Healthcare & Life ScienceAI & ML

Personalised Recommendations by Leveraging Generative AI with Calm

Enhance user experience and drive business value through increased retention, revenue, and lifetime value by leveraging Large Language Models to generate tailored recommendations and justifications.

Personalised Recommendations by Leveraging Generative AI with Calm

Enhance user experience and drive business value through increased retention, revenue, and lifetime value by leveraging Large Language Models to generate tailored recommendations and justifications.

At a glance

Calm is a software company based in San Francisco that produces a leading meditation and sleep application, helping users improve their overall mental health and wellbeing.

Challenge

Calm was looking to enhance its customer experience through improving its personalised content recommendations.

Solution

Combining a vector database, LLMs, and a hybrid search approach to generate personalised recommendations.

Services Used

  • Amazon OpenSearch
  • Amazon Bedrock
  • AWS Lambda
  • Amazon DynamoDB

Outcomes

  • 100% of recommendations have explainability
  • 25% more accurate recommendations
  • 5 additional metadata properties for customisation

Business challenges

Improving personalisation of content recommendations

Calm recognised an opportunity to improve their personalised recommendations to customers and sought to evaluate the use of Large Language Models (LLMs) to enhance the personalisation of content and improve the user experience.

While Amazon Personalise was being used to assist with personalised recommendations, Calm wanted to increase performance and incorporate a layer of contextual awareness to the recommendation process. The goal was to leverage user data and content metadata to generate more relevant and engaging recommendations for their customers.

“I think the work that Firemind has done has been brilliant and it just continued to improve with each iteration. I love that even just at first glance, you could tell there was an improvement in quality.”

Jonathan Hummel, VP of Engineering — Calm

Solution

Integrating LLMs for personalised recommendations

The Firemind team implemented a solution that leveraged a combination of a vector database, LLMs, and a hybrid search approach to generate personalised recommendations for Calm users. This solution was designed to enhance the personalisation of content recommendations, ultimately driving improved business outcomes for Calm.

A key aspect of the solution was the generation of detailed content descriptions using LLMs. By incorporating the transcript data and metadata associated with Calm‘s content catalogue, the team was able to create more nuanced and contextual descriptions of each item. This provided the LLMs with richer information to draw upon when matching content to user preferences and personas.

The final step of the solution involved the LLMs providing justifications for the recommended content. These explanations, addressed directly to the user, highlighted how the suggested items complemented their mood and persona. This added layer of transparency and personalisation was designed to enhance the user experience and drive greater engagement with the recommended content.

Personalised user profile

The solution utilised the user's recent engagement with Calm's content to tailor recommendations for individuals based on their preferences.

Generated detailed content description

By incorporating the transcript data and metadata associated with Calm's content catalogue, the team was able to create more nuanced and contextual descriptions of each item, providing the LLMs with richer information to draw upon when matching content to user profiles.

Discover the Calm app

You can download the Calm app for both Google Play and the App store.

100%

of recommendations now come with explainability

The LLMs provided justifications for the recommended content, highlighting how the suggested items complemented the user's mood and persona.

25%

more accuracy for recommendations

The combination of personalised user profiles, detailed content descriptions, and the hybrid search approach led to a 25% improvement in the accuracy of the recommendations.

Added value

The solution added 5 new metadata properties per recommendation, such as title, description, mood-based, sleep-based, and duration, enabling more customisations and personalisation.

Get in touch

Want to learn more?

Seen a specific case study or insight and want to learn more? Or thinking about your next project? Drop us a message below!