How Small Data Can Become Big Data’s Secret Weapon

How Small Data Can Become Big Data’s Secret Weapon

In recent years, companies have bowed down to big data and hailed it as the next game-changer. But even as big data keeps getting bigger, its promises somehow seem smaller.

It’s a common sentiment. Even marketers and executives at sophisticated Fortune 500 companies are struggling to wrap their arms around big data and mine it for unique, actionable insights. It’s true that leading companies now understand their consumers better than ever before. The trouble is, their competitors have access to the same data. And as a result, when marketers are asked how their knowledge differs from that of their competition, many are left scratching their heads.

The answer is, it’s probably not different.

Everyone in the fast-moving consumer goods industry is looking at the same behavioral, demographic, transactional and share data, which offers only a rearview mirror view of a category’s dynamics. These are certainly critical to track, but without anything additional, they don’t amount to a competitive advantage.

Think about it this way: It doesn’t matter what happened yesterday if you don’t understand why. The secret to success today lies in layering big data with “smaller” data like primary research, interviews, ethnographies and quantitative surveys.

In our experience with multinational brands, many large companies have started to neglect these secondary techniques in the mad dash to exploit big data. That’s why we need to emphasize that both types of data have their place, especially when they work together. For example, the benefit of primary research is that it helps you uncover the “why” behind the “buy.” Through primary research, companies can probe specific areas of interest in order to understand everything from what makes their brand sticky to the weird and wonderful ways consumers are actually using their products.

Companies can then take what they learn from small sample sizes and apply it the much broader scope of big data. Tying these data sets together makes them superadditive—fancy math-speak for making the whole greater than the sum of its parts. Segmentation is a prime example of this. It’s also been a pillar of marketing strategy for decades. Big data doesn’t replace smart segmentation, but it can certainly make it more powerful.

By linking primary research to big data, we can improve the robustness and practicality of foundational frameworks like segmentation. We can even create predictive algorithms that increase the precision of go-to-market strategies and initiatives. When done right, this makes segmentation a much more powerful tool to guide a company’s marketing, innovation, product and investment decisions.

Integrating big data sources around a survey-based demand frameworks allows companies to delve into a much more granular understanding of consumers, brands, competitors and channels, or what we typically refer to as the Demand Landscape.

Thanks to advances in data science, companies can tie next-gen Demand Landscape frameworks to big data for much broader and more significant applications.

New techniques allow us to simultaneously build the segmentation while developing the algorithms to find segments in other data sets. This allows us to connect segmentation insights to outcomes much faster than ever before. For example, companies can use machine learning to identify custom segments in their CRM databases, store-level loyalty card data or even across a broad household database.

Companies can dive even deeper, creating custom geographies and messaging that resonates with consumers’ specific needs and desires. Using rich behavioral data, marketers are able to create even more granular sub-segments within a primary consumer group to customize messaging based on their real-time behavior.

For example, does it really make sense to advertise coffee to your power user at midnight? Or does it make more sense at 3 p.m. during their afternoon lag? Maybe it depends on which consumer you’re talking to. A college student, for example, might need that caffeine jolt at midnight after all. It’s personalization on a massive, but meaningful scale that creates powerful return on investment results.

Since consumer demand is not static, an enhanced Demand Landscape can help companies keep a pulse on the size and composition of its segments and evolve with consumers through continuous refinement and measurement of outcomes.

This isn’t the old consumer segmentation of yesteryear—you know, the one that sits in a binder on a shelf. It’s a continuously evolving model that’s more advanced than ever. We’re pushing to understand customer demand better every day, using big data layered with primary and secondary data sources to discover new opportunities.

So the next time you dust off your customer segmentation, ask yourself three simple questions:

  1. What does it tell you about the demand that your most valuable customers has that your competition doesn’t already know?
  2. How is it being linked to the big data sets your company uses to make decisions and activate strategy?
  3. Does it allow for a feedback loop to continuously improve the segmentation itself?

If you have a next-gen segmentation, you should have a solid answer to all three. And with those answers comes a much sharper grip on how to win your most valuable customers than your competitor.