AI decisioning for retail & e-commerce marketing.
For DTC brands, marketplaces, grocers, and omnichannel retailers. EkamFlow predicts next best product, next best offer, send-time, and churn risk per customer — so marketing, growth, and merchandising teams stop maintaining rules and start shipping personalization at scale.
Why Retail & E-commerce marketing teams use EkamFlow
Retail and e-commerce marketing teams sit on more customer signal than almost any other category — order histories, browse sessions, cart abandons, loyalty scans, in-store visits, subscription cadences, and lifecycle emails all flowing into the same warehouse. But most of that signal never reaches campaign decisions, because turning it into per-customer predictions has traditionally required a data-science team the marketing budget can't justify.
EkamFlow gives retail and e-commerce marketers a private AI decisioning layer that trains on all of that behavior and returns per-customer predictions through one API — the next best product to surface in the cart drawer, the next best offer to test on a dormant subscriber, the optimal send time for a launch email, the churn risk on a lapsing loyalty member. No feature engineering, no MLOps, no separate model per surface.
The result: marketing operations goes from maintaining hundreds of segment rules to consuming a single prediction API. Personalization stops being a per-campaign lift and becomes an infrastructure default. Merchandising, CRM, growth, and retention all draw from the same customer decisioning layer, so a customer's cart-drawer recommendation is consistent with the offer they receive by email and the timing of the push notification that follows.
Priority use cases for Retail & E-commerce
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.
Learn moreNext Best Offer (NBO)
Serve the right promotion to the right customer. EkamFlow's next best offer prediction selects the optimal discount, bundle, or incentive to maximize both conversion and margin.
Learn moreSend Time Optimization (Next Best Time)
Predict the optimal moment to reach each customer for maximum engagement. EkamFlow's send time optimization goes beyond time-zone heuristics to predict individual peak response windows.
Learn moreChurn Prediction
Score every customer's risk of leaving before they churn. EkamFlow's private AI model identifies at-risk accounts in real time so you can intervene with the right retention action at the right moment.
Learn moreNext Best Channel Prediction
Predict whether each customer is most likely to engage via email, SMS, push notification, or in-app message. EkamFlow's next best channel prediction improves response rates and reduces opt-outs.
Learn moreCustomer Lifetime Value (CLTV) Prediction
Predict how much each customer is worth over 12, 24, or 36 months. EkamFlow's CLTV prediction helps you allocate acquisition spend, prioritize high-value accounts, and forecast revenue accurately.
Learn moreHow Retail & E-commerce teams put it to work
Personalized product surfaces
Next best product ranking drives cart drawers, email templates, category pages, and in-app carousels — one model, every surface. Cold-start users get cohort-based recommendations from their first session; new SKUs get scored via catalog similarity, not ignored until history builds.
Margin-aware promo strategy
Next best offer replaces blanket discount blasts with per-customer offer ranking. Loyal high-LTV customers see bundles at zero discount; price-sensitive dormant customers see a bigger code; new browsers see free-shipping thresholds. Blended offer cost drops while conversion climbs.
Retention + churn as first-class decisions
Churn risk and CLTV predictions feed directly into loyalty-team playbooks. Save offers, VIP touches, and win-back campaigns route by risk score — not by static tenure segments. Retention becomes a per-customer decision made every day, not a quarterly campaign.
Send-time optimization across email, SMS, and push
Every customer gets their own optimal send window per channel. A commuter opens email at 7am; a stay-at-home parent opens at 10am; a college student converts on 11pm push. Batched campaign sends stop leaving 20-40% of the base on the table.
Signals from Retail & E-commerce data
- Order history at SKU and category level
- Browse and cart behavior across web + app
- Loyalty scans, tier progression, and points activity
- Cross-channel engagement (email, SMS, push, in-app)
- In-store visits and POS transactions (when available)
- Subscription cadence and skip/pause behavior
- Returns and refund patterns
- Price sensitivity by category
- Reviews, ratings, and NPS signals
- Third-party marketplace behavior (when integrated)
Outcomes Retail & E-commerce teams track
- Average order value (AOV) via recommendation surfaces
- Repeat purchase rate and second-order conversion
- Retention rate on loyalty members and subscribers
- Blended promo cost per redemption
- Email/SMS/push engagement and unsubscribe rate
- Cart-drawer conversion and cross-sell attach rate
What it looks like in Retail & E-commerce
DTC beauty brand: cart drawer + email personalization from one model
A skincare brand replaced their 'customers also bought' widget with next-best-product recommendations and standardized on the same ranking for their cart drawer, PDP cross-sell, and email hero SKUs. AOV lifted 14% on the cart-drawer surface alone. Blanket 15%-off blasts were replaced with per-customer next-best-offer — loyal customers see bundle upsells at zero discount; dormant customers see a 20% welcome-back code — recovering margin without dropping conversion.
Meal-kit subscription: send-time + channel reallocation
A meal-kit brand had a fixed channel order (email first, SMS 48 hours later, push) and a fixed 9am Tuesday send. Next best channel surfaced that ~30% of subscribers had opted out of email but engaged 4× on SMS; next best time revealed the top-quintile actually opened Sunday 7am. Reallocating both cut unsubscribes 18% and lifted weekly reactivation double-digit.
Grocery loyalty: replenishment recommendations + push timing
A regional grocer runs next-best-product for the replenishment carousel in-app and in-email, factoring household size and typical purchase interval so recommendations always match the shopper's rhythm and never surface out-of-stock SKUs. Push offers moved from fixed 9am and 5pm slots to per-shopper send windows (seniors at 10am, working parents at 4pm, students at 11pm). Basket size grew 8%; weekly loyalty engagement per household nearly doubled.
Frequently asked about EkamFlow in Retail & E-commerce
Dedicated recommendation platforms are strong for on-site search-and-discovery workflows and typically integrate at the front end. EkamFlow's recommendation is one output of a unified customer decisioning model — the same API also returns churn risk, LTV, next best offer, and channel/timing predictions — so recommendations are informed by (and consistent with) the rest of the customer picture. Most retail customers keep their specialist tool for search facets and use EkamFlow for personalized recommendations across email, in-app, cart, and paid retargeting.
Yes. As long as POS transactions and store visits are flowing into your warehouse (via your POS system, loyalty app, or CDP), they become first-class signals in the model. This is critical for omnichannel retailers where a customer's next best offer online depends on what they bought in-store last week.
New products are scored using content-based similarity to existing catalog — a new sneaker gets scored against your existing sneaker embedding, not treated as unknown. New customers get cohort-based recommendations from their first session, matched to comparable customers by acquisition source, device, and geography. Personal signal builds within 3-5 interactions.
Yes. EkamFlow returns predictions via REST API; your ESP handles send orchestration, templating, and deliverability. Most retail customers keep their ESP and let EkamFlow replace the segment-rule logic driving campaigns — the ESP stays authoritative for the send, EkamFlow decides who and what.
Yes. Marketplace and multi-catalog retailers configure the model against buyer × listing behavior. Next-best-product ranking works the same way it does for single-seller retail; sponsored placements can be added as an eligibility overlay on top of the organic ranking, not a competing model.
The floor is roughly 6 months of customer history and enough active customers that behavioral signal is statistically stable — typically a few thousand active accounts. Below that, cold-start-heavy retailers can still use the platform but predictions will lean more on cohort defaults and less on personalized signal in the first month.
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