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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
© Algolytics. All rights reserved. 3
WHAT WE DO?
We provide
• Analytical software
• Advanced analytical
services
• Bespoke analytical
applications
to address our customers’ needs:
© Algolytics. All rights reserved. 4
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 CRMIntegrated Analytical
Platform
© Algolytics. All rights reserved. 5
OUR CLIENTS
© Algolytics. All rights reserved. 6
PREPAID CHALLENGE
How to influence customer to recharge more & increase ARPU?
How to approach (segment) them?
Quantity Lack of information
Prepaid customers
© Algolytics. All rights reserved. 7
PREPAID CHALLENGE
• Little demographic data
• Only reliable – behavioral data
usage & recharges
Lack of information
Prepaid customers
© Algolytics. All rights reserved. 8
RECHARGING BEHAVIOR
Regular Keeping account alive
When no moneyOccasional irregularities
© Algolytics. All rights reserved. 9
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
© Algolytics. All rights reserved. 11
PROBLEM & SOLUTION
How to approach Prepaid users?
t
Recharge
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
© Algolytics. All rights reserved. 12
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
© Algolytics. All rights reserved. 13
7 recharges / year
EFFECT OF INFLUENCING RECHARGE PATTERN
t
Recharge
classical
segmentation
9 recharges / year: +2 avg recharges / customer
t
Recharge
recharge
date
estimation
+10 € per user * 100 000 responding customers = +1 million € revenue
© Algolytics. All rights reserved. 14
FURTHER APPLICATIONS OF RECHARGE MODELS
Up-selling
activities
Anti-churn
incentives
Retention
activities
Recharge
history
sequence
Recharge
predicting models
t
Recharge
Estimated recharge
date for each customer
© Algolytics. All rights reserved. 15
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
Follow news on analytical solutions
at Algolytics.com/blog
click here to open
www.algolytics.com
info@algolytics.com
Contact us

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Increasing Revenue of Prepaid Customers by Recharge Segmentation Models

  • 1. IIR Conference extract, Amsterdam 2011 Telecommunication Customer Segmentation & Intelligence Increasing Revenue of Prepaid Customers by Recharge Segmentation Models
  • 2. If you persist in trying to be all things to all people, you will fail. Seth Godin, We Are All Weird
  • 3. © Algolytics. All rights reserved. 3 WHAT WE DO? We provide • Analytical software • Advanced analytical services • Bespoke analytical applications to address our customers’ needs:
  • 4. © Algolytics. All rights reserved. 4 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 CRMIntegrated Analytical Platform
  • 5. © Algolytics. All rights reserved. 5 OUR CLIENTS
  • 6. © Algolytics. All rights reserved. 6 PREPAID CHALLENGE How to influence customer to recharge more & increase ARPU? How to approach (segment) them? Quantity Lack of information Prepaid customers
  • 7. © Algolytics. All rights reserved. 7 PREPAID CHALLENGE • Little demographic data • Only reliable – behavioral data usage & recharges Lack of information Prepaid customers
  • 8. © Algolytics. All rights reserved. 8 RECHARGING BEHAVIOR Regular Keeping account alive When no moneyOccasional irregularities
  • 9. © Algolytics. All rights reserved. 9 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
  • 10. 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
  • 11. © Algolytics. All rights reserved. 11 PROBLEM & SOLUTION How to approach Prepaid users? t Recharge 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
  • 12. © Algolytics. All rights reserved. 12 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
  • 13. © Algolytics. All rights reserved. 13 7 recharges / year EFFECT OF INFLUENCING RECHARGE PATTERN t Recharge classical segmentation 9 recharges / year: +2 avg recharges / customer t Recharge recharge date estimation +10 € per user * 100 000 responding customers = +1 million € revenue
  • 14. © Algolytics. All rights reserved. 14 FURTHER APPLICATIONS OF RECHARGE MODELS Up-selling activities Anti-churn incentives Retention activities Recharge history sequence Recharge predicting models t Recharge Estimated recharge date for each customer
  • 15. © Algolytics. All rights reserved. 15 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
  • 16. 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
  • 17. Follow news on analytical solutions at Algolytics.com/blog click here to open