AI Product Recommendations

Adobe Sensei-powered recommendation blocks that show each shopper the products they're most likely to buy — not the same bestseller list everyone else sees.

Increase Your Average Order Value

26%

Of revenue at top retailers driven by recommendation engines

10–30%

Typical AOV increase after AI recommendation implementation

Recommendation placement types across homepage, PDP, cart, and email

Manual rule maintenance once the model is trained on your data

Recommendation Types We Implement

Every placement serves a different intent — we choose the right algorithm for each position.

Customers Also Bought

Collaborative filtering based on real purchase co-occurrence. Shows products that buyers of this item consistently add to the same order.

Viewed This, Bought That

Bridges the browse-to-buy gap — shows the products shoppers actually convert on after viewing the current PDP.

More Like This

Content-based similarity using product attributes, category, price band, and visual features for catalogue browsing pages.

Homepage Personalisation

First-visit and returning-visitor recommendation blocks personalised to session behaviour and purchase history.

Cart Cross-Sell

High-margin complements surfaced in the cart drawer, tuned to avoid recommending what the shopper already has.

Email & Push Triggers

Post-browse and post-purchase recommendation sequences delivered via your ESP — personalised per recipient, not per campaign.

How We Implement

Data Audit

Review your order history volume, catalogue size, and existing analytics setup. Adobe Sensei requires sufficient behavioural data — we assess readiness before committing to an approach.

Configuration

Sensei Product Recommendations module installed and connected to your catalogue and behaviour data. Placement strategy agreed for each page template.

A/B Testing

Each recommendation block launched as an A/B test against the existing experience (manual rule set or empty slot) so the revenue impact is measured, not assumed.

Optimisation

Monthly review of click-through and conversion data per placement. Algorithm type adjusted where data suggests a different recommendation strategy would perform better.

Whether you're launching your first online store or scaling a global brand, we're here to help you succeed in the digital marketplace.

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