Generative AI

Using predictive models to reduce customer churn by more than 20%

Industry

Telecom

Year

2017

Churn reduction

20%

Client base

1 million

Type

AI development services

Telecom

Building the AI-based platform with a recommender system for one of the biggest Polish telecoms with almost 1 million customers.

About the project

The Client was struggling with growing customer churn, which was their biggest problem, and they couldn’t prevent it due to multiple factors, including ineffective customer retention strategy, lack of essential information, and too long feedback loop.

Our tasks

Churn prediction & reduction

Creating a 360-degree view of each customer along with predictive models enhancing 360-view with churn predictions.

Enabling product recommendation

Deploying predictive models enhancing 360-view with product recommendations.

Delivering suggestions

Developing tools delivering suggestions on how to take care of churn-prone customers to front-line employees.

Integrating models

Integrating created models with the client’s systems.

Updating models regularly

Keeping track of the feedback loop to keep predictive models up to date.

01

Challenges

1. Ineffective company retention strategy

The customer retention strategy was based on randomly contacting as many of the customers whose contract would expire in the next couple of months as possible, and giving hefty discounts to the ones that have already canceled their contract. The client had a huge number of product bundles (aka offers) that were created “just in case”. The number was so high that it was literally impossible for the sales force to learn how to sell the services, and they ended up learning a few selected offers that were the easiest to sell.

2. Lack of data on churning customers

The staff was lacking information about which customers were most likely to churn and what the factors impacting such risk were. The feedback loop on how employees’ actions and campaigns affected sales metrics (especially churn) was extremely long; as a result, decisions on who to contact and what should be offered were based on the gut feeling of the front-line sales force and their managers instead of hard data.

3. No data-driven strategy for churn reduction

Many of the employees’ actions were based on guessing and randomly choosing customers to contact or offers to make, so it was difficult to reduce churn, and using the old strategies and techniques made it virtually impossible. Instead of making use of data, they were trying to make use of their intuition. As our client’s employees put it: “We were blind on one-and-a-half eye”.

Laptop displaying a data visualization platform

02

Solutions

Technology we used

AWS Lambda

AWS SQS

Azure ML

MongoDB

Python

03

Project Results

10x ROI

During the pilot, we nearly doubled the goals, saving our client over $39k every month and much more than that after rollout – and that is not taking into account the cost of acquiring customers in place of those that left for the competition. After full system rollout, our client ended up with more than 10x return on their investment.

Adopting a customer-centric culture

The focus on the customer instead of the abstract concept of Revenue Generating Unit clarifies how the company as a whole is perceived by customers; our telecom is no longer looking at “internet numbers” separately from “TV numbers”. It’s important to realize that for a customer it doesn’t matter what department they work with, if they’ve had bad experiences with one service, they aren’t likely to choose another one from the same provider.

Company-wide AI implementation

Artificial Intelligence was implemented company-wide. Predictions using machine learning models are used not only in sales & retention but also in other departments, for example, to determine which pieces of infrastructure should be upgraded to prevent churn. What’s more, new models can be introduced by the client’s employees themselves.

Improving project management practices

We introduced Agile & DevOps practices in our client’s organization which proved to be a successful way to run the project in their company. During the last couple of years, it was the first IT project for our client that was finished on time and within budget (or actually well before the deadline with some budget left).

Triggering digital transformation company-wide

The good practices from that initiative triggered company-wide transformation as their IT finally got arguments and “green light” for practices they have been trying to introduce and business got the speed of delivery and stability that was desperately needed.