In her presentation, University of Maryland's Marketing professor and Director of the MS in Marketing Analytics, Wendy Moe, discusses current ways we measure and track social media, what is wrong with these common ways, and what we can do to fix them. She goes over that you need to track your brand behavior in order to accurately track your social media.

1. SOCIALMEDIA.ORG/SUMMIT2013ORLANDO
Social media intelligence
WENDY MOE
UNIVERSITY OF MARYLAND
DECEMBER 9–11, 2013

2. Social Media Intelligence
Wendy W. Moe
www.wendymoe.com
@wendymoe

3. Current practices

4. What’s wrong with these practices?
• Scalability
• Analyst bias
• Venue effects
• Selection effects that favor buzz-worthy topics
• Social dynamics that favor the extreme
• What exactly does the of number of mentions or average
sentiment mean? (correlation with offline survey = .008)

5. 1. Understand the behavior
2. Implications for observed metrics and trends
3. Integrating social media with traditional sources of market intelligence
Social Media Intelligence

6. Breaking down the behavior
WHO?
WHAT?
WHERE?
ONLINEOPINION • Posters versus lurkers
• Behavioral biases in posting
decision
• Expressed sentiment (versus
true underlying opinion)
• Topics vary in terms of their
“buzz-worthiness”
• Venue differences lead to
venue effects

7. Behavioral Biases
Pre-Purchase
Evaluation
Purchase Decision and
Product Experience
Post-Purchase
Evaluation
Incidence
Decision
Evaluation
Decision
Posted Product Ratings
EXPERIENCEMODELINCIDENCE&EVALUATIONMODELS
SELECTION
EFFECT
ADJUSTMENT
EFFECT
What influences posting behavior?
• Opinion formation vs. opinion
expression
• Opinion formation, in theory, is a
function of satisfaction
• Opinion expression is subject to a
variety of biases and dynamics
– Expert effects
– Multiple audience effects
– Bandwagon vs. differentiation
• Example: Opinion polls and voter
turnout

8. How does dynamics affect what we observe?
Variance
Average
Activists
Post frequently
Attracted by lack of consensus
More negative
Variance and volume make them more negative
Low Involvements
Post infrequently
Deterred by lack of consensus
More positive
Variance and volume make them more positive

9. Brand Tracking
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ProportionofPositiveComments
Observation Month
Blog
Forum
Microblog
Aggregate
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
DistributionofComments
Observation Month
Blog
Forum
Microblog
Other
Venue Correlation
Blogs .197
Forums -.231
Microblogs -394
Average .008
Correlation with offline
brand tracking survey

10. What influenced expressed sentiment?
General Brand Impression (GBI)
Venue Venue-specific dynamics Message topic

11. Product and Attribute Effects
How much variance exists across focal topics related to the brand?

12. GBI and Offline Brand Tracking Surveys
• Potential for GBI as a lead
indicator
• Correlation with survey (t)
– GBI = .376
– Avg sentiment =.008
– Blogs = .197
– Forums = -.231
– Microblogs = .394
• Correlation with survey (t-1)
– GBI = .881
– Avg sentiment = .169
– Blogs = .529
– Forums = .213
– Microblogs = .722
8.75
8.8
8.85
8.9
8.95
9
9.05
9.1
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6 7 8 9 10
AverageSurveyResponse
GBI
Month of Overlap Period (t)
GBI in month t-1 Survey in month t

13. GBI and Stock Price
(DV=monthly close)
Coeff StdErr p-val
Constant -69.045 34.044 0.070
S&P* 0.104 0.031 0.008
GBI(t) -16.695 10.324 0.137
GBI(t-1) 30.693 10.375 0.014
Adj R-sq .475
* Closing price in month
S&P Index
GBI
Lagged GBI

14. Using Social Media Intelligence for Brand Tracking
• Significant social dynamics exist
• Encourage a variety of opinions to include the moderate majority.
This encourages discussion and insulates impact on sales.
• Social media behavior varies across venue formats
• Monitor multiple sources of SM data
• Account for source effects in SM data
• Neglecting to account for venue can bias sentiment inferences
• Prevalence of attributes mentioned in social media depends on
venues monitored
• Potential to use social media for market research
• Adjusted measure (GBI) can serve as lead indicator
• Model-based measure vs. disaggregate metrics

15. Questions?
www.BuildYourSMI.com
Coming in January 2014
wmoe@umd.edu
@wendymoe
www.wendymoe.com

16. SOCIALMEDIA.ORG/SUMMIT2013ORLANDO
Learn more about past and
upcoming events
DECEMBER 9–11, 2013
SOCIALMEDIA.ORG/EVENTS