Improved Subscriber marketing


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Improved Subscriber marketing

  1. 1. SUBSCRIBER PREFERENCEMODELINGA unique approach to ARPU maximizationfor the satellite television industry
  2. 2. Contents Glossary Executive Summary About the Client Business Situation Solution Space Hansa Cequity SPM Framework Business Benefits Client Testimonials About Hansa Cequity Glossary DTH (Direct to Home) A term used to refer to digital satellite television broadcasts intended for direct home reception, received by a satellite dish and set-top box. ARPU A measure of revenue generated per user or unit, ARPU (Average Revenue per Unit) allows companies to analyse revenue generation and growth at the unit/user level.
  3. 3. Subscriber Preference ModelingExecutive SummaryHansa Cequity enabled a leading direct-to-home digital satellite television providerincrease its Average Revenue per User with an end-to-end deployment of itsproprietary customer engagement model that saw increased levels of customerengagement and higher incremental revenues from customised campaignmanagement strategies.About the ClientOne of the leading digital satellite television providers, the company is a joint venturebetween Indias leading business house and a global broadcasting giant. This casestudy discusses strategies employed by Hansa Cequity to enhance the clientsAverage Revenue per User, through customised consumer campaigns and efficientdata management solutions.Business SituationBusiness SpaceFrom its mass emergence in the 1990s to the present, digital satellite television hasgrown to become one of the fastest growing consumer electronics products of alltime. Trends for the future indicate innovations in interactive TV services, and cuttingedge 3D viewing experiences. However, the subscription based business modelemployed by leading service providers was associated with high costs of acquisitionand retention. It became imperative for service providers to design sales andmarketing strategies adapted to individual consumer types and viewing preferences.Existing Campaign AssessmentTraditionally, the client relied on mass campaigns across various media to promoteoffers, packages, and products. Targeted on the entire customer base withoutspecific individual customer insights, these were run on a daily, weekly and monthlybasis throughout the year. These campaigns were classified as: a. Event based campaigns b. Educational campaigns c. Campaigns where existing subscribers would be sold higher value packs d. Campaigns where customers would be targeted just before pack expiry date 1
  4. 4. Subscriber Preference ModelingCampaign ImpactLower response rate | Customer churn | High campaigning costs | Drain on thevarious channel resourcesReasons for churn and low response rate: a. Confusion and lack of clarity on intended message, owing to differing marketing messages through various channels b. Packages offered were not found to be of any relevance to individual needs and preferences. c. Low level of satisfaction with the brand.Solution SpaceThe Need for a Better Solution a. Customers needs are evolving according to their tenure of association with the brand b. There is a need to leverage customer information (transactional) lying idle in repositories, for effective segmentation and marketing c. It is vital to determine the life stage customers belong to, before running the campaigns,and if the campaign channels are being utilized effectively d. The one size fits all strategy will not generate the requisite ROI, or maximize the ARPU e. The need of the hour is to focus on what customer wants rather than what we would want to sell themSolution ConceptualizationThe idea was to leverage available customer data and determine patterns to predictindividual viewing preferences. The focus was placed on locating individual behaivourtriggers that would in turn suggest a need, which would enable the client to offer thebest offer/package to fit this persons need. 2
  5. 5. Subscriber Preference ModelingSubscriber Preference ModelingFrameworkUnlike conventional mass campaigning, Hansa Cequitys Subscriber PreferenceModel enabled the client to offer each individual user the package/offer mostpertinent to his needs and wants, and thus drive conversions.About Subscriber Preference ModelingThe Subscriber Preference Model is based on a hosted data mart designed fromanalytical data culled from customer transactions over time. This database is used tocreate a Propensity Model which gives insights into the probabilities andpredispositions among customers towards buying a particular product / offer /package. The propensity data for each customer enables the sales executive to makeinformed decisions on the product/offer to sell to the person. The feedback fromcustomer transactions is used to further fine tune the marketing process for theparticular individual, and the demographic.Salient points: a. The SPM model enables the client to offer the Right Offer for the Right Subscriber b. The framework enables effective segmentation of subscribers based on their needs c. Identifies the order of campaign events for each opportunity with subscribersAnalytical Data Mart Subscribers’ spends Demographics DATA MAGNITUDE Call Centre Records 5 Million Customer Base Active Services 55Million Transactions Deactivation Behavior 60Million Call records Subscriber Self-Care 5 Million Customer BasePackage Characteristics Approx 250 fields per subscriber stored as a ETL snapshot Campaign feedback also added back to the data mart Summary & Roll Ups for enrichment Marketing Variables LTD Values & Scores Master Tables Subscriber Events 3
  6. 6. Subscriber Preference Modeling Data mart Statistics: 5 Million Customer Base | 55 Million Transactions | 60 Million Call records | Single view subscriber snapshot of approx 250 fields per subscriber | Continuous updation of campaign feedback for enrichment Key Highlights a. Rich information repository for predicting subscriber behaviour b. Campaign insights flow back for fine-tuning the campaign c. Enables quick planning of specific product campaigns by slicing and dicing the portfolio along various derived variables and parameters Propensity Model Salient points: a. 10 models were built for 10 target products (6 cross-sell and 4 up-sell) b. Customer base was segmented basis their specific needs c. Successful identification of factors that significantly influence the decision to hold a package d. Utilization of various statistical measures to determine the preferred product e. Assisted product sequencing for campaigns f. Determination of top 3 product choices for each subscriber g. Provided "Quick time-to-market" for various packages and offers Marketing Design 1 RS 2 3 OFFE TEST Product Sequence Offers are designed for each product Campaign list Email SMS campaign campaign sent to Inbound sent sent call center If Subscriber requests for follow-up Instant Feedback mechanism Unification ofcampaigning across Campaign list sent to Outbound call centermultiple channels by assigning unique campaign codes 4
  7. 7. Subscriber Preference ModelingUnique Offerings: Offer hierarchy management | Unique codes for unification acrosschannels | Channel optimization strategy | Test and learn environment creationKey Features of SPM Model a. Effective customer base segmentation based on subscriber behavior b. Creation of new offers, which included up front packs and discounted packs c. Effective use of SMS campaigns d. Designed campaigns to be executed via in-bound channels e. Creation of monitored dashboards covering a wide range of variables and parameters, made available to client on daily basis f. An effective tracking and monitoring functionality to measure performance of individual models, offers, and product sequencing methodologiesBusiness BenefitsThe Hansa Cequity AdvantageBeginning from conceptualization, creation of the analytical data mart, propensitymodel, and the subsequent execution, this project was completed within thecommitted time of two months.Business benefits a. Triple fold increase in customer response rate b. Reduced churn by 3%, and won back de-active customers c. Increased average product holding per subscriber by almost 10%. d. 230, 000 packages added, including cross-sell and up-sell packs, over a period of 4 months e. Pitching and conversion rates at the call centre increased by 10% for in-bound customer calls. f. Quarterly incremental revenue of almost $3 million, and a net incremental revenue of almost $2 million - a 200% lift g. Reduced redundant campaigns and introduced new campaigns 5
  8. 8. Subscriber Preference ModelingClient Testimonials"Substantially Reduced Subscriber Churn"Thanks to Hansa Cequitys Subscriber preference model. We are now better ableto provide the Right Offer to Right Subscriber and also better engage with themat each opportunity leading to substantial reduction in subscriber churn. Keep upthe great work!- Head, Subscriber Marketing"Improved Campaign Response Rates"Hansa Cequitys Subscriber preference modeling framework was a key differentiatorin our Marketing efforts. It led to solid business results with our Campaign responserates improving over 3 times the base rate. Thanks for a fantastic job.- Chief Marketing Officer 6
  9. 9. Subscriber Preference ModelingAbout Hansa CequityHansa Cequity is a provider of technology enabled, analytics driven end-to-endcustomer and marketing intelligence solutions. Our goal is to help companies buildprofitable, empowered customer relationships and engaging experiences byleveraging customer information with analytics and technology tools. We build andleverage the power of marketing databases and automation applications using best-in- class customer marketing processes.Website http://blog.hansacequity.comContacts Ajay Kelkar Chief Operating Officer Amit Pote Business Development amit.pote@cequitysolutions.comAddress India: 105-106, 1st Floor, Anand Estate, 189-A, Sane Guruji Marg, Mahalakshmi, Mumbai - 400 011 Ph: +91 22 43453824 Fax: +91 22 43453840 USA: 626, Grove Evantson, Chicago, IL 602 7