Image courtesy of medium.com

In today’s digital landscape, customer engagement is paramount. Our client faces a significant challenge: customers are churning due to a lack of engagement on their platform. This disengagement places undue stress on the business, as efforts to generate interesting content are not reaching the appropriate audience.

Business Objective

Business Problem

In today’s digital landscape, customer engagement is paramount. Our client faces a significant challenge: customers are churning due to a lack of engagement on their platform. This disengagement places undue stress on the business, as efforts to generate interesting content are not reaching the appropriate audience.

Business Objective

Our primary objective is to direct customers to relevant products and content to increase their engagement and, consequently, decrease the subscription churn rate. By tailoring recommendations to individual customer preferences, we can enhance user experience and foster long-term loyalty.

Challenges

Several challenges impede our progress:

1. Third-Party Marketing Platform: Marketing efforts are conducted on a third-party platform with suboptimal opening rates.
2. Inconsistent Recommendations: The current system for generating similar video playlists provides inconsistent and irrelevant product recommendations, failing to captivate the audience.

Our Approach

To tackle these challenges, we will implement a robust solution using AWS Personalize. Here’s a detailed outline of our approach:

Step 1: Update the Similar Videos Playlist with AWS Personalize

AWS Personalize is a powerful tool that leverages machine learning to create personalized recommendations. By integrating this tool, we can significantly improve the relevance of the video recommendations.

Step 2: Create an ETL Pipeline to Generate Training Data

A crucial component of our solution is the creation of an ETL (Extract, Transform, Load) pipeline. This pipeline will:

• Extract data from various sources.
• Transform the data into a suitable format for training machine learning models.
• Load the processed data into a data warehouse for easy access and analysis.

Step 3: Follow the Machine Learning Evaluation Process

To ensure we select the best-performing model, we will:

• Iterate through different machine learning models and parameters.
• Evaluate each model using standard metrics.
• Continuously refine our approach based on the evaluation results.

Step 4: Promote the Final Model to Production

Once we identify the best-performing model, we will:

• Promote it to the production environment.
• Grant website engineers permissions to access the model in real-time, enabling them to populate the similar videos playlist dynamically.

Next Steps

To further enhance the platform, we recommend:

• Creating Personalized Recommendations: Tailor content for customers’ favorite categories and the trendiest content.
• Automatically Generating Playlist Titles: Use machine learning to create attention-grabbing playlist titles.
• Personalizing Customer Search Results: Adjust search results based on their relevance to individual customer profiles, ensuring a more personalized experience.

Conclusion

By leveraging AWS Personalize and implementing a robust ETL pipeline, we can transform the customer experience on our client’s platform. Personalized recommendations will not only increase engagement but also reduce subscription churn rates. Our data-driven approach ensures that content reaches the right audience, fostering long-term customer loyalty and business growth.

Contact us today to learn how we can help you implement a personalized recommendation system tailored to your business needs. Let’s work together to reduce churn and enhance customer engagement through innovative, data-driven solutions.