Introducing: New Recommendation Strategies
Based entirely on purchase behavior, our freshly built recommendation algorithms leverage key conversion-related data to help drive results.
New recommendation strategies enable businesses to personalize product recommendations based on purchase behavior.
Recommendation engines are the Swiss Army knife of every online business’ toolkit. They provide a powerful, flexible tool to increase conversions, raise average order value (AOV), and also build strong and loyal relationships with each customer.
A recommendation engine can shape the whole experience for visitors on a site, improving how shoppers discover and engage with the brand overall based on customer behavior and data. For retailers, product recommendations are especially valuable as they aid shoppers, helping them find what they want more effectively by providing personalized, relevant recommendations at the appropriate time based on individual preferences, shopping behavior, and activity. In the process of getting shoppers more engaged, recommendations also provide key insights and the opportunity to better understand who the customer is in order to delight them, add value, and improve the overall relationship with a brand.
Recommendations are an integral part of Dynamic Yield’s personalization platform, and clients like Sephora and Jewelry.com have already seen great success from leveraging our AI-powered recommendation engine to deliver recommendations that are always up-to-date based on real-time shopper activity and behavior. Because we’ve recognized the increasing impact of this tool for online retailers, our recommendation engine is in a constant state of enhancement, and now, includes a brand new set of recommendation strategies.
Introducing Recently Purchased-Based Strategies
Based entirely on purchase behavior, our freshly built recommendation algorithms capture and leverage key conversion-related information to help drive things like the replenishment of repeatedly purchased items all the way to recommending items commonly bought together with a big purchase item.
With algorithms powered by the most important actions and strongest signals of interest, these Recently Purchased-based Strategies make for high-yielding, personalized recommendations.
And now, businesses have five additional recommendation algorithms they can select from when building a campaign:
- Last Purchased Items: Showcases products from the user’s most recent purchase
- Recently Purchased Items: Showcases products from the user’s recent purchases
- Purchased with Last Purchases: Showcases products usually bought together with items in the user’s most recent purchase
- Purchased with Recent Purchases: Showcases products usually bought together with items from the user’s recent purchases
- Viewed with Recently Viewed Items: Showcases items usually viewed together with items recently viewed by the user
For example, retailers selling products that need to be replenished (ranging from pet food to cosmetics) can use the Recent Purchased Items strategy to boost repeat purchases on their site. Additionally, businesses can implement a Purchased with Last Purchases or Purchased with Recent Purchases strategy on the homepage for all visitors who bought an item in the past week to showcase complementary items while they’re still relevant.
Learn more about recommendations with Dynamic Yield, below:
Armed with new strategies, marketers and merchandisers have more tools in their arsenal to supercharge their recommendation efforts.
Want to see Dynamic Yield’s product recommendations in action? Use our Product Recommendations Use Case Explorer to see how they’ll look on your site!