Data Normalization
This is the tool we use the most to find hidden insight in complex retail, restaurant and even ecommerce systems.
Here's a simple example:
The chart on top is showing total sales by state for a national brand.
This chart looks the same for pretty much all national brands.
CA, TX, FL are almost always the high sales states.
But in the bottom chart...
We've normalized that same data for population of the state.
Think of that chart as units-per-thousand-people.
Now we have a different lense into sales performance.
It's no longer CA, TX, FL as our big states...
It's TN, LA, ME, even OK!
You can see how, regardless of the business, you may start to think differently about how well a given market is performing.
In this case, you might look at TX and reasonably infer that you have opportunity to grow there... Where in the top chart you may not come to that conclusion.
Again, a simple example to demonstrate the point... There are a TON of ways to normalize all kinds of data.
The point is, we're evaluating things on a different scale, migrating the various attributes to a level playing field, which brings light to their real differences.
Very few people do this.
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