- 3 min
RFM segmentation
Recency, frequency, monetary: grouping members with three numbers.
- 3 min
Basket analysis in store
Products bought together, for shelf placement and bundle promos.
- 3 min
Customers without a member card
What you can still learn from anonymous transactions.
- Unit 35 bitesAbout 15 min
Recommendations in retail
Next best offer, personal vouchers, cross-merchandising and suggestions from store staff.
- 3 min
Next best offer
Choosing the single most relevant offer for each member.
- 3 min
Personal vouchers
Vouchers that drive new purchases, not discounts on shopping that would happen anyway.
- 3 min
Cross-merchandising
Placing complementary products together based on basket data.
- 3 min
Clienteling: suggestions from store staff
Giving store staff a list of recommendations for loyal customers.
- 3 min
Seasonal offers
Recommendations for Ramadan, Lebaran and the new school year from member history.
- Unit 44 bitesAbout 12 min
Content and media recommendations
Watch next, related articles, freshness and diversity.
- 3 min
Watch next and read next
How video and news platforms choose what comes next.
- 3 min
Engagement vs satisfaction
Why clicks and watch time can mislead, and more honest signals.
- 3 min
Freshness and news
Content that goes stale fast needs different recommendations from a film catalogue.
- 3 min
Diversity and filter bubbles
Keeping recommendations varied so audiences are not stuck in one topic.
- Unit 54 bitesAbout 12 min
Measuring impact
Holdout groups, uplift and long-term metrics, not just vouchers redeemed.
- 3 min
Holdout groups
Setting aside some members who get no recommendations, as a comparison.
- 3 min
Redemption isn't impact
A voucher used by a loyal shopper may only cut your margin.
- 3 min
Uplift
The difference in behaviour between those who got the recommendation and those who didn't.
- 3 min
Long-term metrics
Retention, visit frequency and customer value over a year.
- Unit 64 bitesAbout 12 min
Trust, privacy and fairness
Member consent, the "creepy" line, editorial values and data-protection rules.
- 3 min
Consent and member data
What members agree to when they sign up, and what Indonesia's data-protection law means for loyalty programmes.
- 3 min
The creepy line
A recommendation that knows too much can drive customers away.
- 3 min
Fairness in offers
When different customers get different prices and offers.
- 3 min
Editorial values in media
Balancing the algorithm with editorial judgement about what matters.
Questions about this track
Learning
How long does Product Recommendation for Retail & Media take?
About 75 minutes in all: 6 units made of 25 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 Product Recommendation for Retail & Media 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 MarketingMetrics, funnels, A/B tests, attribution and cohorts — the numbers behind marketing decisions.
- 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.
Read while you wait
Guides from the Datamew team, free on the web today.
How to Write a Good Prompt: A 5-Part Framework for Work
A good prompt gives the AI five things: context, task, input, format and limits. It says who the work is for, what to do, which material to use, how the answer should look and what to avoid. Then you check the answer and ask a follow-up.
7 min read
How to Learn AI From Zero, Without Coding
Yes, you can learn AI without writing any code. To use AI well at work you need four things: a basic idea of how it works, a sense of where it fails, practice with one or two free tools, and the habit of checking every answer before you use it.
8 min readBe first on the app
Leave your email and we will tell you the day Datamew lands on your phone. One email at launch, no spam.
- Coming soon onApp Store
- Coming soon onGoogle Play