February 23, 2017

Hiding In Plain Sight

In recent years my business shifted ... I used to spend a lot of time analyzing marketing data. These days, I spend a lot of time analyzing the impact of merchandising decisions on business performance.

Why?

I think it comes down to the tools you use to analyze your business.
  1. Your merchandising team typically uses 1980s technology to make decisions (unless you are at H&M or Zara, of course). Even if you have new systems, you are using new systems to produce old metrics.
  2. Your marketing team is obsessed with response/conversion to marketing tactics. The craft has been devalued by Google Analytics - you either pay Adobe or IBM a hundred thousand for advanced analytics or you use Google for free analytics - but make no mistake, none of the tools are designed to help you understand how merchandise performs. If you want to see how 29 Pinterest referrals converted, slice and dice it a thousand ways. If you want to see how 29 new items progressed to winning status? God help you!
We end up looking for solutions in the wrong places. Sales down 5%? Figure out what is wrong with the conversion funnel.

Meanwhile, I'm analyzing merchandising data and the answer is hiding in plain sight.

Do me a favor. It's Friday, and you don't have anything better to do. Here's the query I'm asking for.

Step 1: Identify the year an item was "born".

Step 2: Create variables for each year - sum demand for the item by year. Use 2/24 - 2/23 as your "year", or use calendar year, or use fiscal year, the definition of year isn't that important.

Step 3: Segment items based on the year the item was "born". Count how many items were introduced by year. Then sum demand by year for items "born" in a certain year. If you have five years of data available, you will have five rows in your dataset, and you will have five columns summarizing annual sales totals for each year (row).

Take a look at your table.

You should see what the "life of an item" looks like.

Companies that are struggling discontinue items too quickly, or fail to introduce enough new items, or introduce too many new items that fail to perform well. You'll be able to see the impact of bad decisions over time.

Ok - go run the query. It's easy to run, and many of the issues with your business will be hiding in plain sight.