AI: the engine that extracts the power of consumer data
Artificial intelligence is opening up a new window into the digital traces that consumers have shared for several years, that we haven’t been able to interpret fully before. Now, new data-driven consumer insights and groundbreaking ways to work with segmentation are within reach.
The digital traces we share reveal intimate details of our behaviors, preferences, and consumption habits. It has long been debated whether people will accept that businesses and public organizations have such extensive knowledge of our private lives. However, for every data sharing backlashes such as GDPR, revelations of leaked passwords, and the use of data to manipulate elections, people have continued to give away more and more data, and have likely pushed that development further. A reasonable explanation of this behavior is that the services we receive in return for our data far outweigh the costs and risks associated with sharing our personal data.
A shift in consumer attitudes
At the start of the millennia, getting promotions tailored to our own behaviors brought an uncanny feeling that a commercial entity had come a bit too close for comfort into our personal sphere. Today there is an expectation that the offers we receive as consumers should be personalized after our behaviors and preferences, otherwise the business that is behind the promotion will not be seen as knowledgeable about is customer base.
Consumers are also no longer passive receivers of products, services, and communication. The growing importance of self-expression and consumption patterns that are increasingly driven by interests, rather than demographics, results in more varied identities. Social groups centered around a shared interest, that often has a strong influence on consumption, is getting more common and consumers themselves are messengers, co-creators, and participants in more niched microcultures. Understanding these microcultures and creating services and promotions that are attractive to the right target group is the essence in succeeding with data-driven consumer insights.
From individuals to dividuals
Old ideas of a coherent individual are giving way to the notion of a significantly more complex dividual, built on multiple and temporary identities. Additionally, our lives are increasingly defined by social media, search engines, and networks, and new life patterns challenge the traditional demographic structures. In summary, traditional segmentation tools are slowly but surely becoming less accurate and have to be replaced or complemented by something new, while the digital traces left behind by consumers are increasing at a furious pace.
Data is just the fuel
So how do you handle all the data you have or can get access to? If data is the new oil, a new engine is needed that can process the data and take it further. Otherwise, it will just end up being an unstructured mess with great potential. Data's equivalent of an engine is spelled AI. Artificial intelligence, smart algorithms for pattern recognition and machine learning are the keys to get the most out of heaps of consumer data at a level that not even the sharpest market analyst can reach.
Example: The Modern Family
In a world where mothers and fathers would be lightly aggrieved to be bundled together in the homogenous groups of parents of small children, Kairos Future carried out a study on consumer tribes in this target group a few years ago. Tribes are segments of consumer groups that are built on particular behaviors, consumption patterns, or values rather than demographic or geographic factors.
How did the Modern Family project happen?
Using a combination of AI-driven pattern recognition and qualitative analysis, texts on parenting from social media were analyzed and synthesized. From these five distinct parent tribes emerged: Super parents, Uncompromising parents, Decent parents, Success parents, and Chill parents. The parent groups have marked differences – not depending on where they live, socio-economic status, or age – but by values, behaviors and consumption patterns.
In stage two a number of markers were identified for each tribe, that together reflected the complexity and multifaceted image that make up the groups. The counterparts for these markers were subsequently identified in comprehensive quantitative consumer surveys, through which we could estimate the size of each group, their demographic and geographical distribution, purchasing power, media habits, and shopping styles.
The insights and quantification of the tribes were later used together with our clients to generate development portfolios and marketing plans, which in the long run shaped their long-term innovation, positioning, and communication work.
Kairos Future has worked with our own tool Dcipher Analytics in these types of mappings. It gives us the possibility, without coding, to quickly find patterns and connections in large volumes of data. Please get in touch with Henrik Dillman to learn more about how you can work with data-driven consumer insights, netnography, and qualitative methods.