Case 1

Purpose

The client, a paper based products manufacturing company wanted to reduce its customer churn in its online business

Approach

Our team aggregated the customer level data into a single source of truth and we then proceed to identify the customer behavior using some simple exploratory data analytics techniques

Due to disparate nature of every customer, to bring some regularization we clustered the customers in different categories based on various behavioral attributes like demographics, average time on site, average number of products they buy etc.

We then calculated customer lifetime journey using proprietary supervised learning techniques like Markov chain models and Bayesian models

The final end to end solution was wrapped around a python web framework with integrated learning algorithms with a thin client facing the customer

Outcome

The client was successfully able to identify more than 90% of its customers that were at the end of its buying life-cycle journey and hence were about to churn

An inclusive strategy was devised to target those customers with lucrative promotional offers and mailers to ensure stickiness to the brand

The customer churn was reduced by more than 30% compared to previous quarter and the overall sales grew by 8-10%


Case 2

Purpose

The client, a leading clothing retail, wanted to understand the best discounts to offer to customers

Approach

The problem statement was taken and broken down into smaller problems:

  • Which products would be most responsive to discount promotions
  • What are the discounts to be offered on promotionally driven products

We initially collated product data from multiple sources and all the products were clustered using proprietary unsupervised learning techniques

We then built price elasticity models across products to find out where the products stood in terms of promotional responsiveness

The promotionally responsive products were filtered out using SWAT analysis based on price elasticity and customer satisfaction scores.

A differential simulation pricing modelwas used on the filtered products to find the optimized promotional discounts for every product

Outcome

There was a rapid shift from heuristic based pricing to a data driven pricing in the client organization which led to quick and insightful decision making

The client saved $1.5 million in promotional and marketing spend and the overall sales shot up 12% during the promotional period


Case 3

Purpose

A leading baby products manufacturing company wanted to gauge the impact and effectiveness of its promotion strategy

Approach

This was a special engagement as we helped the client in entire experiment design and we were engaged with the client throughout the journey

Initially we collated all the customer related attributes and then classified the customers based on their behavioral pattens and affinity towards the customer's products

We then chose a pool of test customers to roll out the promotion by gauging their behavioral attributes and using sampling across stratas of customer

The behavior of test pool customers was compared to the control pool customers. The customers in control pool were chosen using hypothesis testing and statistical analysis.

The final promotional effectiveness was gauged using a multiple regression model which used customer behavioral factors, market trends and product attributes to create a robust all-inclusive solution

Outcome

Using this experiment our clients were able to identify the product segment and region combination that were most receptive to a particular type of promotion

The final solution helped them to simulate multiple test promotions on product segments and then gauge the promotion effectiveness to select the most optimal promotion for particular product