Big Data Is Not at All Hype: GRIT Commentary 2018

Based on the $187B in projected spend on big data solutions, it’s clear that big data is more than “hype.” Given this is four times the market research industry, it’s hard to argue it’s anything other than mainstream. The GRIT Report shows 70 percent of respondents are leveraging big data or considering it, putting it close to the top for “Emerging and New Methods.” However, based on conversations with clients and industry peers, there is a disconnect with this statistic—and what I’d characterize as experimentation versus full adoption. I believe market researchers haven’t adopted big data solutions at scale because the ones that currently exist aren’t agile, nor do they drive predictable ROI like they have for the advertising and marketing industries.

In 2011, programmatic advertising spend totaled $2.8B. Enabled by big data, it’s projected to grow to $32B by the end of the year. The reasons why are simple. First, the big data ecosystem that supports advertising is robust and has matured greatly over the last decade. Second, programmatic advertising provides a demonstrable ROI— marketers can target specifc audiences in an automated process and close the loop with purchase data to calculate lift. So how can market research experience similar results?

It’s important to illuminate the magnitude of big data and its sources to understand its potential in research. The data management platform (DMP) we leverage at GutCheck has 900M consumer profles with 10k attributes. Put into perspective, you’d need to run 500M surveys to produce a similar amount of data. We believe the solutions the market research industry will develop will combine proprietary data from client surveys and other sources with thirdparty big data. While anyone can leverage a DMP to drive insights, brands can augment their proprietary content (segmentations, concepts, creative tests, CRM data, etc.) with a DMP to draw out truly unique insights.

For example, we ran 1,500 respondents through a segmentation for a client using our Agile Attitudes & Usage™ product. Once respondents were segmented, we indexed them against our DMP with three explicit goals: create rich personas for each segment; identify potential switchers and which competitive brands provide the greatest opportunity to target; and identify which target audiences in the DMP would provide the most reach for media spending. The results were compelling. The segments had specifc personality traits that are 4x more prevalent than those of the general population, which had signifcant implications for communications positioning. We found a large number of switchers available in the market and identifed two primary brands to position against to capture the most switchers. Finally, we identifed audiences that are 8x more efcient than the ones they were currently targeting. The fndings provided our client the answers they needed to effectively message and activate against their retail conquests to grow market share.

Driven by a robust technology ecosystem, solutions like this can be incredibly agile in nature. Due to automation and standardization, the cost of running big data augmentation is a trivial cost of the project; the time to generate insights is measured in hours.

This is our future as market researchers. Within 2 years, you’ll be hard-pressed to fnd a survey that’s not augmented or appended with big data. Long-term, these solutions will allow us to ask consumers fewer questions and generate deeper insights. Turning this $187B spend on big data into valuable insights will signifcantly change our industry and the way we do research; when we understand the full impact and how to leverage big data solutions effectively, it’s not at all hype.