Customer retention and churn analytics

Pitch

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%

Proposal

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, customers’ 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. Then 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%.

Modelling

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%.

Result

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%.