Introduction

For marketers, the ability to provide strategic and precise recommendations can significantly impact the effectiveness of their marketing efforts and contribute to the overall success of the business. The recommendations are based on the purchase history. Hence, the probability of the user liking the product would be higher, enhancing the user’s engagement with the App. Recommendations can serve as a tool for engagement, prompting customers to interact more with the brand, whether it’s through exploring new products, spending more time on a platform, or participating in loyalty programs.

For example, a quick-service restaurant (QSR) can send tailored content showcasing items based on the individual’s past purchases. It can also display new products that match their affinity to increase product discovery. Over time, this can increase the variety and checkout size of known user purchases, which encourages customer loyalty and increases revenue from returning customers.

In this article, we will create an AI recommendation to recommend menu items based on the user's purchase history.

Expected Result

In this case, marketers would analyse the user’s behaviour and, based on the user’s purchase history, send recommended menu items to the user. This would allow the user to try out new products that he might like based on the AI or machine learning (ML) algorithm of recommended items.

Create a Product Catalog

In this section, let us create a catalog with the following heading and content. You must have the following mandatory fields in the catalog:

Create a Recommendation

In this section, let us create a recommendation based on the user's actions.

1. Navigate to the MoEngage Dashboard and select Content > Recommendations from the left navigation. The Recommendations page is displayed. For more information, refer to Creating User Action Recommendations.
2. At the top right corner, click + Create recommendation. This will take you to the first step, "Select recommendation model."
   
3. Select the Recommended items model and click Next. It takes you to the second step "Create recommendation." Enter the following details:
   1. Recommendation name: Enter a name for the recommendation.
   2. Recommendation description: Enter a description for the recommendation. This description will help you understand the model's aim.
   3. Catalog: Select the catalog on which the recommendation will be worked.
4. The model should be as follows:
   
   Here, the recommendation model would work based on the AI/ML algorithms based on the user’s purchase history. Ideally, recommendation models consider all the events such as Product Viewed, Browsed, Add to Cart but in this case, we are filtering the products out based on the Product Purchased or Purchase History.
5. Click Create to save the changes and run the model. Its status will be Active on the Recommendations home page.

Now that we have created the recommendation, let us create a Push campaign and personalize it using Recommendations.

Create a Push Campaign

In this section, let us create a campaign.

Step 1: Target Users

1. Navigate to the sidebar on the left and click Engage > Campaigns and click + Create campaign or click + Create new > Campaign.
2. Under Outbound, select Push > Periodic.
   
   You are taken to the first step, "Target users," of defining your campaign.
3. Enter the following details:
   • Team: Select a team if your organization has teams enabled for your account. 
   • Campaign name: Enter a name for the campaign.
   • Campaign tags: Select the required campaign tags.
     
4. In the Target Audience section, select All users.
   
5. In the Target Platforms section, select Android.
   
6. Click Next to move to the second step, "Content," where you can define the content for your Push campaign. 

Step 2: Content

1. Select the template that you would like to use. For our example, select Basic notification.
   
2. In the Message title field, enter a title.
3. While defining the message, select the product set generated through the recommendation you defined. Enter “@” and search for the name of the recommendation that you built, in the Push Personalization pop-up.
   

   

   

   Using recommendations, you can personalise the campaign—title, body, and landing page. For example, if the client wants three different communications, you can have three different communications with three different recommendations in each.

   You can also put this in an Email in the same order.

   

   Row 1 is Recommendations for Choice 1; Row 2 is Recommendations for Choice 2; and Row 3 is Recommendations for Choice 3. These recommendations will be filtered based on availability and size.

4. Click Next to move to the third step, "Schedule and goals," where you can define your campaign's schedule and goals.

Step 3: Schedule and Goals

1. In the Send campaign section, select when the campaign should be delivered to your users and the periodicity of delivery.
2. Change the deliverability settings based on your requirements. For more information about these settings, refer to Create Push Campaigns.
   
3. Click Publish.

Conclusion

In this article, we created an AI recommendation to recommend menu items based on the user's purchase history.

Recommendations are a key to increasing and improving the app's usage. These outbound recommendations not only nudge the users, but sending them at the right time with the right set of products also entices the user to try more and increase stickiness with the App.