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Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurturing and retaining customers

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We are at a point of inflection of embedding Advanced Analytics everywhere.

If you are interested in learning about:

1) Why should we cross the Cigital Chasm
2) Which of the areas should one focus
3) Which of the problems should one focus on
4) What are the opportunities / challenges / mitigations

then this is for you.

Published in: Data & Analytics
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Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurturing and retaining customers

  1. 1. www.globalbigdataconference.com Twitter: @bigdataconf 1
  2. 2. 2 Crossing the Digital Chasm Applying Advanced Analytics to Acquire, Nurture & Retain Customers Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
  3. 3. 3 Vishwa Kolla Head of Advanced Analytics John Hancock Insurance, Boston MBA Carnegie Mellon University MS University of Denver BS BITS Pilani, India  Advanced Analytics CoE, Maturity Model  Customer Analytics (entire value chain)  Machine Learning  Scoring Engine  Optimization  Simulations  Forecasting & Time Series • 15+ Years • John Hancock Insurance • Deloitte Consulting (Industries –Insurance, Retail, Financial, Technology, Telecom, Healthcare, Data) • IBM • Sun Microsystems Business Analytical (Math, Stats) Technical (Programming) Expertise Experience Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
  4. 4. 4 BACKGROUND Digital is Everywhere Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Digital Social Mobile Cloud Analytics
  5. 5. 5 In God we trust. All others – please bring me data W. Edwards Deming
  6. 6. 6 BACKGROUND The focus of this discussion is All Things Analytics Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Digital Social Mobile Cloud Analytics
  7. 7. 7 BACKGROUND Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Digital Social Mobile Cloud Analytics Analytics powers the remaining Digital components
  8. 8. 8 BACKGROUND Digital Chasm Digital Chasm is the gap between early adopters and early majority Global Predictive Analytics Conference | March 7 - 9 | Santa Clara 2.5% Innovators Early Adaptors 13.5% Early Majority 34% Late Majority 34% Laggards 16% Digital Adoption Life Cycle
  9. 9. 9 NEED 2001 – 2013 CAGR Revenue (Firm | Industry) Source: 2001 – 2013 Revenue figures from Capital IQ 3% 3% 3% 1% 5% 7% 7% 8% 10% 12% Digital Chasm translates to money left on the table; Crossing it is necessary Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Digital Chasm 2.5% Innovators Early Adaptors 13.5% Early Majority 34% Late Majority 34% Laggards 16%
  10. 10. 10 OPPORTUNITY Prospect Acquire Nurture Retain / Win-back Opportunity exists across the entire customer value chain; Determining focus area is important Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
  11. 11. 11 Lack of direction, not lack of time, is the problem. We all have 24 hour days. - Zig Ziglar
  12. 12. 12 OPPORTUNITY Global Predictive Analytics Conference | March 7 - 9 | Santa Clara All roads lead to improving Customer Life Time Value Customer Life Time Value (Simplified) 𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝐶𝐿𝑇𝑉𝑗 = (𝑅𝑒𝑣 − 𝐶𝑜𝑠𝑡𝑠) (1 + 𝑑)𝑖 − 𝐴𝑐𝑞. 𝐶𝑜𝑠𝑡 𝑛 𝑖=1 𝑚 𝑗=1 i = year index j = customer index d = discount rate Value Operating Costs m, number of customers Acquisition Costs n, longevity Rev, spend / share of wallet
  13. 13. 13 PRIORITIES Prospect Acquire Nurture Retain / Win-back 4x less expensive to retain than to acquire Retention / Win-back is where most initiatives start Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
  14. 14. 14 PRIORITIES Prospect Acquire Nurture Retain / Win-back Grow share of wallet; Customers are sticky Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Switching costs (in most industries) are high; 1-2 years of stick time is the sweet spot
  15. 15. 15 PRIORITIES Prospect Acquire Nurture Retain / Win-back Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Growth drives brand equity and valuation Growth is the priority; Customers are sticky
  16. 16. 16 PRIORITIES Prospect Acquire Nurture Retain / Win-back $100 K - $300 K in annual subscription fees per source Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Purchased data is (relatively) less (or more) expensive; It is also most curated
  17. 17. 17 FRAMEWORK Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Advanced Analytics (AA) can help in several ways Prospect Acquire Nurture Retain / Win-back Advisor / Agent  Segmentation & Profiling  Likelihood to recommend  Referral  Performance  Product Recommendations  Personalization  Leads  Social Network analysis and influence scores  Geo-spatial analysis  Coverage analysis Customer  Segmentation & Profiling  Likelihood to buy  Likelihood to qualify  Like customers  Product recommendations  Personalization  Social Network Analysis and Influence analysis  Likelihood to recommend Marketing  Mix Optimization  X-Sell & Up-Sell  Social listening Advisor / Agent  Integration (with industry standard applications)  Industry standard scores and pricing Customer  Triage prediction  Risk class determination  Risk class prediction  Likelihood to misrepresent  Misrepresentation detection  Likelihood to smoke  Likelihood to get declined  Morbidity analysis  Co-morbidity analysis  Mortality analysis  Post-issue analysis  Protective Value analysis New Data Sources Integration  EMR  EHR  Telematics Product  Next best offer  Simplified issue  Price elasticity  Engagement  Assumption development / Experience studies Customer  X-Sell Social  Social listening  Influencers and Advocates Claims  Fraud detection  Likelihood to commit soft fraud  Claim severity  Likelihood to litigate  Expedited adjudication Telematics  Simplified issue product development  Preferred Pricing and Discounts Advisor / Agent  Propensity to recommend  Propensity to refer  Performance Customer  Likelihood to lapse  Likelihood to win-back Business Integration
  18. 18. 18 FRAMEWORK Global Predictive Analytics Conference | March 7 - 9 | Santa Clara An AA Maturity Model helps with visioning Area Inception Emerging Developing Mature Best In Class People Thought leaders build on the business case to integrating AA Leaders learn and understand through experimentation Leaders build strategic partnerships for success Leaders assemble / build an in-house teams Advanced Analytics enables and drives strategic initiatives Processes Leaders recognize the need for a consistent process Experimentation is both limited and controlled Increased tolerance for experimentation and for failure Emphasis is on experiment design as opposed to on execution Rapid experimentation, fail fast and improvise Technology Leaders recognize technology is a key enabler Environments are scattered Single environment enabling a few selected projects Single environment with multi-tenancy (projects, resources) Multiple environments with SDLC-like maturity Data Leaders identify broadly the data sources required Data sources are integrated in an ad- hoc basis Data is integrated in an ad-hoc basis Data sources are integrated enabling hypothesis testing Data is central to all decision making Governance Leaders recognize the need for governance and complexity A governance process is laid out A governance process is adhered to in pockets A governance process is adhered to across projects Governance can be traced and reported on Benefits from Advanced Analytics
  19. 19. 19 FRAMEWORK Global Predictive Analytics Conference | March 7 - 9 | Santa Clara AA Process Maturity is THE differentiator Data Inputs Advanced Analytics Data and Insight Consumers Structured Semi-Structured Un-Structured ODS Business Users / Data Scientists Executives Operational Users Problem Definition Model Strategy Data Engineering Model Build Implementation & Governance 1 2 3 4 5 Define business problem, objectives and engagement model Translate Business Problem into a Modeling and a Scoring Problem Prepare, normalize and curate raw data into a modeling ready form Build and validate Predictive Models using Champion Challenger Process Use scoring equations from Champion model and deploy the model into production environment
  20. 20. 20 PROSPECTING Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Prospecting is a conglomeration of several small(er) problems  Awareness (Aided and un-aided recall)  Drivers (Price elasticity, Value)  Trigger points (Life stage indicator models) What 1. Too complex to understand (Simplify message, not product) 2. High price sensitivity (Volume vs. Margin) 3. Un-timely identification of trigger points (Omni- Channel – e.g., Live Ramp vs. PA Model build)  Channel (Awareness, Response, Conversion)  Prospective population identification  Touchpoint repetition How 1. Too high error rates in response modeling (Unit of analysis = individual) 2. Exhausted target population (Sub-Prime sampling with caution – cost / benefit analysis) 3. Insufficient response rates (Ad-stock models)  Profiling (Population & Sample(s))  Indexes  Clone(s) | Look alike(s) Who 1. Too Big to Profile (Stratified Sample) 2. Too many unknowns (Just focus on signal) 3. Too many / insufficient clone attributes (Actionable (to build Persona) attributes) 4. Low match rate (Commercial vs. in-house) Business Problem Sample Analytical Problems Sample Challenges and Mitigation
  21. 21. Rules Based Model  Several insignificant but important patterns  Supervised and Un- supervised Learning methods including  Clustering & Segmentation  Market Basket  PCA Predictive Model  Few significant and important patterns  Supervised Learning methods including  GLMs  Non-linear (Neural Nets)  State Space  Genetic Algorithms 21 ACQUISITION Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Acquisition is a trade-off problem; Commercial solution involves finding the optimal mix In-Person Survey / Form 3rd Party Aggregated PredictivePower(Lift) Data Acquisition Costs 3rd Party Collected Data Sources Model Forms Commercial Solution
  22. 22. Data Collection  Identify Unit of analysis  Curate (Collect, De-identify, Cleanse)  Merge  Repeat each time period 22 NURTURE & RETENTION Global Predictive Analytics Conference | March 7 - 9 | Santa Clara Nurture & Retention are BIG data collection & engineering (maturity) problems Internal Data (80%) External Data (20%) Data Engineering (Create Longitudinal View) Predictive Models  Profiles on variety of dimensions  Engagement Index  Product Affinity  Next Best Offer  Likely to Lapse  Likely to Refer / Recommend Customer Product Point in Time Snapshot What data should I keep? 1Q Look back 2Q Look back 3Q Look back 4Q Look back
  23. 23. 23 Discipline is the bridge between goals and accomplishment - Jim Rohn
  24. 24. Relevant Data Set 24 AA Journey Core Inputs (Model Build) Historical Data Raw Data Additional Inputs (Test) Modeling Data Set Core Inputs (Model Build) Additional Inputs (Test) V a l i d a t e TestTrainRelevant Data Noise DataPartitioning DataExtraction DataEngineering ApplyFilterRules DataAggregation Predictive Model Build Scoring Engine Development Live Scoring Engine Evaluate FinalModelEquations RollouttoProduction Data Integration Model Integration Systems Integration Real – time Scoring Engine Development Service Layer Development UI Engine QC Engine Business Objective – Any Predictive Model 1 2 Uni-VariateAnalysis Bi-VariateAnalysis 3 4 5 Problem Definition Model Strategy Data Engineering Model Build Model Implementation & Governance 1 2 3 4 5 01/## Current Getting to the finish line involves careful planning and execution
  25. 25. 25 CLOSING Global Predictive Analytics Conference | March 7 - 9 | Santa Clara  Data mining is for real and not “entirely” hype  Prioritize Process over immediate Purpose  A structured process is critical  There is no pixie dust  QC every step along the way
  26. 26. 26Global Predictive Analytics Conference | March 7 - 9 | Santa Clara THANK YOU!

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