We had a notion here at TRC that by the middle of March most New Year’s Resolutions would have been tossed by the wayside, either in favor of giving up something meaningful for Lent, or the simple acknowledgement that this just isn’t the year to lose 25 pounds. Would folks who made a resolution at the beginning of the year still be keeping that resolution 3 months later?
We kicked around a few hypotheses, and then went about testing them using our online panel of consumers:
- Younger consumers would be more likely to make resolutions than older ones (we figured they hadn’t become jaded by their resolutions not working out over time)
- People would be more focused on issues relating to their health (losing weight, exercising more) than other types of resolutions.
- Most folks who made a resolution would have dropped it by the 3-month mark
So how did our predictions fare?
Tags: Psychology, Consumer Behavior
In his opus Thinking, Fast & Slow, Nobel winner Daniel Kahneman (click
On a trip to Las Vegas in November 2011 I was twice presented with an option to move to the head of the line – for a price. I could take advantage of “early check-in” by paying $25. And I could get my buffet breakfast right away without waiting in line, again for a small fee. The buffet sign struck me as peculiar, since the 4 people ahead of me didn’t really constitute much of a “line”. I snapped a photo.
Yes, it is a rather important issue and can be approached in a variety of ways. My purpose with this post is not to provide a comprehensive answer, but look at one specific solution based on what I recently read. The book is Thinking, Fast and Slow, the Nobel Prize winner
Prior to my current tour at TRC I was a partner at a small data mining boutique that had a simple objective: support the sales and marketing goals of our clients by helping them stem attrition. While the goal may have been simple, how we set out to do this was not. See, we took upwards of 30 months of time-series data on all of their customers and applied some mind-numbing statistical techniques that identified patterns in the data that preceded an eventual behavior. In most cases that behavior was a customer terminating their relationship with our client. Once our system identified the patterns that preceded attrition, we would then continue to feed it customer data each month and it would dutifully output a list of customers that were likely candidates to attrite. In addition, for each customer on this golden list we provided the prediction "trigger", or the thing that they did that was responsible for the system flagging them as a high-risk customer.
I was shopping for groceries with my 12-year old son the other day -a quick trip to the store that qualified us for the express check-out line. On the way out he said to me:
A recent 


