Retail & E-commerce · Product recommendation engines
Your recommender is showing popular products — not the right product for this shopper
Most e-commerce recommendation engines are popularity engines. They surface what sold last week to everyone, regardless of what a specific shopper has browsed, bought, or searched for today.
Banao builds recommendation engines that mix real-time behavioural signals with catalogue attributes and LLM-interpreted intent, then instruments each surface — homepage, product detail, cart, post-purchase — against the metric it is supposed to move. A surface that does not shift AOV or repeat rate gets turned off.
Myntra— engineering pods on search, discovery, and personalisation at fashion marketplace scale.
What a Banao recommendation deployment includes
A recommendation engine is not a single model — it is a real-time signal pipeline, a ranking layer, per-surface integration, and instrumentation. We own all four.
Multi-signal ranking per surface
Collaborative filtering, content similarity, and LLM query understanding are blended per surface — homepage, PDP, cart, search — so the ranking model uses the signal that is actually predictive at that point in a session.
Real-time feature store
Shopper actions in the current session — clicks, add-to-cart, search terms — feed the model in milliseconds. The recommendation at step three reflects what the shopper did at steps one and two, not what they bought six months ago.
Cold-start handling for new products and new visitors
A fast-turnover catalogue breaks models trained only on historical interactions. We handle cold-start with content-based fallback and staged confidence thresholds so new SKUs get exposure and new visitors get relevant results from session one.
Per-surface instrumentation and A/B gating
Each surface — homepage carousel, similar-items rail, post-purchase strip — ships behind its own A/B experiment. We measure click rate, add-to-cart rate, and AOV per variant and only promote what wins.
Category manager explainability
Merchandising teams can see why a product ranked where it did on any surface — which signals drove it up, which pulled it down — and can pin, exclude, or boost without touching model weights.
We run our own growth on the systems we build for clients
Banao operates a ~300-person engineering company on its own AI products before those products reach a client storefront. Vikaas runs Banao's own demand-gen pipeline, including the targeting and sequencing logic that the same team builds for e-commerce growth clients.
The recommendation and targeting patterns we bring to your PDP have already had to survive a commercial operation that depends on them week to week.
- VikaasRuns Banao's own demand-gen and targeting pipeline end to end.
- InterviewGodScreens Banao's own engineering hires every week.
When a recommendation engine is the wrong call
Most personalisation vendors will quote you an engine regardless of whether the conditions for it to work exist. We would rather spend a scoping call ruling it out.
- Thin interaction history: collaborative filtering needs volume. If your catalogue is large relative to monthly orders, curated merchandising will outperform a model, and a model will only add latency and maintenance cost.
- No funnel instrumentation: if you cannot measure lift per surface today, step one is measurement, not a model. Building a recommender onto a funnel you cannot read is how the previous personalisation project failed.
- Catalogue churn faster than the model retrains: in verticals with very high SKU churn — fast fashion, daily deals — session-based and content-based signals must dominate. If your data team cannot support a regular retraining cadence, the Discovery Sprint will surface that before you spend on a build.
How we start — fixed-price, low risk
The previous personalisation project probably started with a platform pitch. We start by finding which surface has a measurable gap and whether a model closes it.
- AI Discovery Sprint2 weeks · fixed price
We instrument your recommendation surfaces, analyse interaction data per surface, and rank the plays by the AOV, repeat-rate, or conversion gap they close. You walk out with a prioritised surface map, baseline lift maths, and a go/no-go per surface — yours to keep whether or not you continue. If you proceed, the Sprint fee is credited against the build.
- Build
Data unification first, then the feature store, then the model. We integrate with your storefront — Shopify, headless, or custom — and ship v1 of the highest-priority surface in weeks behind an A/B test, with Core Web Vitals as a gate.
- Production & continuous learning
Models retrain on live shopper behaviour on a regular cadence. Each new surface ships the same way: A/B first, promote on measured lift. Your team owns the codebase from day one.
Frequently asked questions
How is this different from the recommendation widget our platform already includes?
Platform-native widgets are popularity engines with basic rules. They cannot blend real-time session signals, handle a fast-changing catalogue, or instrument lift per surface against a business metric. The gap shows up when you try to measure whether the carousel is actually moving AOV — most merchants find it is not.
How many product interactions do you need before the model is useful?
It depends on the surface and the signal mix. Collaborative filtering needs meaningful monthly order volume to surface non-obvious affinities. Below that threshold we rely on content-based and session-based signals, which work from day one but do less personalisation heavy lifting. The Discovery Sprint quantifies exactly where your data sits on that curve.
Can it handle a catalogue with hundreds of new products added every week?
Yes, with explicit cold-start handling. New products get content-based representation from day one — attributes, images, descriptions — and graduate to interaction-weighted ranking as data accumulates. We set confidence thresholds so a product does not get buried because it has no history yet.
How do we measure whether the engine is actually earning its cost?
Each surface ships behind an A/B test with a defined primary metric — AOV for a cart recommender, click-to-cart for a PDP similar-items rail. We measure those metrics weekly and surface the results in a dashboard your e-commerce and merchandising teams can read without a data scientist present.
How fast can a recommendation engine reach production?
A typical path: 2-week Discovery Sprint, then 6–8 weeks to v1 on the highest-priority surface behind an A/B test. Banao's ~300-engineer bench means delivery starts in weeks, not the quarters a single in-house ML hire would take.
Find out which recommendation surface is leaving the most AOV on the table
Bring your current conversion rate, AOV, and repeat-purchase rate. In 45 minutes we will identify the surface with the largest measurable gap and what a recommendation engine would move.
Book a 45-min scoping call