Understanding next best product analysis
Generated by Klaviyo AI
Next best product analysis predicts which product individual shoppers are most likely to purchase based on their behavior, purchase history, and profile data. It scores your entire catalog against each customer to surface personalized recommendations that update as new data comes in.
- Personalized product scoring: uses browsing history, past purchases, and cart activity to rank products most likely to convert for each specific shopper
- Cross-channel consistency: delivers the same prediction across email, SMS, push, and other channels from a single customer record
- Reduces manual work: eliminates the need to hand-build product blocks for each send, especially valuable for lean teams managing large catalogs
Next best product analysis is the process of predicting which product an individual shopper is most likely to want next, based on their behavior, purchase history, and profile data. It scores your catalog against each person and surfaces the item they're most likely to buy.
Say someone has bought running shoes twice, browsed compression socks last week, and abandoned a cart with a hydration pack. The model reads those signals together and ranks the products most likely to convert for that specific person. Two shoppers browsing the same category can see different recommendations because their histories point in different directions.
Key features of next best product analysis
Here are the core features that make it work:
- Behavior and purchase scoring: Models weigh browsing, past purchases, cart activity, and profile attributes to score which product to surface next for each shopper, and rankings update as new behavior comes in.
- Catalog-aware matching: Predictions draw on live product data so recommendations reflect current inventory, pricing, and availability.
- Cross-channel delivery: The same prediction can render in email, SMS, push, and other owned channels from one customer record.
- Fallback logic for sparse data: For new shoppers or thin histories, predictions can fall back to category bestsellers or similar defaults until enough signal exists. That limit is real: sparse profiles get a safe default first, then sharpen once purchase and browse history build up.
Benefits of next best product analysis
Here are the key advantages:
- Higher conversion: Shoppers see products that match what they already browsed or bought, which cuts the search friction that sends people elsewhere.
- Larger average order value: Relevant cross-sell and up-sell picks get shoppers to add items they'd want instead of a random discount on unrelated stock.
- Stronger retention: After the running-shoes shopper checks out, the next send can reference what they already bought and suggest the item the model ranks next, so each purchase teaches the following recommendation.
- Less manual merchandising work: Lean teams managing large catalogs don't have to build product blocks by hand for each send.
When next best product analysis makes sense
If you're just getting started with personalization, next best product analysis can replace one-size-fits-all product grids in welcome, browse, and post-purchase messages with picks tied to what each person actually did. If you're already running mature programs across email, SMS, and push, it can score once per profile so you aren't hand-building a separate product grid for each channel.
Klaviyo is the autonomous B2C CRM, with next best product recommendations grounded in real-time customer data. Get started with Klaviyo today.