92% of companies are still dealing with obstacles to successful big data projects, according to global research by CA Technologies.
Across industries, the adoption of big data initiatives is way up.
Spending has increased, and the vast majority of companies using big
data expect return on investment.
However, companies still cite
a 'lack of visibility into processes and information' as a primary big
data pain point. Modeling customer segments accurately can be impossible
for marketers who don’t understand why their customers decide to make
purchases.
Many marketers applying big data to programmatic advertising or email
marketing initiatives understand patterns. With sufficiently
high-quality and recent insights, marketing departments can create
segments and offers that reflect reality. However, experts are
predicting that the next step for marketing will be the adoption of 'thick data' for behavioral understanding.
What is Thick Data?
Data-driven marketing is the act of making educated guesses about human
behavior, based on historical patterns and other analyses. Product
development, offer creation, and email campaigns are, at best,
well-informed guesswork about your customers. Thick data can represent
the missing piece by explaining why humans act the way they do.
Harvard Business Review
(HBR) defines thick data as a tool for developing 'hypotheses' about 'why people behave' in certain ways. While big data can indicate trends
in behavior that allow marketers to form hypotheses, thick data can fill
in the gaps and allow marketers to understand why their customers are
likely to take certain actions.
While 'thick data' is recently receiving a great deal of attention among
big data thought leaders, it’s not a new concept. There’s little
difference between 'thick' data and 'prescriptive analytics,' both of
which represent advanced maturity in marketing big data. By shifting
your focus from predictive big data to forming and testing hypotheses,
marketers can better understand how their buyers will act in the future.
Where Does Thick Data Come From?
Historically, big data has been transactional, while thick data has been
qualitative. For data-driven brands of years past, insights into
consumer behavior were typically derived from behavioral observation,
voice of the customer (VOC) or Net Promoter Score (NPS) surveying, focus
groups, or other time-intensive research methods.
Today, insights into consumer behavior can come from a variety of
sources. Thanks to social media, internet of things technologies and
other drivers of big data, marketers can gain insight into why humans
act the way they do with data sources such as:
● Online or Mobile Behavior
● User-generated social media content
● 3rd-party transactional data
Studies indicate that currently, 95% of brand research into consumer
preferences is performed manually, using methods such as surveying or
focus groups. However, in an era where consumers produce thousands of
insights each day from mobile usage, online shopping and social media
updates, the insights are easy to obtain.
How Thick Data Can Benefit Your Marketing Results
One of the most famous examples of thick data application belongs to Lego, who BIGfish
reports was on the brink of financial collapse in the early 2000’s.
After several failed repositioning attempts, the brand engaged in a 'major qualitative research project' to understand why the 'emotional
needs of children' at play weren’t being met by Lego’s current
offerings. After observing and analyzing countless hours of video
recordings, Lego was able to successfully reposition their products and
resurrect their status as an important toy brand.
While Lego’s use of thick data occurred in an age where analytics tools
were far less sophisticated or widely available, the concept offers
lessons to contemporary marketing teams. By applying attitudinal,
social, and other preference-driven data to your marketing analyses, you
can understand what your customers actually need. Yesterday’s focus
groups have been replaced by the trail of qualitative insights consumers
leave on their mobile devices, in apps, and at sensor beacons. For
brands that are willing to listen, there’s remarkable potential for
prescriptive analytics.
If your marketing goals for the year to come include a better
understanding of your customers, integrating more qualitative and
attitudinal big data insights can allow you to unleash the power of
thick data. The use of a Data Exchange Platform can allow brands to connect directly with
3rd-party data vendors, to gain real-time access to insights on why
their buyers act the way they do.
