Recommend what fits each customer, and prove it with a controlled test.
Recommendation and ranking models use browsing, purchase and catalog data to order products, content and offers for each visitor, within merchandising rules and consent settings — and every change is measured against a holdout.
- 01Collect events and catalog
- 02Generate candidates and rank
- 03Apply business rules
- 04Set rules and review
- 05Test against a holdout
The workflow
E-commerce, digital product and CRM teams who decide what customers see on the site, in the app and in email, and the merchandisers who set rules for it.
- Every visitor sees the same homepage and the same "you might also like" list.
- Merchandisers hand-curate rails that go stale within days.
- Email segments are broad, so most customers get offers that do not fit.
- Personalization vendors report uplift that nobody can verify independently.
Collect events and catalog
SystemConsented behavioral events, purchase history and catalog attributes are collected into a shared feature store.
Generate candidates and rank
AIModels select candidate items for each customer and context, then rank them by predicted relevance.
Apply business rules
SystemStock, margin, exclusions and merchandising priorities are applied before anything is shown.
Set rules and review
PersonMerchandisers adjust rules, pin or exclude items, and review what each segment is being shown.
Test against a holdout
PersonEach change runs as an experiment against a control group, and results are reviewed before full rollout.
Where people stay in control
Merchandisers control rules, exclusions and campaign priorities, and can see what any segment is shown. New models and major changes go out only after a controlled test is reviewed. Customers who have not consented to tracking get non-personalized defaults.
- Conversion and revenue per visitor against a holdout group
- Click-through on recommendation placements
- Catalog coverage, meaning how much of the range gets shown
- Opt-out and unsubscribe rates
Data and integrations
- Behavioral event data with consent status
- Order history and customer identifiers
- Product catalog with attributes, stock and margin
- Experimentation or feature flag tooling
Realistic boundaries
- New customers and new products have little history, so defaults and content-based signals carry more weight at first.
- Personalization only uses data customers have consented to, and sensitive attributes are excluded.
- Results are reported from your own tests; we do not quote uplift figures in advance.
How we build it
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- E-commerce DevelopmentStorefronts, catalog, checkout and the integrations behind them, built so the operations team can run the store without developers.
- Data Analytics & BIAgreed metric definitions, dashboards and analysis designed around the decisions your teams actually make.
- Retail & CommerceCustomer service, catalog operations, demand planning and personalization — connected to the commerce platforms and order systems you already run.
- Travel & HospitalityGuest service across channels and languages, disruption handling, property operations and personalization — connected to reservation and operations systems.
- Technology & SaaSAI features inside your product, developer tooling, support operations and platform engineering — built by a team that ships software, not just prototypes.
Questions buyers ask
Enough interaction history to learn from — usually months of events and orders across a reasonably active catalog. With less, simpler approaches such as popularity by category and content similarity often perform as well, and we will recommend those first.
Every change runs against a holdout group that sees the non-personalized experience, so the difference is measured on your own traffic. Our analytics work sets up the reporting.
Map this workflow with us.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.