Revenue Growth

Intelligent Product
Recommendations

Put the right offer in front of the right customer at exactly the right moment.

The Challenge

Large catalogues create cross-sell paralysis

When a business sells hundreds or thousands of products, sales reps cannot hold the entire catalogue in mind for every customer conversation. Cross-sell and upsell opportunities are missed not from lack of intent, but from lack of a systematic way to surface them.

Generic recommendations — "customers also bought" — ignore the specific context of each account: their industry, their purchase history, their lifecycle stage, and the moment they are in right now.

Signals we use

Individual purchase history · Peer group buying patterns · Product affinity & co-purchase rates · Lifecycle stage & tenure · Engagement with previous recommendations · Catalogue complementarity · Seasonal & contextual triggers

Our Approach

How we build it

1

Catalogue & Purchase Mapping

We structure your product catalogue and map historical purchase sequences — identifying natural co-purchase clusters, product journeys, and the categories that consistently expand wallet share.

2

Collaborative & Content-Based Filtering

We combine collaborative filtering (what similar customers bought) with content-based signals (product attributes and customer profile) — producing recommendations that are both personalised and explainable.

3

Lifecycle-Aware Ranking

Recommendations are ranked not just on affinity but on timing — surfacing offers that fit where each customer is in their relationship with your business, not just what they statistically resemble.

4

CRM & Rep Tooling Integration

The top recommendations per customer are embedded in your CRM visit prep, email workflows, or sales dashboards — with clear reasoning so reps can own the conversation, not just read from a list.

Typical Outcomes

What to expect

Higher
Cross-sell attachment rates across the customer base
Larger
Average deal and order size through relevant upsell surfacing
Lower
Churn risk as customers expand into more of the catalogue
Daily
Recommendation refresh as purchase behaviour and inventory evolve
Related Case Study

Cross-sell intelligence across 1.6M SKUs

A Fortune 500 manufacturer deployed product recommendation models to 7,000 sales leaders — surfacing buying and cross-buying patterns for 11 million customers across a catalogue of 1.6 million SKUs, at a scale no manual approach could match.

Read the case study →

Explainability drives adoption

Reps adopt recommendations they understand. We build in clear reasoning — "customers in this segment who bought X typically added Y within 90 days" — so the recommendation becomes a conversation starter, not a black box directive.

What cross-sell opportunities are you missing today?

We can model your catalogue and purchase history to show the opportunity map before the engagement begins.

Map my cross-sell opportunity