Then, visualize where they are and vary the size/color of the store symbols to convey profitability. You'll have to get lat/lon coordinates for each store, and you should also do this for key competitor stores. Are there are any obvious patterns or trend?ĭepending on the geographical distribution of the 50 stores, it may be helpful to visualize where they are using Google Earth, say.
Grocery store planogram series#
real estate quality, demographics, square footage)?Īggregate data at a monthly level and create a rolling 24 month time series for each store, as well as an overall series for the chain. Is there anything in common within these subgroups (e.g. You could create a simple "weekly scorecard" showing this information. For example, report the average, median, and some basic distributional information like min, max, and 25th/75th percentiles. You want to focus on the latter as much as possible.įind a nice way to summarize the per-store-revenue and margin statistics you have. Presumably these summaries will lead to follow up questions, and that is where you can work with your colleagues to distinguish between analyses resulting in information that would be nice to know, versus analyses that affect specific decisions. Here are a few ideas that are focused on summarizing the chain. Otherwise, you risk being seen as the geeky data guy out of touch with the needs of the business.
Here are some ideas to get started, but I'll start with a key rule I try to follow: Always tie your analysis back to specific business decisions that will be influenced by what is learnedĮven better, start with the business question of interest and let that guide your work. To be clear, an issue for me is that there are extremely rich data but there is not much of an appetite for studying and presenting that richness. My present job involves analyzing a lot of retail data, so yes, I share your pain.