Behaviour-based product recommendation

Personalising product recommendations is essential for improving the user experience and increasing sales. By using audience segments based on browsing behaviour, you can offer relevant products tailored to users' preferences. This use case describes the steps required to implement personalised product recommendations and the associated benefits.

Objective:

Improve the user experience and increase sales by offering product recommendations based on browsing behaviour.

Description :

1. Creation of audience segments:

   • Use Commanders Act segmentation tools to define segments based on user interactions, such as products viewed, added to cart or purchased.

   • Ensure that segments are correctly configured and that data is securely shared with the recommendation solution.

2. Sharing segments with a product recommendation solution:

   • Integrate your Commanders Act platform with a product recommendation solution.

   • Use Commanders Act APIs to send user segments to the recommendation solution.

   • Configure segments based on user interactions to suggest products in line with their browsing behaviour.

3. Display product recommendations:

   • Integrate product recommendations into your website to enhance the user experience.

   • Use tailored messages to encourage users to explore and purchase recommended products.

4. Performance tracking:

   • Track the performance of product recommendations by analysing click-through, add-to-cart and conversion rates.

   • Analyse data to understand the impact of recommendations on sales and user engagement.

5. Continuous optimisation:

   • Use the insights gained to refine your strategies for personalising and optimising product recommendations.

   • Repeat the personalisation process for continuous improvement of the user experience.

Examples of use :

• Example 1: A user consults several similar products without buying. The recommendation solution suggests complementary or similar products based on their previous interactions.

• Example 2: A user adds a product to the shopping basket. The recommendation solution suggests accessories or complementary products to increase the value of the basket.

• Example 3: A user buys a product. The recommendation solution suggests similar products or updates to encourage future purchases.

Associated benefits:

• Increased personalisation: Offer relevant product recommendations based on users' browsing behaviour.

• Increased sales: Encourage purchases by presenting products that match users' preferences.

• User engagement: Improve the user experience by offering personalised suggestions.

• Continuous optimisation: Use behavioural data to refine recommendations and maximise conversions.

Conclusion:

By offering product recommendations based on browsing behaviour, you can not only improve the user experience, but also increase sales and visitor engagement. This approach makes it possible to target users with relevant suggestions tailored to their preferences, contributing to greater satisfaction and optimised sales results.

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