Why do some stores or branches do so well while others struggle? What can we do to help underperformers improve? How can you crack the success code of your physical distribution?
We all know it’s difficult and frustrating to benchmark stores and find meaningful clusters. Anyone who has tried knows the results often don’t feel right. I know this from many projects and I want to share two practical examples with you.
Case from Chocolate Brands Store Chain
A chocolate brand operates its own stores. Typically, they track a few key numbers: sales per store, foot traffic, conversion rate (the share of visitors who buy), and average basket size.
While this data is helpful, it’s usually not enough to explain performance differences. You need to quantify the real success drivers — and that often means adding qualitative factors.
In this case, we added data on:
The buying power of the region
Tourist volume in the area
Store location type (railway station, airport, shopping mall, city center)
When you link these factors in a causal network, you see that foot traffic is by far the biggest sales driver — contributing to about 90% of sales. Conversion rates and basket size also matter but play a smaller role.
We also found that foot traffic depends heavily on local buying power and tourist presence. Locations like railway stations have higher conversion rates than malls. This insight helps you focus: manage what you can. While you can’t always change a location, you can influence traffic with better exterior signage and promotions that pull people in.
Case from a Consumer Electronics Brand
A consumer electronics brand wanted to boost sales. They had lots of data from different electronics retail stores and ran multiple initiatives: in-store demos, staff training, and mystery shopping to measure how strongly staff advocated for the brand.
Again, benchmarking or clusting those that gave confusing results.
Running a causal modeling, they found that in-store demos boosted sales for about a week but had no long-term effect. Staff training, however, improved both mystery shopping scores and advocacy levels — and advocacy turned out to be the real long-term sales driver. So the key wasn’t whether a salesperson recommended the brand first, but how convincingly they advocated for it.
Again, the lesson is clear: raw data is rarely enough. You must qualify it with relevant factors and categorize your stores properly to see what truly drives performance. Further you need to look for cause-effect-relationships not descriptive clusters or comparisons.
A Simple Framework
So how do you crack the code of physical distribution success? Follow these three steps:
Theorize: Start with a blank sheet. Write down the data you have — and what you believe influences it.
Qualify: Gather and enrich your data with relevant context: local buying power, tourist volume, location type, store setup, or any factor you believe impacts performance.
Analyze: Use Causal AI or, if that’s too complex, start with multivariate regression. This approach reveals what drives sales so you can focus on what to manage.
The biggest pitfall is simple benchmarking — comparing stores without context. It’s unfair and misleading because performance depends on overlapping factors. Instead, predict sales and manage the key drivers.
This is how you crack the code of success for your physical distribution.
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