Increase AOV with AI Personalization
Almeta ML learns what each customer is likely to add to their order and shows it on the product page, in the cart, after checkout. Offers update in real time as the shopper browses.
Stores report a significant increase in order values compared to recommendation systems built into their ecommerce platforms.
Almeta ML trains on your store’s data: views, searches, carts, orders, returns. For each shopper, the model reads the current session, past history, and cart contents, then predicts which products they are most likely to add to the order. Each store gets its own models, trained on its own catalog and order history.
A product to purchase together with what they’re buying
A better version of what they’re considering
Every business is different. Real-time machine learning models analyze each of your customers and personalize product offers at every step of the funnel. You can customize the offers with advanced business rules controlled from Almeta ML web console or with code.
AI product sorting & filtering
Similar items, alternatives
Cross-sell & upsell
Next best offer & timing
Most recommenders look at what sold together. Every Almeta ML prediction draws on 20+ signal types across three families: live behavior, the context of the visit, and your catalog and operations.
What this shopper is doing right now — and what they’ve bought before.
The circumstances of the visit — down to the weather outside.
Your business reality — what’s in stock, where, and what it earns.
The model learns each shopper’s price range from what they browse and buy. Upsells step up only as far as the shopper will realistically go; cross-sells fit the budget left in the cart.
No more $1,899 suggestions for a $60 shopper — and no $12 add-ons for someone furnishing a terrace.
Increase AOV with personalized cross-sells and upsells
Click on a question below to see the answer. If you need any help, please contact us.
Our customers report a meaningful increase of their conversion rates. Almeta ML personalizes offers for each customer, and filters out duplicates, owned products, and high-return items. You control how many offers appear at each step. Post-purchase offers carry no checkout risk: the customer has already placed the order.
Built-in blocks show aggregated co-purchase statistics — the same products for everyone. Almeta ML scores offers for each shopper, separates complements from substitutes, applies your margin and stock rules, and re-scores offers during the session. This leads to substantially higher order values and improved conversion rates.
Behavioral events (product views, add to cart, purchases) and your product catalog. A few thousand events are enough for useful predictions. Most stores import historical data, so models train the day you connect.
About as complex as installing an analytics tag. Add the web tag, use a pre-built integration (Shopify, BigCommerce, WooCommerce, Adobe Commerce), or send events through the API. You embed offer blocks on product, cart, and thank-you pages, or fetch offers through the API for email and custom placements. Full integration typically takes 1–2 weeks. If you prefer, we install and configure everything for you.
No. Models train, deploy, and retrain automatically. You set business rules in the Almeta ML console.
Almeta ML is built to learn the lifecycle of each product and customer. The model filters out products the customer recently purchased, unless a repeat purchase of that product is likely.
Yes. This is an important use case Almeta ML was built for. One-of-a-kind inventory is tricky for most recommendation systems: a product that sells once has no sales history to learn from. Almeta learns from similar items, visitor behavior, and product characteristics, which enables immediate smart cross-sells and upsells for unique products.
We offer usage-based pricing, plans start at $99 / month.
Almeta ML does not collect any personally identifiable information about your customers, unless you choose to include it in your data.
If you use the web tag, Almeta ML will only collect the events you choose to track. By default, the web tag generates a random customer ID and saves it to local storage. You can choose to include your own customer ID in your data.