Leveraging Big Data for bigger revenue.


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Leveraging Big Data for bigger revenue.

  1. 1. This document is offered compliments of BSP Media Group. www.bspmediagroup.com All rights reserved.
  2. 2. Leveraging Big Data for Bigger Revenues Deploy a data-driven marketing approach to improve service consumption Africacom, October 2013 Copyright © 2013 Comviva Technologies Limited. All rights reserved. 1
  3. 3. Agenda Declining wallet share Challenges being faced The operator continues to be relevant Monetizing existing services The power of Analytics Need for a new market-place Bring to bear power of Recommenders Use Cases Case studies 2
  4. 4. Growth has subdued 6% Y-o-Y decline in mobile connection growth, 4% Y-o-Y decline in ARPU 120 100 97 Africa SIM per subscriber 104 90 84 73 80 60 40 20 0 2011 2012 2013E 2014E 2.10 SIM per subscriber Net additions (mn)) Africa net mobile connection additions 2.03 2.05 2.05 2.00 2.00 1.96 1.95 1.91 1.90 1.85 1.80 2015E 2011 2012 2103E 2014E 2015E Markets maturing, customers spreading spend across multiple networks Africa mobile ARPU 6.8 6.6 6.5 0% -2% 6 -3% 4 -2% -2% -4% -6% 2 -8% 0 2011 -8% 2012 -10% 2013E 2014E 2015E ARPU (US$) ARPU growth 80 60 53.9 57.9 63.8 68.0 70.9 10% 40 8% 7% 20 4% 0 2011 2012 12% 10% 8% 6% 4% 2% 0% Revenue growth 7.0 Revenue (US$ mn) 7.6 ARPU growth ARPU (US$) 8 Africa mobile revenue 2013E 2014E 2015E Revenue (US$ mn) Revenue growth 3
  5. 5. Growth  Growth can no longer come from acquisition  Can growth come from higher consumption?  Are we leaving money on the table? 4
  6. 6. Operator continues to be relevant Serious brand value – established over years Trust built on relationship - people depend on you You own the subscriber – biggest asset Added confidence – Regulatory oversight Source: Wireless Intelligence, WHO, World Bank, ITU 5
  7. 7. Many CSPs are adopting a high digital services strategy • App Stores & Rich Media Orange partners with Deezer for music streaming • IP Communication • Scope for emerging market operators to focus on: mHealth • Health • Education Econet acquires TN bank to extend banking services • Government • Finance • Content/media mEducation • Agriculture • Law MTN partners with DStv for mobile TV streaming mFinance 6
  8. 8. Challenge is in monetizing existing services Service discovery is a challenge Dormancy is a challenge Making out-reach relevant and contextual is a challenge 7
  9. 9. Reducing dead weight loss Bring together Buyer and Seller Match right product to right customer Implement 3rd degree price discrimination Send promotions at right time Price inelastic Price elastic Demographic segmentation to identify price conscious users Superior Customer Experience Personalization and Recommendation 8
  10. 10. Customer-side engagement is the key Who the customer is: Demographic information, life stage, transactional patterns, device type, social group Where the customer is present: When a person would take an action: Location & network environment Real-time information, customers’ intent and action at a specific time and place • Operators have large volumes of untapped data  Power of analytics to understand and bring context to engagement  Potentially treat each subscriber as unique 9
  11. 11. From old order to new Reebok 2013 ad Mass market engagement Personalized engagement 10
  12. 12. Source: businessinsider.com Wonderful thing called the recommender systems 35 % percent sales generated from recommendations 75% of the content consumed comes from the recommendation engine 11
  13. 13. The paradigm In progress  Analytics to micro-segment (even N=1)_ based on behavior and profiles  Cross-product into matching products with micro-segments  Reach via more than one touch point: Customer value personalization across channels Email Social Mobile 67% 44% 40% Web display 36% 67% says it is important for emails to be personalized, followed by social media (44%), SMS (40%) and web display ads (36%) 12
  14. 14. Customer data is an unused growth asset Data inputs Uses of data Transaction data Drive customer engagement Demographic data Enhance customer experience Location data Customer data Deliver smarter services that generate new revenue streams CRM data Enhance service quality by better network capacity planning Unstructured data Generate reports for business planning  Customers‘ trail of information, coming from many channels, provide rich insights into their specific needs and preferences 13
  15. 15. Rules of buyer-seller engagement have altered Mass marketing Batch & blast Aligned to campaign calendar Customer-triggered Aligned to customer lifecycle One-way communication Dialogue/interactive Business & channel silos Integrated & informed Manual/semi-automated Periodic Fully automated Real-time/ near-real time “Segment of one” marketing 14
  16. 16. Deepen engagement over the lifecycle of the customer Acquire Use cases Pricing: Real-time Offers  Incentives for the first top-up  Discount on VAS trials Grow Rewards & incentives: Winback  Bundled pricing plans  Location based pricing  Personalized real time offers  Next best offers  Data/VAS/mMoney promotions  Location based offers  Churn propensity scoring  Winback campaigns  Customer experience optimization Churn control: Recomm endation: Retain  Service/content recommendations  Loyalty programs  Tenure based personalized rewards 16
  17. 17. Map engagement to customer transactional behavior Balance 30 25 Spend offer Pay «Avg spend +US$2», Get X MB data High balance, subscriber just topped-up his account 20 Activate This week your calls are %50 discounted 15 10 Top-up stretch Top-up «Max top-up amount», Get Y on-net mnts 5 0 Balance drops below US$5, subscriber uses mainly SMS lately Though customer’s balance Recover top-up is in credit, he has stopped Top Up «Avg top-up amount», Get 2Y on-net mnts using the services Zero balance for an abnormal period. Subscriber has not responded to a top-up Incentive Time 17
  18. 18. Improve share of telecom spend among multiple SIM users Analyze customer data patterns to identify multi-SIM customers Send personalized campaigns to multi-SIM users Silent period during a day Device type (multi-SIM) Service usage pattern Variance in recharge pattern Scientific algorithms Customer data Inactivity patterns Multi-SIM customer 1: Active during night from 8pm to 12am Multi-SIM customer 2: Uses data service only Multi-SIM customer 3: Makes on-net calls only Discount on calls during day-time: Recharge with ‘8to-8 day’ pack and get 50% discount on all calls from 8:00 am to 8:00 pm Voice and data bundle: Recharge with ‘More data’ pack and get 1GB data usage and 50 free voice minutes for a month Discount on off-net calls: Recharge with ‘off-net call’ pack and make off-net calls at price of on-net calls 18
  19. 19. Optimize service experience with next best offers  Intensive data user - video constitutes 90% of data consumption  Current data pack: 2GB data, 7.2 Mbps download speed for US$15  Based on customer ‘s data usage pattern, the agent recommend s an appropriate data pack  Frustrated with high buffering time and poor video quality Calls the customer care executive to complain about poor video browsing experience Next best data offers: Priority 1: Video pack $20 Priority 2: Video pack $25 Priority 3: VAS pack $ 30 The customer care executive offers $20 video pack to customer that provides higher browsing capacity and speed Customer subscribes to the $20 video pack $20 video pack offer: Enjoy 3GB of access to video websites and 200 MB of free access to other website at 21 Mbps 19
  20. 20. Proactively anticipate churn events • Flag churn indicators • Accord appropriate weights • Calculate churn score for each customer • Based on churn score identify customers with high propensity to churn • Preemptively send personalized campaigns to high-risk customers to contain churn Churn indicators Last recharge date Last call/SMS/ data usage Age on network Service usage trends Device type (multi-SIM) Class of service Churn prediction High propensity to churn Customer care interactions Location Social network data CS: Churn score 20
  21. 21. Improved service discovery with personalized recommendations Generates relevant playlist based on: Generates relevant playlist based on:  Customer’s demographic profile  Customer’s demographic profile  Wisdom of crowds  Wisdom of crowds  Customers music preferences and transactional  Customer’s unique preferences and transactional patterns patterns First-time Frequent users users Recommended My Songs songs: Black Back in I will always love you (AC,DC)…. (Whitney Huston)…. Bartender(T When you believe Pain)…. (Mariah Carey)…. Drops of Jupiter Love is all we need (Train)… (Celine Dion) 543211 Customer is an R&B music fan. Purchased 2 Whitney  Young adult Houston tones in the last 6  College student months Recommendation engine Recommended My Songs songs: up my life You light Lose yourself (Debby Boon)…. (Eminem)…. Symphony No.9 In da club (50 cents)…. (Ludwig van(Jay99-problems Beethoven)…. Z)….. 543211 In last 4 visits to ‘the RBT portal’  Baby boomer storefront customer selected  Local businessman hip-hop music Recommends Dials RBT popular hip-hop Recommends Dials portal RBT and rock songs R&B songs portal Recommends Dials RBT Recommends Dials RBT pop hip-hop & portal popular tracks from portal songs the Seventies RBT portal RBT portal , 21
  22. 22. Case Studies 22
  23. 23. Reactivate revenues from inactive users  Leading Nigerian operator with 40 million connections Operator Challenges  Predominant prepaid multi-SIM market: Each customer owns 2.4 SIMs Revenue generated from Winback base  20% inactive base: US$ 581 mn is the approx. annual opportunity loss from inactive users  Inefficient marketing: Existing push-based blanket SMS and OBD promotions failed to address inactive users  Winback detects presence of inactive customer on the network Solution  Sends a campaign to the customer in real-time improving reach and ensuring higher conversion Operator’s market share  Achieved campaign reach rate of 49.5% and campaign response rate of 15%  ROI recovered within a month Results  Generated revenue of US$ 17.7 million for the operator in 6 month Winback Launch  After the winback launch, operator market share grew by 2.14% from Dec’12 to Mar’13 23
  24. 24. Indian operator registers 167% increase in tone sales Challenge Problem of plenty 850,000 audio clips Solutions Result Complex service discovery Unable to monetize long tail Top 20 songs generate 48% sale Lengthy menus Multiple short codes MyLikes recommends relevant tunes to customers based on their music preferences, transactional & demographic profile and wisdoms of crowd Increased in sales between sales Nov’12 & Jul’13 167 % MyLikes tone sales A tone is sold after every Decline in share of top 20 bestsellers 48 % 268 % MyLikes revenues Monetization of long tail 535 calls without MyLikes 198 calls with MyLikes Pre MyLikes 43 % Post MyLikes 24
  25. 25. Challenges & Tools 26
  26. 26. Mahindra Comviva’s Revenue Plus -- A unique CVM solution that drives revenue growth by enabling revenue planning, customer engagement & retention management Campaign measurement & reporting Revenue planning Revenue Plus Automated customer profiling & segmentation Campaign execution & fulfillment Campaign design & definition 27
  27. 27. In conclusion “Average is for marketers who don’t have enough information to be accurate ” --Seth Godin 28
  28. 28. Please Visit us at Booth Number C08 29
  29. 29. Thank you Visit us at www.mahindracomviva.com Disclaimer Copyright © 2013: Comviva Technologies Ltd, Registered Office at A-26, Info City, Sector 34, Gurgaon-122001, Haryana, India. All rights about this document are reserved and shall not be , in whole or in part, copied, photocopied, reproduced, translated, or reduced to any manner including but not limited to electronic, mechanical, machine readable ,photographic, optic recording or otherwise without prior consent, in writing, of Comviva Technologies Ltd (the Company). The information in this document is subject to changes without notice. This describes only the product defined in the introduction of this documentation. This document is intended for the use of prospective customers of the Company Products Solutions and or Services for the sole purpose of the transaction for which the document is submitted. No part of it may be reproduced or transmitted in any form or manner whatsoever without the prior written permission of the company. The Customer, who/which assumes full responsibility for using the document appropriately. The Company welcomes customer comments as part of the process of continuous development and improvement. The Company, has made all reasonable efforts to ensure that the information contained in the document are adequate, sufficient and free of material errors and omissions. The Company will, if necessary, explain issues, which may not be covered by the document. However, the Company does not assume any liability of whatsoever nature , for any errors in the document except the responsibility to provide correct information when any such error is brought to company’s knowledge. The Company will not be responsible, in any event, for errors in this document or for any damages, incidental or consequential, including monetary losses that might arise from the use of this document or of the information contained in it. This document and the Products, Solutions and Services it describes are intellectual property of the Company and/or of the respective owners thereof, whether such IPR is registered, registrable, pending for registration, applied for registration or not. The only warranties for the Company Products, Solutions and Services are set forth in the express warranty statements accompanying its products and services. Nothing herein should be construed as constituting an additional warranty. The Company shall not be liable for technical or editorial errors or omissions contained herein. The Company logo is a trademark of the Company. Other products, names, logos mentioned in this document , if any , may be trademarks of their respective owners. Copyright © 2013 Comviva Technologies Limited. All rights reserved. 30
  30. 30. Leading Indian operator: Maximizing music sales with personalized recommendations  Problem of plenty: Expansive catalogue of 850,000 audio clips menus and high IVR browsing charges negatively impacted repeat sales  “Me-too” marketing: Predominant use of “batch and blast” marketing techniques, resulted in low conversion rates of 0.2% 1.1 million Operator Challenges Comparative trend in tone sales  Complex service discovery: Multiple short codes, lengthy 1.3 1.4 1.1 0.7 0.2 0.2 0.4 0.2 0.1 Nov'12 Dec'12 Jan'13 Feb'13 Mar'13 Solution Tone sales generated via MyLikes  MyLikes simplifies service discovery by recommending relevant tunes to customers based on their music preferences, transactional & demographic profile and wisdoms of crowd Tone sales generated via channels not integrated with MyLikes  Integrates with multiple channels - IVR, virtual number and inbound dialing - can be extended to Web and Search  100% increase in tone sales on MyLikes compared to 0% on channels not integrated with MyLikes  126% increase in MyLikes service revenues Results  379% higher customer conversion on MyLikes as compared to channels not integrated with MyLikes  A tone is sold every 184 calls on MyLikes as compared to 535 calls on channels not integrated with MyLikes US$ ‘000 Between November 2012 and March 2013: MyLikes revenue 124.9 75.8 81.9 89.9 55.3 Nov'12 Dec'12 Jan'13 Feb'13 Mar'13 Revenue generated via MyLikes 33
  31. 31. Customer-side engagement 34