Teppei Syokudo - Improving Store Performance: ESK Data Analysis Methodology
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Data Exploration
In exploring the sales-labour data, we first found the mean sales per hour from both MW and RP. It can be seen that the mean sales for MW peak from 11:00 to 13:00 during lunchtime and 18:00 to 20:00 during dinnertime. Likewise, RP’s mean sales peak from 11:00 to 13:00, and from 17:00 to 19:00.
We hypothesized that staff who are more productive will perform better on average as compared to the shop sales on an hourly basis. We explored staff performance by looking at a common measure of labour performance, which is staff productivity.
We attributed hourly sales to each of the staff present in the shop at that hour. We then took an average of the attributed sales for each of the staff by dividing his total sales with the total number of hours that he worked.
However, we realized that there was an hourly effect on retail sales, which affects the labour productivity of the staff. This means that on an absolute basis, if Staff A and B both work the same number of hours, but A works during peak hours, and B works during non-peak hours, A’s labour productivity (Store Sales / Number of hours worked) will be higher than B. This might lead to a possible misrepresentation because Staff A might be poorer at customer service or upselling as compared to B. This leads to a need for data standardization on an hourly basis. For more information on the methodology used, please refer to the Data preparation section.
After standardizing the data, we proceeded to rank them based on their standardized labour productivity and took the top 5 performers, as well as the bottom 5 performers for each store, and plotted their hourly sales, compared to the shops’ average sales.
Our hypothesis is partially true because the top 5 performers almost perform higher than the mean shop sales but mostly during peak hours. The bottom 5 performers also perform lower than the mean shop sales but mostly during peak hours.
This implies that there is value in identifying high performers that perform on a consistent basis. Firstly, we can benchmark staff performance using the top performers. Secondly, we can qualitatively assess the behavior of top performers that affect sales and develop means to train the rest of the staff to be like them.
