Research Update: Navigating Digital Analytics for Social Marketing

 Image from www.pixabay.com labeled for noncommercial reuse

Image from www.pixabay.com labeled for noncommercial reuse

In 2012, an online survey by the Pew Research Center showed that 99% of the 1,244 arts organizations participated in the survey have their own website, 97% have a social media presence on Facebook, Twitter, YouTube, Flickr or other platforms, and 56% have profiles on between four and nine social media sites.

Does a pervasive online presence mean a strategic use of digital technology? Probably not. Three years later, arts organizations were still slowly grasping the importance of digital analytics for their social media efforts.

According to the 2015 Arts Industry Digital Marketing Benchmark Study on over 130 arts organizations surveyed by Capacity Interactive, a digital marketing consulting firm, only 41% of organizations checked their social media insights at least once a week. Among those relatively digitally skilled arts organizations who were surveyed, Facebook was the primary platform to “post and promote video”. (Note the survey pool was not randomly selected in order to reflect the reality among most arts organizations, who might not even have the resources to respond participate in a survey by a 3rd party.)

  Source: 2015 Arts Industry Digital Marketing Benchmark Study, June 2016,    Capacity Interactive

Source: 2015 Arts Industry Digital Marketing Benchmark Study, June 2016, Capacity Interactive

82% of the arts organizations participating in the Pew survey used social media to “engage with audiences before, during, and after events”. If social media has so much potential, can we as arts managers afford to let go the social data that is constantly accumulating on the web? In a social marketing report conducted by Forrester Consulting in 2014 on companies with revenue of one billion dollars or greater, “nearly three out of four enterprise companies use data and insights to identify social marketing strategies that influence customers.” Additionally, in an article published by Mckinsey, corporations are tapping into the social data to “draw strategic meaning from social-media data and develop a "social intelligence". Although these approaches are more likely relevant to Fast-Moving Consumer Goods (FMCG), how can a local community-based arts organization draw insights from the information gathered on social media when we don’t have the luxury to rely on market research firms?

Clearly, there is a dire need for effectively using analytics tools to direct organizational social media efforts.

In the following pyramid of web analytic data tborrowed from Eric Peterson’s Web Analytics Demystified, information on the uniquely identified users is the most valuable data but also difficult to obtain. Data on unique visors will suffice for the purpose of analyzing engagement with our potential audience.

  Source: The pyramid model of web analytics data, Page 57, Chapter 4, Web Analytics Terminology

Source: The pyramid model of web analytics data, Page 57, Chapter 4, Web Analytics Terminology

Facebook is most effective in gathering data on unique visitors as it records actions to users instead of cookies. For example, when a user is signed in to his or her Facebook account, we can track metrics in aggregate terms across all browsers and devices, and then target the segment of consumers that user belongs to. Forrester Consulting also discovered that volume metrics (fans, followers, community members) and engagement metrics (comments, responses, sharing/shares) are the two most commonly used sets of metrics to measure the business value of social marketing efforts by many of the world’s largest companies.

  Source: The 2014 State of Enterprise Social Marketing Report, Page 17

Source: The 2014 State of Enterprise Social Marketing Report, Page 17

Conversion rate has always been a challenge to measure as its’s hard to gauge how many impressions it takes for a customer to take action. But why do conversion rate discrepancies occur between Google Analytics and other social analytics, such as Facebook Insights? Google Analytics relies on cookies, which are unique to each web browser used by the person. It also offers Multichannel Funnel reports to measure and compare attribution from the interaction among various channels and campaigns. Check out the short video below on how to set it up:

If you don’t want to go through that, another way to cross-reference Facebook Insights and Google Analytics is by integrating the data gathered from Facebook Insights and Google Analytics. We can fine tune our social marketing efforts on Facebook by investigating the traffic Facebook sends to our website, assuming that driving Facebook users to the website is one of our social media goals. In the following demonstration, I will start with who the audience members are on our Facebook page and website respectively:

Facebook Insights provides demographic data on the reach and engagement. We can cross check this information on Google Analytics to see who is most likely to click through on Facebook and continue that engagement.

Comparing to the demographic information on Google Analytics, we can see that even though males aged between 18-24 consistently have a high reach and engagement rate on Facebook, the website sessions from that segment are relatively low. Male Facebook users aged between 35 - 44 have a lower reach and engagement rate than those of female Facebook users aged between 25 - 34, but they are much more likely to continue browsing the website than the younger female Facebook users.

This kind of structured data is relatively to draw insights from, especially when information of a relational database is organized on the aggregate level. However, “human-generated and people-oriented content”, such as social media status, message board thread, blog posts, and web search histories are unstructured, as Darin Stewart explained in his blog post “Big Content: The Unstructured Side of Big Data.” Unstructured data “can be a direct line into the hearts and minds of customers. Blogs, tweets, comments, and ratings are a reflection of the current state of public sentiment at any given point in time.”

As I continue this research, I will dive deeper into unstructured data, such as investigating the dynamics surrounding a potential audience’s interaction with a website after clicking through from another social media sites. Additionally, I will look into unstructured data available on social media and provide insights relevant to arts organizations.

Are you curious about how to identifying arts consumers and their conversation online? Or about how to use proprietary software and open-source software to analyze unstructured data for actionable insights? Comment below if you are interested in these or any other web analytic topics, and stay tuned!