Articles

Becoming A Social Media Marketing Superhero

Posted by Jim Iott on Sep 16, 2016 2:30:22 PM

Are you hoping to become a social media marketing superhero? The importance of social media as an engagement channel and rich resource for customer insights is well established. Brands have been on a continued quest to harness the underlying value contained within the considerable volumes and varieties of social media and other digital data sources. Increasingly, they are turning to predictive analytics to uncover customer behaviors and sentiment, identify emerging trends, and ultimately predict consumer intent and future behavior. Embracing predictive analytics will not turn you into a social media marketing superhero overnight - so don’t show up to work in your tights and cape yet. It is however, an important next step in the evolution of your social media marketing strategy.

 

Big Numbers Are Drawing A Big Response

Social media participation is continually expanding and becoming a prevalent engagement channel:

  • There are more than 2 billion social media users worldwide. 
  • Of the more than 320 million people living in the United States in 2015, more than half or 186 million people are active social media users, of which 160 million are active on mobile social networks.
  • 156 million users are on Facebook, while LinkedIn, Twitter and Instagram boast 107 million, 52.9 million, and 60.3 million users respectively. 
  • On average, active users spend 2 hours and 43 minutes on social media sites, about an hour less than they spend watching television (3 hours and 40 minutes on average).*1

With the growing impact of social media, investments in predictive analytics have been increasing as well.

  • According to a recent IDC study, growth in applications incorporating advanced and predictive analytics will accelerate in 2015 - growing 65% faster than apps without predictive functionality.*2
  • Within major enterprises, the most preferred solutions were data analytics at 65%, and, trailing close behind, visual dashboards (47%), data mining (43%), data warehousing (40%), and data quality (39%).*3

 

Social Media Monitoring and Predictive Modeling

In their simplest form, predictive models use historical data and statistics to model and predict the future. By analyzing social media tweets, likes, shares, and other user-generated content, marketers are able to monitor customer behavior and sentiment and predict future behavior.

Let’s take a simple example: Bob and Betty Jones just found out they are expecting a baby. The newly expectant parents share the exciting news through their social networks. The Jones’ are now in the market for baby furniture, strollers, clothing, and all kinds of baby-related goods and services. Predictive analytics tie the social media activity such as baby-related posts, hashtags, and phrases, etc., with the future buying behavior, enabling brands to identify and proactively engage with segments of similar, expectant couples.

Using social media monitoring to predict behavior is especially useful under the following scenarios:

  • Life Stages: Life stage events such as college matriculation and graduation, relocations, marriage, children, and retirement, all trigger dramatic changes in product and service needs, brand affiliations, and purchase behaviors. These same life events also trigger unique social media activity that can be identified and acted upon.
  • Brand and Product Sentiment: Social media analytics can also be used to monitor conversations to determine whether sentiment is positive, negative or neutral concerning your brand, products or services, or customer experiences. These consumer opinions provide valuable insight and can have significant implications regarding band positioning and marketing, product and service modifications, and service standards.
  • Product Launches: Social media analytics can be used to identify potential customers and influencers interested in or in need of specific products or services, and most receptive to promotional campaigns.

 

Taking Social Media and Predictive Analytics To A Superhero Status

Like a young Clark Kent growing up in Smallville, Kansas, many companies are still in their adolescence when it comes to social media and predictive analytics, and have formidable challenges to overcome before achieving the status of superhero marketers.

Predictive models improve as the number and variety of data inputs increase.  Unfortunately, many marketers continue to struggle with unifying social media and other digital data with siloed CRM data and 3rd party life stage and demographic data. These challenges tend to be complex and may require changes in corporate vision, structural re-alignments, and investments in new technologies and skillsets. 

In the long-run though, these investments will enable companies to greatly improve the ability to predict customer behavior and sentiment, anticipate important trends, and engage customers in real-time social media strategies. Beyond social media marketing, these changes will lead to overall improvements in customer engagement and marketing program outcomes. These changes also enable more agile, omni-channel marketing strategies, and more attributable measurement – and will put you well on your way to social media superhero status and beyond!

References:
(1): Statisitca: Statistics and Market Data on Social Media & User-Generated Content, http://www.statista.com/markets/424/topic/540/social-media-user-generated-content/
(2): IDC FutureScape: Worldwide Big Data & Analytics 2015 Predictions, December 2104, http://www.idc.com/getdoc.jsp?containerId=prUS25329114
(3): IDG Enterprise, 2015 Big Data and Analytics Survey, March 2015. http://www.idgenterprise.com/report/2015-big-data-and-analytics-survey