Re-Designed Thinking
How Big Data Ethnography Technology will transform our approach to the Design Thinking process.
“We have entered the age of Belief-Based Consumption.” When my business partner and CEO, Ujwal Arkalgud, said these words back in 2013, I had no idea how much the emerging shift in “WHY” we buy would change product positioning, innovation, and commercialization.
But here we are.
It’s 6 years later and now, 64% of consumers around the world make purchases based on their beliefs.
As a result, the traditional approach to brand building (and product commercialization) that so often focused on a brand’s essence first, and the consumer’s values second, is being abandoned for a more emotionally intelligent approach.
The model is essentially being flipped on its head. Now, we start with the consumer first, and the brand second. It begins with researching and deconstructing the consumer’s needs, problems, and challenges.
The goal?
A consumer-first approach that eases the tension that comes with introducing new products or solutions into the life of the consumer. It is a more human-centric approach to connecting with people. And it is making Design Thinking more relevant than ever before.
Revisiting the Design Thinking Process
As many of you are likely aware, the fundamentals of Design Thinking are simple to list, but hard to do:
The challenge is that the Design Thinking process is not a step-by-step procedure.
When fully and properly utilized it is an iterative approach that allows the team to move forward and backward, learning as they go. As you can see in the flowchart below, the power of Design Thinking is to follow knowledge that presents itself to better inform, better assess and better create.
The better you understand the consumer the better you define the problem. The better you define the problem the better the ideation. The more you prototype and test, the better you understand the consumer and the better you can generate ideas. And so on.
Iteration forces the strongest ideas to rise to the top.
Yet, despite the importance of iteration, there are very few research and insight discovery tools that can be leveraged throughout the process.
How traditional research can limit Design Thinkers.
The first step of the Design Thinking process involves developing a deep sense of empathy towards the consumer you are designing for, to gain insights into what they believe, what they need, what they want, how they behave, feel, and think, and why they demonstrate such behaviors, feelings, and thoughts.
Often, Consumer Insight and Innovation teams will leverage social science-based methodologies like ethnographic research to be the foundation for the project. But while immersive ethnographies can provide rich insight into consumers, they pose a large challenge. It is a costly and long process.
Which is why it is sometimes skipped in the early phases altogether.
They also often rely on a sample size that is relatively small and it is difficult to know if you have analyzed a representative sample of the population. This makes it very difficult to run a regression to predict if the findings are growing or shrinking in culture.
Because of these limitations, designer thinkers often only explore the emotional DNA of consumers during the initial stages of the process. Meaning that if you are iterating based on learnings that present themselves in later stages, you can't leverage social science-based learning to provide context later in the project.
This last limitation is important because the iteration that is so critical to the Design Thinking process is not possible using most research methodologies. So instead, insight professionals are forced to take a bookend approach where there is “Discovery” research done at the outset of the project, and “Validation” research done at the end of this process.
Technology to the rescue.
When it comes to understanding and empathizing with consumers, advancements in word vector technology represent a significant leap forward in enhancing our ability to analyze relationships across words, sentences, and consumer interaction. We can now go beyond rudimentary pattern recognition and sentiment analysis and instead, go deeper, to identify the unspoken needs of consumers.
With the right data (and more importantly, the right amount of data) we can recreate consumer culture in a virtual space by providing machines with the information they need to identify and map the topics we discuss, and how they impact and influence all the corresponding topics that exist in culture.
Marry this with advancements in cataloging and coding social science data and the result is technology that doesn't just track emerging waves of interest - but it can analyze the words we use and reveal dominant motivations, attitudes, values, and fears. This can shed light on how consumers assess what is important to them, and what is something that they will likely avoid.
Context is key.
Traditional approaches to NLP such as one-hot encoding and bag-of-words models are useful in some applications. But the challenge when analyzing consumer culture is that they do not capture information about a word’s meaning or context.
As a result, potential relationships, such as contextual closeness, are not captured across a collections of words. In contrast, word vectors take words, transform them into multidimensional continuous floating point numbers and then maps semantically similar words in geometric space.
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The beauty of representing words as vectors is that they lend themselves to mathematical operators - meaning we can identify topics and trends, and make them measurable, comparable and predict their volatility and growth over time.
Context makes insight on demand possible.
In January of 2019, MotivBase launched the world's first and only big data ethnographic research tool designed to decode the hidden meanings behind consumer interactions. The vector technology enables the discovery of ethnographic insight, using big data (millions of consumer interactions online) and allows us to map how topics are growing in relevance to consumers.
Secondarily, we can match ethnographic factors like motivations, attitudes, values, and fears to the discourse that surrounds a topic or trend. So as long as a topic or trend has enough volume of engagement, we can make social science data (the meanings behind topics/trends) measurable and comparable over time, allowing us to not only improve the accuracy of our predictions, but also capture nuanced changes in cultures and subcultures that otherwise might have been missed.
Now, like typing a search term into a search bar, we can identify where a topic or trend sits in culture today. We can map its volatility over the past few years, and we can predict how fast it is growing.
We call this technology Big Data Ethnography and it can deliver details around who is engaging with this culture, including their socio-demographics, their ethnographic DNA and related topics that are driving growth or causing a culture to decline.
And it can deliver it in the time it took you to read this sentence.
What this means for the Design Thinking Process.
The Design Thinking Process is iterative, so that you can constantly augment your course based on new learnings. The benefit of Big Data Ethnography is that instead of feeling like you have one shot at research at the beginning of the process, you can “keep going back to the well” to explore the cultural meanings and consumer needs as information and learnings reveal themselves.
Let’s look at the Iterative Approach to Design Thinking with this in mind.
Empathy
Much like an infield ethnography, Big Data Ethnography allows you to explore the culture of the category or product universe that is relevant to your product. By analyzing and comparing cultures, you can start to identify the dominant emotional needs. Big Data Ethnography allows you to study hundreds of thousands (if not millions) of consumers to identify the dominant unspoken motivations that shape their engagement with a topic or trend you are looking to understand. But it also highlights the dominant values, attitudes and the fears that give you a rich and empathetic understanding of what they are trying to achieve in life.
While many of these findings can be identified in some infield, social science-based methodologies, they are often costly and time-consuming. Instead of waiting months to start your process, you can build a detailed understanding of the consumers' needs in a few days.
Define
As you look to define the parameters of a project, Big Data Ethnography allows you to take the Ethnographic DNA of a consumer, and identify products that are in the market that are already emotionally solving these needs. By analyzing customer review data, Big Data Ethnographic tools can give you tangible examples of solutions that are in market to provide you with a clearer line of sight to whom you are competing with, how they are positioning themselves and what key ingredients, benefits or technologies are making them stand out to the consumer.
Ideas
As you look to identify potential solutions that leverage your bench strengths, Big Data Ethnography allows you to size cultural markets and determine if they are growing and how fast. This means, that you can run real-time regressions to stress-test ideas before engaging the broader team or investing in the prototype stage.
Prototype
As we have seen in the Design Thinking Process diagram, Prototyping leads to learning, and a return to the Ideation phase. Once again, Big Data Ethnography allows for an iterative and circular process of where the team can stress test the cultural relevance of potential changes influenced by the prototyping phase. This back, and forth process is like sharpening a blade, leading to a more emotionally AND functionally relevant consumer solution.
Testing
Testing often leads to additional learning that can take us back to more ideation. In essence, consumers try a solution and say what they like and what they don’t. Similar to the prototype stage, Big Data Ethnography can be used as you return to the ideation stage to improve your thinking based on consumers’ direct feedback.
But the goal of taking a more iterative approach to supplementing the Design Thinking Process with social science research, is that you will have already solved for the emotional and social needs of the consumer. The more we have a contextual understanding of their motivations, attitudes, values, and fears, the more likely it is that the solution you are ready to test is relevant to your desired consumer.
This is why Big Data Ethnography is critical to accelerating the Design Thinking Process. More context improves every stage of the process, driving better solutions because you can better anticipate the needs of consumers.
A...B...C...
Innovation teams are increasingly being pressured to deliver more impactful products and solutions in less, and less time. The goal of accelerating and scaling social science-based research is to provide the critical cultural data that helps us understand the often irrational and emotional drivers that shape consumer decisions.
Insight Discovery Platforms will allow consumer insight and innovation teams to constantly ask themselves “Do I understand the needs of my potential consumer?”
But it will also allow them to better understand the data and information that they surface throughout the Design Thinking process.
Because, now, for the first time, they can A… B… C…
Always… Be… Contextualizing.
To learn more about Big Data Ethnography visit MotivBase.com