Big data is here and the amount of information that needs compiled, organized, and analyzed seems to grow exponentially every day. Digital marketing and web applications, even for small business, often have quite a bit of data that needs analyzed and communicated. For this reason, it’s wise to spend some time thinking through the data collection and the insights you intend to communicate. Typically the simplest way to effectively complete this process is by starting with the end dataset in mind and working backwards.
We were recently brought to a project that was well underway to compile and display the data collected from a particular market segment. The goal was to analyze the responses of the market segment to highlight industry trends and ultimately create a whitepaper report that would be used as a call to action in digital marketing campaigns.
The idea itself was great. The intention was to create charts and graphs illustrating the trends, provide some editorial insights on what those trends meant, and explain the likely impacts in the short and long term for this particular industry segment.
There was one major problem. The survey didn’t have any quantifiable questions. Instead everything was asked in a free form essay response style. While there were lots of insights and opinions in the data, there was no way to display it simply without making major inferences and assumptions.
The solution was a follow up set of questions that were easily quantifiable and made up the heart of the industry trends that could be charted and graphed. While this did get the information needed, the response rate was not nearly as high as the first survey request. With some forethought, the extra step would have been avoided and there would have likely been a more robust response to the quantifiable questions.
The end goal of most data collection, in digital marketing or elsewhere, is analysis and improved awareness. Start your data collection with an understanding of what information you want to pull and how it can best be displayed. A large portion of the data should be easily analyzed through avarages or trends with free form responses used only to clarify a particular opinion. Then craft your data collection from systems or people to meet those requirements. That forethought will save a lot of headaches and rework trying to make sense of data that is not well suited to the end goal.