Show the same smart recommendations everywhere a customer looks

A recommendation engine that only lives on the product page misses two-thirds of the surfaces where it could actually drive a sale. This builds catalog-aware recommendations that work across web, email, and app.

Marketingexperience-optimizerEcommerceAdvanced7 steps7 outputs

+3 more

Recommendation Personalization Segment Experiment Content Asset Journey Dashboard

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What it does

Builds catalog-aware product recommendations that surface consistently across web, email, and app, rather than being confined to a single channel.

You get

cross-surface catalog recommendations

How it works

Build the recommendation model

create_recommendation

Recommendations drawn from your product catalog using shopper-similarity and item-similarity models.

Produces:Recommendation

Place them on each surface

create_personalization

Rules for what shows on product pages, in the cart, after purchase, and in email.

Produces:Personalization

Split shoppers into cohorts

Create Segment

New visitor, browsing, returning and lapsed cohorts, each getting a differently tuned set of picks.

Produces:Segment

Test against the alternatives

Run Experiment

A/B variants comparing personalized picks against trending products and an editor's pick.

Produces:Experiment

Build the email modules

Generate Content

Recommendation blocks that drop into campaign and journey emails.

Produces:Content Asset

Follow up with high-intent browsers

Create Journey

A journey that sends personalized product picks to shoppers showing strong buying intent.

Produces:Journey

Track what recommendations earn

Build Dashboard

Click-through rate, revenue attributed to recommendations, and how each surface performs.

Produces:Dashboard

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How to build a GTM brain (and why memory is not the hard part)

Five layers, in order: capture, retrieval, source truth, permissions, feedback. Most teams build the first one and stop. That is a storage unit, not a brain.

How to use Claude for GTM: the setup I actually run

The five prompts I run as standing jobs: a morning brief, same-day follow-ups from call notes, a ninety-second account brief, a weekly account watch and a Friday pipeline read. Plus the one permission setting that makes an agent safe to leave running.

In the words of50+ live tenants.

“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

“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

“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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