The complementary approach to tying Big
Data (quantitative) against Thick Data (qualitative) can deliver deeper
insights.
Big Data in marketing is all about
capturing every interaction during the customer journey, then using machine
learning algorithms to process and analyze the data to produce a greater
understanding of consumer behavior. But, there’s a gap in there.
As it stands today, Big Data-powered,
machine learning-driven consumer insights are good for producing quantitative
driven insights. Even emotions and sentiments can be captured, quantified and
fed back into the equation. But Big Data can’t capture everything.
Consider a consumer goods company
trying to get their packaging just right. They’re hearing that customers are
frustrated with the current situation, but no matter how much Big Data driven
number crunching they do, they can’t find the solution. That’s because Big Data
isn’t enough. The company needs to take a closer look at how customers interact
with their product — how they handle it, unpack it and
react to it. And they have to observe a large sample of customers. Next, they
need to apply some distinctly human intelligence to
interpret the customer experience and monitor their emotions throughout. Big
Data can’t do that.
There are millions of situations where
quantitative insights need to be paired with qualitative data to really be
useful.
What kind of store layout will make
customers feel welcome?
What unexpected ways are customers
using your product?
What’s the demand for a new product in
a completely different category?
The bottom line is this: the best insights come from a
combination of quantitative and qualitative research. They complement each
other. And if you want a fuller picture for further fine tuning, you’ll have to
keep them both in mind.
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