We’ve all heard the buzz about Big Data
promise. Specific to market research, the discussion largely centers on whether
Big Data will replace traditional qualitative methods and quantitative surveys
as a primary approach to developing strategic insights. Marketers often favor
Big Data as winner-takes-all in this debate; while market researchers
frequently argue that Big Data provides a useful complement to traditional
research, rather than being a replacement.
Although the debate is fascinating, I’d like to take a
practical approach to help you begin to think about how Big Data could drive strategy and insight
at your company:
- What can we learn from Big Data?
- How can it help us improve business outcomes?
- Where might Big Data fit into our existing insights program?
- Let’s start by considering the types of questions Big Data
and traditional research help us answer and then move on to a series of thought
starters to help spark ideas about how to leverage your company’s Big Data
The What and the Why
of Customer Behavior
At this year’s ESOMAR and CASRO conferences, our own Greg
Mishkin and Dr. Reg Baker addressed the roles Big Data and traditional research
play in understanding the customer experience and providing intelligence to
drive business decisions. To paraphrase:
It turns out that Big
Data (and behavioral observation in general) are really useful when it comes to
describing what people do. But they cannot effectively determine why people do
it. The why is best identified by talking to customers and asking them
questions. That’s the job of traditional qualitative and quantitative research.
The magic happens when
Big Data are combined with traditional market research, allowing attitudes and
motivations to be projected onto a large population. Optimizing the interplay
of Big Data and traditional research to jointly understand the what and the why
is the foundation for Market Strategies’ Continuous Improvement Cycle, which
minimizes business risk while maximizing business impact.
Two Plus Two Equals
While the notion that the whole is greater than the sum of
its parts is clich??, it does tell the story. Combining the behavioral
information from Big Data with psychographic and attitudinal information from
traditional market research helps companies make more complete, connected and
confident business decisions. It allows us to understand why people do what
they do. It allows us to understand what can be done to positively influence
customer behaviors. And it limits the risks associated with resulting decisions
and actions, while maximizing the impact to the business.
Each company and industry has a somewhat unique Big Data
exhaust. But the general business questions we all seek answers to are fairly
similar. I’ve chosen three strategic issues to frame our thought starter
- Customer Churn
- Call Center Monitoring
- Market Segmentation
Although these issues are merely the tip of the iceberg in
terms of the types of insights Big Data can help us uncover, they provide a
useful starting point to get the conversation going.
As you read on, keep in mind that we are talking about
combining Big Data with survey research. We’re looking at the what together
with the why. We’re jointly deriving insight from both behaviors and attitudes.
And we’re talking about using projective techniques so that we can marry survey
insights from a sample of customers with a larger Big Data source that includes
all customers (and sometimes prospects) of interest.
Brands do many things to assess customer churn: Company
records are examined to analyze the churn rate. Surveys are sent to lost
customers in post-hoc efforts to understand why they departed. Focus groups or
in-depth interviews are conducted among at-risk customers to uncover the emotional
forces behind their intentions to leave.
These are all valuable approaches. But suppose we take a
combined approach. What more is possible if, over time, we project customer
satisfaction measures and churn diagnostics (our traditional data source) onto
our customer database (our Big Data source)? Here are some of the issues we
could explore by leveraging Market Strategies’ Continuous Improvement Cycle
- Churn Lifecycle‘At
what point in the relationship does dissatisfaction typically occur? Does it
emerge at two days, two months or two years? Does dissatisfaction taper off or
remain steady over time? How long after dissatisfaction surfaces does churn
occur? What is the window for intervention?
- Churn Contagion‘When
dissatisfaction occurs, is it isolated to individuals, or can it spread to an
unhappy customer’s network? Can we identify behavioral or contextual triggers
that are related to widespread dissatisfaction and churn among customers with
common experiences or contexts?
- Churn Pathways‘Is
a single behavioral, contextual or attitudinal trigger driving a majority of
dissatisfaction and churn? Or are there churn pathways involving combinations
of experiences and attitudes that need to be examined as a process to address
dissatisfaction? Where is the threshold in the experience pathway? How can we
best test and monitor interventions for maximum impact?
We’ve all had the experience of calling customer service and
hearing a recording that says, ‘This call may be monitored for quality
assurance.’ Well, it turns out that those recordings are a source of Big Data.
What’s really special about this type of Big Data is that it uniquely contains
information about the social exchange between your brand (via the service rep)
and the customer.
As with churn, there are a variety of traditional approaches
to monitoring call center performance. These methods usually involve fielding a
survey within a short window of the interaction that asks customers to evaluate
the performance of the service rep; whether their issue was resolved to their
satisfaction and how likely they are to recommend the brand as a result of
their transaction experience. These efforts can be massive, often representing
the largest chunk of a brand’s total research budget.
To address our clients’ interests in gleaning incremental
insights, Market Strategies developed a Calibrated Monitoring approach that
combines expert ratings of call center recordings (the Big Data source) with
subsequent survey ratings from a sample of the same interactions from customers
(the traditional data source). This approach allows us to explore answers to
questions that we couldn’t ask using either approach alone, such as:
- What is the impact of representative accent or dialect on
the customer’s willingness to recommend our brand?
- Are our representatives showing appropriate empathy, and how
is the display of empathy related to customer satisfaction?
- As a result of the representative’s interaction with the
customer, did the customer’s demeanor improve, stay the same or remain
unchanged from the beginning to the end of the call?
Now consider what we could do if we append the call detail
records, or CDRs, to these data and watch to see if the service interaction
(and any subsequent intervention) is associated with positive or negative
customer behaviors as time moves forward.
Market segmentation is one of the most exciting areas where Big
Data can help brands minimize risk and maximize impact. Market Strategies’
blended approach to creating segments has always emphasized the combination of
behaviors, attitudes and other variables as a genesis for developing
segmentation stories that are strategic and actionable. The emergence of Big
Data provides us with a richer array of options for incorporating behavioral
and channel marketing linkages into our segment views.
Below are two ideas that illustrate how Big Data could help
you better understand and frame your market:
- Consumer Population
Data‘Imagine a segmentation approach that uses both Census data (our Big
Data source) and customer needs and preferences (our traditional source) as a
foundation for creating segments. From a strategic standpoint, such an approach
could be useful to assess regional candidates for footprint expansion or to
drive the development of needs-based products that vary by geographic factors.
From an action standpoint, this approach could identify pockets of customers that
are most likely candidates to receive tailored messaging. And these customer
pockets could be split into test/control groups to pilot an examination of the
effectiveness of different marketing treatments.
- The Internet of
Things‘What could be done by incorporating telematics data into your
segmentation story? Consider data generated from health monitoring applications
on smartphones; from vehicles as they are driven; or from cable boxes as
consumers watch programming or flip through channels. Each of these data
streams contains information on product usage which, when combined with
attitudes and customer evaluations of the product, could produce an interesting
segmentation that connects needs, usage and satisfaction. Once key segments are
identified, in-depth-interviews (another traditional source of data) can be
conducted to understand pain points and delighters in the product experience.
The insights from this qualitative approach could, in turn, drive the
development of improvement programs to address pain and expand delight. A
handful of improvement programs could be evaluated among test and control
groups to determine their effectiveness, which could be measured by both
changes in usage (?? la Big Data) and attitudes (?? la survey measures like
The combination of Big Data with traditional qualitative
methods and quantitative surveys is a critical step toward ensuring your
segmentation has the strategic value that’s necessary to guide your brand and
the tactical content needed to facilitate segment specific program development
Making It Work for
I hope that some of these ideas have sparked your interest
in Big Data and how its marriage to traditional qualitative and quantitative
research can deliver a superior level of insight and confidence in decision
Have you determined how to balance your approach to insight
development across traditional qualitative, quantitative and Big Data
approaches? Are you confident you have the right mix and level of connectedness
across data types to minimize risk and maximize impact for your company?
About the Author: Contact
Dawn Palace at 734.779.6860 or Greg Mishkin at 404.601.9561 of Market Strategies to learn how we
can help your company understand the what and the why of customer experience to
make more confident and connected business decisions. Greg will be presenting
“Traditional Market Research and Big Data Integration: A case study in
what works and what doesn’t” at TMRE
in Nashville October 21-23.