The Social Media Performance Model, as presented at McCombs Workshop on Social and Business Analytics. Dr. Gary Wilcox developed the Social Media Predictive Model (SMPM) that uses predictive analytical techniques to identify statistically relevant activity such as likes, clicks or community growth. The SMPM empowers marketing teams to make data-informed business decisions.

1. 306% increase
in conversions
with 1% increase in
Twitter Followers
Social Media Performance Model [SMPM]
The Relationship of Social Media Activity to Website Traffic & Conversions
Measurement of social media is critical to marketing. Yet, according to a recent
Forrester report, almost 66% of interactive marketers are not currently measuring
their social media marketing initiatives. The ability to draw actionable insights
through informed decisions is the most important aspect affecting the success of
social media marketing performance as it relates to business.
Helping to achieve this, the Social Media Performance Model (SMPM) was developed.
SMPM is set up as a predictive multivariate statistical model where multiple variables
are tested to understand social media performance. Using traditional media variables,
like reach and frequency, engagement variables are used to detect audience or
community response to social media content. This is highly valuable for companies,
especially those investing in social media.
Gary B. Wilcox, Ph.D. Department of Advertising & Public Relations, The University of Texas at Austin
Kristen Sussman, M.A. President & Founder Social Distillery, Inc. Austin, Texas
Provide an evaluation within a B2B environment of
a multivariate statistical model’s ability to measure
and determine the importance of key predictor
variables, within a social media environment.
Purpose
Research Questions
Can the SMPM measure reach, frequency
and engagement variables that are related to
consumer website traffic and conversions?
Can the SMPM provide a measure of the
importance of those variables?
Your SMPM uses daily social media activity
such as Facebook Posts, Tweets and Retweets,
among others, as predictive variables and
is dynamic in that any length of time can be
examined. The model tests for significant
relationships between outcome and response
variables. Outcomes, or goals of social media
communication, such as website pageviews,
visits, unique visitors and sales, are used as
response variables.
SMPM is applied to a company’s social media
activity and an analysis is done, isolating
predictor variables that are statistically related to
the response variables. Managing a company’s
social media presence by altering future content
messaging tactics is accomplished using output
results from the model. Digital behaviors change
over time. Therefore, an ongoing analysis is
required for the most effective social media
communication effort.
What Does SMPM do? How Does SMPM work?
Indicators Data Description Source
Visitors Visitors Total Visitors
Google
Analytics
Unique Visitors Unique Visitors
Total Unique
Visitors
Page views Pageview Total Pageviews
Conversions Downloads No. of Downloads
Frequency
Fb Updates/Posts
No. of Updates/
Posts
Spredfast
Tw Tweets No. of Tweets
Engagement
Fb Like
Clicks of Like
Button
Fb Comments No. of Comments
Fb Clicks Clicks of active link
Tw Mentions No. of Mentions
Tw Retweets No. of Retweets
Tw Replies Number of Replies
Tw Clicks Clicks of active link
Reach
Tw Mention Aud.
Size of mention
audience
Tw Retweet Aud
Size of Retweet
audience
Fb Fans No. of Fans
Tw Followers No. of Followers
Summary of Data Sources (Daily)
A one percent increase in Facebook Fans
was associated with a 3.9 percent increase
in website visitors whereas a one percent
increase in Facebook pageviews was
associated with only a 0.28 percent increase
in website visitors. In this case, an increase
of 76 Facebook Fans was associated with
an increase of 3.5 website visitors per day.
Likewise, a one percent increase in Twitter
Followers was associated with a 10.5
percent increase in website unique visitors
or in other words an increase of 142 Twitter
Followers was associated with an increase
of 8.5 unique visitors per day.
When examining the conversion data, Twitter
Followers exhibited the most important
relationship with the conversion downloads.
A 1 percent increase in Twitter Followers
was associated with a 306 percent increase
in conversions. In this case an increase in
2 Twitter Followers was associated with an
increase of 1.2 conversion downloads.
Website Visitor Traffic
As affected by Facebook and Twitter
Beta SE t Prob
Social
Media
Channels
Tw Followers 306.58 99.7275 3.07 0.0065
Fb Clicks 0.4690 0.1535 3.06 0.0068
R2 =.5834; MAPE=16.97; RMSE=.263
Conversions
Beta SE t Prob
Visitors
Fb Fans 3.9054 0.9008 4.34 <.0001
LI Clicks 0.0912 0.0401 2.28 0.0242
Tw Mentions 0.1626 0.0456 3.57 0.0005
Tw Replies 0.1578 0.0635 2.48 0.0141
Fb Pageviews 0.2778 0.0849 3.27 0.0013
R2 =.6056; MAPE=7.52; RMSE=.175
UniqueVisitors
Fb Fans 10.4987 2.9006 3.62 0.0004
LI Clicks 0.1039 0.0395 2.63 0.0095
Tw Mentions 0.1682 0.0442 3.81 0.0002
Tw Replies 0.1797 0.0611 2.94 0.0038
Fb Pageviews 0.2321 0.0824 2.82 0.0055
R2 =.6122; MAPE=7.40; RMSW=.0171
Pageviews
Fb Fans 3.1032 0.6855 4.53 <.0001
Tw Mentions 0.2370 0.0530 4.47 <.0001
Fb Pageviews 0.4429 0.0947 4.68 <.0001
R2 =.4975; MAPE=7.27; RMSE=.206
Website Traffic
The SMPM demonstrates the ability to identify the key predictor variables important within
a social media community providing communication strategists insight for future decision
making. Further, the accuracy measures provide a strong indicator of the SMPM’s measurement
precision across various key metrics.
The SMPM enables data driven insights that are used to effect business outcomes achieved
through social media. For example, the two most frequently occurring predictor variables, in this
case Facebook Views and Twitter Mentions, are used to develop more targeted strategies moving
forward. Using this knowledge, the B2B marketers place more resources and efforts using Facebook
targeted advertising spend to effectively reach a more relevant community. Further, the influencer and
advocacy strategies used across Twitter are repeated to effectively achieve more mentions, and in
turn strategically achieve more leads, during the next B2B campaign.
The SMPM demonstrates the ability to measure reach, frequency and engagement variables
associated with the specific website metrics. Using this knowl­edge, management of the content
marketing strategy and tactical implementation is shifted based on performance measure results.
Most importantly, as it relates to the B2B example within this study, the marketers used SMPM
resulting knowledge to develop marketing strategies and tactics for the company’s largest
annual event which also incorporated the most successful implications learned from the
content marketing campaign.
Conclusion
Introduction Results
+10.5%
with 1% increase in
Twitter Followers
+3.9%
with 1% increase in
Facebook Fans
+0.28%
with 1% increase in
Facebook Pageviews