---
title: "Defining the Business Problem: Framework 1"
description: "This post presents a snippet of a framework for defining the business problem in a systematic way using example. ## The Problem in General Terms A large telecommunications operator is having a major problem with customer retention in their mobile…"
url: "https://sdscope.com/doc/defining-the-business-problem-addressing-a-pain-point/"
updated: "2022-06-02"
category: 4. Defining the Business Problem
---

# Defining the Business Problem: Framework 1

This post presents a snippet of a framework for defining the business problem in a systematic way using example.

## The Problem in General Terms

A large telecommunications operator is having a major problem with customer retention in their mobile telecom business. 8% of mobile telecom customers leave when their contracts expire, and it is getting increasingly difficult to acquire new customers as the mobile telecom market is now saturated.

## The Approach \[1\]

1. **Identify the business problem**
8% of mobile telecom customers leave when their contracts expire

2. **Determine why machine learning is the most appropriate approach**
See [When to Apply Machine Learning](https://sdscope.com/doc/when-to-apply-machine-learning/)

3. **List possible solutions and define key items**

**Solution 1: Predict churn**
*Proposed model:*Build a model that predicts the likelihood of a customer to leave
*Usage:* Run the model every month to identify customers most likely to leave, then offer them an incentive to stay
*Benefit:*Retaining existing customers is much less expensive than attracting new ones. Average Revenue Per User after offering prospective churners would be $2.09 if 20% of prospective churners are retained but would fall to $1.83 if all prospective churners actually churn.
*Data requirements:* a large collection of historical data marked as churn and non-churn for each customer; demographic, behavioral and transactional information about each customer, information about the company’s services
*Capacity requirements:* capacity to build suitable incentives, a mechanism to contact identified customers with the incentive (e.g., by email, text messaging, voice call, etc.)
*Business success:* Reduce churn by 20% in the first 3 months of deployment
*Business failure:* Unable to reduce churn by 18% in the first 6 months of deployment
*Machine learning success:* Precision of 0.85
*Machine learning failure:* Precision of 0.7 or less

**Solution 2: Identify reasons for leaving**
*Proposed model:*Identify a small set of features of the company’s products that are important in building a model that predicts the likelihood of a customer to leave
*Define the rest of the items in the manner done in Solution 1 above*

**Solution 3: Design incentives**
*Proposed model:*Build a next-best-offer model that accurately predicts the likely effectiveness of incentives that could be offered to entice customers with high churn-likelihood to stay
*Define the rest of the items in the manner done in Solution 1 above*

**Solution 4: Recommend a service**
*Proposed model:*Build a model that accurately predicts how much a customer would like an existing service
*Define the rest of the items in the manner done in Solution 1 above*

4. **Prioritize models**
Draw up an implementation priority list according to the feasibility of the above models in terms of data and capacity requirements

5. **Prepare an initial implementation plan**
Develop a high-level implementation plan that can be iterated on as more information is discovered including project schedule, tools and techniques, risk assessment, and resource allocation

## References

1. Inspired by Kelleher, J. D., Mac Namee, B., and DArcy, A. (2015). Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies. Massachusetts Institute of Technology, 64-70

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