Monday, March 6, 2017

Using Geofencing to Observe Shopper Behavior

This post was originally published on the Research Now blog.

It is widely discussed that mobile opens up incredible opportunities for researchers. It is perhaps equally widely discussed that mobile provides challenges for researchers – especially those most reticent to part with, let’s say, more traditional approaches. I could think of a number of examples of this two-sided coin, but I’ll leave all of those, save one, for future discussions.

One that the industry needs to tackle head on is the use of geolocation for understanding shopper behavior. So much opportunity! But logistics and analysis is so hard (for many rooted in market research)! The notion of using geolocation itself for research is no longer new. Geofencing has been used to target people for research for several years – with the most commonly used methodologies centered around delivering a survey to someone when they are in a specific location or after they have left. In many cases this is a viable approach to understanding shoppers – and getting feedback close to the point of experience.

Personally, I’m a fan of targeted and efficient research engagements that ask people to recall their shopping behaviors before they forget them. But I am also a fan of not having to ask what we don’t really need to ask, for example who they are, where they shopped, and when. With this idea in mind, and wanting to piggyback on prior years of researching Americans’ Black Friday shopping habits, we looked to explore how geofencing could be effectively utilized to understand shoppers with minimal active engagement from them. So, last Fall, we brainstormed with Placecast and their savvy team of location-focused researchers on how we could shed new light onto shopping behaviors around this critical time period for retailers.

While we did end up asking some questions directly of people, we managed to glean a lot by matching our panelists’ location data with existing profiling attributes. We discovered, for example, that the most affluent Walmart shoppers came to the store on Black Friday when compared to days leading up to and following that day.

The most affluent shoppers also proved to shop early in the morning in the days immediately prior to and following Black Friday. Understanding who shops where and when is crucial to retailers and advertisers as they try to craft relevant messaging and promotions for holiday sales. Combining geolocation data and associated advanced analytics with known profiling attributes creates a compelling story about shopper behavior, one that can be layered with surveys and other data sources to provide actionable insights.

The industry has an opportunity here – to use geolocation data in a smart way and one that alleviates much of the survey burden often placed on participants.

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