Next Best Product & Recommendations
Predict the next best product, content, or offer for every customer. EkamFlow's recommendation engine — sometimes called next best product — ranks your catalog per customer to drive conversion and engagement.
Next best product — often called a recommendation engine or product recommendation system — is an AI approach that ranks every item in your catalog against each individual customer and returns the top-scoring products, content, or bundles most likely to convert. Instead of surfacing generic 'customers also bought' collaborative-filtering suggestions, modern next-best-product systems evaluate per-customer purchase intent, cart context, browsing signals, and lifecycle stage to produce individually personalized recommendations. The output plugs into product pages, cart drawers, email templates, in-app surfaces, and merchandising rails.
What is Next Best Product & Recommendations?
A recommendation engine uses machine learning to predict which products, content, or actions are most relevant to each individual customer. Modern recommendation systems go beyond collaborative filtering to incorporate purchase history, browsing behavior, contextual signals, and customer attributes for truly personalized suggestions.
Building a recommendation engine in-house is one of the most complex ML projects a company can undertake. It requires specialized infrastructure for candidate generation, ranking, real-time serving, and continuous A/B testing. Most teams either rely on basic 'customers also bought' rules or expensive standalone recommendation platforms.
EkamFlow includes recommendations as part of its unified prediction model. The same API call that returns churn risk and LTV also returns personalized product recommendations — trained on your catalog data and customer behavior. No separate recommendation infrastructure to build or maintain.
How it works, step by step
- 1
Connect your warehouse
Snowflake, BigQuery, Databricks, Redshift, or Postgres. Read-only access to product catalog, order history, and browse-event tables.
- 2
Import catalog + engagement signals
EkamFlow inspects product attributes, order line-items, browsing sessions, and cart events — no schema config required.
- 3
Train private model on your catalog + customer overlap
The model learns which products convert together for which customer types, factoring in both classical collaborative signals and content-based catalog metadata.
- 4
One API call ranks catalog per customer
The API returns the top-N recommended products per customer, with relevance scores and recommendation-type tags (upsell, cross-sell, replenishment, discovery). Sub-60ms latency.
- 5
Continuous catalog and customer updates
New products, deprecated SKUs, and shifting customer behavior are all incorporated in real time. No index rebuilds, no pipeline reruns.
Signals the model uses
- Purchase history at SKU and category level
- Browse and cart behavior (viewed but not purchased)
- Co-purchase and co-view patterns
- Product attributes (price band, brand, category, tags)
- Customer lifecycle stage and cohort
- LTV band (recommend differently for high vs low-value customers)
- Recency and seasonality of category interest
- Inventory level (avoid recommending out-of-stock)
- Margin per SKU (rank on expected margin, not just conversion)
- Reviews, ratings, and quality signals
Where it fits in your stack
- E-commerce: Shopify, BigCommerce, Commercetools, Salesforce Commerce Cloud
- Marketing automation: Braze, Iterable, Customer.io, Klaviyo
- CDPs: Segment, Rudderstack, mParticle, Hightouch
- Search & merchandising: Algolia, Bloomreach, Coveo, Elasticsearch
- CMS: Contentful, Sanity, Contentstack
- Ad platforms: Meta CAPI, Google Enhanced Conversions, TikTok Events API
- Data warehouse: Snowflake, BigQuery, Databricks, Redshift
Build in-house vs. EkamFlow
| Dimension | Build in-house | EkamFlow |
|---|---|---|
| Setup time | 3–9 months (candidate gen, ranking, serving) | Days |
| Team required | ML eng + data eng + MLOps | None |
| Cold-start products | Hand-tuned rules or ignored | Content-based scoring from catalog metadata |
| Cold-start customers | Popularity fallback | Cohort-based personalization from first session |
| Multi-surface support | One integration per surface | One API — email, cart, PDP, search, in-app |
| Margin awareness | Rarely factored | Expected-margin ranking on every score |
| Latency | Depends on serving infra | <60ms per customer × surface |
What it looks like in different industries
A beauty brand replaced their 'customers also bought' widget with next-best-product recommendations. On the cart-drawer surface alone, average order value lifted 14%. Recommendations for first-time buyers surfaced bundles; recommendations for loyalty members surfaced newly launched premium SKUs.
A subscription media platform replaced editorial 'featured content' with per-user next-best-content ranking. Session length rose 22% and cancellation rate on the subscription dropped in the 60-day window after rollout. Content teams still curate; the model picks which curated pieces to surface first for each viewer.
A subscription analytics platform surfaces next-best-feature recommendations in-app — pointing power users toward the specific module they're most likely to adopt next based on their usage patterns. Feature-adoption uplift on the recommended modules is 3× vs. control.
A furniture marketplace ranks the entire cross-seller catalog for each shopper on every PDP. Instead of one recommendation module fighting with three others (recently viewed, trending, sponsored), a single next-best-product ranking drives all surfaces — with sponsored placements re-inserted as an eligibility overlay, not a separate model.
An OTA uses next-best-product to rank hotel, activity, and add-on inventory per traveler on the confirmation page. A honeymoon booker sees a couples spa package; a business traveler sees an early-checkin add-on; a family booker sees a kids-eat-free restaurant offer. Attach rate per booking rose 19%.
An online grocer runs next-best-product for the replenishment carousel in-app and in-email. The model factors household size, typical purchase interval, and current in-stock — so recommendations never surface out-of-stock SKUs and always match the shopper's actual replenishment rhythm. Basket size grew 8% without shifting any pricing.
How EkamFlow does it
Context-aware recommendations
Recommendations factor in churn risk, lifetime value, and purchase history — not just item similarity. High-value at-risk customers get different recommendations than new browsers.
Works across product, content, and offers
Recommend products, articles, features, or promotions from a single model. One API covers your entire personalization surface.
No recommendation infrastructure
Skip the candidate generation pipelines, embedding stores, and ranking servers. EkamFlow handles the entire recommendation stack inside one private model.
Related predictions
Frequently asked about next best product & recommendations
Dedicated recommendation platforms are strong for search-and-discovery workflows and generally integrate at the front end. EkamFlow's recommendation is one output of the same private model that returns churn, LTV, next best action, and next best offer — so recommendations are informed by (and consistent with) the rest of the customer decisioning. Most customers use both: a specialist platform for on-site search facets, EkamFlow for personalized recommendations across email, in-app, and cart.
New products are scored using content-based similarity to existing products in the catalog — a new sneaker gets scored against your existing sneaker embedding, not treated as an unknown. This solves the classic cold-start-item problem that pure collaborative filtering struggles with.
New customers get cohort-based recommendations from their first session — matched to comparable existing customers by acquisition source, device, geography, and any browse behavior in the current session. Personal signal builds within 3-5 interactions.
Yes. Exclusion rules are first-class: exclude out-of-stock SKUs, exclude products already in the customer's account, exclude a category the customer has explicitly opted out of. Exclusions are enforced at ranking time, not filtered after — so the top-N returned always fills correctly.
Yes. The objective is configurable: pure relevance (highest conversion probability), pure margin (highest expected profit), or a blended objective. Most e-commerce customers run a blend that weighs both, so a recommendation that lifts conversion 5% at 40% margin outranks one that lifts conversion 15% at 8% margin.
Any surface with an API integration: product detail pages, cart drawers, email templates, in-app widgets, push notification content, search-page merchandising rails, homepage carousels, and third-party ad platforms (Meta, Google, TikTok product-catalog ads). Same underlying model, different presentation.
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