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Thick Data: Why Marketers Must Understand People's Behaviour

Is thick data another buzzword, or critical to marketing success?

Big Data
4January

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.

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