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

Intended for Knowledge Sharing only

Augment the actionability of Analytics with the “Voice of Customer”Global Predictive Analytics Conference 2017


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


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.


Quick recap of what it is


Customer Analytics, eh

4




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.

http://abcnews.com.co/homepage-newspaper/




…BUT THEN, EVEN RAMBO HAD TO PARTICIPATE IN PERFORMANCE REVIEWS

6https://imgflip.com/memegenerator/7064654/Kanye-West

https://imgflip.com/memegenerator/7064654/Kanye-West




What does Master Shifu say?

7




IN HIS WISE WORDS…

8

“Talk to your

Customers” son

MovieQuotesandMore

http://www.moviequotesandmore.com/kung-fu-panda-2-quotes/




BUT EVEN AFTER LOVABLE FOCUS GROUPS…

9

https://www.youtube.com/watch?v=ybzPpbDbV0Q






…SOMETIMES YOU ARE NOWHERE CLOSE!

10

www.cbc.ca

http://www.cbc.ca/




Why this gap?

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Intended for Knowledge Sharing onlyIntended 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.




What do we propose?

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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 Platfor

m

Reporting

Analytics

Research

Data Product

s

IterativeLoop Why?

Focus on Big WinsReduced WastageQuick FixesAdaptabilityAssured executionLearning for future initiatives

Optimization

…AND THIS IS WHY


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




Sequencing alone isn’t enough, requires a well designed program

16


WHAT IS A VOICE OF CUSTOMER PROGRAM?

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“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/




PLANNING A SUCCESSFUL VOC PROGRAM

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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


TACTICAL DETAILS INVOLVED IN ROLLING OUT A SUCCESSFUL VOC PROGRAM

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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


DATA MANAGEMENT (ILLUSTRATIVE)

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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)

Ow

ned

prop

erti

esAg

ency

Tight & seamless integration across systems necessary to achieve the goal of reasoned actionable insights…

USE CASES (ILLUSTRATIVE)


Product

Marketing

Operations

Risk

Strategy

1. Monitoring throughout PLC2. User Experience issues3. Personalization – FB Connect

1.Promotion effectiveness 2.Brand/Public Relations initiatives3.Cross & Up-sell/Campaign designs

1.Platform uptime2.Conversion3.Quicker sales

1.CRM Effectiveness2.Proactive solutions

1.Brand Awareness, Share of Voice2.Engagement3.CLV

1.Needs assessment & roadmap2.Competitive assessments

1.Fraud/gaming2.Information Security

1.Reduced incoming calls & response times2.Relational NPS

1.Fraud rates2.Complex pattern identifications3.Post incident response

1.Industry and consumer pulse2.Consumer relationship stickiness

Function Potential Analytics Possible metrics that it can help


-evolving-


VOC PROGRAM MATURITY CURVE & ALIGNMENT

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DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTI

VE AIAnalytics 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

http://www.gartner.com/it-glossary/predictive-analytics/

http://www.gartner.com/it-glossary/predictive-analytics/


VOC PROGRAM MATURITY AS DEFINED BY FORRESTER*

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Source: June 27, 2013, “The Path To Customer Experience Maturity” Forrester report

Stan

d al

one

to d

eep

inte

grati

on

Hindsight to Foresight




Bottom line please!

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CUSTOMER ANALYTICS IS TOUGH & NEEDS THE WHOLE TEAM TO KICK ASS - ANALYTICS, RESEARCH, TESTING & PROGRAM MANAGEMENT!

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DesignBolts

http://www.designbolts.com/2016/01/23/kung-fu-panda-3-2016-iphone-desktop-wallpapers-hd/




Why now - the emphasis on integration?

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FEEDBACK, A CRUCIAL ELEMENT IN PREDICTING CUSTOMER ACTIONS/REACTIONS

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Predictive Analytics

Behavioral Analytics

What are the customers

doing?

Voice of Customer

What are the customers telling you?

Platform PerformanceHow are you delivering? Competitive

Are the customers

buying elsewhere?

Social ListeningHow are

customers discussing

you?

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

http://omimbe.from-mn.com/customer-service-life-cycle-126239.html


BENEFITS OF INTEGRATING ANALYTICS, RESEARCH & TESTING

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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.




The parting words…

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SUMMARY


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.

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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.




Appendix

THANK YOU!


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/

https://twitter.com/decisions_2_0













Data & Analytics

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