Points logic involves assigning weights to different products depending on the answers a shopper provides in a quiz. The products with the most points for a given set of responses will be recommended to that shopper.
If you would like to use product tags in your quiz, learn more here.
In this article, you will learn about;
Benefits of points logic
A points-based logic system can be beneficial in a product recommendation quiz for several reasons:
Customized suggestions: Assign points to products based on shoppers' responses to questions about their preferences, needs, or characteristics. The accumulated points then guide more granular and personalized product recommendations.
Scoring relevance: Assigning different point values to each question lets you weigh the importance of certain criteria in the recommendation process. For instance, if a shopper indicates that affordability is a top priority, you can assign more points to budget-friendly products.
Efficient filtering: Use negative weights to down-weight products that don't align with a shopper's preferences, streamlining recommendations and preventing inaccurate suggestions.
Dynamic recommendation page: Only one recommendation page needs to be set up — no need to create pre-determined product combinations. The page dynamically populates with the most relevant products for each shopper.
Selecting points logic
Create a new quiz and select Blank Points Quiz, or edit the logic selector for an existing quiz and select Points Logic.
Configure the maximum number of products to recommend and, optionally, the runner-up count — recommended products that had the next highest point totals. For example, if you configure a maximum of 3 products and 2 runner-ups, the top 3 products appear in the recommended section and the 4th and 5th highest-scoring products appear in the runner-up section.
Enter a title for the recommendation page. This acts as an internal identifier and displays as the title for shoppers. Optionally, add a subtitle to provide additional context about the recommended products.
Toggle on Show "Other Recommended Products" section to display additional products unrelated to the quiz results. Enter a title, optional subtitle, and choose which products to display.
Click Apply.
Adding products and variants
Once you have selected points logic, choose the products you want to recommend.
Below the logic switcher, click + Add Product.
Choose one or more products from the product selector, then click outside the selector when finished.
Hover over a product and click the edit button.
If a product has variants, choose to include All or Selected variants from the dropdown. Star the default selection to choose which variant will be pre-selected on the recommendation page.
⚠️ To assign points to different variants of a product, add the same product multiple times and select a unique variant for each. A product cannot be added more than once if it does not have a different combination of variants.
Assigning products to questions
There are two ways to weight products in a quiz.
Assigning products to each question
Hover over a question and click the edit button.
Hover over an answer option and click Add Products to assign products to it.
Choose the products. To edit the points assigned, click the 3-dot menu and select Edit Weightings.
Assign positive or negative points to configure the weighting of each recommended product. For example, if a shopper answered "sensitive skin", assign a positive weighting to products that suit that skin type and a negative weighting to products that don't.
Click Apply to save changes.
Using the Set All points matrix
The Set All Points matrix lets you set points for all questions and products at once.
Click Set All Points at the top of the questions section.
Add points to each question option / product combination as needed. If no points are assigned to a combination, the product will not be associated with that question option.
Click Set All Points at the bottom of the window to confirm the weightings.
Explanation of weightings
Positive and negative weightings emphasize the importance of certain preferences or characteristics with respect to product recommendations, allowing for a more nuanced and personalized outcome based on each shopper's responses.
Positive weighting
Positive weighting assigns higher points to preferences or characteristics that align with a shopper's needs. For example:
Question 1 (Preferred features): 20 points — If a shopper expresses a strong preference for certain product features such as eco-friendliness or specific ingredients, positive weighting ensures their score reflects the significance of these preferences in the final recommendations.
Question 2 (Budget): 15 points — If a shopper prioritizes cost-effectiveness, positive weighting ensures their budget-conscious choice is given a higher score, influencing the overall recommendation.
Negative weighting
Negative weighting assigns lower points to preferences or characteristics that are less desirable or conflict with a shopper's needs, helping filter out products that don't align with their preferences. For example:
Question 3 (Avoided ingredients): -10 points — If a shopper wants to avoid certain ingredients due to allergies or personal preferences, negative weighting ensures that products containing those ingredients receive lower scores. A significant negative weighting (such as -100) can ensure a product will never be recommended — for example, if a shopper is allergic to that ingredient.
Question 4 (Disliked features): -15 points — If a shopper expresses a strong aversion to specific product features such as fragrance or a certain type of packaging, negative weighting ensures those products receive lower scores, steering recommendations away from them.
For points logic quizzes using products, any product scoring 0 or more points is eligible to be recommended. Any product scoring below 0 will not be recommended.
By using positive and negative weightings together, you can tailor recommendations to better match each shopper's preferences and factors they wish to avoid — creating a more personalized and satisfying experience.