Data Based Decision
Making
Mining Your Business Data for More
Informed Decision Making
What Is Data Mining?
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Data mining is sorting through data to identify patterns and establish
relationships.
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Automatic discovery of patterns
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Prediction of likely outcomes
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Creation of actionable information
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Focus on large data sets and databases for valuable insight
What sort of business issues can it help?
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Marketing
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Customer retention
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Basket analysis – e.g. what’s the next best offer
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Sales forecasting
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Improve return on marketing spend – targeting – identify cross/upsell
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Find prospects where there’s the best chance of a quick win
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Reduce acquisition costs
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Merchandise planning
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Claims analysis
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Credit scoring
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Fraud detection
Example: Telecom Churn Analysis
The Business Pain:
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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.
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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.
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Churn erodes profitability.
What We Need To Know To Fight Churn
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Who is churning?
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Why are they churning?
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What are the alarm bells we can look for?
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What strategies can we employ to limit churning?
How Can Data Mining Help?
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Historic customer data – demographics, transactions,
interactions
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Every customer has a footprint within this data
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Each footprint will identify a behavior
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Mining will identify patterns in behavior
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Patterns are tested and validated for accuracy
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Valid patterns produce models that can be applied to predict
behavior.
Churn – the solution
The Dataset
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Data mining requires large
datasets to ensure statistically
valid results
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Data may come from many
sources as its important to include
all aspects of the business that
may influence churn.
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CRM, accounting, customer
service, surveys
Churn – the solution
Preparation
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Flag records that we know already churned
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Identify variables that may be predictive – for example we would
include Number of service call but exclude phone number
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Build decision tree – the decision tree will identify the variables or
combination of variable that influence churn within the sample
dataset.
Churn – the decision tree
Understanding the results
The first split is on Day Minutes so lets look at this variable
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
Understanding the results
Darker colour indicates
higher correlation to
churn
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
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
Deploy retention strategies
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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 >= 210.480
and < 245.560 and
Eve Mins >= 254.590
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.
Summary
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Retention strategies based on actual historic data
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Deployable to live systems actionable in real time
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Models easily re-evaluated as new products and services added
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Efficient allocation of marketing/retention budget
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Identify systemic issues causing churn e.g. poor customer service