Social Data Intelligence 101: First Seek to Understand

Social Data Intelligence 101: First Seek to Understand

Our earlier blog post this month, “3 Questions That Will Help You Unlock the True Potential of Your Social Data,” shared three questions that enterprise leaders can use to help their teams unlock the true potential of their social data. At TrueVoice, we refer to these important stages of true social data intelligence (as opposed to social monitoring or strategy) as the stages of understanding, visualizing, and activating the intelligence that a company collects through its social and digital platforms. In an effort to help enterprise organizations reach higher levels of context and actionability from their social data intelligence, we’re going to walk through each stage of actionable data and provide insights into how you can use each to dig up deeper, more meaningful insights from the data you already have.

As an executive peeking into the world of social analytics, you’re served up countless reports on social data that feature brand mentions, statistics, and follower counts. But looking at these reports, there’s no obvious indication of white space opportunities, competitive advantages, or even an indication of what you should do with this information. Despite the promise of social data intelligence, you aren’t seeing the benefits of it.

You aren’t alone in this. A recent MIT Sloan Management Review study revealed that almost four in ten respondents don’t use the analytics tools their companies adopt. When asked why, they cited not understanding “how to use analytics to improve the business.” This disconnect between gathering data and actually understanding it represents a huge opportunity in social data intelligence, and it’s one that you can take advantage of.

Protecting Your Intelligence From Dirty Data

When your social media monitoring team collects data, the results are raw. The data is simply numbers, names, and statements that float on an excel sheet without ranking or context. This data could be flecked with sarcasm, second-language mistakes, promoted content, or a number of other social data inaccuracies. In that sense, this data is “dirty” and possibly dangerous to use right out of the gate.

When you analyze your data to achieve true understanding, you’re not simply gathering it or translating it. You’re taking information from a variety of sources and turning “dirty data” into “clean data.”

Context Turns Social Data Into Social Data Intelligence

Cleansing your data is the first step in understanding it. But even clean data is just data. The next step is to evaluate that clean data in order to extract actionable ideas, insights, and information that data alone cannot provide.

While many companies assume that their teams have got this base covered, they often don’t. Just because your organization gathers and reviews the data coming in doesn’t mean it understands how to use it. True understanding of your data reveals relationships and deeper meanings; a context that you can use to act on the insight you uncover.

It’s the same as almost any other product. Take the phone, for example. Using a phone, you can close a million dollar deal. But having the phone in front of you doesn’t show you how to use the phone. Understanding your data is what will help you close the deal.

Action Tips for Understanding Data

Now that you know the value of clean data and context, we’ve got a question for you to consider: can you honestly say that you understand your social data? You know where your data comes from, and you know what much of it means on a literal level. But when you read a report from your social media monitoring team, do you completely grasp what your customers are saying, why they are saying it, and how you should best respond to them?

If you’re like most companies, you might not be there yet. Here are five ways you can work to get there:

  1. Segment Your Audiences – Understanding your data starts with understanding your audience. When you develop intelligence reports, make sure you segment your audiences to represent existing customer segmentation profiles actionable for your enterprise.
  2. Translate Your Data With Linguistic Evaluation – You cannot take your customer’s words at face level because your customers are often literally and figuratively speaking a different language. Work with a network of behavioral and linguistic professionals to go beyond what audiences are saying to reveal why they are saying it.
  3. Evaluate your Data With Subject Matter Experts – Outsource your evaluation and analysis to machines and you’ll miss out on a significant amount of insight. Work with subject matter experts who understand the unique dynamics, movements, and behaviors of your audiences and stakeholders.
  4. Track Your Brand, Product, and Services – Benchmarking and tracking self-reported customer product discussion will help you identify product weakness, whitespace opportunities, and alternative uses for product development. This brings you more targeted insight into where to spend your budget and why.
  5. Identify and Index Your Leaders and Influencers – All follower insights are not created equally. Identify and rank leaders and influencers within your industry so that you can capture relevant digital and social metrics and produce a qualified and respectable measurement of reputation.
  6. Evaluate With an Eye for Data Analysis and Marketing – Finally, do everything possible to evaluate your clean data with an eye for both data analysis and marketing; not one or the other. Data alone won’t help your business succeed. As the Harvard Business Review puts it, “Remember that analyzing data isn’t the point. The point is better marketing.”

You cannot act on your social data if you don’t understand it. Therefore, you need to work with a seasoned team and process to turn your data into intelligence. And that’s what the right social data intelligence partner will do for you: turn massive amounts of social data into actual, actionable social data intelligence that you can use to make better business decisions.

Do you understand your data? What is the greatest challenge for you?