Digital Winners 2013: Johannes bjelland shared


Published on

Digital Winners 7-8 November
Successful marketing with Big Data – A use case from Asia, Johannes Bjelland

Published in: Technology, Business
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Digital Winners 2013: Johannes bjelland shared

  1. 1. Successful marketing with Big Data Analytics A use case from Asia Johannes Bjelland, Pål Sundsøy Telenor Group Research Digital Winners, Fornebu 08.11.2013
  2. 2. Our customers generate an increasing amount of information in our systems What’s in it for Telenor?
  3. 3. For each call, sms and data session: hundreds of data points are stored A number - Caller Date & time B number – Receiving party Type: Call, SMS, Data, et c Data volume Cell_ID: Location IMSI: SIM card TAC: Handset 3
  4. 4. Boosting Mobile Internet uptake in Asia with prediction and SMS marketing Motivation • • • For many in Asia, the mobile phone is their only gateway to the Web. Many customers have internet capable phones, but do not use them The business unit is using state of the art Below The Line marketing process This Pilot was a collaboration with the Mobile Internet Asia project in Digital Services
  5. 5. Selecting the right campaign target groups is key to maximize Campaign revenue Customer attention is valuable and a limited resource! • 6000 yearly SMS campaigns effectively boost customer revenues • Number of campaigns cannot be pushed further • Contact rules: Max 1 offer each 14 days • Efficiency of campaigns can be improved with a big data approach 5 08/11/2013
  6. 6. Machine Learning assists us in selecting optimal target customers from huge data sets Data sources • Traffic usage data • Subscription data • Handset Features • Location • Handset switching • VAS usage 300 variables 40 000 000 customers Who are most profitable targets for SMS campaign ?
  7. 7. Its impossible for a human to relate to all these data (!)
  8. 8. The predictive model learns from existing cases of data conversion Non-convertors ‘Negatives’ Create model Find patterns identifying the data convertors based on historic data Natural Data Convertors ‘Positives’ 2-6 months back: Use Historical data Today: Present time data Non Data Customers today Model deployment Use the patterns to identify likely adopters *Offers are 15 MB & 99 MB data packages offered for half-price Identify and Run Campaign on 200k most likely adopters
  9. 9. The prediction model outperforms existing best practice approach – 13 times better than best practice Actual Campaign Hit Rate Hitrate 7.00 6.42 6.00 5.00 3.76 4.00 PSPM Prediction Model 3.00 Microsegmentation Current best practice Microsegmentation approach 2.00 1.00 0.50 0.70 0.00 P7 Package 15 MB Datadata pack P9 data pack 99 MB Data Package 99% Renewal– the algorithm is optimized to avoid ‘freeriders’
  10. 10. Telenor Data-Driven Development Using data for social good Detecting signals in the data • Use mobile phone data to Improve models for Infectious disease spread • Understand the spread of Dengue fever in Pakistan • Collaboration with epidemiologists from Harvard School of Public Health • Crisis and Disaster Management • Assessing mobility patterns and changes in economic behavior during the Cyclone Mahasen (May 2013). • Goal: Improve efficiency of emergency aid measures • Measuring Socio-economic state based on big data • Collaboration to be set up between UN Global Pulse, World Food Program and Telenor Group. • Food security - food prices and availability
  11. 11. A ‘Big Data’ company is distinguished, not by how many terabytes it sits on, but by the way the company exploits the data in Business! Telenor is taking steps toward becoming a Big Data company • Answering business questions via data mining and ad hoc analysis • Using pilots and data driven marketing to let the customers tell us what they want • Collaborating with world leading research environments within data science • Petabytes is not a prerequisite - What we need is ‘BIG ENOUGH’ Data for business