Find which recommendation logic actually drives more cross-sell revenue

Collaborative filtering, session-based, and popularity-weighted recommendations all sound reasonable in theory, and they perform differently in practice depending on your catalog. This tests all three on real revenue.

Analyticsexperiment-strategistEcommerceStandard1 step1 output

Experiment

Website experiment created on /experiences.

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What it does

Runs a server-side experiment testing which recommendation engine - collaborative filtering, session-based, or popularity-weighted - drives more cross-sell revenue.

You get

a measured best recommendation algorithm for your catalog

How it works

Set up the algorithm test

Run Experiment

Runs a server experiment on the recommendation_algorithm flag with three strategies: collaborative filtering (Customers also bought), session-based (Recently viewed by you), and popularity (Trending now). Only visitors with more than one page view count. The winner earns the most revenue from orders containing a clicked recommendation.

Produces:Experiment

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