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.
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!
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.
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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