Store-Level Sales Data Consolidation and Performance Analysis

The Context

The client had a data room full of store-level sales data. The problem was not missing data. It was that everything was spread out. Different files, different formats, no clear structure. Some files overlapped, some didn’t match properly. Before doing any analysis, they first needed to get the data into a usable form.

What They Asked For

They wanted one file where everything sits together. Not ten different sheets. Not separate files. Just one place where all the data is cleaned and organized. After that, some basic analysis to understand store performance.

Store-Level Sales Data Consolidation and Performance Analysis

What We Did

Started by going through the files one by one. Tried to understand what each file had. This part took time because there was no common format. Then everything was combined. All relevant data was brought into one file. It was messy at first, but at least everything was in one place.

After that, the data was arranged properly. Grouped based on what made sense. Store-level, timelines, and other simple splits. Once the structure was clear, basic analysis was done. Looked at trends. Compared performance across stores. Nothing too detailed, just enough to get a direction.

At the end, a simple deck was created so the client didn’t have to open the raw file again and again.

What Was Not Easy

The size of the data. There was always a chance of missing something while merging files. So extra checks were needed. Also, once everything was combined, the file became heavy. Slow to open, slow to work on.

To fix this, a version with formulas was shared first. The client checked the numbers. After that, formulas were removed and only values were kept. That reduced the size.

What Helped

Keeping things simple. No over-complication. Just clean data and basic structure. Also shared different versions of the file. The client could pick what worked better for them.

End Result

The data became usable. That’s the main thing. The client didn’t have to go back to multiple files again. Everything was in one place. They could look at store performance quickly and move ahead with their evaluation.

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