The Future of Social Intelligence and Sentiment Analysis

© 2014 Converseon Inc. Proprietary and Confidential

Social Listening & IntelligenceThe Next Generation

June 21, 2016

© 2014 Converseon Inc. Proprietary and Confidential 2

Today’s speakers

ROB KEYFounder & CEO of Converseon@robkey

JOEL RUBINSONFormer Chief Research Office at The Advertising Research Foundation

Senior Strategist and Advisor with Converseon

@joelrubinson

Over a decade of social intelligence and consulting leadership. Starts where listening platforms stop.

“Top score consulting/research (5 out of 5), data processing and sentiment analysis (Forrester Wave Q1 2014 Enterprise Social Listening)

Forrester Research

Top Innovator (ConveyAPI technology) in Social Data Mining “for its ability to provide near human level precision at the speed and scale that only software can provide.” Dataweek


“Though social and digital media are rapidly transforming marketing and new tools emerge daily, in most firms the organization of the function hasn't changed in 40 years. How should marketers revamp…to meet the new realities?”The Ultimate Marketing Machineby Marc de Swaan Arons, Frank van den Driest, Keith WeedSource: Harvard Business ReviewPublication Date: Jul 01, 2014.

Social is a game changer

http://hbr.org/search/Marc%20de%20Swaan%20Arons/0

http://hbr.org/search/Frank%20van%20den%20Driest/0

http://hbr.org/search/Keith%20Weed/0


AOL “Buying at Speed” findings across 20+ product categories:• We live in an always connected world where

we browse daily…engaging in shopping behaviors without shopping purpose

• When we ARE shopping, up to 80% of purchases (depends on category) involve digital, lean-forward behaviors

A push-pull media world

McKinsey Consumer Decision Journey

AOL: Buying at speed: how technology empowers the always on shopper. Jan, 2014

http://advertising.aol.com/blog/buying-speed-how-technology-empowers-always-shopper


Consumers become social…with brands

Marketers begin social marketing and social business programs

Marketing research becomes “intrigued”

Social media moves from curiosity to quantified impact

5

The journey marketers and researchers have been on embrace social data

How can we engage in the conversation?

What insights can we gain from social media conversation

Are the data trustworthy?

Does social media have quantitative value

What can we learn about user interests?

How can we drive sales?

Curiosity

Core


Today We’re at a Tipping Point.

…Into your framework for brand success…into your brand research data strategies….to reinvent brand tracking…into your research modalities

….More and more leading brands are getting serious about integrating digital, especially social data – and capturing voice of customer in new ways


But there’s been a problem: too much social data has been a “coin flip.”

Greater insights must begin with better data


A case in point..


The Dirty Secrets of Social Listening (to date)

“Far from being unfixable, however, miscalculations in social-media analyses can already be fixed using methods developed to fix similar problems in studies in epidemiology, statistics and machine learning.”

- ComputerWorld


Reasons: There’s never been more noise and less signal

• How does Sprint analyze what is being said about their company?

• They can’t just search for the word “sprint”• It’s too common a word


Reasons: Booleans Ineffective

("enterprise 2.0" OR "Social Biz" OR "social business" OR "#SocBiz" OR "#SocialBiz" OR "#socialbusiness" OR SocBiz OR SocialBiz OR socialbusiness OR "enterprise social business" OR "enterprise social network" OR "enterprise social grid" OR "social CRM" OR "social BPM") AND ("activity stream" OR "activity streams" OR "application development" OR "best practices" OR "business model" OR "business models" OR "content management" OR "crowd sourcing" OR "customer service" OR "disruptive technologies" OR "disruptive technology" OR "human resources" OR "information technology" OR "next generation social" OR "open social" OR "organizational culture" OR "predictive analytics" OR "product lifecycle management" OR "social analytics" OR "social mail" OR "social portal" OR "social portals" OR "social commerce" OR "customer experience management" OR "innovation management" OR "social app" OR "social application" OR "social applications" OR "social apps" OR "social business app" OR "social business application" OR "social business applications" OR "social business apps" OR "social collaboration" OR "social computing" OR "social learning" OR "social media" OR "social network" OR "social networks" OR "social platform" OR "social platforms" OR "social software" OR "social softwares" OR "social technologies" OR "social technology" OR "soft ware" OR "tool kit" OR "unified telephony" OR "analytics" OR "app" OR "application" OR "applications" OR "apps" OR "benefit" OR "benefits" OR "blog" OR "blogs" OR "bookmark" OR "bookmarks" OR "BPM" OR "collaboration" OR "collaborative" OR "commerce" OR "communication" OR "communities" OR "community" OR "compliance" OR "innovation" OR "connection" OR "connections" OR "CRM" OR "crowdsourcing" OR "engagement" OR "ERM" OR "information" OR "intelligence" OR "intelligent" OR "interaction" OR "interactive" OR "internet" OR "interoperability" OR "learning" OR "mail" OR "marketing" OR "mobile" OR "network" OR "nimble" OR "optimization" OR "optimized" OR "organization" OR "organizational" OR "organizations" OR "platform" OR "platforms" OR "portal" OR "portals" OR "productive" OR "productivity" OR "risk" OR "risks" OR "sales" OR "software" OR "softwares" OR "technologies" OR "technology" OR "tool" OR "toolkit" OR "tools" OR "transparency" OR "transparent" OR "value" OR "voIP" OR "wiki" OR "collective intelligence" OR "customer self service" OR "social grid" OR "content" OR "social content" OR "insights" OR "social selling" OR "social communications" OR "collaborative communications" OR "human capital management" OR "business process management" OR "enterprise resource management" OR "enterprise resource planning")

15% Relevancy


• Is this tweet positive or negative?• It depends on whether you work for Verizon or Sprint• Often, your viewpoint is what makes something positive or negative, but most text

analytics systems are not up to the challenge

• “UberEats just launched in Ottawa but doesn't deliver to my house…I'm moving.”

Every Signal, Target and Entity Must Be Captured and Analyzed

Reasons: Listening Tools Have Lacked Context and POV


The same words mean different things

• An “unpredictable” movie is good, but “unpredictable” braking, not so much

• We like “small” cell phones but not “small” hotel rooms

Much conversation is implicit

• “I spent my entire lunch hour yesterday trying to exchange my American Airline ticket

Reasons: Poor Precision


It’s Simple Math

60% (precision) x 15% Relevancy X Low “Recall” = Problems


The New Era

2016: A New Era


New Deep/Machine Learning Approaches Unlocking Insights

Knowledge-based

Resources

Machine Learning

System Test Data

Training Data/Libr

aries

Semi Supervision: Keeps “humans in the loop” for continuous training

Customizable: Trains to domain and brands (“small” may be good for selling smartphones, bad for hotel rooms)

Accurate: Close approximation of human performance at scale (humans that agree with each other) – generally 90-95%

Scalable: Now allows the accuracy of human coding at large scale and speed

Vertical and Brand/Domain Specific

Custom Classifiers: Enables unlimited number of custom classifiers (intent, purchase phase, etc.)

Vertical and brand specific

High Relevancy and Recall: Isolates key data sets rapidly and at most detailed level.

Data approach as represented by Converseon’s ConveyAPI technology


• Relevance feedback allows you to be in control• 85% accurate

with less thanone hour’s work vs 15% by

boolean

No More Booleans: Machine Learning Captures Patterns That Go Beyond Keyword Specifics


• It had a slightly buttery off-note• I disliked the aftertaste• The taste was decidedly foul• I would never eat this again• The taste of it turned my stomach• This the worst tasting #@&*% ever

New Metrics: Intensity


New Metrics: Emotions

Plutchik Wheel of Emotion

Converseon analyzes emotion in social conversation


High Recall: Entity (Facet) Level Analysis


High Recall, Precision, Relevancy Example

negative4%

neutral15%

positive81%

Cool Whip Sentiment:Convey

negative3%

neutral84%

positive13%

Cool Whip Sentiment:Industry Standard

Industry standard sentiment analysis for Cool Whip over-indexes neutral, failing to represent true consumer opinions of the brand and identify brand advocates:

Industry Standard ConveySentiment Precision 38% 83%

Relevancy 46% 91%


New Metrics: Emotion and Intensity Analysis Example

Emotion analysis reveals the insight that Thanksgiving is when consumers turn to social media to share their feelings about Cool Whip, associating it directly with the joy of Thanksgiving dinner with family

N/ALow

IntensityMedium Intensity

High Intensity

joy 10% 45% 28% 14%disgust 0% 1% 1% 0%

anticipation 0% 1% 0% 0%

anger 0% 0.1% 0.3% 0.1%

sadness 0% 0.1% 0.1% 0%

trust 0% 0% 0% 0%

surprise 0% 0% 0% 0%

fear 0% 0% 0% 0%

OctNovDecJanFebMarApr

MayJunJul

AugSepOct

0 100 200 300 400 500 600

Monthly Expressions of Joy in Mentions about eating Cool Whip

2014

2015

Emotion and Intensity in Messages about Eating Cool Whip

“I'm so excited for thanksgiving just for pumpkin pie. With a giant tub of cool whip. And some apple cider. Maybe with a side of turkey.”

Source: Converseon analysis of public online records, November 2015.


Custom Classifiers Unlock New Insights

Fact / Opinion Product Application Consumer Intent

Brand Personality Unmet Needs Influencers

Patient Journey

Sentiment

Adverse Events


0%

25%

50%

75%

100%

5%

17%11%

35%

17%

11%

12%

6%

Customer Journey Analysis Example

Smartphone Category

0%

25%

50%

75%

100%

11% 14% 13%19%

54%35%

14%22%

41%

14%

22% 42%42%

27%

Fear

Distraction

Apprehension

Pensiveness

Acceptance

Trust

Serenity

Surprise

Interest

Sadness

Annoyance

Disgust

Anger

Anticipation

Joy

Multiple Categories

Problem

Recog

nition

Inform

ation

Search

Compe

titive

Evalua

tion

Purcha

se

Decisio

n Post

Purchas

eProb

lem

Recogn

ition

Inform

ation

Search

Compe

titive

Evalua

tion

Purchase

Decisio

n Post

Purchas

e

Emotions in the Purchase Funnel


Network Analysis is Being Blended with Social Intelligence for More Advanced Influencer Analysis

Twitter Network BetweennessBetweenness Centrality is a measure of ability to broker communication between individuals. Interacting with individuals who have high betweenness can expose your messages to more influencers.

Legend• Arrow: Indicates that one

influencer mentions another on Twitter.

• Node Size: Nodes increase in size with higher numbers of overall Twitter followers.

• Node Color: Indicates influencer’s centrality.

High Centrality Intermediate Centrality Low Centrality

© 25


New Metrics: Audience Analysis

WOMEN make up 70% of Cool Whip Advocates

80% of Cool Whip Advocates are WHITE

13-17 18-24 25-34 35-44 45+0%8%

73%

18%

1%

White79%

Black16%

Hispanic4%

73% of Advocates are aged 25-34

Food 58%

Religion 58%

Beverages 49%

Humor 49%

Nutrition 41%

Parenting 39%

Fitness 39%

School Life 38%

Health Care 38%

Family 37%

Cool Whip Advocates are interested in FOOD, FAMILY LIFE, and HEALTH

@AshleyKfit Ashley Kaltwasser AthleteTop interests: Fitness, Multimedia, Travel

@how2girl Courtney SixxModel and step-motherTop interests: Fashion, Family, Charity

@manwhohasitallComedian and fatherTop interests: Parenting, Nutrition, Beauty

Top Advocate Influencers are minor celebrities – comedian, model, athlete


Multiple Peer Reviewed Studies Have Shown Predictive Capabilities of Convey-Powered Data

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Professor Wendy Moe and David Schweidel, conducted analysis of social conversation versus offline brand tracking using Converseon data..

New WOMM Media Mixed Modeling Study (utilizing Converseon data)

95% precision + 85% Relevancy +High Recall


Enabling Mainstreaming and Integration

Business Outcomes

Brand Equity (Brand Preference &

Advocacy)Sales / Share Outcomes

Meaning & Engagement

Meaning (Language, Attributes,

Culture, Consumer Interest Profiles)

EngagementBehaviors

(Community Size & Behavior Toward

Brand)

Purchase Disposition

(Path to purchase)

Marketing Effectiveness Spending

Impressions(Amplification via Paid,

Owned vs. Earned)

Return(Marketing Return on

Investment)

Social Inputs/Classifiers:

Brand Equity: Consumer preference + advocacy; Social NPS (actual advocating versus intent)

Brand Meaning: language, topics, emotions, imagery, opinions, perceptions

Engagement Behaviors: Interacting with brand beyond functional purpose. (likes, retweets, etc.)

Purchase: Purchase funnel analysis; intent to buy


…Across Organizations

Product Launch, Unmet Needs, Segmentation, Outliers, etc

Brand TrackingInnovation

Customer Journey/Advocacy

Advanced Enrichment


3. All the way to bright

Measure and analyze social media to demonstrate its importance to the marketing organization Contribution to sales Predictive value Listening for the unexpected Segment conversations by customer

groups Seek single truth Data not APP level!

Use social media as a way of transforming brand tracking• Lighten the survey load by

tracking brand beliefs via social

• Be agile…as the marketplace changes, no need to fear trend disruption from adding attributes

Turn social media into trustworthy information• Establish rigorous standards

for determining conversation relevance and sentiment

• Use the same data for modeling, KPIs, and brand tracking


Thank [email protected]

@robkey

The Future of Social Intelligence and Sentiment Analysis

Data & Analytics

Transcript of The Future of Social Intelligence and Sentiment Analysis