1. Data Based Decision Making
Mining Your Business Data for More Informed Decision Making

2. What Is Data Mining?
Data mining is sorting through data to identify patterns and establish relationships.
Automatic discovery of patterns
Prediction of likely outcomes
Creation of actionable information
Focus on large data sets and databases for valuable insight
Machine learning – software runs to apply proven algorithms
Classification as well as predictive modeling
Business decisions based on model results
Larger data sets yield statistically confident results

3. What sort of business issues can it help?
Marketing
Customer retention
Basket analysis – e.g. what’s the next best offer
Sales forecasting
Improve return on marketing spend – targeting – identify cross/upsell
Find prospects where there’s the best chance of a quick win
Reduce acquisition costs
Merchandise planning
Claims analysis
Credit scoring
Fraud detection

4. Example: Telecom Churn Analysis
The Business Pain:
Most telecom companies suffer from voluntary churn. Churn rate has a strong impact on the life time value of the customer because it affects the length of service and the future revenue of the company. For example if a company has 25% churn rate then the average customer lifetime is 4 years; similarly a company with a churn rate of 50%, has an average customer lifetime of 2 years.
It is estimated that 75 percent of subscribers signing up with a mobile carrier every year are coming from another mobile provider, which means they are churners. Telecom companies spend hundreds of pounds to acquire a new customer and when that customer leaves, the company not only loses the future revenue from that customer, but also the resources spent to acquire that customer.
Churn erodes profitability.

5. What We Need To Know To Fight Churn
Who is churning?
Why are they churning?
What are the alarm bells we can look for?
What strategies can we employ to limit churning?

6. How Can Data Mining Help?
Historic customer data – demographics, transactions, interactions
Every customer has a footprint within this data
Each footprint will identify a behavior
Mining will identify patterns in behavior
Patterns are tested and validated for accuracy
Valid patterns produce models that can be applied to predict behavior.

7. Churn – the solution The Dataset
Data mining requires large datasets to ensure statistically valid results
Data may come from many sources as its important to include all aspects of the business that may influence churn.
CRM, accounting, customer service, surveys

8. Churn – the solution Preparation
Flag records that we know already churned
Identify variables that may be predictive – for example we would include Number of service call but exclude phone number
Build decision tree – the decision tree will identify the variables or combination of variable that influence churn within the sample dataset.

9. Churn – the decision tree

10. Understanding the results
The first split is on Day Minutes so lets look at this variable

11. Understanding the results
We can examine the Day Minutes variable to understand what the typical usage is. In this case the median is around 175 min

12. Understanding the results
Darker colour indicates higher correlation to churn

13. Understanding the results – the key nodes
Higher than median Day time
usage without Intl plan
Higher than median Day time & evening
usage without VM
Low day usage with high number
of customer service call
High day usage with no VM

14. Retention strategies High usage valuable customers
- provide Intnl plan to this group
High usage valuable customers
- Provide VM service to this group
Lower value customers with
high service cost. Maybe too high
maintenance to be profitable
High usage high value customer
so provide VM service

15. Deploy retention strategies
Each node of the decision tree that we wish to action provides the criteria that we can apply to identify new and existing records that fall within that node
Day Mins >=
and < and
Eve Mins >=
and VMail Plan not = 1
These criteria are used to create rules within the organisations CRM to identify records that
fall within each actionable node, thus allowing retention strategies to be deployed on an on-going
basis, as customers move in and out of actionable groups.

16. Summary Retention strategies based on actual historic data
Deployable to live systems actionable in real time
Models easily re-evaluated as new products and services added
Efficient allocation of marketing/retention budget
Identify systemic issues causing churn e.g. poor customer service