Suggestions for Analytics of POS data of a Convenience Store??

Im working on a project for a chain of gas stations that want to increase its convenience store performance/revenues. They have 300 gas stations but only 9 stores but will expand to 300 stores in the next 2 years. The POS data source don’t identify or give user level data, only transactions level data.

7 responses on "Suggestions for Analytics of POS data of a Convenience Store??"

  1. Often, The Business(tm) has some notion of the things that they value or need to know and you can start by trying to see what they’re interested in and asking if this data helps with that.

    I think if I had this data and had to spitball uses I’d be looking at finance/accounting use cases (income/rev/spend/etc), warehouse/logistics (number of products sold at different locations), and marketing use cases around trying to measure the effectiveness of ad spend.

  2. with the transaction data get the ROI per store and see if it makes sense the effort to open new ones and see what happen with the low performing ones that its better to close those and maybe try in a different locations ( in which they don’t have near more convenience stores)
    Good luck!

  3. Average customer total ring value.
    Average gallons per gas sale.
    Average inside ring.
    Fountain sales.
    Average inside sales impact by outside average gallon price.

  4. You could send data from POS to Google Analytics via measurement protocol thus enabling you to analyze it using the built in reports which they have. Even if the data is anonymous, you can still do some analysys and see the most popular days or hours in terms of sales, the average sale price, items, if some coupon codes were used, etc.

  5. Conduct two types of analysis. First exploratory analysis to search for any trends. This will help answer your question to determine what is valuable from the POS data. Then once you have some trends reformat for explanatory analysis to extend the communication of the trends to the business 😎

    Edit: try and plot financial data against demographic data (you can use another dataset) to help the company predict what sort of sales figures they can expect based on what area they open the remaining 291 stores

  6. Aggregate data into hourly chunks, daily chunks etc, but aggregate by several types of products. Total, Food, Drink, Prepared food, misc, whatever hypothesis you may have that is driving the stores revenue.

    Then you could fit an ARIMA, ets, or any sort of model and tack on any explanatory variables you may have. You could also plot out the products over time and look for co variance, i.e., possibly drink drives food sales but drink sales may be driven by the season rather than any food being sold.

  7. Definitely look at associated sales with fuel to see if higher margins can be obtained with add-ons or upsell.

    Look at transactions per hour as an input to the labour required to run the store.

    Look at distribution of pump usage (if the line item includes the pump number) to see which ones need more attention in terms of water buckets, paper towels, cleanliness.

    Look at potential queue length if you have estimate of transaction timing. Some methods of payment take longer than others, high basket size takes longer etc.

    Look at basket composition.

    Look at benchmarking the stores and finding those unique differences. Some will sell more of particular products.

    Look for potential out of stocks (outlier detection on regularly sold lines)

    Look at promo performance. Do you get the expected uplift In the early weeks, should you cycle promos more frequently?

    Look at method of payment type, combine with census data to learn about the customers in the area

    Then do all the normal trending stuff.

    Source: I used to do exactly this job for a national fuel provider. Feel free to to pm me for more info.

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