{"id":2005,"date":"2026-08-11T17:00:00","date_gmt":"2026-08-11T22:00:00","guid":{"rendered":"https:\/\/tolinku.com\/blog\/?p=2005"},"modified":"2026-03-07T03:50:24","modified_gmt":"2026-03-07T08:50:24","slug":"product-recommendation-deep-links","status":"publish","type":"post","link":"https:\/\/tolinku.com\/blog\/product-recommendation-deep-links\/","title":{"rendered":"Product Recommendation Deep Links"},"content":{"rendered":"\n

Product recommendation deep links combine two powerful concepts: algorithmic recommendations and direct in-app navigation. Instead of showing a generic "You might like these" carousel in the app, you send personalized deep links through email, push notifications, and SMS that take users directly to products they are likely to buy.<\/p>\n\n\n\n

For personalized banners, see personalized smart banners: dynamic content that converts<\/a>. For the complete e-commerce guide, see deep linking for e-commerce apps<\/a>.<\/p>\n\n\n\n

How Recommendation Deep Links Work<\/h2>\n\n\n\n

The Flow<\/h3>\n\n\n\n
    \n
  1. Your recommendation engine identifies products a user is likely to purchase (based on browsing history, purchase history, similar users, or trending items).<\/li>\n
  2. You generate a deep link to the recommended product or collection.<\/li>\n
  3. You deliver the link via email, push notification, SMS, or in-app message.<\/li>\n
  4. The user taps and lands directly on the recommended product in the app.<\/li>\n<\/ol>\n\n\n\n

    Link Structure<\/h3>\n\n\n\n
    https:\/\/yourstore.com\/recommended\/:userId\/:productId\n<\/code><\/pre>\n\n\n\n
    Or for a personalized collection:<\/p>\n\n\n\n
    https:\/\/yourstore.com\/for-you\/:userId\n<\/code><\/pre>\n\n\n\n
    The :userId<\/code> parameter lets the app fetch the user's personalized recommendations. The link itself does not contain sensitive data; the personalization happens server-side when the app loads.<\/p>\n\n\n\n
    Recommendation Types and Deep Links<\/h2>\n\n\n\n
    "Because You Viewed" Recommendations<\/h3>\n\n\n\n
    Trigger: User viewed a product but did not purchase.<\/p>\n\n\n\n
    Push notification: "Still interested in the Nike Air Max?"\nDeep link: \/product\/nike-air-max-90?ref=viewed-reminder\n<\/code><\/pre>\n\n\n\n
    This is the simplest recommendation type. The user showed intent; the deep link brings them back to complete the purchase.<\/p>\n\n\n\n
    "Frequently Bought Together" Recommendations<\/h3>\n\n\n\n
    Trigger: User purchased one product from a commonly paired set.<\/p>\n\n\n\n
    Email: "Complete your setup: Customers who bought the MacBook case also bought these."\nDeep link: \/collection\/macbook-accessories?ref=bought-together&anchor=SKU-456\n<\/code><\/pre>\n\n\n\n
    The deep link opens a curated collection of accessories related to the recent purchase.<\/p>\n\n\n\n
    "Based on Your Tastes" Recommendations<\/h3>\n\n\n\n
    Trigger: Algorithm identifies products matching the user's preference profile.<\/p>\n\n\n\n
    Push notification: "New arrivals we think you'll love"\nDeep link: \/for-you?ref=taste-match\n<\/code><\/pre>\n\n\n\n
    The \/for-you<\/code> deep link opens a personalized page generated by the recommendation engine.<\/p>\n\n\n\n
    "Trending in Your Category" Recommendations<\/h3>\n\n\n\n
    Trigger: Products gaining traction in categories the user frequently browses.<\/p>\n\n\n\n
    Email: "Trending in Running Gear this week"\nDeep link: \/browse\/running-gear?sort=trending&ref=weekly-digest\n<\/code><\/pre>\n\n\n\n
    "Price Drop" Recommendations<\/h3>\n\n\n\n
    Trigger: A product the user viewed or wishlisted has dropped in price.<\/p>\n\n\n\n
    Push notification: "Price drop: The jacket you liked is now 30% off"\nDeep link: \/product\/leather-jacket-789?ref=price-drop\n<\/code><\/pre>\n\n\n\n
    Price drop notifications have some of the highest conversion rates because the user already showed purchase intent and the price objection has been reduced.<\/p>\n\n\n\n
    Personalization Strategies<\/h2>\n\n\n\n
    Segment-Based Recommendations<\/h3>\n\n\n\n
    Group users into segments and send segment-level recommendations:<\/p>\n\n\n\n
    \n\n\n\n\n\n\n\n
    Segment<\/th>\nRecommendation Type<\/th>\nDeep Link<\/th>\n<\/tr>\n<\/thead>\n
    New users (0-7 days)<\/td>\nBest sellers in their browsed categories<\/td>\n\/browse\/:category?sort=best-selling<\/code><\/td>\n<\/tr>\n
    Active browsers (no purchases)<\/td>\nProducts with high conversion in their price range<\/td>\n\/product\/:id?ref=first-purchase<\/code><\/td>\n<\/tr>\n
    One-time buyers<\/td>\nComplementary products to their purchase<\/td>\n\/collection\/goes-with\/:purchased-sku<\/code><\/td>\n<\/tr>\n
    Repeat buyers<\/td>\nEarly access to new arrivals in preferred categories<\/td>\n\/new-arrivals\/:category?access=early<\/code><\/td>\n<\/tr>\n
    Lapsed users (30+ days)<\/td>\nWin-back with top-rated products<\/td>\n\/for-you?campaign=winback<\/code><\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Time-Based Personalization<\/h3>\n\n\n\n
    Send recommendations at the right time:<\/p>\n\n\n\n
    \n\n\n\n\n\n\n\n
    Signal<\/th>\nTiming<\/th>\nExample<\/th>\n<\/tr>\n<\/thead>\n
    Browse without purchase<\/td>\n2-4 hours later<\/td>\n"Still looking? Here's what you viewed"<\/td>\n<\/tr>\n
    Abandoned cart<\/td>\n1 hour, then 24 hours<\/td>\n"Your cart is waiting"<\/td>\n<\/tr>\n
    Post-purchase<\/td>\n3-7 days later<\/td>\n"Complete your look"<\/td>\n<\/tr>\n
    Replenishment<\/td>\nBased on product lifespan<\/td>\n"Time to reorder your face cream?"<\/td>\n<\/tr>\n
    Seasonal<\/td>\nStart of relevant season<\/td>\n"Your summer wardrobe essentials"<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Measuring Recommendation Deep Link Performance<\/h2>\n\n\n\n
    \n\n\n\n\n\n\n\n
    Metric<\/th>\nFormula<\/th>\nGood Benchmark<\/th>\n<\/tr>\n<\/thead>\n
    Click-through rate<\/td>\nClicks \/ Delivered messages<\/td>\n5-15% (push), 2-5% (email)<\/td>\n<\/tr>\n
    Recommendation acceptance rate<\/td>\nPurchases \/ Recommendation clicks<\/td>\n10-25%<\/td>\n<\/tr>\n
    Revenue per recommendation<\/td>\nRevenue from recommended purchases \/ Recommendations sent<\/td>\nVaries by AOV<\/td>\n<\/tr>\n
    Recommendation coverage<\/td>\nUsers receiving relevant recommendations \/ Total users<\/td>\n70%+<\/td>\n<\/tr>\n
    Recommendation diversity<\/td>\nUnique products recommended \/ Total catalog size<\/td>\n20-40%<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    A\/B Testing Recommendations<\/h3>\n\n\n\n
    Test the recommendation algorithm, not just the messaging:<\/p>\n\n\n\n
    \n\n\n\n\n\n\n\n
    Test<\/th>\nVariant A<\/th>\nVariant B<\/th>\n<\/tr>\n<\/thead>\n
    Algorithm<\/td>\nCollaborative filtering<\/td>\nContent-based filtering<\/td>\n<\/tr>\n
    Number of products<\/td>\nSingle product deep link<\/td>\nCollection of 5 products<\/td>\n<\/tr>\n
    Timing<\/td>\n1 hour after browsing<\/td>\n24 hours after browsing<\/td>\n<\/tr>\n
    Channel<\/td>\nPush notification<\/td>\nEmail<\/td>\n<\/tr>\n
    Personalization depth<\/td>\nCategory-level<\/td>\nProduct-level<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Technical Considerations<\/h2>\n\n\n\n
    Privacy and Data<\/h3>\n\n\n\n
    Product recommendations use personal data (browsing history, purchases). Ensure compliance with privacy regulations:<\/p>\n\n\n\n