Before you act on the data, consider the possible fallout.
The mistake I see most with clients is simple. They make a decision based on the numbers in front of them and assume everything else will stay the same.
One change. More revenue. That’s the whole forecast.
In 2012, JCPenney decided to stop playing games with prices.
The company had been running hundreds of promotions a year. Customers rarely paid full price. So management cut most coupons and sales and lowered everyday prices instead.
Made sense on paper.
Customers hated it.
Sales at established stores fell 19% in the first quarter. Foot traffic dropped 10%.
CEO Ron Johnson admitted, “Coupons were a drug, they really drove traffic.”
But I think there was something more to it.
A coupon wasn’t just a lower price. It made customers feel like they were getting a special deal. A backstage pass that other shoppers didn’t have.
Take that away, and you take away part of the reason they came in.
The data showed what customers paid. It didn’t explain why they bought.
Sales fell 25% that year. Johnson was gone the following spring.
And the company had rolled out the strategy without first testing how customers would respond.
That’s not just a JCPenney problem. I see it with clients all the time.
The decision looks fine on its own. The trouble is what happens next.
Customers adjust. Competitors respond. Behavior changes in places your dashboard isn’t looking.
So I ask one question before making a significant decision:
“And then what?”
Then I ask it again.
“And then what happens after that?”
Write down three possible consequences of your decision. Include at least one you’d rather not see.
Then test the change on a small scale. Keep a comparable group untouched so you can measure what actually happened.
The data tells you what happened under yesterday’s conditions.
Your decision changes those conditions.
Make sure you’ve considered what happens next.
Rock on.