Enhancing e-commerce services with ML-powered churn prediction calculation

Executive summary

Client background

Our client delivers comprehensive e-commerce and subscription management solutions designed to monetize digital goods, online services, and SaaS in diverse sectors. They provide a cloud-based e-commerce platform that streamlines recurring billing, enhances customer experiences, and encompasses worldwide compliance and payment functionalities.

Business challenge

The client offers businesses an e-commerce platform that comes with a marketing email campaign feature. They aimed to boost its capabilities by introducing features for customer segmentation, precise churn rate prediction, and creating personalized marketing campaigns.

Value delivered

By successfully implementing Machine Learning models into the e-commerce platform, we helped the client enhance their service offering. As a result, they experienced improved customer retention and acquisition, leading to a boost in their overall profitability.

Success story in detail

Business challenge: improving service quality of the e-commerce platform with Machine Learning

Our client wanted to improve the effectiveness of the email marketing campaign feature of their e-commerce platform. The main requirement was to integrate Machine Learning models to analyze the likelihood of a customer canceling their subscriptions. This would enable e-commerce platform users to send effective personalized email campaigns, increasing their customer retention.

Also, the company aimed to enhance the efficiency of creating marketing campaigns by implementing automation, thereby reducing a substantial portion of the manual work previously needed.


Sparknet approach: conducting a PoC and applying MLOps best practices
During the PoC stage, we developed a prototype and designed the architecture for the functionality that would automate the marketing feature within the client’s e-commerce platform. We created a WBS (Work Breakdown Structure) to effectively organize, plan, and execute all project activities, focusing on template management, email sending, data layer, and statistics. Then, based on the PoC results, we decided to build an AWS-based solution using the cloud-native approach and serverless best practices. This approach reduces maintenance costs and makes the platform flexible for integration with a Machine Learning system. Additionally, our team applied MLOps (Machine Learning Operations) best practices for automating email campaigns to enhance the reproducibility and efficiency of the campaign process. Implementation: setting up a serverless workflow and Machine Learning infrastructure Sparknet team has helped implement a multi-tenant Machine Learning-based solution within the client’s e-commerce platform. It enables accurate subscription churn prediction and helps target subscribers more effectively. We have set up a serverless workflow for email campaigns utilizing AWS Lambda and AWS Step Functions. AWS Step Functions define the sequence of steps needed for email campaigns. We integrated AWS Lambda to generate personalized email content, send emails, track user interactions, and update campaign analytics. As a result of the migration to AWS, customers can configure their email campaigns through APIs. Every customer can reuse the campaigns by setting different configurations, such as lists of receivers, schedules for sending emails, and segments of churn probabilities. Additionally, we have assisted in setting up Machine Learning infrastructure. We began by gathering data about end-user behavior during an email workflow. Then, our AI engineers analyzed this data and calculated churn probabilities, which indicated the likelihood of a customer discontinuing their subscription. We used AWS SageMaker to develop predictive models that determine the likelihood of user actions, such as subscription churn or engagement with email content. Sparknet team enabled the automatic sending of customized emails based on the churn probability data. For instance, in cases of high churn probabilities, specific users receive tailored email campaigns designed to motivate them to continue with their subscriptions. On the other hand, more loyal users receive regular marketing emails. Additionally, our engineers have used churn probabilities to create dashboards with calculated revenue for clients. These dashboards provide a comprehensive view of user interactions with email campaigns. Finally, we have used React to develop the front-end part for the template manager, where customers can move ready-made blocks to build their email campaigns. This feature significantly reduced the time required to compose emails since users no longer have to do it manually.
Value delivered by Sparknet: improving customer experience, business efficiency, and revenue growth
We have helped the client integrate Machine Learning models that calculate churn probabilities into their e-commerce platform. We have also set up a serverless workflow based on the AWS stack and created configurable email campaigns. As a result, our client benefited in several significant ways:
  • Automated marketing interventions for user segments based on subscription churn probabilities;
  • Enabled client segmentation that increases customer retention rates with personalized marketing activities and, subsequently, boosts revenue;
  • Reduced maintenance costs and made the system more flexible by migrating to the AWS stack;
  • Improved operational efficiency and customer experience by implementing a template manager that allows to quickly create targeted email campaigns without requiring a significant amount of manual work.