About the Client

The client operates in the fintech industry, focusing on streamlining payment processing for SMBs. They offer a reliable and secure platform that handles all types of financial transactions, helping businesses improve their payment workflows. With plans for continuous growth, retaining customers was a priority to ensure steady revenue and support their future scaling.

Project Overview

The client partnered with us to improve their ability to predict and mitigate customer churn. With the help of Databricks, we implemented an advanced solution for data integration, predictive modeling, and personalized retention strategies, enabling the company to enhance customer loyalty and engagement.

Key Challenges

High Churn Rate

A significant number of SMB customers were leaving the platform, affecting long-term revenue.

Data Fragmentation

Data from transactions, user activity, and customer support was stored in different systems and/or different formats, making it difficult to analyze customer behavior effectively.

Generic Retention Strategies

Without insights into which customers were likely to churn, the company’s retention strategies lacked personalization and impact.

Plan of Action

We consolidated data, developed predictive models, and implemented personalized retention strategies. Real-time analytics were used for ongoing monitoring and adjustments.

Key Objectives

Unify Data

Consolidate data from various sources for a comprehensive view.

Predict Customer Churn

Develop a model to identify at-risk customers and reduce churn.

Enhance Retention

Implement personalized strategies based on churn predictions.

Real-Time Analytics

Provide insights for informed decision-making and timely adjustments.

Support Growth

Ensure scalability for expanding needs.

Approach & Execution

#1. Data Consolidation & Transformation

Challenge: Data was scattered across multiple systems and formats.
Solution: We leveraged Databricks to bring together transaction records, customer activity logs, and support interactions into a unified environment. This streamlined their data processes and improved the accuracy of predictive models.
Approach:

Data Consolidation: Databricks’ built-in data connectors enabled us to bring together transaction data, support logs, and platform usage data into a unified environment.
Data Cleaning & Transformation: Used Spark within Databricks to remove duplicates, handle missing values, and standardize formats. Key features such as transaction volume and customer activity were developed to enhance the predictive model.

#2. Churn Prediction Model

Challenge: A reliable model was needed to predict which customers were likely to churn.
Solution: We developed a Random Forest classifier using Databricks’ MLlib to analyze historical data and predict churn risk. This model was trained to identify patterns and factors indicating potential churn.
Approach:

Model Development: Built and trained the Random Forest classifier on cleaned and transformed data, focusing on features like transaction activity and support interactions.
Evaluation: Assessed model performance using accuracy, precision, and recall metrics to ensure reliable predictions. Databricks facilitated scalable model training and fine-tuning.

#3. Targeted Retention Strategies & Visualization

Challenge: The client needed to apply personalized retention strategies based on churn predictions.
Solution: We used the churn model to identify high-risk customers and implemented targeted retention strategies. Insights were visualized using Tableau, integrated with Databricks for real-time tracking of strategy effectiveness.
Approach:

Retention Strategies: Designed offers and enhancements such as discounts and improved support for at-risk customers.
Visualization: Created interactive dashboards with Tableau to provide real-time insights into churn risks and monitor the effectiveness of retention efforts.

Results

20% Reduction in Churn

Achieved a significant decrease in customer churn.

40% Increase in Customer Engagement

Personalized offers led to better customer engagement.

Valuable Insights

Gained actionable insights into customer behavior.

Team Composition

Data Engineers: 2
Data Scientists: 2
Business Analyst: 1
Tableau Specialist: 1

Conclusion

Our collaboration with this fintech startup resulted in a 20% reduction in customer churn through a data-driven approach using Databricks. By consolidating data, developing predictive models, and implementing targeted retention strategies, we provided real-time insights and actionable plans to improve customer engagement.

This project highlights the power of advanced analytics in driving customer retention and supporting business growth in the fintech sector.

Testimonials

Our Slam Book

Tony Lehtimaki

DIRECTOR - AMEOS

Spain

Very professional, accurate and efficient team despite all the changes I had them do. I look forward to working with them again.

Antoine de Bausset

CEO - BEESPOKE

France

They are great at what they do. Very easy to communicate with and they came through faster than I hoped. They delivered everything I wanted and more! I will certainly use them again!

Vivek Singh

MARKETING & SALES HEAD - VARMORA

Gujarat

I really liked their attention to detail and their sheer will to do the job at hand as good as possible beyond professional boundaries.

Nimesh Patel

DIRECTOR - COVERTEK CERAMICA

Gujarat

Excellent work, and on time with all goals. Communication was very easy, and knowledge of work was excellent. Will be working with them on upcoming projects. I highly recommend.

Craig Zappa

DIRECTOR - ENA PARAMUS

United States

"I would like to recommend their name to one and all. No doubt" their web app development services cater to all needs.

Neil Lockwood

CO-FOUNDER - ESR

Australia

Aglowid is doing a great job in the field of web app development. I am truly satisfied with their quality of service.

Daphne Christoforidou

CEO - ELEMENTIA

United States

Their team of experts jotted down every need of mine and turned them into a high performing web application within no time. Just superb!

Customer Churn Prediction & Retention for a FinTech Organization

Industry

Fintech

Project Type

Location

Timeline

Team Size