The data that has been analyzed is supermarket sales data that covers the years of 2017, 2018, and 2019. Within this data it looks at a lot of different items including but not limited to; Location, Gender, Customer Type, Product Line, and Total Sales. These are the items that I evaluated to answer my question about how the business can best expand marketing and make more effective business decisions. By using these allocations I was able to break down the data and refine it all the way down to see the largest customer type and what products they preferred to buy from what locations on specific days of the week. I found this information to be beneficial to a decision maker as they would be able to use the data and visualizations I constructed to see exactly what customer base that should be targeted for specific products in specific locations on particular days of the week. If a business can identify when and where they are making the most money they can reallocate resources to potentially make more money in that market or evaluate what a specific location is doing to get all of the additional percent of total sales and replicate that in other locations to try to experience the same positive results.
In this first very broad spectrum graph you can identify that the largest portion of customers are “Normal” female customers. Normal is also meaning that they are nonmember customers as the other category is member customers who accounted for almost 49% of Total Sales. This is interesting certainly because this means the majority of your sales are to customers that are not members of the supermarkets and should be marketed to as outside customers that could also become member customers of the supermarkets.
In Figure 1. I identified that the highest percent of total sales was from normal women customers. Therefore, in Figure 2. I only looked at normal women customers across all locations to see where those customers are making up the highest percent of total sales to identify if there was a location that was responsible for an interesting amount of total sales but after analyzing the charts there was not much difference based on the location they were all pretty similar as far as the percent of total sales but Mandalay did account for the highest percent of total sales when filtered on normal women customers.
In Figure 3. I decided to look at what product line in particular that normal women customers in Mandalay were purchasing. I thought this could maybe give some insight on if there was a product line that really stood out to this specific group of consumers that was making the normal nonmember women customers the highest % of total sales customer base. After analyzing the data and making the chart there was nothing surprising other than the two highest product lines were very similar in how much they accounted for in total sales and those two product lines were Fashion accessories at just over 18% and Food and Beverages at just over 17%. With this information I thought it was good to look at to see if there was a product line that maybe needed to be removed or altered but with the consistency across this customer base in one location I would say they are all doing pretty well.
In Figure 4. I decided to see if there was a trend during the days of the week that this particular customer base was purchasing the products that consisted of the highest % of total sales. In this figure you can see that Fashion Accessories are in gold and Food and Beverages are in blue. What I found with this analysis was the most interesting information I found and that is that normal women customers account for the highest percent of total sales in Mandalay of Food and Beverages on Friday’s and Saturday’s. I found this very interesting because this means that they are coming out and purchasing the highest levels of food and beverages on Fridays and Saturdays and could be customers of other products during that time. Another interesting piece was that the highest percent of total sales of Fashion Accessories occurred then on Sunday’s in Mandalay by normal women customers.
Moving forward I would say that management should look at a way to cater to those nonmember customers of normal customers in this scenario because normal women customers account for the highest percent of total sales across all locations. I think this is something that should be looked at because usually members have perks and benefits and most likely are return customers, so I believe that if the highest percent of total sales are going to nonmembers I think those customers need marketed to that way they can become members of the supermarkets. Furthermore, looking at the days in Figure 4. that account for the highest level of sales you can see that Thursday is a very slow day for sales of nonmember women customers in Mandalay so this is potential a time and a place where labor could be cut down if it is not required if sales are going to be lower during that particular day for those specific product lines.