How to Choose Customer Data in Advertising

In the last part of this series titled, “How to Choose a Target Audience in Advertising”, we discussed how marketers can begin to plan out their advertising campaigns before they meet with prospective location data providers. As marketers begin to evaluate potential data providers, they need to determine the types of data used and how it is collected. Let’s say a fitness company wants to launch a new advertising campaign to promote their latest exercise bike. They want to reach customers who regularly go to spinning classes, since we know this set of consumers bikes for exercise.

Selecting the Right Type of Customer Data in Advertising

There are two different types of data for advertising: deterministic and panel data. Deterministic data is data built on actual consumer behavior. In contrast, panel data is modeled and based on a sample set of customer data. If deterministic data is available – use it. Deterministic data, which reflects what consumers really do in their daily lives, will always be more accurate than panel data, which makes assumptions about the behavior of consumers with similar characteristics. However, not all deterministic data is created equal. For this reason, you’ll also want to understand how your data provider is validating their results.

Modeled vs. Deterministic Data

How Geo-fencing Impacts Customer Data in Advertising

Returning to our example, the fitness company will also need to ask the data provider what type of geo-fences they use (grids, pin and radius, polygons). There are multiple techniques used by location data providers to record a consumer attendance at a location, and some techniques are more precise than others. If the geo-fence at that location is tightly drawn – ideally, a hand-drawn polygon that reflects the actual size of the studio – you can be reasonably sure that mobile devices captured within the geo-fence reflect studio customers.

If the geo-fence is drawn as a pin with surrounding radius,  it may overlap adjacent businesses, resulting in attendances captured at the restaurant or clothing boutique next door. Some data providers use grid systems that register the activity of customers  observed at all businesses within a city block-sized area. While useful to understand the composition of a shopping center or neighborhood, it’s not a good fit for this ad campaign.

For this reason, it’s essential to understand the precision of the geo-fences used to capture consumer attendances. Polygonal geo-fences are the most precise, and come closest to reflecting the actual size of the venue in question. Other geo-fencing methods will yield audiences with a greater margin of error, reflecting the geo-fence’s overlap with adjacent venues or businesses.

How Metadata Impacts Customer Data in Advertising

Lastly, the fitness company will need to understand what, if any, metadata is used to validate a consumer attendance.  The fitness company verified that the data provider has a well-drawn geo-fence around the spinning studio. But what happens if the same studio also hosts yoga, ballet and self-defense classes? Unless the location data provider also has a record of associated events and related metadata, the geo-fence will capture all attendances at the venue, and not just customers who attend spinning classes. In this case, the company should take the time to ask what processing is being done on the backend to exclude people who live in the apartments above, or who work at the front desk every day. These attributes will affect the quality of the customer data – and the performance of your advertising campaign.

In the third part of this series on target audiences in advertising, we’ll discuss Scale & Reach.

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Next:  Scale & Reach

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