Selling Text Analytics to your boss

SELLING TEXT ANALYTICS TO YOUR
BOSS
Nov 2014

Intro – speaker, the talk & quick intro of the audience.
Quick summary of challenges with Text Analytics
Role play (what mistakes are done today?)
The elevator pitch
Proposed tactical approach
References for reading materials
CONTENTS
Intended for Knowledge Sharing only 2

INTRO
About me:
"As Director, Analytics for the Digital Developed Markets department at Visa Inc., I am responsible
for helping the Leadership & Stakeholders with actionable insights derived from Analytics. The
business questions span the whole spectrum across the Product, Marketing, Sales and
Relationship. We leverage any of the various options, i.e., Strategic analysis, Advanced Analytics,
Text Analytics or Mining depending on the problem being solved."
The talk:
Content will range start with why does Text Analytics need a special session on convincing boss,
followed by a role play summarizing current mistakes, a sample elevator pitch for your boss and a
proposed execution plan. The content is tailored for Mid to Senior Level Managers trying to
convince Leaders/Executives/Heads. It doesn’t provide any technical details –methodologies,
tools, vendors or hardware investments.
*Disclaimer: Participation in this summit is purely on personal basis and not representing VISA in any form or matter. The talk is
based on learnings from work across industries and firms. Care has been taken to ensure no proprietary or work related info of any
of my the firms I have worked with is used in any materials.

The elevator pitch
CONTENTS

TOUGHEST ELEVATOR PITCH TODAY
Don't you know I'm human too?

WHY DO THEY SAY NO?
 PERCEPTION ISSUES – too niche, too specific problem solving.
 COMPLEX & CHALLENGING – variety of data sources, structure, time
consuming
 RoI UNCLEAR
 LACK OF TRAINED RESOURCES
 MY CURRENT SUITE OF ANALYTICS IS ENOUGH

ARE ALL THOSE STATEMENTS TRUE?
 PERCEPTION ISSUES – too niche, too specific problem solving.
 COMPLEX & CHALLENGING – variety of data sources, structure, time
consuming
 RoI UNCLEAR
 LACK OF TRAINED RESOURCES
 MY CURRENT SUITE OF ANALYTICS IS ENOUGH
MYTH
TRUE
TRUE
TRUE
FALSE

The elevator pitch
CONTENTS

How is it done today, more or less 
https://www.youtube.com/watch?v=BUsiI47ol6g

The elevator pitch
CONTENTS

NECESSARY COMPONENTS IN AN ELEVATOR PITCH
 30 sec summary of the idea, customer & value proposition
 What is it?
 How does it help, aka, what will s/he get out of it?
 How it works?
 Target Customers
 What do you need?
 Why this and nothing else (USP & SWOT Analysis)
 How will you grow this?
Get your boss hooked,
Once hooked, the detailed explanation…

THE ELEVATOR PITCH
Text Analytics is a “Mind Reading” Tool. Can tell us how Consumers perceive
our products and what their needs are. It complements the other Analytical
techniques to answer the why questions.
We will use it to help Brad (a Product Manager) prioritize Password reset
experience by showing him the magnitude of bad user reactions. It will be a
quick survey followed by Text Analytics.
We have identified an $1M+ Opportunity in Password reset experience via
Text Analytics.

WHAT IS IT AND HOW IT HELPS?
 Text mining/Analytics is the process of deriving high-quality information from
text with the goal of turning text into data for analysis, via application of natural
language processing (NLP) and analytical methods.
Source: http://en.wikipedia.org/wiki/Text_mining
http://en.wikipedia.org/wiki/Text_mining#Text_mining_and_text_analytics
 Text mining usually involves the process of structuring the input text (usually
parsing, along with the addition of some derived linguistic features and the
removal of others, and subsequent insertion into a database), deriving patterns
within the structured data, and finally evaluation and interpretation of the
output.
 e.g. frequency distributions, pattern recognition, tagging/annotation,
information extraction, data mining techniques including link and association
analysis, visualization, and predictive analytics.

A TYPICAL EXAMPLE OF FINDINGS OR INSIGHTS…
 Overall NPS: 70% (Promoters: 80%; Detractors: 10%)
 What are the Promoters happy about?*
 60% of the users love the simplicity of use of Mobile App
 40% feel the recommendations are relevant
 30% like the two level authentication feature
 What are the Detractors are unhappy about?*
 10% hate the password reset experience
 8% feel that password reset link takes too long to reach their email inbox
 5% feel that the text updates don’t provide sufficient information
 What new features did the users ask for?
 Monthly reminders
 Functionality for the receivers to confirm the payment
 FX conversion change alerts
*depends on the how the questions were framed in the surveys

A TYPICAL IMPACT SIZING…
KPIs
Monthly unique users 20,000,000
#Page Views per user 40
#Total Page Views from Users 800,000,000
Quantifying
the problem
#users trying to reset Password 2,000,000
Users from the survey who hate the current
experience (consistent and all detractors
cited this as the reason)
100%
Assuming everyone who tries to reset
password hates the experience, so users at
risk
2,000,000
We would be left with 720,000,000
Conservative Realistic Aggressive
Impact Sizing
($)
By changing the experience, we decrease
detractors by
50% 80% 100%
and save these many Page Views 40,000,000 64,000,000 80,000,000
At a CPM of $2 (Cost per 1000 impressions),
we improved Ad Revenue by
$80,000 $128,000 $160,000
Annual Revenue impact $960,000 $1,536,000 $1,920,000
Resetting the current Password reset experience can save at least 1 MM annually…

HIGH LEVEL STEPS INVOLVED…
Objective
Translation to Analytical
Framework
Data Collection and
Preparation
Analysis, Validation &
Verification
Actionable insights and
impact sizing
A/B Testing
Rollouts
• Understand the core needs, expected outcomes, generate hypotheses and know if it’s
an Unstructured or Structured problem
• Consult with User/Market Research team on the applicability of the text input to your
problem statement and how/where to get the data from.
• Decide on Analytical methodology, e.g., Word bubble, Link, SVM
• Data Collection: Structured (Surveys, Call Center logs) or Unstructured (Reviews, Social
media comments, Blogs, Articles)
• Data Preparation: Crowd sourcing for tagging, Cleaning (Stemming, Stop words,
normalization, adjectives & entities, preposition removal, usage patterns, n-grams)
• Data Transformations: TF, IDF, Medians, Max, transformations, Dim reductions, etc.
• Marrying with internal data: Activity, Clickstream, Demo or Geo, Product.
• Execution of Analysis on one sample and validation on another.
• Verification of insights with other established processes, checking for reliability.
• Generate recommendations and impact sizing (current problem and other areas)
• Champion vs. Challenger testing of the impact.
• Rollouts – New product, feature, changes, etc.
1
2
3
4
5
6
7
Hmmm, what’s this?

HIGH LEVEL STEPS INVOLVED…
Objective
Translation to Analytical
Framework
Data Collection and
Preparation
Analysis, Validation &
Verification
Actionable insights and
impact sizing
A/B Testing
Rollouts
1
2
3
4
5
6
7
Analyst & Stakeholder
Analyst, Researcher, Data
Instrumentation, & Data Manager,
Developer, Data Scientist
Analyst, Data Manager, Data Scientist
Analyst, Data Scientist, Stakeholder and
SME, Researcher
Analyst, Stakeholder, Leader
Analyst, A/B Testing, Stakeholder,
Developer
Stakeholder, Leadership & Executives
Yeah, but who does it?
Knowing what is the key need
Right framework, right data, right
questions
Most challenging, time consuming, and
tricky phase (smarts, patience &
determination needed)
Next most challenging – iterations on
methodology/data to get to a acceptable
lift
Realistic sizing and allied applications of
insights
Buy-ins
Corporate Strategy shifts
Easy?

POSSIBLE SOLUTIONS SET BY STAKEHOLDERS …
MARKETING
PRODUCT
CUSTOMER SUPPORT
• SOV, Brand awareness and Perception
• Sentiment in Media coverage
• Competitive and industry benchmarking
FUNCTIONS USE CASES
STRATEGY
RISK
• Consumer Pain points – Experience, Product stability, use
cases, etc.
• Machine logs analysis for user interactions studies
• Informs Analytics of Consumer Sentiment, reactions,
expectations, pain-points and helps in tailoring better
actions
• Market and User Research
• Competitive and industry benchmarking
• Gaming behavior and hacker communities for new use cases
Others (Legal, HR,
Finance)
• Contract verifications, Backgrounds, etc.
• Resume parsing, Career Planning, Talent Management, etc.

WHAT DO YOU NEED?
 Executive support must.
 Stakeholder buy-in and strong advocacy and support.
 Clearly defined and communicated objectives mapped to company initiatives
 Clearly defined and accepted success criteria for Text Analytics Initiatives
 Tools & Technologies:
• Non-Coders: KNIME, Rapidminer
• Semi-Coders: SAS, Angoss & Polyanalyst
• Full on Coders (Advanced Users): R and Python
 Type of Talent: Closely tied to business needs. However one advanced
programmer (Python, R) and an analyst are needs. 50% time of a Researcher and
20% time of Stakeholder customer needed.
 Partnership with User/Market researcher team needed to frame right
questions and make right readings from the data.
 Partnership with Data Instrumentation and Data Managers to collect,
prepare, execute and ramp up this process.

USP & SWOT…
STRENGHTS
• More aha and surprises
• Gut>Emotions>Metrics
• Consumers feel respected and
connect better when you talk to them
THREAT
• Big data technologies need to plan for
ever changing text data & privacy
concerns (Volume, variety, velocity
and veracity)
• More devices, more challenges
OPPORTUNITIES
• Cross functional team to standardize
the data collection and preparation
• Buy-ins on success criteria
• Train the analysts
• Evolving NLP techniques, vendors and
market buy-ins
Closest to truth! Insights from metrics based analysis is Analyst’s reasoning
and Insights from Text Analytics are Voice of Consumers!
WEAKNESSES
• Cost and complexity of execution*
• RoI – various ways to look at it
• Not create and forget (nuances during
automation)
• Lack of resources
*Unique data collection changes (low response rates of surveys), internationalization expansions, etc.

HIGH LEVEL THUMBRULES OF WHEN TO DO VS. NOT…
 Minimum and reliable sample size (>=5% or 10K+ users are leaving some
active or passive feedback somewhere).
 Share of Voice, Brand awareness and perceptions have stabilized and product
has been live for atleast a year for users to get familiar with it.
 High Level analysis reveals correlation between Portfolio KPIs and relational
NPS/SOV/call center metrics/Brand metrics.
 BI, User Research, Analytics, A/B Testing have all been properly invested and
strengthened in that order. Text Analytics and Mining usually go together into
helping ramped up business. These two are also dependent on the previously
mentioned practices for success and show most value when all others feel or
are not able to answer the questions.
 100% accuracy isn’t required but a strong and reliable directional insight is
the need of the hour.

The elevator pitch
CONTENTS

THE THREE STAGES OF EXECUTION
PICK
PROVE
SELL
• Stakeholder discussions to find out pressing
questions
• Prioritize – Requester; urgency; impact; efforts
• Choose “highest PR potential” problem for POC
• Create action plan – data collection, cleansing,
methodology, timelines, expected outcome
template, success criteria.
• SWAT team – Stakeholder rep, Analyst & Dev or
Data Scientist
• Check-ins & documentation of what worked and
did not, do’s/don’ts, challenges & nuances.
• Insights communication & Impact estimation.
• Champion vs. Challenger measurement.
• Highlight victories – underdog story, winning
against the odds, challenges faced, etc.
• Ramp plans: hiring, cost, time, areas where it can
be used
• Branding – Internal, and if possible, external
too, make it ‘cool’ and desirable.
Best to go
prepared to
you boss
Boss buy-in
would be
really good
Boss &
Stakeholders
primary doers.
Should go up
to Execs.

STILL NOT CONVINCED?
Not surprising, every change agent has it tough…
 Just do it (forgiveness is better than permission),
 Unsure of ability to pull off or too much to do already? No time or people or
tool!
 freelancers/contractors, vendors, POCs
 either yourself by learning it or
 informal partnership with a data scientist or a coder in the company
 Cost concerns:
 innovation labs, done by university students under NDA
 Run a company innovation contest
 Your boss unsure if Stakeholders will like it?
 Get it done and sell to Stakeholders boss
 Brownbags or other team meetings
 Your boss just doesn’t want to do it: Create a Watsapp!
…satisfaction of getting it done and value add is huge enough reward.

REFERENCES

SAMPLE EXECUTIVE SUMMARY
Objective
• Set up Text Analytics infrastructure to transform unstructured consumer inputs into actionable insights
Methodology
• POC to analyze the utility for our business and recommend a go/no-go decision
Key Findings
• Things worked
• That didn’t work
• Who are the largest customers
Recommendations
• Go/No go – Yes, but with as a cross functional team and clear success criteria
• How big an opportunity?

Some interesting applications of Text Mining/Analytics,
Project Dreamcatcher:
http://www.slate.com/articles/news_and_politics/victory_lab/2012/01/project_dreamcatcher_how
_cutting_edge_text_analytics_can_help_the_obama_campaign_determine_voters_hopes_and_fear
s_.html
J.K.Rowling caught 
http://blogs.wsj.com/speakeasy/2013/07/16/the-science-that-uncovered-j-k-rowlings-literary-
hocus-pocus/
Movers and Shakers I follow in this space:
Seth Grimes
http://altaplana.com/TAS08-TextAnalyticsForDummies.pdf
http://www.slideshare.net/SethGrimes
http://altaplana.com/grimes.html
Junling Hu
http://www.aboutdm.com/
SOME ADDITIONAL STUFF TO READ ABOUT…
*No endorsements or claims of validity/reliability. Used it myself and wanted to share with you.

Good foundation on Text Mining from Statistica
http://loyaltysquare.com/text_mining.php
http://www.statsoft.com/Textbook/Text-Mining/button/3
http://www.nltk.org/book/
KNIME use cases (Google search sufficient for Angoss, Rapidminer, SAS whitepapers yields):
http://www.meetup.com/Bay-Area-KNIME-
Users/events/199339222/comments/418637362/?itemTypeToken=COMMENT&a=uc1_rd&read=1&
_af_eid=199339222&_af=event
Text Mining Video tutorials by Rapidminer
https://www.youtube.com/watch?v=hpvda_Rfg3s&list=PLeE4eVNDyo0VKTTiPg0zlX_OYiWII-sow
Text Mining & Analytics on Courseera
https://www.coursera.org/specialization/datamining/20
https://www.coursera.org/course/textanalytics
On Udemy (Text Mining for Bloggers)
https://www.udemy.com/text-mining-for-bloggers/
IF YOU WANT TO LEARN YOURSELF…
*No endorsements or claims of validity/reliability. Used it myself and wanted to share with you.

Here is my contact, reach out may be 
On Linkedin
https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a
*Linkedin has all the other contact details – Email and Phone, etc.
Slideshare
http://www.slideshare.net/RamkumarRavichandran/
Blog coming soon…
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Selling Text Analytics to your boss

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