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Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
Increasing Revenueof Prepaid Customers by Recharge Segmentation Models
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Increasing Revenue of Prepaid Customers by Recharge Segmentation Models

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Recharge-Based segmentation, presentation from Telecommunication Customer Segmentation and Intelligence Conference

Recharge-Based segmentation, presentation from Telecommunication Customer Segmentation and Intelligence Conference

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  • 1. Increasing Revenue of Prepaid Customers by Recharge Segmentation Models IIR Conference extract, Amsterdam 2011 Telecommunication Customer Segmentation & Intelligence
  • 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’ business needs
  • 4. © Algolytics. All rights reserved. 4 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 OFFER
  • 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. info@algolytics.com www.algolytics.com

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