Top 4 Reasons Why You Should Take a Big Data Analytics Approach to Research

Big Data is generating a lot of buzz these days as a way to gain market insight from aggregate consumer data. What people mean by the term “Big Data” is still evolving but, pretty much, it’s any large data set from which you can look at your consumer, category and / or market. It isn’t data you necessarily request (like hosting a focus group or fielding an online study); it’s data being generated by systems we created for other purposes - years of Twitter posts, Internet connected sensors (e.g.: a fridge that records when the door is open and for how long) or millions of retail transactions - that when examined, can be revealing about the world around us.

 

However, you are thinking about big data all wrong if you think it’s a free replacement of all your current research. As a marketer, marketing strategist or marketing researcher you have very specific information needs that are not easily substituted. Big data still cannot answer why.

 

There is a lot of raw data available, surely it can be mined for value marketers can exploit - the trend is real and applicable, so what does it mean for your business?

 

Here are 4 reasons why a big data analytics approach could be valuable to you:

 

Direct inquiry bias is the bane of conventional research. It betrays our best interest as researchers -respondents answer questions as they think we want to hear, producing rationalizations, not truth. The most effective insight collection involves asking indirect questions or tasking participants in a way that reveals human truths. Large sample of digital behaviors are the ultimate indirect questions: based on behavioral economics you can see what people engage in (i.e.: Internet search terms) or buy without actually asking; the size of the data set offers increasing validity for findings. See Google’s Flu Trends Project brought about by the study of flu-related searches, proven to be more reliable than the US CDC’s ability to track the same global flu trends. 

 

 

Big Data analytics doesn’t really explain “why” but it shows you a number of correlations, which can confirm or challenge your hypotheses (or reveal something entirely unexpected). As Mayer-Schonberger and Cukier write in their book Big Data, correlations provide “a really good proxy for a phenomenon”. They cite Wal-Mart’s discovery of the positive relationship between Pop-Tart sales and hurricanes to exemplify this. Wal-Mart has no idea why this relationship exists in the US, but at the sound of the hurricane weather warning you can bet end caps and store displays are full of those delectable, flaky, little quadrilaterals. What’s more, businesses may discover correlations between consumer behavior, or shifts in demand, and factors completely beyond their control. Also, consider how even the largest, most ambitious surveys still often fail to yield complete understanding when you drill down to the lowest levels.

 

We have found that Big Data is optimized when combined with smaller data points such as a targeted number of in-home visits or a ‘perfect’ image from Instagram. One initiative we completed involved looking at 2 years of social media content on a single medical condition. While we could provide quantitative findings related to words used, brands mentioned and sentiment expressed, we could also dive deep into a single blog or user account to show the images and comments posted to bring the story to life. No matter what our source, researchers always tell a story - Big Data analytics is no different, if you use it correctly.

 

In a recent project, we combined large consumer surveys (6000+ person segmentation study) with 3 years of sales data, all from one category. From this, a market structure was developed creating a map of all category transactions by occasion, need and consumer; our client now has a comprehensive picture of the entire market. To put the icing on the cake, because the client combined the market structure with the consumer segmentation they were already using, their ability to integrate these findings into their business increased significantly. Most importantly, the map revealed to our client where opportunities lie that a)would not cannibalize their current sales; and, b)reveal the white space in the market they can tap in to. If you set up these rather conventional tools properly, you can mine through a very large data set looking emergent patterns of behavior – which ultimately lead to unexpected findings and business opportunity discovery.

 

Big data research can help you see your business in a whole new way. The information you need to generate new strategy and find opportunities for profit and growth could be sitting on a server somewhere right now. We encourage you to rethink your market research initiatives and consider integrating big data analytics into your overall strategy. Sure, the numbers are endless and ugly, but often patterns are hidden within distracting spreadsheets and tables. In the hands of skilled analysts and strategists, gold can be found.

 

 

This content was provided by Fresh Squeezed Ideas. Visit their website at www.freshsqueezedideas.com.