Segmenting Consumers to Predict Future Trends

Segmenting Consumers to Predict Future Trends


In the digital media space, it's challenging enough for companies to stay on top of consumer trends, much less attempt to get ahead of them. A digital distribution company approached Vital Findings with exactly that goal in mind: to identify and profile lead users of digital media in order to optimize and tailor future product development, a goal calling for an in-depth consumer segmentation. However, the team at Vital Findings believed that our segmentation analysis needed to go further than just the typical profiling of heavy users, since consumer attitudes can be leading indicators of future behavior. We needed to allow for the possibility that there were different types of heavy users (e.g. those who approach digital media as a substitute for physical media vs. those who see it as a supplement), and that some average users might share some of their key underlying needs, an overlap that would point to areas where early adopter trends were likely to become mainstream. Using statistical techniques such as factor analysis and k-means cluster analysis, we allowed the data to drive the segmentation while using our industry expertise to ensure that the factors behind the data were actionable for our client. Finally, the Vital Findings team developed personas based on each of the final segments, helping our client's marketing and development teams visualize their target consumers.


Provide a leading digital distribution company with a means to identify and profile "lead users" -- consumers whose attitudes and current behavior are key indicators of future trends -- while also identifying needs shared by average users, an indication of trends likely to reach the mainstream. The client also needed a way to bring the segments to life for their management team, which meant that the Vital Findings team would need to develop personas of each segment to help visually communicate their characteristics and attitudes.


In addressing this challenge, the team at Vital Findings decided to begin the project by reviewing the client's past attempts at segmentation. The team used data mining techniques to analyze the client’s previous segmentation work and identify which methods showed promise, which were actionable, and what was missing. Based on this analysis, the team decided to focus our new segmentation study on four key factors: movie affinity, shopping style, new technology adoption, and digital vs. physical attitudes and usage.

Instead of relying on simple demographic characteristics, such as age and income, to define our segments, we let our more advanced approach dictate how many segments might exist. In poring over the data, Vital Findings utilized factor analysis to determine which attitudes drove the most differences among consumers, and k-mean cluster analysis to develop segments of consumers who shared similar attitudes. In addition to measuring consumers' self-reported attitudes, the team took a novel approach to the analysis, adding in behaviors from other categories (e.g. digital music, social networking) to give the final segments a broader context.

Advanced Analytic tools - K-mean cluster analysis
Advanced Analytic tools such as K-mean cluster analysis allow us to visualize complex data sets and bring them to life through further research and personification

Once the six segments were revealed in the data, the team went to work profiling the segments and constructing personas for each. For example, one digital-leaning segment preferred digital media for the cost savings of streaming, and were more female than other digital users. Another digital-leaning segment was more male, and exhibited attitudes toward movie collecting that were shared by a physical-leaning segment of male disc collectors. The team honed in on the key mindset elements of each different segment to understand how and why the segments were making their decisions, leading us to broader needs such as value, self expression, and social acceptance.

In order to ensure that the learnings from the segmentation would continue to resonate throughout the organization, the team translated these key factors into a predictive algorithm and typing tool to enable the client to accurately identify each segment group quickly and easily. This typing tool lived beyond the project, as the client team was then able to use the tool to identify target segments in every subsequent market research study that they performed.


The digital distribution company was able to differentiate among heavy users, and reveal the underlying needs that would likely become mainstream. Further, because the upfront analysis work was thoughtful and collaborative, the findings uncovered trends relevant to the company’s partners as well, such as retailers and advertising agencies. Armed with these insights, the company will be able to ensure its products’ success in its partners’ stores and digital outlets.

Advanced Analytics Research


A good segmentation should be revealing, thought-provoking, and insightful, but also intuitive and easy to evangelize. Vital Findings can dive deep into the heart of a market, digging out synergies and trends previously untapped, by using advanced statistical tools to make sense of data. Our team can also clearly communicate our technique and findings, de-mystifying the "black box" that can often come to symbolize advanced analytics.

Finally, with our qualitative and design research expertise, we can bring segments to life using ethnography to develop segment personas and design deliverables such as posters, immersion sessions, and mini documentaries. While the data may be in-depth and precise, the presentation is always clear and actionable due to our design-centric approach.

Some examples of the type of questions that would fit well with this methodology:

  • I've got the early adopters, but who's the next likely adopter of my product and how can I meet their needs?
  • Which traits of my lead adopters are likely to become mainstream?
  • How can I develop one segmentation and still feel confident that my strategic planners, marketers, and product designers will all use it?
  • My service reaches a large swath of the market now, but how can I make sure that I'm anticipating future needs?


  • Segmentation
  • Personification
  • Multivariate Analysis
  • Data Mining
  • Algorithm Development

Innovative Methods

Creative Advanced Analytics:
Not only did we utilize advanced analytical tools such as factor analysis and k-mean clustering in order to interpret data, but we also applied these techniques in creative ways. We took the time to evaluate prior attempts at segmentation, and created an analytic framework for the segmentation before jumping into field.

Unique interpretation of data:
Having an enormous amount of data is only as helpful as the team that can analyze and explain it, and segmentation is often a "black box" analysis conducted by outside statisticians. Our team understands how the statistics behind segmentation work, and was able to add novel variables to the analysis that cut across multiple questions. We also developed variables from related categories like digital music, which added a broader context to the analysis.

Quick turnaround:
Multivariate analysis can often take months. By developing our analytic framework from the beginning, our team was able to complete the statistical work in a matter of weeks.

Design sensitive presentation:
Our design team ensured that the findings were visually compelling and that the segment personas gave the client an intuitive feel for their needs. The team also used graphics to help communicate the key similarities and differences across segments.