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Deriving economic value for CSPs with Big Data [read-only]
 

Deriving economic value for CSPs with Big Data [read-only]

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Presentation by Dr.Vinod Vasudevan, CEO, Flytxt at Tavess Telecom Analytics and Big Data Forum, 11th September 2013, Dubai

Presentation by Dr.Vinod Vasudevan, CEO, Flytxt at Tavess Telecom Analytics and Big Data Forum, 11th September 2013, Dubai

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    Deriving economic value for CSPs with Big Data [read-only] Deriving economic value for CSPs with Big Data [read-only] Presentation Transcript

    • Measurable Economic Value for CSP’s from Big Data Dr. Vinod Vasudevan, CEO, Flytxt
    • 2confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Agenda What can Big Data do for Telecom? Key aspects that maximises the benefits. Illustrated with real-life examples.
    • 3confidential© 2013 Flytxt. All rights reserved. 11 September 2013 What is Big Data? Big Data: Creating economic-Value from high-Volume, high-Velocity, high-Variety information assets with high-Veracity using new techniques of information processing. Creating transparency Micro-segmentation Enabling experimentation Replacing/Supporting human decision making Innovating new business models, products & services Enables Analytics engine Subscriber data Real-time network data Internet Data Storage Decision models Operational metrics Business intelligence Customer experience Targeted marketing Real time network behaviour Value of Big Data Source: Analysys Mason
    • 4confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Economic Value of Big Data for CSP’s References IDC: Worldwide BigData Technology and Services 2012-2015 Foreast E&Y: Global revenue assurance survey 2013 Gartner- Market Trends: New Revenue Opportunities and Profitability for Telecom Carriers (Developed and Developing Markets), 2015 Gartner: Market Trends: Worldwide, CSP Mobile Marketing and Advertising, 2010 Analysis Mason webinar: Key software approaches to make the most of analytics in telecoms, 2012 *All figures in Billion USD, predicted for 2017 Economic Potential for CSP’s ~ 250 Billion USD p.a
    • 5confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Big Data in Telecom Always had the best digital data; but not the systems or processes to derive benefit from the value in that data! Volume Variety Velocity BI CCM HLR IN NEED NEW THINKING, NEW PROCESSES AND NEW TECHNOLOGIES
    • 6confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Any Data can be relevant Mission is to increase data usage. Facebook site has one of the highest data consumption Whitelist Facebook BE PREPARED TO USE ANY AND ALL DATA Data usage shot through the roof. Substantiated the appetite for data and therefore 4G However the data revenues and the business case for 4G all but evaporated
    • 7confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Case Study: Precise targeting and clustering Operator anchored advertising campaign in Bangladesh to upsell high end smartphone >300% ROI on mobile Ad campaign for handset upsell. >90% precision in micro-segmented location based campaign. >2% CTR on mobile campaign to drive brand’s site traffic. • Data from device Management System • Data from SGSN/GGSN CDRs • IN Decrement Data • Billing Data • MSC CDRs • Data from GIS • Customer Master, DND, VIP lists etc. as usual Location Handset Model Data usage Spend Significantly increased mobile Ad ROI: Creates a new revenue stream for the CSP
    • 8confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Case Study: A South Asian Operator Data / Category Volume Subscriber Profiles 65 Million, 3.9 TB of KPIs and Insight Store N/W Data Sources/day 4500+ Data jobs from varied data sources like - Base File, Daily Usage, Recharge Event, IN Decrement, VLR, GPRS Usage, Incoming MOU, ARPU,IMEI, WAP logs, Content Purchase & Browse, Device Management Data, Retailer Info, Total No. of Rows Processed/day 175Bn at a data integration frequency of 5 Min. to 24 Hrs. Campaigns & Conversions 543,616 segmented offers in a year , 53 Million
    • 9confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Bucket 1 Bucket 2 Bucket 3 Hard, Soft, Probabilistic, Heuristic?
    • 10confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Not just predictive … 1. Predict Churn Propensity Depth of Analysis Prescriptive Predictive Exploratory Descriptive Behavioral BusinessValue 2. Genuine Risk of Churn or just Deal Digger 3. Cause Identification & Prioritization 4. Next Best Action to Win Back Subscriber 5. Measure the entre process & feed into them
    • 11confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Case Study: Real-Time Closed Loop Contextual Recommendations Continuous Insight Engine iTag Assembly Adapter Online Batch mode Supervised/ unsupervised learning Predictive modeling Behavior prediction Service/channel affinity Sociographic Physiographic Millionsofsubscribers Thousandsofproducts Hundredsofcontexts Impact Generated 1 Bn Recommendations per annum 2% conversions 10.2M USD annual incremental revenue Contextual Recommendation
    • 12confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Real time makes a huge difference
    • 13confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Case Study: Real-Time On-Trigger Campaigns Subscriber Profiles – 35 Million base KPI’s – 10 Real-Time + 160 Others Total Rows Processed/Day -1.6 billion rows at a data integration frequency of 2 Minutes to 24 Hrs. Total Campaigns per day – 250+ Campaigns/day KPIs & Insight Updates – 1 billion updates per day System Specifications Impact Generated Generating almost 1.2% incremental revenue month on month Real-time on-trigger campaign yield 40% to 300% higher conversion rate Prominent real time events – Current Balance, Recharges, Data usage, Long distance usage, On- net Usage, Roaming OG/IC. 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 1 2 3 4 5 Performance Real-time Vs. Non Real-time Campaigns (March-July 2013) Series1 Series2 Real-time Segmentation Real-time Analytics Real-time Tracking Real-time Fulfilment Real-time Action Contextual Grading Scheduled Rule Experimentation Business Consulting +
    • 14confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Real-time Visibility and Measurements Managed Multi- Channel Communication EXECUTE Sample Actions: 1. As soon as a zero-usage subscriber activates send Best fit offer 2. Send two wheeler visual MMS to subscribers who are Commuters but not long distance travelers with free helmet offer on test drive. 3. Offer Best Fit Data Upgrade to CSP subscribers segmented on consumption , pocket size & Handset type Real time , Integrated, Closed Loop Measurement and Reporting Real-time Analytics Real-time Actions Real-time Visibility, Fulfilment <90d 90-180d > 180d >=300 KES Diamond Top 1% Platinum Next 9% Gold Next 40% Silver Next 50% Ivory New Silver ARPU/ % of base AON Gold Ultra New
    • 15confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Advocacy Phase Delighted customer brings in more customers Across Customers’ Perpetual Lifecycle 1 Value Time 2 3 4 5 6 7 Whom to acquire Customer Joins Loyalty Retention Acquisition Phase Handholding Phase Usage Phase-1 How good is the Service Experience? Usage Phase-2 retain the right customers Migration Phase Prepay Post-pay Post-pay Prepay Neglect Phase Predict churn & retain the right customer Customer Churns Baby Care Campaigns Retention, Multi-wave, Interactive Campaigns Churn Prevention Campaigns Loyalty Enhancement Campaigns Churn Prediction
    • 16confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Case Study – Micro Segmentation Campaign Each segment further micro-segmented based on ARPU drop/ Recharge /Usage with priority score Suitable offers are pre-designed against each micro-segment Effectiveness of the campaign is measured against the conversions from the control group Iterative Campaigning addressing non responsive subscribers with updated offers
    • 17confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Listen Carefully to what Data speaks … An Operator in India 10M pre-pay subscribers, >5 years with Operator > $12 ARPU An Operator in Europe All contract subscribers AON for many is 100 days Month-on-month change in plan charges (reduction!). Both are sure to lose all those subscribers! Indian psychology does not accept post pay The other views Pre-pay as non-serious mobile service! Make these subs pay-as-you-go Make all of them post pay
    • 18confidential© 2013 Flytxt. All rights reserved. 11 September 2013 … and Combine it with Decision Sciences Auto: Promote 3G pack to all mid and high 2G data users. Manual: Exclude 3G dongle users Auto: Identify Clusters demonstrating youth characteristics Manual: Ignore cluster with heavy international traveller Auto: Promote recharge packs through multiple channels Manual: Use OBD for Indian rural segment Auto: Better to stop HVC Retention campaigns as there is only .1% conversion Manual: This conversion is still good for HVC segment, continue with the campaign. Insights Decision ActionFeedback Manual intervention for contextual decisions Context Design Execution Monitor Data from source systems Work flow management Feedback analysis and planning Data science Decisionscience Operations Analyst DataAnalysis
    • 19confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Full Service: Technology, Consulting, Execution KPIs INSIGHTs RECOMMENDATIONs ACTIONs PREDICTIVE MODELS FILTERING STATISTICAL CLASSIFIERS SOFT CLUSTERING CORRESPONDENCE ANALYSIS TIME SERIES ANALYSIS AGGREGATION COVARIANCE TWO-PASS ALGORITHM K MEANS CLUSTERING SCORING ITERATED FILTERING NESTED SAMPLING EXPECTATION MAXIMIZATION SOCIAL NET MODELS PREDICTIVE MODELLING TIME SERIES ANALYSIS ANALOGICAL REASONING PREDICTIVE INFERENCING MATRIX REASONING GENERALIZATION STATISTICAL SYLLOGISM REDUCTIVE REASONING SET COVER ABDUCTION PROBABILISTIC ABDUCTION ABDUCTIVE VALIDATION ABDUCTIVE REASONING LOGIC BASED ABDUCTION INDUCTIVE REASONING BAYESIAN INFERENCE SUBJECTIVE LOGIC ABDUCTION Increase ARPU Reduce Churn Improve QoS Increase CSAT Reduce Cost Increase Loyalty Improve MarginNew Revenue Stream Faster, efficient, Lower TCO
    • 20confidential© 2013 Flytxt. All rights reserved. 11 September 2013 2.8% contribution to gross revenue Incremental revenue from 48% of subscribers 48% improvement in usage drop over control group 24% conversion for retention campaign, with significant gains Stable base increased by 20% 8% conversion rate for trigger based pack promotions 30K+ successful monthly online payment recommendations 32 Million Shillings incremental revenue in a month >300% ROI on mobile Ad campaign for handset upsell. >90% precision in micro-segmented location based campaign. >2% CTR on mobile campaign to drive brand’s site traffic. Some more case study results
    • 21confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Flytxt Overview – About Us 200+ employees consisting of Marketing Consultants, Data Scientists & Analysts, R&D Experts, Software Engineers Management team with 200+ years in Telecom Dutch corporation with Global Development Centre at Trivandrum, India and offices at Delhi, Mumbai, Dhaka, Lagos, Nairobi and Dubai Sample text Our vision is to create >10% economic value for telcos from their data using Big Data Solutions Flytxt solutions increase revenues, margins and customer experience for CSPs Products based on patent pending DLU framework implementing complex analytics Serving many small & large operators across continents totaling 400M+ subscribers, via a mature CTE model Proven: 2% to 7% economic benefit to customers Emerging market innovation that has high potential and relevance to the developed markets Vision, Mission & Impact Customers (Operators, Brands) Company Awards & Achievements Sample text IEEE Cloud Computing Challenge B.I.D International Quality 2013
    • 22confidential© 2013 Flytxt. All rights reserved. 11 September 2013 Thank You Dr. Vinod Vasudevan Contact: vinod.vasudevan@flytxt.com www.flytxt.com