Foot traffic data is spinning around us with a dizzying effect.
Why are we losing our equilibrium when looking at the conclusions of data? Think back on the days of Analytics 101, garbage data in -> garbage results out. What’s the value of data unless it captures what is really going on?
We all have the same problem – we want an accurate view of people. Methods that only capture WiFi devices are missing a big part of reality; many people simply turn WiFi off and 65% of phones in use are still “feature phones” (no WiFi available). AirPatrol’s own studies have found that WiFi-only detection represents less than 4 out of 10 people.
With information like that, we’re not only missing an accurate count; we’re missing opportunities and skewing the data we have. If you look at the facts, feature phones are likely in the hands of the 55+ crowd as the younger generations want need the newest device. This age group is in the majority of devices that remain unseen with WiFi-only detection. Concurrently, this age group also has the most disposable income. Now your data isn’t only miscalculating the number of people in the area, but it’s skewing the data to younger age groups, a smaller disposable income and stores that attract a younger demographic. Not quite as accurate as you hoped for!
Studying foot traffic is about understanding human behavior and using those observations as a base to build tomorrow’s better business decisions.
Data becomes better with each added metric. With each measurement, conclusions are more exact and become better predictors of the future. As mentioned in The Problem with Data, data is nothing unless it tells you something more than what you’re able to observe. With so many numbers and eye-catching graphs flying around, it’s easy to get dizzy and confused but the basics will always ring true – you get out of it what you put into it and questionable input creates blurry results.