NORQ AI

Recommendation systems for retail

Building a recommendation engine from scratch on Google's own store data - the public GA4 export of the Google Merchandise Store - and publishing every result, including the ones that did not work. The question behind the series: does a recommendation engine add 2% or 12% to a shop's revenue, and how would you know?

Part 1

Your analytics data is lying to your recommender

Product views over-counted 13.6 times, nine product IDs for one sweatshirt, and a crawler responsible for 29% of all product-page views. What has to be fixed before any model is trained.

Part 2

A recommendation engine written in 30 lines of SQL beat the bestseller list by 60%

Five models on the same held-out January sessions. The winner has no training step, the textbook ranking loses fifteen points, and the purchase-only model comes last.

Part 3

Measuring a recommender honestly

What an offline test cannot tell you, and the ladder from a held-out score to a live holdout group. Coming soon.

Customer lifetime value

Which customers are worth paying for? Worked through on two years of real order history from a UK online retailer, ending with the part that changes a budget: handing a predicted value back to the ad platform that bids on your behalf.

Part 1

Your customer acquisition cost is judged against the wrong number

The top 10% of customers produced 64% of two-year revenue, the first order was 14% of a customer's value, and first-order value identified one in three of the customers who ended up mattering.

Part 2

Customer value becomes predictable after 90 days, not after the first order

How long a retailer has to wait before an acquired customer stops being a guess: 42% of the eventual top decile visible from the first order, 67% after 90 days, and where the curve flattens.

Part 3

Teaching Google Ads what a good customer is

Value-based bidding on predicted lifetime value, and the export that makes it possible. Coming soon.

Every figure in these articles is reproducible from public data: the ga4_obfuscated_sample_ecommerce dataset published by Google through the Cloud Public Datasets Program, and UCI Online Retail II (Chen 2012, CC BY 4.0). The queries are in the articles.

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