The symptoms are always the same. Tired red eyes, cramps in the dominant hand, and the burdensome trifecta of back, neck, and shoulder pain, all to dig through large volumes of data to identify the predominant “what” and “why” of consumer conversations often resulting in inaccuracy and inefficiency.

Many social monitoring and listening platforms have lightened the load for humans, as there is a necessity for human analysis to derive insights. Effective analysts, brand insight directors, and managers acknowledge thatthere will always be a mix between humans and technology in achieving the end goal of actionable insight. What should be the appropriate balance of humans and machines in this equation?

One of the most common mistakes to avoid in seeking this balance is the lack of prioritization of content. Remember to ask the following questions:

  • What is the goal for your review?
  • What is your objective criteria that represents your desired voice of your customer?
  • How do you identify it in its various nuances?
  • Is there a hierarchy of issues you are looking for?
  • How will you tag and track your results for subsequent analysis and consolidation?
  • How will you present your findings?

If you neglect to answer these critical questions you can easily fall into the “garbage in…garbage out” trap. Your results will only be as good as your curated data set. The question is how do you perform this curation for your own analysis?

Now many companies have taken the first step by providing dashboards and static spreadsheets with relevant analysis. Unfortunately, even with these tools, individuals are compiling this information using anything from a flurry of Microsoft Office applications to pen and paper to transform the data into a presentation-ready format. Despite increasingly sophisticated technologies, humans are ultimately deciding what information is relevant to their organization and how they are planning to take action on their findings.

The symptoms can be alleviated with the right dose of proscription and prescription in how you look at your data, process it, and capture your findings.