Grouping consumers is a crucial stage in the market research process, whether before or after insight teams implement research tasks, grouping consumers in a way that is relevant to the research and business can help provide great context to insights.
By grouping consumers, insight teams and brand stakeholders are better able to see exactly who is in their current customer and consumer base , as well as who their target audience should be in both of those categories. With this, stakeholders can make more relevant and accurate decisions based on high-quality data, and insight teams can tailor research experiences to each segment based on their own wants, needs and communication styles. So segmentation really impacts the ways in which market research and strategic business decisions work out.
But, there are challenges to grouping consumers on both the insight teams’ and stakeholders’ ends, and there are a few ways to choose from that we can go about grouping consumers depending on the research experience and objectives involved.
Segmentation is the overall term for dividing up a group of people and putting them into groups based on similar characteristics. When you want to segment a group of consumers, you already know who the target audience is going to be, and can segment them as soon as they come into the research project based on characteristics you already understand.
There are a of ways to segment a group of consumers, from pain points you want to research and psychographics, to a specific niche and historical analytics. There are numerous benefits to employing segmentation tactics both in research and business strategies, mostly to do with improving direction and enhancing focus, but also build deeper affinity and connections to the customers and consumers within those tailored segments.
Segmentation such as this can be used for any type of research, and is encouraged as one of the easiest forms of grouping consumers. It allows stakeholders and insight teams to better understand those respondents within a panel or community beyond simple demographic data before the research has even started — this in itself allows research tasks and communications to be tailored to the desires and comfort of each segment and better chances of long-term respondent engagement in one or more research projects. For example, some segments might prefer the aloofness and format of survey research, while others will undoubtedly prefer the intimacy and conversation of focus groups. Further to this, research teams can call upon specific segments to different research tasks, or mix the segments to gain a better, wider representation of all respondents in their customer base.
But there are challenges to segmentation that need to be kept in mind:
- Firstly, understanding that consumers can belong to multiple segments at once, and planning needs to be made so that they are either able to become a part of multiple segments or insight experts must decide on which segment they fit in with best.
- Similarly, keeping segments precise and relevant is the next challenge that must be accounted for — if those segments aren’t accurate or relevant, then all research done with those segments will not be as reliable in quality.
- Allowing your segments to evolve organically is the last challenge to consider wisely. As consumers evolve, so will their ties to the segments available. Some consumers might move between segments, losing their affinity with their original segment and finding an affinity with a new one based on how their experiences, opinions and behaviour change. Allow those respondents to move freely between segments to maintain relevance and high-quality insights.
The practice of Clustering is the version of segmenting that happens after the respondents have been recruited and participated in research tasks. Instead of grouping people that stakeholders already have a base understanding of before the research begins, clustering simply identifies what people do most of the time based on the data gathered from research tasks, and this allows insight teams and stakeholders to predict what customers are likely to do without boxing them into rigid groups from the start. Cluster analysis uses a mathematical model (or machine learning and algorithms) to discover groups similar to customers based on finding the smallest variations among customers in each group.
While this means it might take a little longer to understand who the customers are, the grouping of customers happens with complete accuracy based on the behavioural and research data they provide. The groups then created are distinctly tailored to the respondents within the research projects, and accurately reflect their common actions, experiences and opinions to stakeholders, who are then able to make successful decisions based on a fuller informed understanding. This particular technique is best for longer-term research experiences that provide the time needed to gather a full understanding of the consumers at the stakeholders’ fingertips. The more research is done, the more refined the clusters become, and thus the more tailored research and business decision-making can be too.
Just as with segmentation, there are many shared and unique challenges to using cluster analysis:
- The first challenge that is shared with segmentation is to allow the clusters to evolve organically. This can be done a lot quicker and effectively than with segmentation, as the practices for forming those clusters in the first place are the same as those that allow the clusters to evolve — take the data shared by respondents and use them to refine the clusters and help respondents move between depending on their suitability.
- The first unique challenge to take note of is understanding which clusters are actually important from the data provided. Take care not to make too many distinct clusters, as while this might help with making informed decisions, those decisions are likely to only impact a few customers out of the entire customer base. Find those clusters that are relevant to the stakeholder organisation, and then populate them with the respondents who match the cluster criteria.
- Lastly, clustering is a slower process than segmentation, as it means sifting through a high volume and variety of data to create those clusters in the first place and then doing so continue to make sure those clusters are relevant and accurate. Take the time to do this though, as it will benefit both the research experience and the stakeholder business more in the long run.
Creating Impactful Personas
Similar to both clustering and segmentation, Personas are fictional characters that are made by insight experts and stakeholders before research experiences have started and are then refined and informed by the data generated from consumer research. This goes beyond segmentation through the creation of distinct ‘characters’ to represent the groups of consumers rather than simply using criteria to identify them. These personas represent groups of consumers within a target audience depending on demographic and behavioural data of the stakeholders’ choice, and can be used to embody different groups of customers to show how they evolve and behave when interacting with the brand — each persona will also document the different expectations and needs each persona has in regards to the brand.
Personas are a great form of grouping consumers, as they bring a level of personality to the task, allowing stakeholders and insight experts to better relate to the different groups. These personas are able to represent and communicate with stakeholders across the organisation, keeping them engaged and up to date with the latest insights and evolutions for better decision-making at all times. They are particularly useful for customer communities, where insights are derived at all times of the day both within and outside of scheduled research tasks, as customers communicate with each other in forums, blogs, comments and such rather like an exclusive social media platform.
As with segmentation and clustering, there are challenges to heed wisely:
- Just like with the other two forms of grouping, allowing these personas to evolve organically to retain relevance is crucial to the success of insights generation and activation across the organisation. If those personas that stakeholders rely on are not accurate then the decisions made based on them will fail, and the engagement in research will wane, with stakeholders relying on old-fashioned gut feelings rather than solid insights.
- Similar to the clustering challenge, having too many personas or having irrelevant personas will have the same negative impact as not allowing them to organically evolve with new insights and data. But also, having too many personas will have the same impact as having too many clusters, the decisions made based on each niche will not have a great impact, and so creating a few core personas to catch most if not all of the customer base will provide better direction and chances of success.
- Restricting the persona criteria to demographic data will be the downfall of anyone who uses this grouping method. Demographic criteria might form the foundation if there are commonalities between customers, but better to rely on behavioural data and insights on opinions and experiences to form these personas, as they will more accurately reflect the customer base and catch more customers in one persona than simple demographic information.
Grouping for Better Insights
Whether you are segmenting, clustering or creating impactful personas, it is guaranteed to result in more accurate decision-making on all sides of the equation if that grouping is done right. On the stakeholders side, it means more accurate decision-making, more access to insights and a well-rounded understanding of both the target audience and the current consumer base that will feel the impact of all decisions.
On the insight team’s end, grouping consumers will help them design more targeted research experiences for whatever decisions client stakeholders have coming up — they can quickly recruit the right research respondents and generate accurate and relevant insights in a quicker timeframe with half the effort and resources.
For research respondents, they are able to participate in customised research tasks that are more likely to engage them, more likely to be on a topic they’re interested in and worded in a language they instinctively understand. And the decisions made on the back of that research will result in a positive impact on their brand and customer experience.
This article was originally published on the FlexMR Insights Blog and can be accessed here.