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Churn Analysis for Microfinance

Churn Analysis for Microfinance

An ML-based web app to predict whether a customer currently paying off a loan will stay with the company to get another loan or not

Shanelle Recheta

March 22, 2021

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  1. F CHURN ANALYSIS 01 for a Microfinance Institution TEAM FAST

  2. F 02 Alleviate Empower Grow Providing financial and non-financial services

    to improve the economic well-being of the poor. TEAM FAST AND FURR-IOUS
  3. 03 Why are clients defaulting? Why are they dropping out?

    "To serve them, we need to retain them." F TEAM FAST AND FURR-IOUS
  4. F 04 Improved Products and Services Reach target loan portfolio

    by 2023. More Happy Clients, More Loyal Clients Decrease in client dropouts Growth and More Lives Changed Reach more clients, touch millions of lives by 2023. TEAM FAST AND FURR-IOUS Value Proposition
  5. 05 1 2 3 4 5 50 40 30 20

    10 0 F Tenure in Years • Business Intelligence • Descriptive Analytics • Inferential Analysis To study the key factors related to dropout cases from the identified products of the company using:
  6. 06 Underlying Magic Extract Retrieve data from the database using

    Microsoft SQL. Explore Gather initial intel on the structure of the data using Excel,Tableau and and Python Visualize Extract insights and tell the story of the data using Tableau F Clean Data preparation through the use of Python Communicate Present findings to the stakeholders using Tableau and Powerpoint Deploy Turnover of the final data set, notebooks, tableau files, and presentations to our client Delivered minimum viable products while continuously getting feedback from client on how to further improve the project. Ways of working: Agile Methodology SQL
  7. 07 Domain Knowledge Data Issues & Validation F Challenges Encountered

    Data Wrangling Techniques Short Timeline SQL, Tableau, Python Tools
  8. 09 F Data issues uncovered helped the client realize the

    need for better structure of data and design of the system light-bulb ideas Actionable Insights and gave them on how to improve their system application control and data collection

    2 out of 5 clients dropout Client’s first loan and year are critical stages in retaining them.
  10. 10 F Actionable Insights New clients are more sensitive in

    their experience hence, higher tendency to drop out.
  11. 11 F It’s not just about the product. It’s also

    about customer experience. Actionable Insights
  12. 11 Design Referral System Enhance Data Collection • Train staff

    as “brand ambassadors” • Optimize process for convenience Conduct Further Studies • Market segmentation • Social media analytics F Recommendations Improve Customer Experience • Collect more customer data • Regular Customer Satisfaction Surveys • Data Validation Controls • Encourage customers to refer • Make it easy and rewarding
  13. 12 Team Fast and Furr-ious Grace Database Master, Query Specialist

    Queenie Pythonista, Data Wrangling Expert Angel Pythonista, Data Wrangling Expert Airees Tita of Tableau, Visualization Specialist F