20 Reasons Why Social Analytics Is A Nightmare — And What To Do About It

Social media managers and analysts have one of the toughest jobs in digital marketing today.

Imagine trying to be an expert in content marketing, acquisition and retention marketing, brand management, word-of-mouth, customer advocacy, paid media, reputation management and viral marketing — all across five different media channels and dozens of accounts. This is a day in the life of a social media manager.

Due to its complexity, social media is also one of the least understood of all marketing channels. Learnings around social media management are largely limited to high-level best practices that are not backed by numbers. Though progress has been made, there remains a mountain of challenges around building a solid social analytics practice.

Current Challenges Around Social Data

Here are some of the issues that make it difficult to analyze social media data:

  1. Proliferation Of Social Networks. Brands report using an average of seven social networks, with 15% of brands having a presence in ten or more.
  2. Lack Of Consistency In Metrics Across Networks. What is the value of a given social interaction on a given social network? Consider Facebook Likes, Comments, and Shares vs. Twitter Favorites, Replies, and Retweets.
  3. Management Of Multiple Social Properties. According to surveys, companies manage an average of 178 corporate-owned social media properties.
  4. Difficulty In Aggregating Data Across Properties. Even simple reporting can be time-consuming, depending on the number of channels, metrics, and properties you need to juggle.

The challenges above deal with data fragmentation and complexity. The good news is that this class of problems can be largely alleviated by using a tool or platform to automatically pull in data from different sources.

  1. Limited Control Over Content Topic & Delivery. Some portion of social media content, such as product news, promotions, or brand imagery, is not fully under control of the social media manager.
  2. Susceptibility To Influence From External Events. The current World Cup being one example, social media is highly reactive towards news and events, causing constant fluctuations in results and making comparisons difficult.
  3. Concurrently Running Campaigns & Initiatives. Oftentimes, there are a number of multiple campaigns and initiatives running concurrently within any given property, and it can be difficult to separate their results.
  4. Existence Of Negative Actions. Due to the variety of interactions, negative results have a prominent place in social media in the form of dislikes, un-follows, negative comments, etc. This complicates measurement further.

The issues above are with complexity and lack of organization. This is particularly problematic for analytics, since without classification, it is easy to mistake the effect of one variable (such as topic) for another (such as post type). Organization can always be done manually (e.g., in Excel), but at scale, this requires an automated solution to classify and tag messages based on content. (You’ll need to identify potentially influential variables within the data in order to create more consistent sub-samples.)

  1. Lack Of Evergreen Content & Environment For Testing. There is no such thing as static content in social, which makes testing nearly impossible. (Pinned posts and tweets are about the closest it gets, but the metrics only apply to profile/page views.)
  2. Extremely Rapid Content Turnover. Research shows that the half-life of social posts lasts only around 3 hours, unless they are promoted. This again leaves little room to test and iterate on any given piece of content.
  3. No Support Of Cohort-Targeted Organic Actions & Results. Targeting or tracking results for groups of known individuals is limited to paid media. This makes split testing impossible for organic content.
  4. Uncontrolled Organic Content Serving. Facebook’s falling organic reach has been causing a stir recently. It serves as an example of marketers’ lack of control over if and how organic content is served on social media.
  5. Frequent Feature Updates & Changes. Typical of new media, social networks make frequent updates to features and functionality, affecting “what works” and in some cases making learnings obsolete.

The challenges above are regarding lack of control, particularly in regards to organic content and the inability to do split testing. While these problems are native to the social media environment and cannot be resolved easily, insights can be extracted over time by averaging out uncontrolled-for factors via the sheer volume of data. If the issues around the organization are resolved first, the volume of data required will be far smaller, and learnings can be accumulated correspondingly faster.

  1. Inability To Track Impact Of Engagement. Organic posts can be tracked only for click-through events: engagement actions cannot be traced directly to an off-site activity.
  2. Cross-Effects Between Different Engagement Actions. Shares impact reach which in turns impacts likes and comments, but likes and comments can also increase the feed ranking of the post, increasing opportunity for exposure. This jumble of different actions influencing one another makes it difficult to separate their individual impact.
  3. Difficulty Of Quantifying Value Per Engagement. Due to the above reasons, it is oftentimes impossible to even estimate how much a share or retweet is actually worth to the business.
  4. Difficulty In Demonstrating Accurate ROI. For all of the above reasons, it is notoriously difficult to calculate an accurate ROI for social media. Case studies do exist, but conversions directly attributable to social referrals are likely only a portion of total revenue impact.

The challenges around demonstrating impact of social media will be the most difficult to find a solution for, as there is simply no easy way to tie certain marketing activities to impact. However, with advances in marketing impact modeling techniques such as marketing mix or attribution, there is hope that a viable solution will be found in the future.

  1. Varying Audience & Social Norms Across Networks & Properties. Social networks are widely varying in nature and audience, and require different content strategies.
  2. Difficulty Of Obtaining External Expertise. Relying on experienced agencies is often times not an option. Due to branding and customer interaction aspects of social media, only 27% of firms choose to outsource efforts.
  3. Multiple Roles In Sales Funnel. There was a time when social media was seen purely as an upper-funnel awareness play, but recent developments are making direct response campaigns viable as well. This makes social media strategies more multifaceted and even more complex.

This final set of strategic challenges should resolve themselves once many of the other issues are fixed. As you establish a solid social analytics strategy and learnings accumulate, the full potential of social media as a marketing channel can be applied towards the strategic objectives.

The Future Of Social Analytics

While social media is one of the most complex marketing channels in existence, it also has high potential due to its wealth of data and application towards multiple marketing functions and strategies. However, for social marketing to become truly data-driven, the issues above need to be tackled head-on.

With advancements in technology and toolset, it should only a matter of time before these challenges around social analytics are resolved.

Opinions expressed in the article are those of the guest author and not necessarily Marketing Land.

Related Topics: Analytics | Analytics & Marketing Column | Channel: Analytics | Facebook: Marketing | Social Media Marketing | Twitter: Marketing

Sponsored


About The Author: leads product marketing at Origami Logic, a cross-channel marketing intelligence solution for modern marketers. With a career of 8 years in marketing and analytics spanning various functions, Kohki's focus has always been on translating data into strategy, simplifying the complex, and bridging the gap between data and organizational silos.



Sign Up To Get This Newsletter Via Email:  


Share

Other ways to share:

Read before commenting! We welcome constructive comments and allow any that meet our common sense criteria. This means being respectful and polite to others. It means providing helpful information that contributes to a story or discussion. It means leaving links only that substantially add further to a discussion. Comments using foul language, being disrespectful to others or otherwise violating what we believe are common sense standards of discussion will be deleted. You can read more about our comments policy here.
  • Marc Smith

    Hello! May I suggest having a look at the free and open NodeXL social media network analysis platform for the familiar Excel spreadsheet. NodeXL (http://nodexl.codeplex.com) connects to a variety of social media data sources and extracts networks. NodeXL can then automate the process of analysis, visualization, reporting and publication of insights into social media networks. Network metrics make it easy to find the people in key positions or roles in the network. Content analysis quickly summarizes the messages exchanged. Recent research created with the NodeXL application has revealed the variety of social media structures that form when people link to one another (http://pewinternet.org see: “Mapping Twitter topic networks”). Social media platforms vary widely, but all social media create social networks. NodeXL makes creating a social network map and report about as hard as making a pie chart.

  • Mike Lindsay

    Apparently the “what to do about it” chapter was left out of the publication. When can we expect to see that? .. because we already knew everything printed in this article

  • Kohki Yamaguchi

    Hi Mike, the proposed solutions are in the italicized sections. Here is the lowdown: solve scale and organizational challenges using technology. Segment the data to decrease unwanted diversity (variance) as much as possible. Use statistical or algorithmic modeling to reach an ROI estimate if absolutely necessary; otherwise settle for proxy metrics. Automate all of the above as much as possible so that you have time to actually analyze and strategize.

 

Get Our News, Everywhere!

Daily Email:

Follow Marketing Land on Twitter @marketingland Like Marketing Land on Facebook Follow Marketing Land on Google+ Subscribe to Our Feed! Join our LinkedIn Group Check out our Tumblr! See us on Pinterest

 
 

Click to watch SMX conference video

Join us at one of our SMX or MarTech events:

United States

Europe

Australia & China

Learn more about: SMX | MarTech


Free Daily Marketing News!

Marketing Day is a once-per-day newsletter update - sign up below and get the news delivered to you!