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Social Media Performance Model Poster

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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.

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Social Media Performance Model Poster

  1. 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