Here's an example. On a recent project, we learned that two merchandise divisions move in opposite directions.
One merchandise division performed like this:
- 1st Order = 16% of demand.
- 2nd Order = 19% of demand.
- 3rd Order = 21% of demand.
- 4th Order = 23% of demand.
- 5th Order = 24% of demand.
- 6th - 10th Order = 28% of demand.
- 11th - 15th Order = 34% of demand.
- 16th - 25th Order = 33% of demand.
- 26th+ Order = 27% of demand.
Another important merchandise division performed like this:
- 1st Order = 14% of demand.
- 2nd Order = 13% of demand.
- 3rd Order = 13% of demand.
- 4th Order = 12% of demand.
- 5th Order = 11% of demand.
- 6th - 10th Order = 10% of demand.
- 11th - 15th Order = 9% of demand.
- 16th - 25th Order = 7% of demand.
- 26th+ Order = 5% of demand.
Now, if you want to optimize performance, you have a series of tactics to employ.
- Prospect catalogs that feature the second merchandise division.
- Best customer catalogs that feature the first merchandise division.
- Email campaigns that feature the first merchandise division.
This data is aggregated and smoothed out and nearly impossible to notice within most of our reporting systems. Honestly, it's a simple query, one that can be run in about two minutes, with 30 lines of programming code.
So go have your measurement guru run the query for you, and see what you can learn about when customers purchase merchandise.