This is a seminal study that provides substantial evidence that social media is not an upper funnel influence on brand awareness, but rather a metric reflecting the customer-brand experience which has a direct impact on customer purchase behavior.

1. Monetising Social Media
Engagement and theMeasurement
Measuring the Value of the
Customer Experience

2. FORWARD
• Contained in this document is valid proof that
social media brand conversations can be
quantitatively measured and monetized. This
represents a significant development and
innovation in marketing measurement. It
monetizes the customer experience and
brings tangible and measureable value to the
voice of the customer.

3. THE AGENDA
• Who we are
• SocialMedia Measurement: Search for a New & Valid Approach
• Leveraging SocialMedia Engagement Metrics for Deeper Insights
– Monetizing Social Media Word-of-Mouth & Marketing: The Marketing
Mix Model
– The volumetric impact of Positive and Negative Buzz
– Determining Which Social Channels are Driving Brand Performance
– The Key Factors or Reasons Driving Consumer Engagement with Your
Brand
– Using Social Metrics to Monetize the Value of Marketing Sponsorships
– Valuing Facebook marketing campaigns
– Leveraging Social Media Analytics to Find Out Why Consumers Use
Your Brand
• SocialMedia Measurement and the Blue Ocean?

4. A NEW APPROACH TO SOCIAL MEDIA MEASUREMENT: THE
SOCIAL ENGAGEMENT INDEX OR SEI
• This analysis will trace the development and value of an approach for measuring consumer
engagement on social media. This approach is called the “Social Engagement Iindex” or SEI.
• This metric is based on an algorithm developed with the assistance of Dr. Boyd Davis,
Professor of Applied Linguistics at the University of North Carolina and Dr. Peyton Mason,
CEO of Next Generation Marketing Insights The involves four steps and it leverages
linguistics science heavily.
1. First we scrape large quantities of social media conversations filtered for topics on the
specific brand of interest. The data source is social media sites like Twitter, Facebook
and Blogs; but this same method has also been used to “score” customer reviews
from various hospitality review sites like Hotels.com and Trip Adviser.
2. We next divide these conversations data into positive and negative sentiment
conversations.
3. Then we apply the algorithm which quantitatively “scores” each of the positive and
negative groups along the two linguistic dimensions of “emotional affect” and
“personalization”. This scoring algorithm applies the science & rules of Linguistics.
4. We finally time-code each conversation and aggregate into a time series metric.
5. This approach differs from standard sentiment metrics, text analytics and even
natural language processing because 1) it evaluates the entire conversation not just
key word, 2) it evaluates conversations within context and 3) it is based on language
structure and rules, not just counting words.

5. DERIVING THE SOCIAL ENGAGEMENT IINDEX: THE NUTS & BOLTS
5
1. SCRAPE ALL SOCIAL MEDIA CHANNELS FOR
BRAND CONTEXT CONVERSATIONS, E.G.
BRAND MENTIONS
2. DIVIDE INTO POSITIVE & NEGATIVE REVIEW
GROUPS. FURTHER DIVIDE INTO KEY TOPICS.
3. DERIVE ENGAGEMENT INDEX BY
CONVERSATION FROM 30 LINGUISTIC RULES
TO “SCORE” MINED BRAND/TOPIC SOCIAL
MEDIA CONVERSATIONS
4. TIME CODE BYWEEK & AGGREGATE
METRICS
POSITIVE
REVIEWS
NEGATIVE
REVIEWS
POSITIVE
SCORES
NEGATIVE
SCORES
SOCIAL MEDIA
CHANNELS
LOW MED HIGH
High
Med
Low
EMOTIONAL SCORE
PERSONALIZATION SCORE
8
7
6
5
4
3
2
1
0
SEI SCORE
Per 1
Per 2
Per 3
Per 4
Per 5
Per 6
Per 7
Per 8
Per 9
Per 10
SEI Ratio
* Next GenerationMarketing Insights, 2011

6. THE SOCIAL MEDIA MEASUREMENT LANDSCAPE AND THE
VALIDATION TASK
• Presently, there are 3 approaches to social media measurement:
– Standard Sentiment Method. This approach classifies conversations as being primarily
positive, neutral or negative with respect to the subject. Metrics are aggregated purely
by word-count or frequency
– The Influence Approach. This approach scores conversations based on the influence of
the author (number of friends, connections, etc.). Klout score is an example.
– The Language or Linguistics Method. This approach uses Linguistic science or rules to
classify sentiment and score the conversations based on some measure of emotion or
intensity from the language & context of the conversation. This is the Social
Engagement IndexMethod we have developed.
• When comparing methods, we were limited with comparison on the Influence Approach,
since this is an individual scoring algorithm and there is no aggregate time-series sentiment-based
metric to correlate to brand sales.
• The key to understanding the attraction of any metric for statistically measuring its impact on
a brand begins by looking at simple correlations to brand sales. In this context, we took one
client brand and compared Standard Sentiment Metrics to the Social Engagement Index in
terms of their core statistical correlation to brand sales over time.

7. AVAILABLE SOCIAL MEDIA “SENTIMENTMETRICS” FALL SHORT AS A TOOL
FOR MEASURING ROI
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.
SOCIAL ENGAGEMENT INDEX POS/NEG RATIO
METRIC 6 POS/NEG RATIO
METRIC 5 POS/NEG RATIO
METRIC 4 POS/NEG RATIO
METRIC 3 POS/NEG RATIO
METRIC 2 POS/NEG RATIO
METRIC 1 POS/NEG RATIO
8.2%
21.2%
8.8%
3.1%
11.2%
-2.3%
83.1%
-20% 0% 20% 40% 60% 80% 100%
METRIC 1 POS/NEG RATIO
METRIC 2 POS/NEG RATIO
METRIC 3 POS/NEG RATIO
METRIC 4 POS/NEG RATIO
METRIC 5 POS/NEG RATIO
METRIC 6 POS/NEG RATIO
SOCIAL ENGAGEMENT INDEX
POS/NEG RATIO

8. CASE STUDIES: THE SEI METRIC AND LINKS TO BRAND SALES
• To fully leverage the SEI for our clients, the task is to understand its
impact on their business.
• To do this, we do exploratory analysis to see how relevant the
metric is to the customer demand of a number of clients.
• Then we utilize the SEI within a full marketing response (aka, mix)
model in order to not only understand its impact on the business,
but also how it interacts with and is affected by direct marketing.
• First, however, we compare the customer SEI metric to 3 clients’
customer demand over time.
– Our comparisons come from three different clients, one in the food &
beverage industry, a telecom client and one from the hospitality
industry.
– The specific SEI metric we use here, and in our model, is the SEI ratio
of positive to negative tonality conversations.

9. IN THE TELECOM INDUSTRY, THE SEIsm IS STRONGLY CORRELATED TO
NEW CUSTOMER ADDITIONS (85%)
600
500
400
300
200
100
0
250
230
210
190
Customer Adds Index New Customer Sales Index SNI Ratio Index
170
150
130
110
90
70
6/16/2009 7/13/2010 8/9/2011
SEI Positive/Negative Index
9
TELECOMNEW CUSTOMER ADDITIONS

10. IN THE FOOD & BEVERAGE INDUSTRY, THE SEIsm MIRRORS
COMPANY SEASONAL PATTERNS (84%)
10
TOTAL FOOD & BEVERAGE SALES
250
200
150
100
50
0
160
150
140
130
120
110
100
90
80
07/08/08 08/04/09 08/31/10
Retail Sales Index
TOTAL Retail.Sales SNI Positive/NegativeRatio
SEI Positive/Negative Index
SEIsm IS A REFLECTION OF TOTAL "WORD-OF-MOUTH" AND A PROXY FOR CONSUMER GOOD WILL

11. FOR A HOSPITALITY CLIENT, SEIsm IS A STRONG CORRELATE TO NEW
BOOKINGS (77%)
TOTAL HOSPITALITY BOOKINGS
160
140
120
100
80
60
40
20
-
140
120
100
80
60
40
20
-
1/6/2009 1/6/2010 1/6/2011
SEI Positive/Negative Index
Bookings Index
Bookings.Index SNI Positive/Negative Ratio
THE METRIC USED WAS “ONLINE” REVIEW SITES FOR HOTELS, RESORTS AND CRUISE LINES. WE
FOUND THE METRIC TO BE A PROXY FOR CUSTOMER SATISFACTION 11

12. MONETIZING THE VALUE OF SOCIAL MEDIA THROUGH
MARKETING-MIX MODELS
• To fully leverage the SEI for our clients, the task is to understand its
impact on their business.
• By incorporating SEI metrics into marketing response (aka, mix) models,
we can:
– Come to a better & more precise understanding of how social media buzz
affects a client’s business performance
– Understand the impact and interactions of the client’s marketing and media as
it affects social media conversations about their brands.
– Provide strategic guidance as to the most effective ways for monitoring and
managing social media conversations on brands
• Our task is to build a “nested model” where SEI is both a dependent and a
predictor variable. In the former, SEI is a function of all media & marketing
efforts. In the latter, sales is a function of all media & marketing plus the
SEI.
– This approach not only enables us to understand the impact of Social Media
Word-of-Mouth, but also the influence of media and marketing on social
media brand conversations.

13. A TWO-STAGE MODEL IS USED TO QUANTIFY THE DIRECT IMPACT OF
MARKETING AND SOCIAL MEDIA ACTIVITIES ON SALES
1. Model the Social Engagement Iindex as a function of key marketing & media
drivers
2. Model retail time-sales as a function of media & marketing drivers plus the SEI
Total Retail Sales Contribution Sub-model
Net Contribution
of Marketing
2%+6%
+6% +13% = 27% Social Media
56%
7%
2%
5%
11%
19%
35%
Conversations
(SEI)
Base Sales Conventional Marketing Campaigns Mkting campaigns on SM
13
19%

14. A KEY INSIGHT DISCOVERED IS THAT NEGATIVE-SENTIMENT
CONVERSATIONS HAD A SIGNIFICANTLY GREATER EFFECT THAN
POSITIVE ONES
+4.4%
+16.5%
20%
15%
10%
5%
0%
Increase Positive Decrease Negative
Total Retail Sales Sensitivity of Response
(chg in Sales vs chg in Sentiment components of "Engagement")
positive
ENSURE THAT YOUR BRAND IS REDUCING NEGATIVE TONED CONVERSATIONS
30%
20%
10%
0%
-10%
-20%
-30%
-100% -50% 0% 50% 100%
Sales Impact
Sales Impact
Change in "Engagement" by Tone
Absolute Response (Based on Standard
Favorable Move)
negative
14
(Assumes 100% Decrease in Negative is not realistic)

15. HOW SOCIAL MEDIA CHANNELS ARE DRIVING BRAND
ENGAGEMENT AND SALES
15
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
130
120
110
100
90
80
70
60
2009 2010 2011
Engagement Score
Sales Index
Sales & Total Social Network Engagement Drivers by Channel
Facebook Twitter Boards Blogs &Groups Sales Index

16. WHAT ARE THE “STORIES” OR CONTENT SHARED ABOUT YOUR BRAND
16
THAT IS MOST RELEVANT TO DRIVING REVENUE?
The key topics/subjects of conversation about brands are scored. we can understand the reasons
behind brand social media performance and can quantify these in terms & monetize their value
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
130
120
110
100
90
80
70
60
2009 2010 2011
Network Index Score
Sales Index
Engaging Social Topics for a Restaurant Chain
Promotion A Place to Hang Out To Meet Friends and Associates Product B Product A Sales Index

17. 35
30
25
20
15
10
5
0
UNDERSTANDING THE KEY EVENTS AND FACTORS DRIVING
SPONSORSHIP AND BRAND ENGAGEMENT
SEI has been successfully used to measure and monetize the value of sports sponsorships
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Super Bowl
Sponsorship Engagement
NFL-Football
Sponsorship Engagement
PGA Golf
Sponsored PGA
Tournament

18. VALUING AND MONETIZING FACEBOOK MARKETING CAMPAIGNS
• Because our Social Engagement Metric is part of a larger model, we can value events
such as Facebook Campaigns. As shown below, the net consumer engagement is not
necessarily the same as the volume of comments or the number of Likes.

19. DETERMINING THE RELATIVE IMPORTANCE OF KEY CONCEPTS OR
SOCIAL CONTENT IN DEFINING THE BRAND
Most Important Positive Drivers:
Place2HangOut
>5.46= 211
1. The Brand & Place
9.1%
2. For Meeting People
3. The Beverages
Positive SEI
3.93 = 100
4. The Store Atmosphere Place2HangOut
<5.46 = 83
91.9%
ToMeetPeople>
9.43 = 325
2.6%
ToMeetPeople<
9.63 = 188
6.5%
Beverage>14.0
= 466
0.6%
Beverage<14.0
= 288
1.9%
To Meet People
>5.4 = 229
3.8%
To Meet People
<5.4 = 85
85.5%
Beverage
>6.4 = 271
7.7%
Beverage
<6.4 = 74
77.8%
Place2HangOut
>3.6 = 126
5.9%
Place2HangOut
<3.6 = 76
71.9%
Atmosphere
>5.2 = 211.1
1.6%
Atmosphere
<5.2 = 67
70.3%
These starts show an average SEI score of 100; and each level indicates a higher or lower SEI based on an SEI score for a topic. The percent represents the percent of the sample in
each segment.
19

20. THE “BLUE-OCEAN” OF SOCIAL MEDIA
MEASUREMENT: KEY INSIGHTS
• By linking a metric of “Social Network Engagement” to client sales, we have shown that this
approach shows great promise as a diagnostic for understanding social media’s impact on a client’s
business by including it as an input into marketing response (aka,mix) models. We have thus
succeeded in measuring and monetizing the impact of social media on consumer demand.
• We have also shown that these data provides deep insights about what moves brand performance
in the market place, a new depth of understanding that could be considered a blue-ocean
innovation.
• Some of the key lessons that we have learned include:
– The impact of the SEI on brand performance tends to mirror the phenomena of “word-of-mouth”, which is a
known critical driver of most brands, but traditionally difficult to measure.
– Negative sentiment towards a brand have substantially greater impact on its performance than positive. It is
imperative that firms address expressed issues with these consumers and prevent the negative buzz from
going widely viral.
– For service based firms like the hospitality client, the social engagement from online reviews represents a
measure of customer satisfaction, which is a dominant driver of these businesses. The social engagement
metric here represents a promising tool for deriving customer service ratings for various business domains
from such sites as Yelp, Expedia and Trip Advisor.
– Our approach to Social Media Measurement provides a wealth of insights into why consumers buy your
brand, how consumers engage with your brand and sponsorships and what particular social channels are
most important in driving your brand. It monetizes the consumer experience.
– That the direct impact of SEI on business is large and significant. A brand’s marketing and advertising has
some effect on the SEI which in turn, affects sales.
– We learned that the value and ROI of marketing is greatly enhanced due to the indirect effect it has on sales
through its direct impact on Social Media Engagement (SEI )
– That our SEI metric is no Holy Grail, but it shows much promise in delivering un-matched insights on how
social media conversations have a direct and tangible impact on company performance.

21. ABOUT US
 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.

22. OUR EXPERIENCE

23. WAS THIS INTERESTING?
• Please contact for a direct discussion
• Michael Wolfe, Principal
Bottom-Line Analytics LLC
(o) 770.485.0270
(m) 678.314.8446
mjw@bottomlineanalytics.com