“Thick data is data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories and behaviour models of their world.”
We are all familiar with the explosion of big data, with organisations investing a lot of time and money analysing massive data sets to obtain insights to gain the competitive advantage, but organisations are struggling to turn data into value.
- Big Data – Relies on Machine Learning
- Thick Data – Relies on Human Learning
Thick data is qualitative information that provides insights into the everyday emotional lives of consumers. It goes beyond big data to explain why consumers have certain preferences, the reasons they behave the way they do, why certain trends stick and so on.
Harvard Business Review defines thick data as a tool for developing ‘hypotheses’ about ‘why people behave’ in certain ways. While big data can indicate trends in behaviour 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.
Thick data comes to us in the form of a small sample size and in return, we get an incredible depth of meanings and stories. While big data loses resolution when we shrink the sample size to that required for Thick data.
Thick data will dent the assumption “If we give data to the right people at the right time, they will change their behaviour” and the organisation will be more successful.
Do you understand the emotional way in which people use your products or services? This will help your organisation develop a better understanding of your customers.
Two key areas that link tightly with Thick data are Single Customer View & Design Thinking.
Single Customer View – (SCV) is an aggregated, consistent and holistic representation of the data known by an organisation about its customers that can be viewed in one place, such as a single page. If you know more about your customer’s behaviour and cultural preferences from thick data this adds value to your view of your customers and personalised product offerings.
Design Thinking – (DT) is a methodology used by designers to solve complex problems and find desirable solutions for clients. A design mindset is not problem-focused, it’s solution focused and action oriented towards creating a preferred future. This method takes into account your thick data findings, meaning your products will be designed to meet the needs of your customers.
Should we be looking at Thick Data to gain a deeper understanding and gain value from all that big data?
A few stories to consider:
Big data will tell you that in 2013, Samsung was able to sell 35 million more smartphones than Apple. But what can these companies really do with this data? Pat themselves on the back or hang their heads in shame? If you are in the market for a smartphone, you’re not going to buy a Samsung just because they sold 35 million more than Apple.
As a customer, you probably don’t even know this information. You may, however, buy a Samsung because they offer a multitude of models that you can customize to your preferences, and Apple’s product line is less diverse. Or perhaps you won’t buy an Apple smartphone because it’s not quite as durable, or they don’t have as wide a selection of phone colours as Samsung.
Using thick data to figure out “why” more people are buying from Samsung is key for both companies to move forward and either keep dominating the market, or reinvent to gain dominance. At its core, business is about making bets on human behaviour, and those bets backed by thick data are what business models should be based around.
Nokia in 2009 was the world’s largest mobile phone company, ethnographic work in China identified lots of indicators that led to the conclusion that low-income consumers were ready to pay for more expensive smartphones.
To take advantage of this Nokia needed to replace their current product development strategy from making expensive smartphones for elite users to affordable smartphones for low-income users. Nokia did not know what to do with these findings as they said the sample size of 100 was weak and small compared to their sample size of several million data points. In addition, they said that there weren’t any signs of these insights in their existing datasets.
We all know what happened to Nokia!
Microsoft bought them in 2013 and it only has three percent of the global smartphone market. There are many reasons for Nokia’s downfall, but one of the biggest reasons was that the company over-relied on numbers. They put a higher value on quantitative data, they didn’t know how to handle data that wasn’t easily measurable, and that didn’t show up in existing reports. What could’ve been their intelligence ended up being their eventual downfall.
Lego – a successful company that was near collapse in the early 2000’s because they lost touch with their customers. After failed attempts to reposition the company with action figures and other concepts, Jørgen Vig Knudstorp, CEO of the Danish Lego firm, decided to engage in a major qualitative research project. Children in five major global cities were studied to help Lego better understand the emotional needs of children in relation to Legos. After evaluating hours of video recordings of children playing with Legos, a pattern emerged. Children were passionate about the “play experience” and the process of playing. Rather than the instant gratification of toys like action figures, children valued the experience of imagining and creating. The results were clear; Lego needed to go back to marketing its traditional building blocks and focus less on action figures and toys. Today, Lego is once again a successful company, and thick data proved to be its saviour.
As you have read Thick Data is not new, but to benefit from the findings we need to be open to behaviours and preferences that will not show up in our existing data sets.
How do you get moving with Thick Data?
- Start with a rigorous process with the executive team to identify where the most promising sources of value and benefit will be.
- What do you need more insight on – product / service?
- Define the case studies on where you will focus your attention.
- Prioritise. Start with one or two case studies to demonstrate initial value and prove the process and method.
- What data do you already have or have access to?
- Where are your gaps in knowledge and data? How will you fill these?
- Seek external help for conducting hours of interviews, analysing videos and listening to conversations, while looking for an answer to your defined questions.
- Pull out interesting themes and contrasts.
- Demonstrate your value and present your results to the business.
Figuring out how to make the most of Big & Thick Data, and what is the right proportion to use of each is a debatable topic.
A success story from Samsung – “What does the TV mean in the modern household?
The brand used Thick Data to better understand their customers’ relationship with TVs. Only by using rich, multi-format and multi-approach research were the brand able to discover some key insights.
Research revealed that to most people, TVs aren’t electronics, but furniture.
From this essential insight, the Samsung team redesigned their TVs, going for a modernist approach and changing their marketing strategy, based on customer feedback.
Therefore – even though we live in a digital era – there are occasions when human observation or face to face communication can make a difference and bring invaluable benefits to your business, especially when used alongside online research techniques.
Good luck with discovering your data’s emotions and preferences.