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.

…BUT THEN, EVEN RAMBO HAD TO PARTICIPATE IN PERFORMANCE REVIEWS
6
https://imgflip.com/memegenerator/7064654/Kanye-West

What does Master Shifu say?
7

IN HIS WISE WORDS…
8
“Talk to your
Customers”
son
MovieQuotesandMore

BUT EVEN AFTER LOVABLE FOCUS GROUPS…
9
https://www.youtube.com/watch?v=ybzPpbDbV0Q

…SOMETIMES YOU ARE NOWHERE CLOSE!
10
www.cbc.ca

Why this gap?
11

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?
13

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

…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?
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/

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

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

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…

USE CASES (ILLUSTRATIVE)
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

-evolving-
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

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

Bottom line please!
24

CUSTOMER ANALYTICS IS TOUGH & NEEDS THE WHOLE TEAM TO KICK ASS
- ANALYTICS, RESEARCH, TESTING & PROGRAM MANAGEMENT!
25
DesignBolts

Why now - the emphasis on integration?
26

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?

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

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.

The parting words…
30

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.
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.

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/

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