Problem

Our client, a retail bank, was struggling to increase their term deposit subscriptions. They were unsure of how to effectively target potential customers and increase their conversion rates. They needed a solution that would allow them to predict which customers were most likely to subscribe to their term deposit campaign.

We knew we had to find a way to analyze the bank's data and build a predictive model that would help them identify the best prospects for their campaign.

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Solution

We used R programming language and analyzed the bank's dataset using hypothesis testing techniques and literature review. We selected variables based on these techniques and incorporated them into our logistic regression model. Our team built a machine learning model that predicted whether or not a customer would subscribe to the term deposit campaign.

We then checked the model based on assumption checks for statistical significance. Once we confirmed its accuracy, we provided our client with recommendations on how they could use this model to target potential customers more effectively.

Impact

Increased subscription rates by X%
Identified top prospects for targeted marketing efforts
"This project completely transformed our approach to marketing our term deposit campaign. We are now able to target potential customers more effectively and have seen significant improvements in our subscription rates."

Team

Team Member #1 - Data Analyst
- Analyzed the bank's dataset using hypothesis testing techniques and literature review
- Built a machine learning model that predicted subscription status based on selected variables
Team Member #2 - Statistician
- Checked the model based on assumption checks for statistical significance
- Provided recommendations to the client on how to use the model to target potential customers more effectively

Bank Boosts Term Deposit Subscriptions with ML Model