increasing revenue of prepaid customers by recharge segmentation models
TRANSCRIPT
IIR Conference extract, Amsterdam 2011
Telecommunication Customer Segmentation & Intelligence
Increasing Revenue
of Prepaid Customers
by Recharge Segmentation Models
If you persist in trying to be
all things to all people, you will fail.
Seth Godin, We Are All Weird
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WHAT WE DO?
We provide
• Analytical software
• Advanced analytical
services
• Bespoke analytical
applications
to address our customers’ needs:
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ALGOLYTICS OFFER
Fraud Risk
Modelling and
Analysis
Recommendation
Systems
Loss Forecasting and
Stress Testing
Credit Risk
Modelling and
Analysis
Bespoke Analytical
Applications
Collections Modelling
and Analysis
Analytical CRM
Integrated Analytical
Platform
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OUR CLIENTS
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PREPAID CHALLENGE
How to influence customer to recharge more & increase ARPU?
How to approach (segment) them?
Quantity Lack of information
Prepaid customers
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PREPAID CHALLENGE
• Little demographic data
• Only reliable – behavioral data
usage & recharges
Lack of information
Prepaid customers
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RECHARGING BEHAVIOR
Regular Keeping account alive
When no money Occasional irregularities
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BEHAVIORAL-DEMOGRAPHIC SEGMENTATION
• survey / usage / demographic data
■ strategic – for overview
■ hardly applicable segmentation
- mapping surveys to population
■ no direct link
■ static
■ unreliable & weak data coverage
„kids” „seniors” „women at
home”
„young
active biz”
„heavy
multimedia
user”
Revenue
RECHARGE-BASED
SEGMENTATION
■ based on reliable recharge data
■ trigerred by customer actions
„Regular” „Irregular1” „Keeping
alive”
„When
empty”
„No simple
pattern”
■ Directly applicable to revenue
generation
■ Dynamic – reflects current behavior
■ Reliable data
Revenue
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PROBLEM & SOLUTION
How to approach Prepaid users?
t
Rech
arg
e
Credit recharging pattern
Estimated recharge
date for each customer
Predictive
models
do segmentation based on recharging patterns
adapt message to recharge segment
send timely marketing message
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Data
Recharge
history
sequence
Prepaid
transactions
DWH
WHERE IT FITS
Marketing actions
Increase recharge
amount
Recharge
earlier
Shorten recharge
period
…
Scoring models
Estimated
recharge date
Segmentation
by recharge
behavior
Classical
segmentation
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7 recharges / year
EFFECT OF INFLUENCING RECHARGE PATTERN
t
Rech
arg
e
classical
segmentation
9 recharges / year: +2 avg recharges / customer
t
Rech
arg
e
recharge
date
estimation
+10 € per user * 100 000 responding customers = +1 million € revenue
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FURTHER APPLICATIONS OF RECHARGE MODELS
Up-selling
activities
Anti-churn
incentives
Retention
activities
Recharge
history
sequence
Recharge
predicting models
t
Rech
arg
e
Estimated recharge
date for each customer
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BENEFITS OF RECHARGE-BASED SEGMENTATION
Recharge-
based
segmentation
Availability
of reliable data
Directly
links to profit
generation
High model
accuracy
& increased
response
Clear savings
• small but
cumulating
• low cost
Profit
Costs
Average is for marketers who don’t have
enough information to be accurate.
Seth Godin
Recharge-based segmentation
■ Direct
■ Dynamic
■ Reliable data
High response
& Revenue increase
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