Analytics: BUS Model
I’ve often heard people say “the numbers don’t lie” and while that is true in context it doesn’t always give the whole picture. Just looking at raw data isn’t helpful to anyone, so while the numbers may not be lying it doesn’t mean I know what they’re saying. I’m glad there are folks who can transform raw data in to something useful.
One such method used to transform data in personalizing marketing is the BUS model — behavioral, usage, situational. Below is an overview of the BUS model:
Behavioral — What time of day does the customer browse? When does she convert? Which devices does she use? Does she shop via app or mobile web? What are her unstated interests?
Usage — Analyzing usage reaffirms the importance of what marketers have looked at historically; for instance, recency and frequency of purchases, visits, etc.
Situational — Think about circumstantial factors that could be affecting user behavior. “Maybe you see a certain segment of customers is highly disengaged; you need to understand why, and a lot of times that may have to do with their initial experience (i.e. maybe you had low inventory when they first browsed and they never came back, maybe your mobile check-out experience was less than ideal and you lost them forever). From there, you can develop targeted campaigns to address these contextual situations and work to re-engage the customer.”