There are seemingly infinite data sources that marketers can utilize to help find their audience across media. The challenge they face is deciding how much to invest in data to maximize their return; in other words, will spending heavily on specific data to find the audience result in higher revenue than if they used a different source, or didn’t use the data at all?
While many marketers spend a great deal of time examining this issue, what’s often overlooked is the accuracy and source of the data they are considering. If marketers use generic or low quality data, then they may end up marketing to people they should avoid, and missing people they want to reach. This is especially true when marketing to the affluent, where financial qualifications play a major role in determining a target audience. To acquire powerful data, and pull from it the most actionable insights, marketers need to identify trusted direct-source data providers and avoid over-relying on survey-based sources.
Marketers know that data about household economics, demographic components, intent, and geographic data can help narrow down an audience. A single woman in her 20s, living in Washington, D.C., with little disposable income likely isn’t the target for a high-end cruise departing out of France. But this methodology only works with highly accurate and granular data, and there is no shortage of inaccurate data available on the market. Therefore, it’s important for marketers to understand where the data comes from and how it is gathered.
One source with a higher potential for inaccuracy is survey-based data, which analyzes a small (usually less than 1%) segment of the population and then extrapolates those results across millions of consumers. First, projecting that small a sample across that large a population inherently leaves plenty of margin for error. An additional problem is that imprecise information gathered in the survey process makes the resulting data less accurate, and there are plenty of ways to introduce inaccuracy into a survey. When it comes to household finances for example, respondents may overstate or understate their wealth, often unintentionally. For example, the affluent may not be able describe their complete economic situation off the top of their head, so they tend to provide incomplete responses.
When it comes time for the marketer to use this data, there are bound to be errors. One factor many marketers don’t realize is that the audience they are trying to reach may actually be much larger or smaller than the data leads them to believe. A post-campaign analysis may find that consumers making $150,000 per year (as defined by the survey-based data) exhibited the highest lifetime value. Through the lens of more precise data, it may be revealed that the ideal target may only make $100,000 per year. So, if the marketer uses those faulty insights for future campaigns, setting a $150,000 cap and continuing to use imprecise data, they could miss out on a large portion of an audience that could deliver high value.
This is where direct-source data comes in. Because it is built on more reliable information, such as anonymized wealth or spending data, it then tends to have a higher level of accuracy. This results in more actionable targeting, measurement, and insights, which are crucial.
For campaign results to drive revenue, they must be based on accurate insights, and data is the foundation upon which marketers build insights. It’s up to marketers to continuously test their data to ensure they are getting the best insights. With hundreds of options, marketers only have the time and capacity to test a few. But they can narrow down by finding direct-source data providers, and then examining which potential data vendors offer the right data management practices, meet risk standards, and act with integrity across their enterprise.
The main thing all marketers should know is that they must question the data. If financial or income related qualifiers are part of a marketing plan, it’s crucial to understand where the data comes from and how it’s collected. There are premium data sources that come at a higher initial cost, but marketers should understand that cost is relative to return. If they opt to spend less on inaccurate data, they’ll likely miss the right people, and drive fewer conversions.
The author is correct that there is a lot of bad data floating around, some of which results from the problems with surveys that he identifies. However, it is obvious he never had a statistics or market research course as an appropriate sample size is not at all related to the size of the universe it represents. An appropriate sample size is a function of several other factors.