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Importance
Data Mining
Analytics
In Marketing
How
to be aof
successful
Stalker
on a mass
scale
Tim Manns
Importance of Data Mining Analytics In Marketing
Overview
•
The data mining challenges facing Optus marketing group
•
Infrastructure issues for scaleable data mining
•
Executive summary of the data processing we undertook
to solve these challenges
•
A case study showing the success of our analysis for
mobile telephony customer retention
•
A case study for fixed line telephony segmentation analysis
used to support successful up-sell campaigns
•
Conclusions and tips
Challenges Facing Telecommunications in Australia
Fixed line to Mobile
substitution
Consolidation of smaller
Telcos / ISP’s / new
entrants who are not at
scale
Threat Reality of
Mobile saturation,
over 90% penetration
levels
Customer
expectations
Regulatory
Intervention
Increasing price
competition
Optus focus is ‘Outstanding Customer Experience’
About Optus
•
Optus is a leading Australian (2nd largest) integrated telco company
- serving more than seven million customers each day.
•
One of the worlds few non-incumbent telco’s that are truly convergent.
•
The company specialises in a broad range of communications services
including mobile, local, national and long distance telephony, business
network services, internet, satellite services and subscription television.
•
“The SingTel Group is Asia’s largest telco company, with operations in
20 countries worldwide, serving approx 80 million customers. Optus is
the most significant financial contributor to the SingTel Group”
- Paul O'Sullivan, Chief Executive
•
“Optus has a clear, single-minded focus on delivering a superior
customer experience.”
- Paul O'Sullivan, Chief Executive
About Tim Manns
•
Senior Data Mining Analyst
– Customer Insights. A team of 5 analysts are responsible for ad-hoc
complex analysis, predictive analysis and customer segmentation.
We run at least 30 separate advanced analyses every month.
•
SPSS Australia (2004-2006)
– Data Mining Consultant for Asia Pacific region, assisting customers
of SPSS Clementine with data mining projects.
•
SPSS UK (1999-2004)
– Global Lead, Technical Support for SPSS Clementine product suite.
•
Hobbies
– Mountain biking, surfing, ‘City Of Heroes’ video game, warm beer.
Customer Insights & Communications Team
Business
Architecture
 Reporting
 Business Requirements
 Maintenance
 Feature Prioritisation
Customer
Insights
Campaign
Delivery
 Customer profiling
 Adhoc reporting
 Predictive analysis
 Segmentation
 Wash Campaign Lists
 Rollout Campaigns
 Tracking
 Campaign Evaluation
Teradata
Warehouse
The Data Mining
Challenges Facing Optus’
Marketing Group
Definition of “Data Mining”
•
Quote;
People talking but they just don't know,
What's in my heart, and why I love you so.
I love you baby like a miner loves gold.
Come on sugar, let the good times roll!
•
The processing of large amounts
people data in order to extract
useful and actionable information
-> To understand the customer,
communicate in a manner they
prefer, and to provide services they
want or need to their benefit. To
establish a trusted relationship!
What is Data Mining for Telecommunications Marketing?
•
Use the data about your customers to understand them and offer
tailored services, products and rewards they actually want
“We Hear You”
•
As a data mining analyst, my role is to
understand the data stored within our
Teradata warehouse, and translate
this into actionable customer focused
information that will benefit Optus
•
This includes analysis to support;
–
–
–
–
–
–
–
Product Usage Reporting
Customer Profiling
Predictive Churn (attrition)
Inactivity Stimulation
Customer Segmentation
Product Up-sell and Cross-sell
Customer Loyalty Rewards
Data Mining &
Customer Insights
Micro site www.wehearyou.com.au
Micro site www.wehearyou.com.au
Infrastructure Issues For
Scaleable Data Mining
Infrastructure Issues for Scaleable Data Mining
•
Clearly define the business problems you want to solve.
•
Centralised warehouse for customer data (single view).
•
Detailed data (finding needles in the haystack).
•
Data Quality (garbage in = garbage out)
•
Capability to act on results (marketing campaign delivery)
•
Experienced and clever analysts to do all the hard work!
•
Business support in favour of data mining
•
Agree on success metrics…
$
Optus Teradata Integrated Data Warehouse (IDW)
Optus has invested in a Teradata warehouse and uses it to
perform in-database data mining for a number of reasons;
•
No duplication of customer details data marts. Data security.
•
Alive data. Avoid delays in using data from any off-line data mart.
•
Parallel processing enables huge amounts of data analysis
to be performed on ‘live’ transactional customer data.
•
The entire customer base can easily be scored in IDW and used
immediately by business applications and CRM systems.
•
Many complex historical tables exist within a dynamic data
warehouse environment (things change!). We can react quickly.
Optus uses SPSS Clementine Data Mining tool
We use a data mining tool that supports in-database mining;
•
Works very well with Teradata data warehouse.
•
Automatic SQL generation (structured query language).
– Typical project can involve 10,000 lines of SQL code
– Saves time to manage large SQL code analysis
•
For example, score ‘neural networks’ as SQL in Teradata
•
Minimal training required, friendly user-interface.
•
High analyst productivity (turn around projects in days).
•
Ability to generate useful graphs and outputs.
•
Comprehensive algorithms and data processing options.
Data Processing Used
To Solve These Challenges
We aren’t drowning in a sea of data…
•
The aim of our analysis is to use data to understand the behaviour of
customers and to also anticipate their future actions and needs.
•
Optus customers generate millions of rows of mobile call data per day.
•
We usually examine usage data for 3 month history.
•
We create 100’s of columns of descriptive data for each customer.
•
We analyse everyone. Business and campaigns select customers.
For example;
Active Customers
Spend more than $20
Top 5% High Churn Risk
100% of Customer Base
60% of Base
2%
Campaign
Case Study:
Mobile Telephony Churn
Analysis for mobile telephony customer retention
Some fictitious example numbers…
•
2 million Post-Paid mobile customers.
•
Average customer spends AUD$50 per month.
•
2% of Post-Paid mobile customers leave each month (40k disconnects).
•
1% are paying customers that voluntarily churn to another mobile (20k).
In the perfect world (we use magic, and no voluntary churn);
– We stop 20k voluntary churn
– 1% of customers will stay for with Optus for extra month.
– Continued $1 million revenue received by the business per month.
In the real world (marketing campaign stops 20% of voluntary churn);
– We stop 4k voluntary churn.
– 0.02% of customers will stay with Optus for extra month.
– A continued $200k revenue received by the business per month.
Predictive Model – Cumulative Gains Chart
Percentage of Churners in Next Month
Fictitious example shows improved identification of churners in the next month
100
20k
90
A predictive model could identify 40% of
future churners by contacting only 10%
of existing customer base
Random Chance
Predicted Churn
80
70
60
50
40
30
20
10
2m
0
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percentage of Post-Paid Mobile Phone Customer Base Today
Results are illustrative examples
The Benefit of Predictive Churn Analysis
•
Customers feel valued. We proactively contact them with a reward.
•
If our retention activities are able to save a small percent of customers, this
will have positive financial impact to the business.
•
Our Post-Paid mobile churn rate is decreasing. Public financial figures.
Post-Paid Mobile Churn %
1.5
1.45
1.4
1.35
1.3
1.25
Churn % of Optus Consumer mobile services
7
Q
2
FY
0
7
Q
1
FY
0
6
Q
4
FY
0
6
Q
3
FY
0
6
FY
0
2
Q
1
FY
0
6
1.2
Q
Churn % each month
1.55
Case Study:
Fixed Line Segmentation
Fixed Line Segmentation: Household Behaviour
•
One problem in our analysis of fixed line telephony usage is that many
individuals may use one home phone. They appear as a single customer.
•
Based upon the household telephony usage, we are able to group
households into one of seven segments.
•
Segments are used for targeted up-sell, cross-sell, and bundling offers.
•
They are also used in conjunction with geo-mapping software to provide
appropriate acquisition offers to other households in nearby locations.
7
Segment A
Segment B
Segment C
Size Of Our Seven Fixed Line Customer Segments
•
Not all segments are the same size
•
Specific features distinguish a customer segment (eg. international calls)
Percent of Customer Base
30
Customer Percent
25
20
15
10
5
0
Segment
A
Foreign
Friends
Segment
C
Segment
D
Segment
E
Segment
F
Segment
G
Size Of Our Seven Fixed Line Customer Segments
•
Not all segments are the same size
•
Specific features distinguish a customer segment (eg. international calls)
100%
30
Customer Percent
25
Percent
20
15
10
5
0
Segment
A
Foreign
Friends
Segment Segment Segment Segment Segment
C
D
E
F
G
Demonstrate How Specific Customer Behaviours Are
Represented In Each Segment
•
Behaviours such as international calling and use of calling cards (or both).
100%
Use Both
Only International
Only Calling Card
80%
Use Neither
60%
40%
20%
0%
Segment
A
Foreign
Friends
Segment Segment Segment Segment Segment
C
D
E
F
G
Campaign Upsell Offers Based Upon Segment
•
Some customers can buy an add-on product for cheap international calls
called ‘WorldSaver’, that costs $5 per month. WorldSaver provides cheap
international calls at 3 cents a minute. Few customers buy WorldSaver.
•
Churn rates amongst international callers is high.
•
Using WorldSaver would cause less revenue to Optus,
-> but big savings for the customer.
•
Over the longer-term this reduces voluntary churn and encourages a
better customer relationship.
•
A test campaign went out to 6k customers. Response rate was 41%.
•
Revenue from these customers is approx 15% lower than before.
•
Churn rate within this customer group is now very low, so total received
revenue is higher than expected (because no customers leave).
Conclusions and Tips
Conclusions and Tips
Make data mining analysis recognised as critical business operations.
• Also get support from groups such as Market Research and Sales
• Agree on success criteria and business definitions (eg. Churn definition)
A few quotes…
• “Build it and they will come” (Theodore Roosevelt)
– Invest in a big data warehouse. Keep as much detail as possible for
as long as possible. Flexible analysis can use this detailed data.
• “The early bird catches the worm”
– Start with simple analysis and start now. Once the business accepts a
few successes, then larger complex challenges can be attempted.
• “Size doesn’t matter, its what you do with it that counts”
– Many millions rows per day… It can still be worthless if it isn’t used to
tackle business problems that will yield reasonable returns on
investment. Try to solve problems that have existing business metrics
or targets in place.
•
Question Time
We hear you!
we hear you .com .au
[email protected]