The everyday experience and touches of customers to your brand constitutes a significant business driver. This study demonstrates how a special metric of this customer-experience, the SEI, can be used in predictive models to measure, monetize and derive significant insights into how your customers define your brand

1. Measuring the Customer
Experience with Social Media
New Developments in Measurement and Analytics

2. Measuring the Customer Experience is Essential
1. It has been found the be the largest single business driver for many
brands
2. Measurement of this experience is considered to be essential for firms
who aspire to be “customer centric”.

3. Millions of social media comments, all reflecting real brand-
customer experiences. As Jeff Bezos said: “Your brand is
what people say about you when you’re not in the room.”
United Airlines is
never on-time and
their service sucks
I love drinking
Coke with pizza!
My iPhone
is an essential
part of my life!
Progressive has the
cheapest insurance
but their claims service
Is terrible!
I bought a Maytag
at Lowe’s and it
cleans like no other
My Honda CRV is
great on gas
economy!
For health reasons
I have cut back
on Diet Coke
The Porsche 911
Is the sexiest
car on the planet!
I keep getting
dropped calls on the
Sprint network!
I love how my son
plays with his Lego
blocks

4. A Unique Way to Mine Social
Conversations
4
Stance
Shift
Syntax &
Structure
Tonality &
Sentiment
Experiential
Statement
Custom
Dictionary Context
Personal
Emotional
Customer Experience
• Leverages 30+ rules of language through a ‘scoring algorithm’ that turns textual data into a scaled
metric called the Semantic Engagement Index (SEITM)
• Is built upon a validated Linguistics approach known as ‘Stance Shift Analysis’
• Takes into account several critical components of conversations usually ignored
• Captures and measures
the value of the customer
experience
• Links closely to sales -
represents brand health
• Uncovers the Why’s and
the underlying drivers
both positive and negative

5. I just got my cool new iPhone from BestBuy,
however, I keep getting dropped calls on the
Brand X 4G network
Positive
Negative
Flag Brands & Relative Importance
Custom coding
Engagement
5
UNIQUE BLA VALUE
1. Evaluate the Entire Conversation
2. Account for Context
3. Adapt to Industry Language, Terms
4. Adjust to Channel Communication (Facebook, Twitter, specialist forums, blogs)
Leveraging social media is about building a
metric based on linguistics principles
Teasing out the nuances of language
Transitional
word (Shift in
Stance)

6. From Millions of Cleaned
social media
Conversations
Powerful social insights on Themes
and topics that are most important
to consumers.
Small Pepermint Afternoon Snack 12Pack
Great Deal Breakfast yum Large
Miss it Get me one Orange on sale
Morning Half Priced got coupon Drive Home
Vanilla Mocha 8 Oz need a hit
Small Pepermint Afternoon Snack 12Pack
Great Deal Breakfast yum Large
Miss it Get me one Orange on sale
Morning Half Priced got coupon Drive Home
Vanilla Mocha 8 Oz need a hit
We Detect Thousands of interesting
“nodes” of Consumer information
Clear Themes
and Topics of
Importance
Emerge
Advanced Analytics to help drive
content strategy and measure social
ROI.
Our Supervised Learning Pattern
Detection organizes the nodes
Adding Structure to Unstructured Data: The Solution Path For Consumer Chaos

7. Fusing SEITM based language
measurement with
advanced analytics to
understand competitive
brand positioning, content
drivers, reputation and
essential elements and
structure of the customer-
brand experience.
Using known tools to listen
and monitor high level
consumer brand-experience
conversations.
Measure language based
on engagement and
importance through the
Semantic Engagement
Index (SEITM).
Listening,
Monitoring and
basic Sentiment
Measuring
Language for
brand insights
Social
Media Advanced
Analytics
Social
Monetization
Applying a trended SEITM
within Media Mix
Modelling to monetise
customer-brand
experience (earned social
media) alongside all other
media and quantify any
synergistic effects.
Extend the Value of Social Media Insights
BLA Social Insights, Analytics and ROI Framework
We will focus on this
specific application of SEI
today

8. Available Social Media “Sentiment Metrics” fall short as a tool
for measuring ROI, but the SEITM shows great promise
-20% 0% 20% 40% 60% 80% 100%
Sentiment Metric 1
Sentiment Metric 2
Sentiment Metric 3
Sentiment Metric 4
Sentiment Metric 5
Sentiment Metric 6
SEI™ POS/NEG RATIO
11.2%
3.1%
-2.3%
8.8%
21.2%
8.2%
83.1%
Figure 1: Compares correlation to sales of $6B client with SEI and sentiment
metrics for 6 leading social data vendors, there is a wide gap.
Sentiment Metric 1
Sentiment Metric 2
Sentiment Metric 3
Sentiment Metric 4
Sentiment Metric 5
Sentiment Metric 6
SEI™ POS/NEG RATIO

9. The correlation* to sales over time shows the SEI™ has Predictive Power
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SEI validation: four categories
Correlation = 86%
Correlation = 84%
Correlation = 81%
Correlation = 83%
Correlation = 83%
*Lead lag analysis has confirmed that causation is only one way – the SEI™ to a large degree is able to
drive hard commercial metrics.

10. SEI validation across ~ 20 diverse brands, both US and international.
Validated more than any other social metric
52%
53%
56%
57%
59%
68%
73%
74%
77%
79%
79%
79%
79%
81%
81%
84%
86%
86%
88%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Haircare Brand
Personal Care Brand 4
Personal Care Brand 3
Personal Care Brand 2
Personal Care Brand 1
DIY Retailer Brand 2
AVERAGE
Business Services
Hospitality Brand 2
Restaurant Brand 3
Cosmetic Brand
Hospitality Brand 1
Softdrink
Restaurant Brand 2
DIY Retailer Brand 1
Restaurant Brand 1
Telecom Brand
Movie 1
Movie 2
SEI/Sales Correlations
10

11. Recent Marketing-Mix Model cases
Customer experience (SEI™ earned media) as the largest sales driver
44.9%
9.1%4.0%2.2%1.8%2.3%
5.9%
29.8%
Brand Gamma Decomposition of Sales
Baseline
TV
Print
Radio
Owned Digital SEO
Paid Digital Mobile
Paid Digital Search
Customer Experience Earned
Digital Social SEI
54.1%
5.5%1.5%
4.4%
2.1%
2.8%
5.9%
23.7%
Brand Beta Decomposition of Sales
Baseline
TV
Print
Radio
Owned Digital SEO
Paid Digital Mobile
Paid Digital Search
Customer Experience Earned
Digital Social SEI
60.4%
4.5%
2.1%
3.3%
1.1%2.1%
5.3%
21.2%
Brand Alpha Decomposition of Sales
Baseline
TV
Print
Radio
Owned Digital SEO
Paid Digital Mobile
Paid Digital Search
Customer Experience Earned
Digital Social SEI
64.8%1.5%0.2%0.3%3.6%0.1%1.8%
27.8%
Brand Omega Decomposition of Sales
Baseline
TV
Print
Radio
Owned Digital SEO
Paid Digital Mobile
Paid Digital Search
Customer Experience Earned
Digital Social SEI
6Copyright 2015

12. Case 1: Defining the Coffee
Retailer Brand and Position

13. For a coffee retailer, we uncovered 26 “content drivers”, which are topical themes and
components of the SEI. We conducted CART regression analytics which arrays these
themes in order of importance for prediction of SEI. Of these 26 drivers, 18 were
beverage or food products, while 8 were topics related to the store experience. Our
findings reveal that the store experience were more important drivers than the products
and were a more important factor in defining the brand.
Insight & Outcomes
The key drivers to Positive SEI™ were:
1. A place to hang out
2. To meet people
3. Atmosphere
4. Beverage Products
The client developed a ‘2 for 1’
promotion to drive store level sales.
This was the most effective promotion
run on any product over the past 3
years, generating a lift in 3 weeks equal
to about 4% of total sales.
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Case 2: Key Content Drivers of Retail Sales
Positive Social
Engagement
100
Place to Hang
Out
211
Place to Hang
Out
83
To Meet People
325
To Meet People
188
Atmosphere
466
Atmosphere
288
To Meet People
229
To Meet People
85
Beverage A
271
Beverage A
74
Note: Separate analysis - Classification & Regression Trees (CART)

14. Brand Positioning Using Socially Engaged Chatter
Meeting Friends
Hanging out

15. Case 2: Social Content Drivers
for Brand Positioning

16. Case 1: SEITM & Marketing Contributions for Zip
78.6%
2.1%
6.8%
3.3%
3.0%
2.5%
2.4%
1.9%
1.1%
0.4%
23.5%
Zip Modeled Incremental Contributions
Baseline
SEI/Mktg Synergy
SEI-Social Media
Radio
POS Signage
TV
Digital Display
Sampling
Pub.Reltns
OOH
• Zip Situation: Zip (masked name) is an “instant” beverage launched by this
beverage retailer in 2009; and was a deviation from its natural brewed products. Zip was
one of the most successful new product launches in the last dozen years. Prior modeling
had shown that Zip actually generated a +3% lift to total retail sales. The successful launch
strategy was aimed at getting maximum trial and exposure with an extensive sampling and
early price promotions. The challenge in year two is to understand how to position the
brand and sustain growth momentum.
• Zip Marketing Contributions :
By modeling Zip using SEITM, we found that
the “buzz and advocacy” stimulated by its
marketing efforts drove almost 7% of its
volume and marketing efforts also helped
boost a sizable “synergistic dividend”.
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17. Case 2: Zip Brand Sales & SEITM Time Correlations
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
-
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
2/23/…
3/23/…
4/23/…
5/23/…
6/23/…
7/23/…
8/23/…
9/23/…
10/23…
11/23…
12/23…
1/23/…
2/23/…
3/23/…
4/23/…
5/23/…
6/23/…
7/23/…
8/23/…
9/23/…
10/23…
Zip Sales
Zip.SEI.Ratio
SEI Ratio Norm
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Tracking the SEITM showed a high correlation to Zip’s first year sales. This was clear evidence of a
powerful and effective effort to generate strong buzz and advocacy toward the brand, with a
strong linkage to sales. SEITM also shows a “leading indicator” relationship to sales.
Note plotted metric is ratio of Positive to Negative SEITM

18. Case 2: Content Motivation Drivers of Sales Conversion for Zip Powder
18
188
3,516
103 128
300
301
350
491
724
930
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
BaselineNetPositiveSEI
GreatAroma
YummyFlavors
GreatGiftIdea
Convenient
TastesGreatColdorHot
TastesGreat
GreatforTakingtotheOffice
TastesLiketheRealThIng
TotalNetPositiveSEI
Zip Powder All Social Channels Engagement Content Drivers
Further analytics of the “content drivers” of SEITM consumer engagement revealed key
drivers to be “tasted like the real thing” and was great for “taking to the office” and enjoying
that original taste of the parent brand. By focusing communications towards these benefits in
year 2, Zip managed to continue a strong 11% growth.
Current
Positioning
Desired
Positioning

19. Case 3: Scoring and Evaluating
Sports Sponsorships

20. We scored SEI for the sponsorships. By investing more in NFL Football and less on NASCAR and
Basketball, this company managed to accelerate YOY growth from 3 to +8% the following year
Example: Assessing Sport Marketing ROI
(65% of marketing budget)
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21.  Bottom-Line Analytics LLC is a consulting group focusing on a broad
portfolio of marketing analytics, including marketing optimization
modeling
 Our modeling experts have a total of over 100 years of direct experience
with marketing optimization modeling. This includes direct experience in over
35 countries and dozens of product categories.
 We are dedicated to the principles of innovation, excellence and
uncompromising customer service.
 Most important, however, we are dedicated to getting tangible and positive
business results for our clients.
ABOUT US

22. Full Service
Analytics
Capability
Social Media ROI
Marketing Mix Modelling
Pricing Optimization
Radial Landscape Mapping
Key Drivers Analysis
Demand Forecasting
Customer Satisfaction Modelling
Digital Performance Analytics Dashboards
Segmentation Analysis
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BLA is a trusted advisor to a wide array of clients
We believe in the continuous innovative application
of analytics to advance customer centric decision
making for improved business performance.

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BLA leadership bios
Michael Wolfeis CEO of Bottom-Line Analytics LLC in the USA. Michael has 30 years of direct
experience in marketing science and analytics both on the client and consulting side. On the former,
Michael has worked for Coca-Cola, Kraft Foods, Kellogg’s and Fisher-Price. He has also consulted
with such blue-chip firms as AT&T, McDonald’s, Coca-Cola, Hyatt Corp., L’Oreal, FedEx and Starbucks.
Michael has broad experience in marketing analytics covering marketing ROI modelling, social media
analytics, pricing research and brand strategy.
Masood Akhtar is the Bottom-Line Analytics partner in the UK and heads the company
efforts across EMEA. Masood is former Director of Analytics for McCann-Erickson and also has
worked for Mintel International Group, JWT, Costa Coffee, Coca Cola, Hyatt Corp. He is an
accomplished econometrician with extensive experience in marketing ROI analytics, marketing
research, market segmentation, social media analytics and marketing KPI dashboards.
David Weinbergeris CMO of Bottom-Line Analytics. David’s career has taken him to such
blue-chip firms as Coca-Cola, Kraft Foods, Georgia Pacific and the Home Depot. David’s consulting
experience has focused on such verticals as retailing, financial services, apparel, consumer products
and insurance. David’s has considerable expertise in the areas of customer analytics, life-time
value, shopper marketing, social media, brand strategy, segmentation and marketing ROI analytics.
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