The baseline audience is what your audience is being compared to, which defaults to the Online Adult Population in the Resonate platform. Think of your baseline as the way to define your universe, or what you want to compare against. Comparing against the Online Adult Population is a great way to get insights for U.S. consumers as a whole.
But changing your baseline helps you get hyper-relevant insights by allowing you to compare audiences to your universe - whether that be the geographic area that you operate, the people visiting your website, people who are interested in your vertical or niche, comparing yourself against a competitor and much more.
Here are a couple of examples of how changing your baseline can improve your marketing.
Comparing Against a Geographical Area
Targeting the geographic area where your business operates helps you eliminate individuals who can't use your products and services and better target those who will.
1. A regional credit union that only operates in six states can set that geographic area as the baseline to restrict their audiences to those six states. Setting that geographic area as their baseline and also including it in their audience definition hyper-filters their research results to only include people who live in the area where they operate.
2. A regional daily newspaper has an audience comprised of tag data from one of their client's websites. They can build a secondary audience using attributes that define the tag data audience. For example, people who are intending to purchase home goods in the next 3 months. They can set the baseline for this secondary audience as the DMA where they operate. Then, they can compare these two audiences to understand their client's website visitors in the area where they operate vs. nationally.
Comparing Against Your Website Tags
Comparing against your own website tags narrows your research to the universe of people who are already interacting with your brand online.
An entertainment company uses their dynamic website tag as the baseline in an analysis to understand who is researching one of the attractions they offer, and of those, who is visiting the ticket purchase page for that offering. This helps them narrow down people who are most likely intending to purchase tickets for that attraction.
They used their dynamic website tag as the baseline audience and set their audience as the website tag for people who visited the ticket purchase page. Then, they chose to filter insights by DMA.
They discovered that the majority of people visiting their website and making it to the ticket purchase page were from Kentucky. Now, they can focus on creating a targeted marketing campaign for this state.
Comparing Against Survey Attributes
Comparing against survey attributes can help you narrow your focus to your specific vertical or niche area of expertise.
A financial company wants to gain a better understanding of what % of investors' portfolios reside in stocks versus mutual funds. Analyzing an audience comprised of their own customers will result in people over-indexing - naturally - for investment behaviors compared against the Online Adult Population. Instead, they can use a custom baseline that includes their own vertical-specific attributes. For example, they can create a baseline of all investors to increase the accuracy and relevancy of their research.
When you are thinking about baseline, it's important to take the Index metric into account. Only Index will change when you change the baseline for an audience, Composition will remain the same. Check out this quick guide to learn more about index and composition.
As a quick refresher:
Index - tells you how much more or less likely it is that your audience exhibits a certain attribute in comparison to your baseline audience. Index helps you to identify attribute values that stand out from others and make your audience unique compared to your baseline.
Index is calculated by dividing your audience composition with your baseline composition. It is important to keep in mind that changing the baseline means you’re changing the calculation that determines the index.
Composition - tells you the percent of your audience that exhibits a certain attribute.
Pro-tip: Finding meaningfulness in the Index metric varies based on data type and business case. For example, as a rule 110-120 is considered a meaningful over-index value for survey data because there is more differentiation between survey values, while 105 can be meaningful for behavioral or tag data.
The range of Index values is also important. If there is a broad range, like 80-305, then an Index of 105 is not meaningful because there are much higher index values. If the range is between 95 and 105, then an index of 100 probably becomes meaningful.
These are general guidelines, but finding meaningfulness in Index ultimately depends on your business context.