Telmar News & Insights

3 Audience Segmentation Techniques Media Planners Need To Know

July 27, 2021


Before we plunge into a discussion about segmentation techniques, let’s put ourselves into the right frame of mind. 

For a moment, think about your own experiences as a consumer.

  1. Do you recall an instance where you were targeted with an ad or offer you thought was really wrong for you? 
  2. Along those same lines, can you think of an ad you were exposed to that was surprisingly on-point? 

Now that you’ve got those two examples for context, step back into your advertising and/or agency-related role.  

Are you looking for ways to more effectively target your audience? Have you recently spoken to, or exchanged email messages with, a client who is counting on you to help them achieve better results for KPIs like reach and frequency? 

Segmentation tools can help you achieve those results. But the more you know about the main types of analysis – and what aspects of a campaign they can help you with, the better.

Let’s step back for a moment. It’s important not to gloss over this next bit.

Segmentation tools enable you to divide an audience into groups that share characteristics or differences with one another. Think of this in practical terms. You can use attributes like attitudes, behaviors, lifestyle, life stage, and demographics to subdivide that larger audience into smaller – ostensibly more responsive – target groups. 

The good news is that some of those segmentation tools can capture a staggering amount of data. The perhaps unfortunate news is that parsing, understanding, and making this data actionable can be quite a challenge. Sophisticated data integration techniques can make multiple sources of data work together for better audience analysis. More specifically, data integration can bring primary data and syndicated (general market) data together or bring two separate syndicated data sets together – like consumer research and media audience research.


So, if you’re looking to conduct better audience segmentation analysis, here are three techniques that should help. 

1.Correspondence analysis

Correspondence analysis is used to identify and understand relationships between markets, brands, and media. Primarily, correspondence analysis helps you identify factors that differentiate between people in a market. But it also helps media planners spot potential market gaps. Correspondence tools help to quickly see these relationships by generating a correlated pictorial perceptual map of your cross-tab results. 

Let’s consider a correspondence analysis meant to reveal which segment of a brand audience aligns with a particular consumer attitude. Some examples of these attitudes might be:

  • “Strongly concerned about the environment” 
  • “Strongly concerned about getting ˜the best price”
  • “Strongly concerned about making a fashion statement” 

Correspondence analysis can also be used to identify the most discriminating or important lifestyle statements if you were preparing to use a cluster analysis tool. (More on that in a moment.)

While media planning software often promises to help you accomplish more, better, and in less time, it’s up to you to make sure it does. So, when evaluating your options, it pays to prioritize three key capabilities above the rest.

2.Cluster analysis

Cluster analysis helps you segment a target audience into multiple smaller target groups (clusters) where the people within each cluster are maximally associated with each other (homogeneous) and the groups themselves are maximally differentiated (heterogenous).

Let’s revisit the example of high-performance car buyers. We know that all high-end performance car buyers are not alike. Cluster analysis will identify and create target segments that can each be analyzed and acted upon with refined marketing and media tactics for more effective reach and efficient media spend.

3.Factor analysis

When you need to reduce a large data set down to a smaller one for easier handling, take a closer look at what factor analysis can do for you.

Factor analysis helps you determine which variables in a large data set have the strongest underlying dimensions that are inter-correlated – and groups these variables into buckets called “factors.” Each factor can become a new variable within the data set and effectively replace all the variables that it represents.

Data reduction is cool, but even cooler is how factor analysis can reveal latent (hidden) information in your data by giving you answers to questions that were never asked. 

Let’s say you’re doing some audience targeting work and for whatever reason the target you’re aiming for is “smartphone super users.” Luckily, you have access to data from a survey that asks a whole bunch of questions about the features respondents use on their smartphone.

However, the survey did not ask any questions about the level of each respondent’s expertise. So, how are you supposed to figure out which ones are “super users?” Trying to identify experts based on one or two advanced features does not work because most people know how to use some number of advanced features. 

This is where factor analysis performs its magic. It enables you to use all the questions asked about features to discover underlying dimensions that are inter-correlated into a factor that can be identified as “Expert User.” 

From this factor a new variable can be added to the data set as “smartphone super user” and it can then be used for segmentation analysis and in all the other audience segmentation tools. Viola!


Summary and Takeaways

Powerful segmentation tools can be tremendously helpful in gaining a deeper and richer understanding of your audience targeting. Insight into audience targeting can fine-tune your tactics, overcome targeting obstacles, and achieve a more cost-efficient use of your budget to reach more potential customers.

But it all starts with the data. If your data is not rich enough to fulfill your needs, data integration can help you wring greater targeting value from the data that you already have.


Discover the Competitive Edge You’ve Been Looking For

You know data integration as a technique meant to help many different players in the advertising ecosystem. Media sales houses use it to gain a clearer picture of their total audience. Advertisers use it to make better media investment decisions. And agency professionals use it to accomplish better results with less resources. 

But integrating data in ways that will truly help optimize your results is rarely easy. For ideas on how to gain an edge, download your copy of our new eGuide called, Supercharge Your Marketing Intelligence with Data Integration.”

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