Using data to bridge strategy to activation

From communications, media and entertainment to telecommunications and manufacturing, many industries have already seen the significant benefits of effectively leveraging big data solutions to drive insights that move their businesses forward. But there’s one industry in particular that is still behind: market research.

The challenge: Adopting data to scale

Without a structure or framework to precisely harness trillions of data points, it can be costly and even impossible to find meaning in large volumes of data in an efficient manner. Given the opportunities that the market research industry has to use third-party data in both day-to-day activities and big-picture decision-making, it’s clear that big data solutions, which have a projected spend of $187 billion, will become mainstream. Those who adopt such solutions will be able to make a data-driven impact on their business.

Based on reports from industry-leading publications in the market research space, a majority of brands and companies are already leveraging big data or considering it as a new method. If market researchers want to help their teams drive personalized and consumer-centric products while increasing revenue and efficiencies, they need to understand the value of implementing the right data solution that enables them to take action and understand the story behind the data. However, 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.

How researchers can bridge the gap

In 2011, programmatic advertising spend totaled $2.8 billion. Enabled by big data, it’s projected to grow to $32 billion by the end of this 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 specific audiences in an automated process and close the loop with purchase data to calculate lift. So how can researchers experience similar results and help bridge the gap between marketing strategy and activation?

It’s important to first illuminate the magnitude of big data and its sources to understand its potential in research. Some data management platforms (DMP) have about 2 billion consumer profiles with 10,000 attributes. Put into perspective, you’d need to run 2 to 3 billion surveys to produce a similar amount of data. We believe the solutions the market research industry needs combine proprietary data from client surveys and other sources with third-party 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 and gain a competitive advantage.

Seeing it in action

To actually understand what kind of an impact this can have, let’s look at one example where a leading brand in the coffee industry leveraged this approach to understand its market position relative to competitors and its target consumer segment. By assessing both behavioral loyalty (actual market share based on purchase data) and attitudinal equity (consumer attitudes and perceptions toward the brand) this coffee brand was able to understand whether it was poised for growth or vulnerable to decline within its category. 

The results showed the brand had an opportunity to leverage attitudinal equity to grow through acquisition. Specifically, the attitudinal equity score and current position in the market showed it could acquire up to 32 percent of current category purchasers from competitors, which would equate to an increase of 4.78 million new customers.

Further, combining other big data attributes from the DMP specific to consumers’ personalities, purchasing habits and media consumption helped create a rich persona for this coffee brand’s target audience. Ultimately, the team was able to: 

  • identify potential switchers and the competitive brands that provided the greatest opportunity to target; and
  • learn which target audience would be the most likely to engage with ads.

Combining survey data and big data meant this brand had a deeper understanding of its audience that made media activation far more efficient. The findings provided the answers needed to effectively message and activate against retail conquests to grow market share.

Making it agile

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

In fact, in another example, a consumer packaged goods brand used a similar approach to enhance the understanding of its consumers and grow through retention. Augmenting billions of big data points with survey data specific to their segments allowed them to explore preferences, practices, media consumption, personality, lifestyle and interests all at once. Within weeks the brand was able to develop profound insights to better understand its audiences, develop product and promotional materials and activate against segments using creative specifically positioned for each one. 

The future of data in market research

Within two years, you’ll be hard-pressed to find a survey that’s not augmented or appended with third-party data. Long-term, these solutions will allow us to ask consumers fewer questions and generate deeper insights – ones that can be applied by teams across the spectrum from marketing strategy and analysis to media activation. Turning this $187 billion spend on big data solutions into valuable, unique insights will significantly change our industry and the way we conduct research. Using the right solutions will not only create common data currencies throughout organizations of all sizes but will also increase fluency, efficiency and effectiveness. On the flipside, those who fail to take advantage of fast, scalable solutions risk being left behind.

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