Social Media Performance Model Poster
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 UNIQUE VISITORS 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
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Transcript of Social Media Performance Model Poster
306% increasein 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 QueStionSCan 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 VisitorsTotal Unique Visitors
Page views Pageview Total Pageviews
Conversions Downloads No. of Downloads
FrequencyFb Updates/Posts
No. of Updates/Posts
Spredfast
Tw Tweets No. of Tweets
Engagement
Fb LikeClicks 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 AudSize 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 trafficAs affected by Facebook and Twitter
Beta SE t Prob
Soci
al
MeDi
a ch
anne
lS 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
ViSi
torS
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
uniq
ue Vi
Sito
rS
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
Page
VieW
S 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 knowl edge, 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
