Customer retention and churn analytics

Business objective

It is a renowned grocery retail brand that sells daily grocery needs through multiple channels such as physical stores, websites, and mobile applications. It is looking to grow its sales and retain customers. They have both subscription-based models and traditional eCommerce buy-sell stores.

  • Data science
  • Deep learning
  • Machine learning
  • Business intelligence


We pitched whether they face any issues in terms of growth, sales, marketing etc. We presented some services from us, including AI/ML solutions such as Recommender system, Sales forecast, Demand prediction, Churn Analysis, etc. They got back to us as they realized they face high customer attrition.

Our success rate to retain customers with our solution is above 85%


We proposed the following services initially:

  1. A thorough analysis of their past orders and transactional data and make data story.
  2. Churn and customer retention modelling predict customers with a high chance of going away in a defined timed window.
  3. Pricing and discount strategy for customers with a high probability of going away.
  4. Personalized email and SMS marketing.

Personlized email palyed vital a role to customer retention success.

Data Collection

At this stage, we requested their data team coordinate with us to provide information about what kind of data they have about their customers, customersโ€™ behaviour and their transactions. We got to know they collect a fair amount of data, and they are the following:

Feature name Type Example
Gender Categorical Male/Female/Unknown
Age Numeric Calculated from birthdate
Is senior citizen Categorical Derived from age
Location Lattitude/Longitude Numeric Derived from address or location
Delivery distance Numeric Calculated from location data
Delivery time Numeric Obtained from transaction data
Has Subscription Categorical Yes/No
Term Categorical Monthly/Annually
Total subscription time Numeric Total days that customer has the subscription
Gaps in subscription Numeric Total days that customer was out of subscription
Payment method Categorical Card/Internet banking/POD
Avg purchase value daily Numeric
Avg purchase value weekly Numeric
Avg purchase value monthly Numeric
Total purchase value Numeric
Avg time spent on the web app daily Numeric
Avg time spent on the web app weekly Numeric
Avg time spent on the web app monthly Numeric
Avg time spent on the mobile app Numeric
Avg daily time spent on each category Numeric One hot encoded for all categories
Avg daily time spent on each product Numeric One hot encoded for all products
Numer of cart abandoned Numeric
Is Coupon used Categorical Yes/No
Avg Discount given per order Numeric
Churned Categorical Yes/No. Labelled.

Explanatory data analysis and Feature Engineering

After collecting these data, we cleaned up the data by removing blank data or imputing missing data. Then we made a data warehouse effort to create a data frame. Thenย we performed univariate, bi-variate, and multi-variate analyses. We are putting some brief points here.

  1. There is a 7.5% churned rate.
  2. Customer who is younger than 34 tends to churn more.
  3. Gender distribution does not contribute anything.
  4. Annual subscription tends to stay more.
  5. More significant gaps in subscription tend to churn more.
  6. Discount played a dominant player to retain a customer and many more.

The churn rate was 7.5%.


We first split the data in train and test split with 80% and 20% ratios. We trained several models from classical machine learning (statistical models) and deep learning such as Logistic regression, Support vector machine, Random forest, ANN, and CNN. We built a classifier to predict customers with a high probability of going away.

Our model test accuracy was 95%.


We measure the outcome of this model by checking how many customers marked to be churned in the next month got retained. After some special treatment early, like providing significant discounts and other perks, more than 95% of those marked customers were retained. Overall the churn rate reduced to just 1.8%.

Overall the churn rate was reduced to 1.8% from 7.5%.

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