Psst Brands... Your Social Media Measurement Inefficiency is Showing
 

Psst Brands... Your Social Media Measurement Inefficiency is Showing

 

Established marketing platforms like TV, print, email, and digital have a standard way of measuring success. In many cases, these platforms have dedicated companies to measure performance like Nielsen for TV and BPA Worldwide for print. Social, however, is an entirely different story, and it’s very possible (and highly likely) you’re measuring content performance incorrectly. Below are some of the top issues with content analysis on social, and tips for best practices to ensure your content is as impactful as possible.

There are four factors that make it difficult to develop social media benchmarks for content performance:

1. Not All Data is Public

When measuring your brand’s performance, whether against specific competitors or an entire industry, it should be easy enough to pull likes, comments, and shares over fans/followers. In fact, this methodology is the industry standard for comparison. Benchmarking services like Unmetric make good use of this data, however, their data collection is limited to what is made available by the platforms. This means that whether you do social measurements yourself or through a service, there will be key metrics you’re missing out on, including key markers like true impressions and click data. Not all posts are intended to drive likes, comments, or shares! When calculating engagement rates, the only usable public data is fan/follower count. Though this approach to gauging performance is not very accurate, it’s currently the best methodology in-market.

2. Different Social Channels Measure Social Impressions Differently

An impression on Twitter is not determined by the same methodology as on Facebook or YouTube. Video views also vary, with Facebook measuring a content view after 3 seconds, compared to YouTube, which considers content ‘viewed’ once a certain completion percentage is reached. Aggregating data across platforms then proves itself inconsistent, and irrelevant.

3. Constantly Changing Algorithms

Reach relies on multiple, and there is no consistent way to assume how much reach a post might get. Often, by the time you have collected enough posts to have a worthwhile sample of data, the algorithm drastically changes rendering your previous data invalid.

4. Not All Social Channels Provide Paid Benchmarks for Comparison

Engagement and click-through rates aren’t the only way to analyze your content’s performance. With promoted posts, it is important to look at the efficiency of spend in driving to an objective. If your aim is driving impressions, pay close attention to your Cost per Thousand (CPM). Driving engagements? Cost per Engagement (CPE). Driving traffic? Cost per Click (CPC). Unfortunately, the networks, including Facebook and Instagram, do not always provide these paid benchmarks for industry or even platform-wide comparison. The best source of data has proven to be eMarketer, which releases industry and channel reports.

So, What’s the Best Practice?

Based on the notes above, take any industry benchmarks with a grain of salt. Use them as a pulse check, but not gospel. “But, how the heck am I supposed to know how my content is performing?” One of the best things you can do is analyze your post by its objective. If a post is intended to send users to your website, measure the click-through rate. Looking at engagement rate for a post not intended to drive engagement can lead to a misinterpretation of the data. If you have a high volume of content, it could also be worth segmenting data further by looking at content series/campaigns or featured products. For example, if your company sells fruit, categorize your posts based on the type (i.e. “banana”, “apple”, “pear”, etc.). Then, examine the engagement rate performance of your engagement objective fruit posts.

When you think of calculating an “average” are you calculating the mean, median, or mode? They are all valid “averages”, but they can represent data in very different ways. A benefit to using median is it better accounts for the volume of outliers we tend to see with social data. By using a median to compare your performance, a 20% engagement rate isn’t going to overinflate your results like an arithmetic mean might. The arithmetic mean is best when the data has a more normal distribution and lacks significant outliers. You probably won’t touch mode in this type of content analysis since mode represents the most frequent occurrence in a data set and is best for categorical analysis. If you categorize your brand/product mentions or customer care messages by issue or topic, mode might be a good way to see which issue or topic is the most-discussed. By measuring against yourself, you can find what is working for your specific audience and what is not.

The TLDR Summary

Social media measurement is still in its infancy. There have been some attempts to develop industry and platform standards, but they are flawed given the accessibility of data, constantly changing algorithms, and differences from channel to channel. For now, working with what is available is the best way to go, keeping in mind the limitations and caveats. When optimizing content performance, the most illuminating benchmarks to look at are those using your existing performance data. The key to thoughtful analysis moving forward is going to be an evolution of a cross-platform standard of measurement as well as a third-party service that can serve the same role for social as Nielsen does for TV and BPA does for print.

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