AWS Only
AI-Powered Hyper-Personalisation for Balance Transfer Credit Cards at Money Supermarket
Money Supermarket (MSM) partnered with Firemind to develop an AI-driven solution aimed at enhancing customer experience and loyalty by offering hyper-personalised support for consumers researching 0% interest balance-transfer credit cards.
AWS ONLY ·
AI-Powered Hyper-Personalisation for Balance Transfer Credit Cards at Money Supermarket
Money Supermarket (MSM) partnered with Firemind to develop an AI-driven solution aimed at enhancing customer experience and loyalty by offering hyper-personalised support for consumers researching 0% interest balance-transfer credit cards.
At a glance
Money Supermarket (MSM) is a leading British price comparison website that specialises in financial services. The platform enables consumers to compare prices across a wide range of products, including credit cards, loans, insurance, and utilities. By providing users with the ability to easily compare different financial products, MSM helps customers make informed decisions and find the best deals tailored to their needs. As a major player in the price comparison market, MSM continually innovates to enhance customer loyalty and offer value-added services beyond just price comparison.
Challenge
Money Supermarket faced the challenge of retaining customers by providing more personalised and value-added services, leading them to partner with Firemind to develop an AI-driven solution that offers tailored credit card recommendations and enhances customer engagement on their platform.
Solution
The solution is an AI-powered tool that provides personalised credit card recommendations and interactive assistance, enhancing customer engagement and loyalty on Money Supermarket's platform.
Services Used
Amazon Bedrock
AWS Lambda
Amazon SageMaker
AWS DynamoDB
Outcomes
20% estimated increase in customer retention
15% boosted estimated conversion rate for balance-transfer credit card applications
Business challenges
Retaining customers through enhanced personalisation in a competitive market
Money Supermarket (MSM) faced a significant challenge in retaining customers amidst a highly competitive price comparison market. In this space, consumers frequently switch between platforms in search of the lowest prices, making customer loyalty a critical issue. Despite MSM’s extensive range of financial products and competitive pricing, the transient nature of customer engagement meant that simply offering a broad selection was no longer sufficient to maintain a strong customer base.
To address this challenge, MSM recognised the need to go beyond traditional price comparison and enhance the overall customer experience. The company aimed to introduce value-added services that would not only capture user interest but also foster long-term loyalty. This led to the decision to partner with Firemind, focusing on developing an AI-driven solution to provide personalised recommendations and interactive support. By leveraging advanced AI technologies, MSM sought to create a more engaging and tailored experience that would encourage customers to stay and interact more deeply with the platform.
"Firemind's PULSE tool was able to quickly and accurately summarise vast datasets of customer surveys, ensuring we could take action against any negative customer experience elements."
Dean Macfadyen, Data Platform Engineer — Generative AI Customer
Solution
AI-powered personalisation for enhanced customer engagement and loyalty
To address Money Supermarket’s challenge of retaining customers in a competitive price comparison market, Firemind developed an advanced AI-driven solution that significantly enhanced user engagement. The core of the solution involved leveraging AWS Lambda to build serverless functions capable of processing customer data and interfacing with various APIs. This setup ensured a scalable and responsive system that could handle personalised recommendations efficiently.
Central to the project was the use of Amazon SageMaker to develop and deploy sophisticated machine learning models. These models analysed customer credit reports and product options to generate tailored recommendations for 0% interest balance-transfer credit cards. SageMaker facilitated the training and optimisation of these models, enabling them to deliver accurate and relevant suggestions based on individual user profiles.
Amazon DynamoDB was employed to manage and store customer session data and credit report information. This NoSQL database provided a high-performance, scalable solution for handling the dynamic data needs of the AI system, ensuring quick retrieval and updates necessary for real-time personalised recommendations.
Additionally, AWS API Gateway played a crucial role in managing the APIs that connected the website to the backend services. It orchestrated the communication between the front-end user interface and the AI-driven backend, ensuring seamless integration and interaction. Generative AI models, including those from Amazon Bedrock (Anthropic Claude 3 Haiku), were utilised to power the conversational element of the solution, allowing users to ask “what if?” questions and receive personalised, factual responses. This comprehensive approach not only addressed the challenge but also set a foundation for future AI-driven enhancements on MSM’s platform.
Advanced personalisation with SageMaker
The use of Amazon SageMaker was pivotal in developing sophisticated machine learning models that provided highly personalised credit card recommendations. By analysing individual credit reports and comparing them with available products, the models delivered tailored suggestions that significantly enhanced the relevance of the advice provided to users.
Efficient data management with DynamoDB
Amazon DynamoDB played a crucial role in the project by ensuring efficient and reliable management of real-time customer data. Its high-performance, scalable nature allowed for quick retrieval and processing of customer information, which was essential for delivering timely and accurate credit card recommendations.
Enhanced user interaction with conversational AI
The integration of a conversational AI interface, powered by Generative AI models, facilitated interactive and personalised user engagement. This feature allowed customers to ask detailed “what if?” questions and receive customised, factual responses, thereby improving the overall user experience and addressing the challenge of maintaining customer loyalty.
Model Spotlight
Claude 3 Haiku
We selected Anthropic Claude 3 Haiku for this project due to its ability to provide concise, precise responses, making it ideal for handling straightforward customer queries in a fast and efficient manner.
With its support for Retrieval-Augmented Generation (RAG), Claude 3Haiku can pull in accurate, personalised data from external knowledge sources, ensuring tailored answers for individual customer needs. This efficiency in communication is expected to contribute significantly to the 20% estimated increase in customer retention, as users value quick, reliable information that helps them make informed financial decisions.
Claude 3 Sonnet
Anthropic Claude 3 Sonnet was chosen for its capability to generate more detailed and elaborate responses. Claude 3 Sonnet is particularly suited for addressing complex inquiries that require in-depth explanations, such as comparing various credit card options or analysing long-term financial impacts.
Like Haiku, Sonnet supports RAG, ensuring that these detailed responses are both informative and factually accurate, personalised according to the user’s financial profile.
By offering more thorough guidance, Claude 3 Sonnet is expected to drive a 15% boost in conversion rates for balance-transfer credit card applications, as it helps customers understand the benefits and implications of different options.
Why Firemind
Money Supermarket chose Firemind for its exceptional expertise in generative AI and large language models (LLMs), which were critical for developing an advanced, personalised customer experience. Firemind’s proficiency in leveraging Amazon SageMaker to build and deploy sophisticated machine learning models, combined with their deep understanding of generative AI technologies, ensured the creation of highly relevant and interactive recommendations. Their skill in integrating these technologies with Amazon DynamoDB for efficient data management and AWS Lambda for scalable serverless solutions provided a comprehensive approach to enhancing user engagement and retention. Firemind’s innovative use of generative AI for conversational interfaces further aligned with MSM’s goals of delivering a more engaging and responsive customer experience, making them the ideal partner for this project.
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