Augment the actionability of Analytics with the “Voice of Customer”

1. Intended for Knowledge Sharing only Augment the actionability of Analytics with the “Voice of Customer” Global Predictive Analytics Conference 2017

2. Intended for Knowledge Sharing only Disclaimer: Participation is purely on a personal basis and does not represent VISA,Inc. in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related info of any firm is used in any material. Director, Analytics & Testing at Visa, Inc. Drive data driven culture and decision making RAMKUMAR RAVICHANDRAN Senior Manager, Analytics at Visa, Inc. Enable strategic decisions via actionable insights MURALIDHARAN DHURVAS

3. Intended for Knowledge Sharing only Disclaimer: Participation in this summit is purely on personal basis and is not meant to represent VISA’s position on this or any other subject and in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related information of any firm is used in any material.

4. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Customer Analytics, eh 4

5. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only FEELS LIKE RAMBO… 5 abcnews.com.co With my Customer 360, I can do Use case analyses, Behavioral profiling, Audience buckets, Cross/Up sell, Recommendation engines, Risk predictions, etc. etc.

6. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only …BUT THEN, EVEN RAMBO HAD TO PARTICIPATE IN PERFORMANCE REVIEWS 6 https://imgflip.com/memegenerator/7064654/Kanye-West

7. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only What does Master Shifu say? 7

8. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only IN HIS WISE WORDS… 8 “Talk to your Customers” son MovieQuotesandMore

9. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only BUT EVEN AFTER LOVABLE FOCUS GROUPS… 9 https://www.youtube.com/watch?v=ybzPpbDbV0Q

10. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only …SOMETIMES YOU ARE NOWHERE CLOSE! 10 www.cbc.ca

11. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Why this gap? 11

12. Intended for Knowledge Sharing only Intended for Knowledge Sharing only CHALLENGES WITH CUSTOMER FEEDBACK 12 Difference between “what they say” and “what they do”. State of mind changes rapidly based on the context on where they were and what they went next. Sometimes customers genuinely don’t know what they want. They just don’t like it or want something better. Few noisy or influential customers may create the noise, but the majority of the target customer base is happy. “Law of substitution”: Customers may hate your product or brand, but still use it, because it’s the best of the lot or vice versa.

13. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only What do we propose? 13

14. INTEGRATION OF ANALYTICS, RESEARCH & TESTING Intended for Knowledge Sharing only 14 Analytics provides insights into “actions”, Research context on “motivations” & Testing helps verify the “tactics” in the field… Strategy Data Tagging Data Platform Reporting Analytics Research Data Products Iterative Loop Why? Focus on Big Wins Reduced Wastage Quick Fixes Adaptability Assured execution Learning for future initiatives Optimization

15. …AND THIS IS WHY Intended for Knowledge Sharing only RESEARCH ANALYTICS Cost/Speed of doing it Ease of Analyzing (Structure) Sample Size Type of Insights Attitudinal Behavioral Attribution Inferred Direct Greatest strength? Finding out a hypotheses Sizing the hypothesis Analytics is the yang to the Research’s yin & Testing adds the swag to the lovely couple… TESTING Response Direct Confirm the hypothesis

16. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Sequencing alone isn’t enough, requires a well designed program 16

17. Intended for Knowledge Sharing only Intended for Knowledge Sharing only WHAT IS A VOICE OF CUSTOMER PROGRAM? 17 “Voice-Of-Customer is the collection of customer feedback from all touchpoints & context and use it in strategic decisions and actions, in pursuit of delivering optimal Customer Experience” https://www.surveygizmo.com/customer-service-surveys/guide-to-voice-of-the-customer-voc/

18. Intended for Knowledge Sharing only Intended for Knowledge Sharing only PLANNING A SUCCESSFUL VOC PROGRAM 18 Strategy Deploy Data Analysis Testing Act  Strategic fit for the VOC program  Deploy VOC across touchpoints and ensure seamless integration with Analytics & Testing  Instrumentation, collection, data blending and platforming  Insight generation either as pulse check reporting, diagnostic analyses, sizing, predictions & monetization  Test & Learn to iteratively validate and/or improve effectiveness of CX recommendations  Ramp the winner variations  Sell, Scale, Transform

19. Intended for Knowledge Sharing only Intended for Knowledge Sharing only TACTICAL DETAILS INVOLVED IN ROLLING OUT A SUCCESSFUL VOC PROGRAM 19 Strategy Deploy Data Analysis Testing Act • Alignment with Strategic goals and sizing of impact on KPIs • Support Executive Sponsor, Champion BU, First adopter BU • KPI flavors: Relational(Influencer vs. Regular), Transactional, BU level • Success Criteria & Cultural transformation • Questionnaire design • Mode: Pop-ups, triggers, emails, interstitial, social, CRM, static • UED: Flow/length/intuitive, Placement, Prominence, CTA, Messaging: insight->channel->context->frequency->response rates & accuracy • Spamming guidelines • Instrumentation: Common ID across systems, touchpoints, channels (Voice & Chatbot Transcriptions), Solicited/Unsolicited feedback connectors • Availability: Part of standard Customer Profile schema as relevant dimensions (dates/themes), Experimentation insights & predictive backfill • Monetizable data products • Descriptive reports & Diagnostic analytics: Cohorts, Trends, Correlations • Advanced Analytics: Identify drivers of feedback & vice versa (KPI impact) • Text Mining: Predictive Sentiment Mining, SOV/Brand Awareness drivers, Theme/entity extraction, Exploratory research/classifications • Champion, Challenger framework to validate effectiveness or iterate towards effectiveness (across UED, data collection (accuracy/response rates), analytical findings, treatments) • Expected RoI on full rollout, learnings/consequences, feedback to strategy • Full rollout, new normal identification and baselining, system dynamics modeling, documentation, leftover gap and new cycle begins

20. Intended for Knowledge Sharing only Intended for Knowledge Sharing only DATA MANAGEMENT (ILLUSTRATIVE) 20 Data Sources Blending & aggregations Awareness, Sentiment, SOV, Theme, Entity, Context Enterprise Data Lake Social: Facebook, Twitter Digital, Open Web, Crawlers CRM Systems Canned feedback systems (Focus Groups, Surveys, Emails) Live front end integrated feedback tools Chatbots Data lake (Internal actions) CX effectiveness, issue/event/ops monitors, performance, Fraud Data Mart (Suitably masked monetizable data product)Analytical Systems Testing Systems Recommendations (Product, Treatments- UX, Lifecycle, Pricing) OwnedpropertiesAgency Tight & seamless integration across systems necessary to achieve the goal of reasoned actionable insights…

21. USE CASES (ILLUSTRATIVE) Intended for Knowledge Sharing only Product Marketing Operations Risk Strategy 1. Monitoring throughout PLC 2. User Experience issues 3. Personalization – FB Connect 1.Promotion effectiveness 2.Brand/Public Relations initiatives 3.Cross & Up-sell/Campaign designs 1.Platform uptime 2.Conversion 3.Quicker sales 1.CRM Effectiveness 2.Proactive solutions 1.Brand Awareness, Share of Voice 2.Engagement 3.CLV 1.Needs assessment & roadmap 2.Competitive assessments 1.Fraud/gaming 2.Information Security 1.Reduced incoming calls & response times 2.Relational NPS 1.Fraud rates 2.Complex pattern identifications 3.Post incident response 1.Industry and consumer pulse 2.Consumer relationship stickiness Function Potential Analytics Possible metrics that it can help

22. Intended for Knowledge Sharing only -evolving- Intended for Knowledge Sharing only VOC PROGRAM MATURITY CURVE & ALIGNMENT 22 DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE AI Analytics Maturity Curve Fit of VOC VOC fit in the analytics maturity curve Fix Elevate Optimize Transform VOC->CX Maturity http://www.gartner.com/it-glossary/predictive-analytics/ Source: June 27, 2013, “The Path To Customer Experience Maturity” Forrester report

23. Intended for Knowledge Sharing only Intended for Knowledge Sharing only VOC PROGRAM MATURITY AS DEFINED BY FORRESTER* 23 Source: June 27, 2013, “The Path To Customer Experience Maturity” Forrester report Standalonetodeepintegration Hindsight to Foresight

24. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Bottom line please! 24

25. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only CUSTOMER ANALYTICS IS TOUGH & NEEDS THE WHOLE TEAM TO KICK ASS - ANALYTICS, RESEARCH, TESTING & PROGRAM MANAGEMENT! 25 DesignBolts

26. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Why now - the emphasis on integration? 26

27. Intended for Knowledge Sharing only Intended for Knowledge Sharing only FEEDBACK, A CRUCIAL ELEMENT IN PREDICTING CUSTOMER ACTIONS/REACTIONS 27 Predictive Analytics Behavioral Analytics What are the customers doing? Voice of Customer What are the customers telling you? Platform Performance How are you delivering? Competitive Are the customers buying elsewhere? Social Listening How are customers discussing you?

28. REALIZATION THAT UPSTREAM FOCUS ON LOYAL CUSTOMERS BETTER FOR BUSINESS Intended for Knowledge Sharing only 28 Source: omimbe.from-mn.com Brand Awareness, Share of Voice, Mentions, Sentiment Open/Click Rates, Inquiries/Site Visits, Questions Conversion, Txns, Sign Ups, Downloads, Searches/Visits per User Repeat Usage, #/$ Txns per User, Support Center, Growth NPS, Referrals, Social Media Sentiment Flow of Insights more upstream = better acquisitions & CPE

29. Intended for Knowledge Sharing only Intended for Knowledge Sharing only BENEFITS OF INTEGRATING ANALYTICS, RESEARCH & TESTING 29 Causation: Feedback data helps us reduce the “unexplained part” and add “whys” as said by Customers to the analytical solutions. Confirm: Integrating Analytics-Research-Testing helps quickly iterate offerings and deliver superior experience sooner than later. Scale: The consistent struggle of sample size with the exploratory research studies can be won over by sizing right proxies via analytics. Monetization: Apart from being an input into predictive models, Customer feedback/survey data can be rich sources of additional information that can be monetized beyond primary use case. Valuation: Integration helps attach a tangible Business RoI, e.g., a happy customer is 3X valuable than non-happy.

30. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only The parting words… 30

31. SUMMARY Intended for Knowledge Sharing only Reasoned actionable insights & decisions are the new normal and require integration of Analytics, Research & Testing to get complete perspective. Big Data and Cloud well positioned to enable the integrations on front end and backend across batch & real time mode, e.g., Google 360. Research supplied “Context” data, Analytics delivered “Action” data & Testing based “Response” data can be packaged into a high value monetizable data product with applications far beyond primary use case. Open Standard (common API protocols) required to facilitate less-friction integration, data pass through and the ability for the Application layer to process & react appropriately, to truly be able to realize this vision. 31 Feedback data handling is tricky (small, fickle, sensitive) and pose data blending/extrapolation challenges. Even trickier is handling the localization nuances. Pioneers have put together best practices to follow.

32. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Appendix

33. THANK YOU! Intended for Knowledge Sharing only Would love to hear from you on any of the following forums… https://twitter.com/decisions_2_0 http://www.slideshare.net/RamkumarRavichandran https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos http://www.odbms.org/2015/01/ramkumar-ravichandran-visa/ https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a RAMKUMAR RAVICHANDRAN MURALIDHARAN DHURVAS https://www.linkedin.com/in/muralidharandhurvas/