Last Wednesday, I visited a Geomarketing congress organized by my alma mater and Geodan. In this post, I’ll discuss how geomarketing on both a macro-level and a micro-level gives you more pieces to the customer puzzle.
Geomarketing focuses on the relationship between spatial awareness and marketing. Here are some of the real-life cases that were presented:
- Understanding customer motives for visiting certain car dealerships
- Determining the location of a new store
- Plotting and targeting households for effective direct mail campaigns
These are all very good and relevant examples of how location affects the marketing process and how proper geomarketing can improve the rate of success. However, they’re all examples of how to perform geomarketing on a macro-level.
Even modern mobile applications like Foursquare operate mostly on a macro-level. People check into a drug store, a restaurant, a train station, et cetera. It can be good information for your business, but it doesn’t tell you how they move about while they’re at your store for example. This is where geomarketing on a micro-level comes in.
The presentation I found most interesting was by Prof. Dr. Jaap Boter who was also my thesis supervisor back when I was graduating. Prof. Boter performed research at a bookstore where customers were asked to wear RFID tags while they were shopping for books. When leaving the store, these customers filled in a questionnaire, and—if they had made a purchase—the receipt was included as well.
Using this information, it became possible to track customer movements inside the store, which revealed interesting insights such as:
- The behaviors of various customer segments
- The differences in customer behavior on different days (e.g. Saturday versus Monday)
- The effectiveness of the store’s layout
These are all very interesting and important bits of data that help you understand the consumption process. Loyalty programs, CRM systems and macro-level geomarketing can help you understand (some of) the causes and effects of a purchase, for example:
- A cause like someone lives near the store so s/he was inclined to visit it
- An effect like someone makes large or frequent purchases
- A cause and effect like a man making a purchase because it’s his son’s birthday
This is also very valuable information, but it doesn’t reveal much about the process. Was this man looking very long for his son’s birthday gift or was he helped out quickly by a store clerk? The combination of customer data, both on a macro-level and a micro-level, help you understand your customer better.
How closely are you stalking your customers?
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