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Suggested reading: Smart Recommender Algorithms

Hook the returning users to reduce bounce rates

Campaign Objective: Don’t disappoint your customers in case of no results. Show them personalized suggestions to keep them browsing.
Page & Audience
- Where: Homepage
- Targeted Audience: Returning Users
- Strategy: Showing selected items for users’ preference.
Campaign Strategy: You can use Chef to broaden the algorithm scope and optimize the 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. Given that recommendations are tailored to each unique user based on their behaviors, we advise you to segment the campaign targeting returning users.
Design: You need to use the Go To Product button and an attractive header.
Limitations: Since the algorithm requires past user events, it's better to use this algorithm on the Main Page, Zero Search Results, and 404 pages. It’s not recommended to use this algorithm on the category and/or the product pages since the recommendations can be non-relevant to the categories.

Campaign Objective: Along with personalized product recommendations, you can offer personalized discounts to give the customer an additional incentive to make a purchase.
Page & Audience
- Where: Home Page or Product Page
- Targeted Audience: Users shopping for discounted products
- Strategy: Promotive products of specific categories
Campaign Strategy: You can use Chef to broaden the algorithm scope and optimize the recommendations. Or,
- Applicable Algorithms: Highest Discounted Items, Viewed Together
- Filter: Category + any of the Selected Categories
Design: You need to 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 viewed.

Campaign Objective: To generate additional interest, they have also moved viewed products to the wishlist section.
Page & Audience
- Where: Home Page
- Targeted Audience: All users
- Strategy: Showing alternative items from the same Brand and Different Category
Campaign Strategy: You can use Chef to broaden algorithm scope and optimize the recommendations. Or,
- Applicable Algorithms: User-Based, New Arrivals
- Filters:
  - Dynamic filter + Brand + Contains
  - Dynamic filter + Category + Does Not Contain
Design: You need to use the Go To Product button and an attractive header.
Limitations:
- It’s not recommended to use these algorithms on the category and/or the product pages since the recommendations can become non-relevant to the categories.
- Understanding newly arrived items based on the first visit, rather than time added to the website.

Campaign Objective: Improve Average Order Value and vendor relations by pushing excess stock, product bundles, and stock clearance products and also assist marketing campaigns using manual merchandising.
Page & Audience
- Where: Category Page or Cart Page
- Targeted Audience: Returning users
Campaign Strategy
- Applicable Algorithm: Manual Merchandising
- Filter:
  - Dynamic filter + Brand + Contains
  - Dynamic filter + Category + Does Not Contain
Design: You need to use the Go To Product button and an attractive header.
Limitations: No real personalization. All users see the same recommendation.

Campaign Objective: Usually known as; “Frequently Bought Together” and “Goes Well With”. By cross-selling, you can improve your average order value right away.
Page & Audience
- Where: Product Page
- Targeted Audience: All users
Campaign Strategy
- Applicable Algorithms: Viewed Together, Purchased Together
- Filter: Dynamic filter + Category + Contains
Design: You need to use the Go To Product button and an attractive header.
Limitations: Only focused on the similarity of the items viewed. No real personalization unless the user is interested in the top items of the website. All segmented users see the same recommendation for an item.

Campaign Objective: The primary goal is to increase the Average Order Value (AOV) by getting the user to buy an expensive alternative.
Page & Audience
- Where: Product Page
- Targeted Audience: All users
Campaign Strategy
- Applicable Algorithms: Substitute Products, Viewed Together
- Filter:
  - Dynamic filter + Category + Does not contain
  - Dynamic filter + Brand + Contains
Design: You need to use the Go To Product button and an attractive header.
Limitations: Only focused on the similarity of the items viewed. No real personalization unless the user is interested in the top items of the website.

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
- Filter:
  - Dynamic filter + Main Category + Contains
  - Dynamic filter + subCategory + Does Not Contain
Design: You need to use the Go To Product button and an attractive header.
Limitations: Only Focused on the similarity of items purchased.

Campaign Objective: Checkout Promotion for Free Shipping, coupon code, etc.
- Where: Cart Page
- Targeted Audience: All users
Campaign Strategy
- Algorithms: Checkout Recommendation
Design: You need to 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: Taking basket amount into consideration, only products that are fulfilling the campaign amount are recommended with checkout recommendation. The purchased together algorithm is applied.

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For the design, CSS should align with the website's UI & UX to ensure consistency and cohesion.

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