Social Media Analytics: Insights from Structured versus Unstructured Data
Let’s be honest … social media is a challenge.
Not only is staying current, active, and “topped off” a chore, but crafting full-scale campaigns that contribute to your business’ and brand’s actual goals can be bewildering. At the same time, the market for social-media continues to grow. According to recent data from eMarketer, “Social Network Ad Spending to Hit $23.68 Billion Worldwide in 2015.”
The question is, “How do you navigate the maze and ensure that the time and effort you invest pays off?”
The answer is social-media analytics.
TechTarget calls it a “practice,” but it might better be called a discipline.
Why? Because analytics aren’t particularly exciting. There’s not much drama in data. At least, not at first.
Still, analytics are the only way to make genuinely informed business decisions and measure your success. This way you know what’s working for your brand, what’s worth exploring further, and what’s a complete waste of time.
Naturally, social media analytics involves numbers and most marketers don’t like numbers. Partly because data seems to undercut the creative process, and partly because accountability is terrifying.
But don’t be put off or intimidated. Insightful analytics aren’t just about raw numbers. In fact, a full fledged social media approach means collecting and interpreting what’s known as both structured and unstructured data.
What is Structured and Unstructured Data?
If you have spent any time reading “best practice” posts about social media metrics or mining social media data for yourself, then you have come across the phrases “structured data” and “unstructured data.” Knowing the difference between the two types and exactly what insights you can and should glean from them is essential to getting the most out of your social media.
So what’s the difference?
In simplest terms, structured data is quantitative, it comes to you within preset parameters an in numerical form. Unstructured data, on the other hand, is qualitative. It comes to you without preset parameters most often in the form of comments and conversations: real humans sharing real words. Each has its own purpose in marketing.
Quantitative data (structured) aims at answering straightforward, numerically-driven questions like, “Who, What, When, Where, How and How Many?”
Qualitative data (unstructured) is about “Why?”
As Social Media Today put it, “While you can glean information from the structured data, analyzing the unstructured data is the only way to uncover insights. It’s also the most difficult part to do (and do well).”
Insights from Structured Data
Structured data is information that’s already organized and is easy to “make sense of.” Most notably, structured data also helps you “optimize your spending and impact across various channels.”
The following are some of the social media insights you can gather from structured data:
1. Growth
In social media marketing, growth is the easiest factor to measure. However, growth doesn’t just refer to your aggregate number of followers and fans. Instead, growth focuses on your increase over time in the number of your followers and fans.
More specifically, growth should also keep track of engagement interactions like retweets, mentions, likes, and shares. This enables you to gage how many people are interacting with your brand now compared to an earlier period.
After uncovering your growth and going back to your objectives, determining what’s working, what’s not, and where to invest becomes data-driven, rather than based on feelings or hunches.
2. Influence
Simply put, influence on social media is the percentage of people in your market who “Go” when you say, “Go.” In other words, it’s your ability to affect how other people think and act.
Measuring influence can be difficult.
Some of the metrics mentioned under growth apply, but only as they relate to action. For instance, click-through-rates (CTRs) — the number of people who don’t just see but click through from your social media posts — is a great source of data.
Another source is Klout. Klout offers you an aggregate score of your social media influence by looking at the number of people you influence, how much you influence them (e.g., mentions, shares, retweets, likes, and clicks), and lastly how influential those people are themselves. A summation of the three elements gives you your Klout score, which is an incredibly structured way to measure this metric.
3. ROI
When you spend time and money on social media marketing, you want to know the results it’s generating and if they match (or nearly match) your projected returns.
In doing this, you must ensure that you have the right numbers because this is the information that will help you decide whether you want to keep investing or withdraw.
The most straightforward way to measure ROI is obviously sales. But depending on your target at the time of investment, you might measure ROI by growth in terms of the social media following. It could also be gaged by the traction your content is creating.
Similarly, you can measure ROI by the influence. Has your Klout score gone up? All this can be used to measure ROI. It all depends on your goals.
Insights from Unstructured Data
Unstructured is harder to make sense of.
By its very nature, it’s not readily available for use or categorization, but the insights you derive are much deeper and can have huge implications.
That’s why Webz.io focuses not just on collecting unstructured data and dumping it in your lap, but cleaning and unifying it into a structured format built around the insights you need. This way that data can be easily integrated into your existing apps, platforms, and analytics software (especially at a large scale).
1. Sentiment
In a recent article, Entrepreneur reported that “Customers that are exposed to a brand on social media are 180 percent more likely to search for that brand on search engines.” This goes to highlight the importance of keeping track of your social media activity and making use of the findings to further optimize your overall marketing.
However, again, it’s not enough to simply aggregate the raw numbers of shares and mentions. What matters is sentiment.
Sentiment is the qualitative measurement of your “customer mood” as it relates to your product, your service, and you. This involves gathering customer opinions, their actual comments, how your brand is being portrayed in the media, and host of other non-quantifiable sources.
Collection of these unstructured opinions and feelings is done through social media scraping, crawling, and extraction.
Remember, you want to know how your customers feel. So it’s vital to manage this collection through mentions, keywords, hashtags, and specific profiles. These help you understand the all important “Why?”
2. Leads
Unstructured data — like what your ideal customer likes, where she shops and why she likes what she likes — helps you acquire more targeted leads.
Without this data, you are just shooting in the dark, or worse, blindfolded. And unless you are Matt Murdock from Daredevil (in which case you must have excellent listening skills), you will fail.
Again, by scraping unstructured data, you’re able to get far more specific about not just the types of people you should target, but the actual people themselves.
It also enables you to send more personalized messages. Real engagement is not about saying “I’m the best” but communicating “this is how I will solve your problem.” You can’t uncover this unless you have the correct, real-world data.
3. Messaging
When you discover who your audience is and where they like to hang out, you also discover what they like talking about and most importantly what exactly they’re saying. It is through unstructured data that you are able to find the verbatim words of your audience and use their exact language to improve your product and craft your own persuasive copy.
Summing Up
Both structured and unstructured data are nonnegotiable in social media analytics.
Structured data helps you understand what direction your company is headed in in terms of growth, influence and the ROI. Unstructured data helps you understand customer sentiment through opinion mining. It also helps you acquire high-quality leads, which saves you a lot of resources that could have gone to waste by targeting all the wrong people. Lastly, with the information you gather on your leads you are also able to improve your products to ensure they solve your customers’ problems and create copy that will show them why you are the one.
