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Intempt

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

1

Configure Website Experiment

Run Experiment

Follow the two-path setup below: configure the experience top-level (Path 1), then author the variant content (Path 2).

Produces:Experiment

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In the words of50+ live tenants.

Jim Stromberg, CEO at StockInvest

We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.

Jim Stromberg

CEO, StockInvest

Eric Gardner, COO at FieldsUSA

Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.

Eric Gardner

COO, FieldsUSA

Tadas Kertenis, Co-founder at Hoperfy

With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.

Tadas Kertenis

Co-founder, Hoperfy

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