Algorithm Strategy Showcases

Suggested reading: Smart Recommender Algorithms

The algorithms power personalized recommendations, drive engagement, increase conversions, and enhance customer satisfaction. These showcases dive into tailored strategies and campaign objectives designed to leverage algorithmic insights across key pages of your website. Whether you aim to re-engage browse abandoners, reduce cart abandonment, or boost Average Order Value (AOV), these data-driven approaches empower you to deliver exceptional shopping journeys while meeting specific business goals.

Hook the returning users to reduce bounce rates

• Campaign Objective: Don’t disappoint your customers if there are no results. Show them personalized suggestions to encourage them to continue browsing.
• Page & Audience
  • Where: Homepage 
  • Targeted Audience: Returning Users 
  • Strategy: Showing selected items according to users’ preferences. 
• Campaign Strategy: You can use Chef to expand the algorithm's scope and optimize recommendations. 
  • Applicable Algorithms: User-Based, Top Sellers + Attribute Affinity
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    The user-based algorithm generates recommendations on the user's second visit. Since recommendations are tailored to each unique user based on their behavior, we recommend segmenting the campaign to target returning users.
• Design: Use the Go To Product button and an attractive header.
• Limitations: Since the algorithm requires past user events, it is best to use this algorithm on the Main Page, Zero Search Results, and 404 pages. Using this algorithm on category and/or product pages is not recommended, as the recommendations may be irrelevant to the categories.

Attract the discount lovers to create a high-volume conversion

• Campaign Objective: In addition to personalized product recommendations, you can offer personalized discounts to provide customers with an extra incentive to make a purchase.
• Page & Audience
  • Where: Home Page or Product Page
  • Targeted Audience: Users shopping for discounted products
  • Strategy: Promotional products of specific categories
• Campaign Strategy: You can use Chef to expand the algorithm's scope and optimize recommendations. Or, 
  • Applicable Algorithms: Highest Discounted Items, Viewed Together
  • Filter: Category + any of the Selected Categories
• Design: Use the Go To Product button and an attractive header.
• Limitations: No real personalizations. All users see the same recommendations, focused on the similarity of items they have viewed.

Re-engage browse abandoners and improve customer lifetime value

• Campaign Objective: They have also moved viewed products to the wishlist section to generate additional interest.
• Page & Audience
  • Where: Home Page
  • Targeted Audience: All users
  • Strategy: Showing alternative items from the same Brand and a Different Category
• Campaign Strategy: You can use Chef to expand the algorithm's scope and optimize recommendations. Or,
  • Applicable Algorithms: User-Based, New Arrivals
• Design: Use the Go To Product button and an attractive header.
• Limitations: 
  • It is not recommended to use these algorithms on category and/or product pages, as the recommendations can become irrelevant to the categories.
  • Understanding newly arrived items based on the first visit rather than the time added to the website.

Cross-sell products to increase conversions

• Campaign Objective: Usually known as “Frequently Bought Together” and “Goes Well With”. By cross-selling, you can immediately improve your average order value.
• Page & Audience
  • Where: Product Page
  • Targeted Audience: All users
• Campaign Strategy
  • Applicable Algorithms: Viewed Together, Purchased Together
• Design: Use the Go To Product button and an attractive header.
• Limitations: Only focused on the similarity of the items viewed. There is no real personalization unless the user is interested in the site's top items. All segmented users see the same recommendation for an item.

Upsell same-brand products to boost Average Order Value

• Campaign Objective: The primary goal is to increase the Average Order Value (AOV) by encouraging users to purchase an expensive alternative.
• Page & Audience
  • Where: Product Page
  • Targeted Audience: All users
• Campaign Strategy
  • Applicable Algorithms: Substitute Products, Viewed Together
• Design: Use the Go To Product button and an attractive header.
• Limitations: Only focused on the similarity of the items viewed. There is no real personalization unless the user is interested in the site's top items.

Bundling complementary products to cross-sell brands and categories to boost AOV

• Campaign Objective: Showing complementary items from the same main category to get the user to buy at the same time.
• Page & Audience
  • Where: Product Page
  • Targeted Audience: All users
• Campaign Strategy
  • Applicable Algorithms: Complementary Products, Purchased Together
• Design: Use the Go To Product button and an attractive header.
• Limitations: Only Focused on the similarity of items purchased.

Pull back cart abandoners and decrease the cart abandonment rate

• Campaign Objective: Explore the promotion for free shipping, coupon codes, and more.
  • Where: Cart Page
  • Targeted Audience: All users
• Campaign Strategy
  • Algorithms: Checkout Recommendation
• Design: Use the Go To Product button and an attractive header.
• Rule: The cart amount should be less than the offer amount (For example, if there is free shipping above 200$, the rule must be that the cart amount is less than 200$)
• Limitations: Considering the basket amount, only products that meet the campaign amount are recommended, along with a checkout recommendation. The purchased together algorithm is applied.