20-minute walkthrough with 2–3 personalized examples on your real pages.
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An e-commerce playbook for product recommendations — deploy Bought-Together bundles, personalized bestsellers, and predictive cross-sell algorithms.
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From the Personyze dashboard, click "Product Recommendation" to launch the setup wizard.
You must tell Personyze when a user views, buys, or adds an item to the cart. You have 3 methods:
event equals "add_to_cart"
(self.personyze=self.personyze||[]).push(['Product Viewed', "PRODUCT_ID"]);
(self.personyze=self.personyze||[]).push(['Product Added to cart', "PRODUCT_ID", 'Quantity', QUANTITY]);
(self.personyze=self.personyze||[]).push(['Product Removed from cart', "PRODUCT_ID"]);
(self.personyze=self.personyze||[]).push(['Products Removed from cart']);
Define *where* this widget appears. You can set it to show only on the Homepage, or only for visitors from North America.
Customize the look and feel to match your brand exactly using the "Customize Look and Feel" step.
Add visual labels to products to increase CTR:
vip_price
In the "Final Touches" step, apply rules to ensure recommendations are business-smart.
Filter products based on User Data variables:
Your homepage must appeal to everyone—both first-time visitors and loyal customers.
Use the "Personalized Recommendations" algorithm. It automatically switches logic:
This ensures relevance from the first click to the hundredth.
Help users find what they are looking for by leveraging context.
Shows items from the category the user engaged with most recently. If they looked at "Drills" yesterday, show "Power Tools" on the homepage today.
Show the top-selling items within the current category. If the user is browsing "Laptops", show the best-selling laptops, not shoes.
Similar to Best Seller, but based on Views rather than Purchases. Good for trending items that haven't converted yet.
Optimize the product detail page to keep users engaged and browsing.
Collaborative Filtering: "People who looked at this shirt also looked at these." Great for providing alternatives if the main product isn't a perfect match.
A stronger signal than just "viewed". It tells the user: "People who considered this item eventually bought *these* items." Excellent for closing sales.
A simple but effective history widget. Helps users compare options by easily navigating back to items they saw earlier.
The final push to increase basket size before payment.
This powerful algorithm looks at the entire contents of the shopping cart to find items that complement the *combination* of products (e.g., Camera + Case -> Memory Card).
Allows manual override. "If Cart contains X, ALWAYS show Y." Useful for strict compatibility rules (e.g., Specific batteries for a specific toy).
Leverage social proof by showing high-desire items. Often triggers impulse adds in the cart.
Bring customers back with highly relevant news.
Deep learning that analyzes a user's full purchase history to predict what they might need next.
Perfect for consumables. It surfaces items the user has purchased before, streamlining the reordering process.
Show the newest items in your catalog, but apply a "Filter by Past Purchase Category" rule (e.g., A "Yoga" buyer sees new mats).
Don't guess which algorithm works best. Test them.
Personyze will split traffic automatically and can eventually auto-allocate 100% of traffic to the winner.
Measure the direct impact of your recommendations with detailed stats.
Common questions about AI product recommendations. Anything else, our team is one message away.
Collaborative Filtering is the core technology behind recommendations like "People who bought this also bought that." It analyzes patterns across thousands of user sessions to find hidden correlations between products without needing manual rules.
Personyze starts collecting data immediately. Basic algorithms like "Most Popular" work instantly. Advanced algorithms like "Personalized Recommendations" typically need a few days of traffic (or a historical data upload) to build accurate user profiles.
Yes. You can use "Managed Recommendations" or "Pins" to force specific items (like high-margin goods or overstock) to appear first, while letting the algorithm fill the remaining slots dynamically.
Absolutely. The engine works identically for content. Instead of "Products," it recommends "Articles" based on reading history, tags, and category affinity. You just need to sync your content feed instead of a product feed.
Yes. By default, Personyze filters out items where stock_status = 0. You can customize this rule in the filtering step to also exclude items with low stock or specific tags.
stock_status = 0
This is the "Cold Start" problem. Personyze handles this by falling back to Crowd Logic (e.g., "Most Popular" or "Trending Now") or using Contextual Data (e.g., "Popular in your City") until the user generates their own history.
Yes. You have full control over the HTML/CSS. You can use our visual editor to tweak existing templates or paste your own custom HTML structure to match your site's branding perfectly.
Direct Revenue counts sales where the user clicked the specific recommended item and bought it. Assisted Revenue counts sales where the user clicked a recommendation but ended up buying *something else* in the same session.
Yes. You can create separate campaigns for Mobile and Desktop. For example, show a horizontal slider of 4 items on Mobile, but a grid of 8 items on Desktop.
Yes. If your product feed contains localized fields (e.g., title_en, title_es), you can configure the widget to dynamically display the correct language based on the user's browser settings or site selection.
title_en
title_es
Yes. The "Cross-Sells for Items in Cart" algorithm effectively creates bundles by suggesting complementary items (e.g., suggesting a Lens when a Camera is in the cart).
Feeds typically sync once every 24 hours. However, you can configure more frequent updates (e.g., hourly) or use our API to push real-time stock/price changes instantly.
Yes. The A/B testing feature allows you to split traffic between two different logic sets (e.g., "Collaborative Filtering" vs. "Most Popular") to see which one generates more revenue.
Yes. Personyze anonymizes user data and respects "Do Not Track" requests. Personalization relies on pseudonymous IDs rather than PII unless explicitly provided by the user.
Personyze scales effortlessly. Our engine is designed to handle catalogs with millions of SKUs and high-traffic sites without latency, ensuring recommendations load instantly.
You have the full playbook — now deploy AI product recommendations that cross-sell and upsell automatically.