Increasing Revenue of Prepaid Customers by Recharge Segmentation Models

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 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


OUR CLIENTS


PREPAID CHALLENGE

How to influence customer to recharge more & increase ARPU?

How to approach (segment) them?

Quantity Lack of information

Prepaid customers


PREPAID CHALLENGE

• Little demographic data

• Only reliable – behavioral data

usage & recharges

Lack of information

Prepaid customers


RECHARGING BEHAVIOR

Regular Keeping account alive

When no money Occasional irregularities


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


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


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


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


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


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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Increasing Revenue of Prepaid Customers by Recharge Segmentation Models

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