By: Kea Wheeler, Senior Project Director
Imagine if your boss told you that she had found the perfect target group based on attitudes and needs segmentation, called Savvy Savers, and wants to conduct research with them.
But once you head off to find this target group, your boss tells you these Savvy Savers also have to drive a certain type of car, be aware of a certain brand, have 2.5 kids, see themselves as innovative, like to try new things, and must be located in Dallas. Welcome to the world of recruiting qualitative research with a segmentation algorithm screener.
What is a segmentation algorithm screener?
Traditional screeners use a set of questions to identify qualified consumers to participate in qualitative research. These questions usually revolve around criteria such as demographics (i.e. age and income) and can include category preference questions.
A segmentation algorithm screener is more complicated. Companies usually segment their market into subsets based on criteria such as attitudes, usage, or needs. These segmentations are usually done through a national quantitative survey. The results provide population subsets that companies usually name in order to speak about these segments of their target market in a more personal way.
Once the segmentation is complete, companies have a list of questions that they feel every named segment, such as the Savvy Savers, will answer the same way regardless of where they live. These series of questions is called an algorithm.
Why are segmentation algorithm screeners problematic?
Not all segmentation screeners are a bad thing. When applied effectively, they can bring companies closer to their target market. Issues arise when expectations are different from reality.
Issue #1: The algorithm target may not be the real target audience
Let’s use our Savvy Savers target as an example of being “the perfect target.” If the potential consumer answers the algorithm questions in a certain way, they fit the desired target market and qualify for the study. However, “this perfect target” is never perfect on an algorithm screener. Clients want potential participants to qualify for the study by answering the algorithm questions a specific way and, in addition, meet a host of other criteria. This means that the “perfect target” is indeed perfect on paper in the segmentation report, but not when it comes to who they want to actually attract in the marketplace.
Issue #2: A national incidence does not always equate to a specific market’s incidence.
Segmentation surveys are typically fielded with a broad geographic scope. This produces a national incidence or incidence rate. For example, if a company determines that the incidence to find a Savvy Saver is 20% nationally, that means that if 100 people across the country were called and screened, one should find 20 people who can be classified as Savvy Savers.
This seems reasonable enough. But qualitative research is not based on national representation. For the most part, qualitative research is conducted in 1-3 markets. This makes it harder to find and recruit the desired target group.
Issue #3: Qualitative research may be completed at a fixed location.
In some Qualitative research methodologies, it is necessary for participants to come to a specific location to participate, which further limits the number of potential recruits because respondents must be within a certain radius of the facility. Couple the limited location with the need for consumers to attend the research on a specific date and at a specific time and the pool of potential Savvy Savers to recruit may have dropped from 20 to 3.
Issue #4: The algorithm may be outdated.
Segmentation studies can be expensive and time consuming. So it is understandable that companies may only conduct a segmentation study once every few years. This may be acceptable for items that take more time to change such as attitudes and beliefs, but things such as needs and usage can change dramatically in a short amount of time. Circumstances can create lower incidence, which means less potential respondents for the qualitative study being recruited.
Issue #5: Algorithms can increase costs and may reduce the number of willing recruiters.
Recruiters dislike algorithm recruits. Seriously, dislike them. This disdain can result in higher per recruit costs or recruiters flat out refusing a project.
One of the reasons recruiters dislike segmentation algorithm screeners is because the algorithm “key” is a huge secret known only to the client and the supplier who conducted the segmentation study. This minimizes the ability for recruiters to “pre-screen” their databases.
Without the pre-screen option, Maya Middlemiss, the Managing Director of research recruitment consultant Saros Research Ltd in the UK and Casslar Consulting in Spain, warns recruiting costs could resemble that of cold calling. In Middlemiss’ article, Recruiting qualitative participants research using quantitative algorithms, she explains,
If we are provided a locked tool, the only thing we can do is apply it after the event during the telephone interview stage – this is more cumbersome and expensive, because it does not enable us to rule out people who are not a fit before the calling stage. Depending on the expected incidence of the desired segment(s), the strike rate – and therefore costs involved in recruitment – may even approach that of cold-calling. That is often a surprise to clients, but it is a consequence of trying to use quantitative tools in qualitative research (April, 2016).
We’ll continue this discussion in part 2 of our post on the use of a segmentation algorithm screener next week, where we will discuss solutions and the value that this type of methodology can provide.