Real behavior. Real insights.
By monitoring real customer behavior organizations can gain insights that drive better business performance.
How well do you know yourself? How well can you forecast your behavior? Well, if you are like most people then you are downright terrible at both. These are two of the things that science shows that people are really bad at. The field of Behavioral Economics is littered with anecdotes of the inconsistencies of what people claim they do and how they actually behave. So, if we can’t trust surveys to tell us what people will do, how do we go about finding out?
The answer lies in observation and pattern recognition from real behavior data. People are inconsistent on an individual level, but quite predictable as clusters within a larger dataset. By using measurements of footfall, queueing, and transactions in retail, or appointments and drop-in in visits in healthcare, or the easily traced paths of visitors on your web site, the patterns start to emerge.
Once you have some insights into which segments that behave in what ways many possibilities open up:
E.g. a service provider can use price discrimination by charging more at busy times to increase profit margins while also evening out resource demand and staff requirements.
Another possibilty is to customize offerings in retail, or do specialist staff scheduling in healthcare, based on Time Targeting. A common pattern that often turns up in customer journey flow data is that senior citizens are up early and often visit stores and institutions in the morning. Youths tend the other way and go shopping in the afternoon and evenings. And the people with busy days make hurried visits at lunch or around the time they get out of the office. Working with these kinds of patterns a business can deliver exeptional service while also exposing segments to more relevant offerings.
As you can see, it is all there in the data, provided you measure behavior instead of asking people for their opinions.