The AB Tasty Recommendation Builder allows you to create tailored lists of items to recommend, using a combination of algorithms and transformations. This flexible system lets you define precisely how recommendations are generated and modified to meet your business needs.
Recommendation Structure:
A recommendation is composed of:
- Algorithms: Generate a list of items.
- Transformations: Modify the list generated by an algorithm.
Drag-and-Drop Interface:
- Build your recommendation by dragging and dropping operations into drop zones.
Drop Zones
FOLLOWED BY Divider:
- Concatenates results from multiple algorithms, avoiding duplicates.
Example: Combine products from two algorithms (e.g., "Most Popular" FOLLOWED BY "New Arrivals").
THEN Divider:
- Applies transformations to modify algorithm results.
Example: Filter, shuffle, or sort the list.
THEN for Transformations:
- Allows multiple transformations in sequence, separated by THEN.
Example Recommendation
Scenario: Create a Multi-Source Recommendation
Algorithms:
First 15 products from [ALGO] User Reco.
FOLLOWED BY 15 products from [ALGO] Most Popular.
Filters Applied to All Products:
Recommendable in newsletter = True.
Marketplace = False.
Accessory = False.
Pickup in store = False.
Steps to Build a Recommendation
Add an Algorithm:
- Select an algorithm and configure its settings.
Transform Algorithm Results:
- Drop a transformation into the algorithm box (e.g., filter or shuffle).
Add a FOLLOWED BY Divider:
- Drop a second algorithm after FOLLOWED BY to concatenate results (e.g., as a fallback algorithm).
Apply Global Transformations:
- Add transformations after the THEN divider to modify the overall results.
Add Exceptions:
Use the "+ Add Exception" button to apply alternate algorithms based on specific conditions.
Preview Your Recommendation
Once your recommendation is built:
- Preview Button: Click to view results.
- Modal Options:
- Set parameters.
- Preview results as a table or list.
- Inspect results for each algorithm.
- Display additional details for each item.
- Check API calls leading to the result.
Operations
Algorithms
- Sorted Items: Example: Top 12 items by revenue over the last 30 days.
- Associated Items: Example: Items frequently purchased with the selected item.
- Similar Items: Example: Items often viewed with the selected item.
- Recommended Items: Reuse a saved recommendation.
- Handpicked Items: Add items manually or by importing item IDs.
- Used Items: Example: Last 12 items bought by a user (requires user data integration).
Transformations
Filter:
Include only items matching a condition.
Example: Show only items where the brand is "Apple".
Dynamic Filter:
Use input variables to make filters dynamic.
Example: Keep products cheaper than the input item.
Sort:
Sort items by a specific field.
Example: Sort by top sales.
If Condition:
Apply transformations conditionally.
Example: If brand = "Apple", show Apple items; if brand = "Dyson", show Dyson items.
Shuffle:
Randomise the order of items.
Use cautiously, as it can disrupt relevance.
Limit:
Restrict the number of items displayed.
Example: Limit results to 20 items.
Exclude:
Remove items based on input variables.
Example: Exclude items already purchased by the user.
Final Notes
- The Recommendation Builder offers powerful tools for creating highly customised recommendations.
- Regularly test and preview your configurations to ensure they meet your goals.
- For additional guidance, consult the platform documentation or contact your Customer Success Manager (CSM).