Teppei Syokudo - Improving Store Performance: ESK Findings
Hypothesis 1
Hypothesis 1: We can increase store productivity by hiring good cashiers who can upsell (increase sales dollar per customer) and serve customers faster (increase customer number).
From the sales process, we know that the cashiers are the most customer facing staff. They are also the most likely to influence customers’ purchase decisions, based on their ability to upsell and cross-sell. This hypothesis looks at identifying good cashiers who are able to consistently increase sales dollars per customer through upselling and/or cross-selling. It also looks at the speed at which the cashier serves the customers, as more customers served within an hour means higher sales, which directly relates to higher store productivity.
For this analysis, we use simple linear regression and apply it to each cashier to see if a particular cashier is able to affect the number of customers, and sales dollar per customer. At the same time, we account for the time of day and day of week effects.
Fit Model. To do so, we use JMP’s Fit Model function. We first go to the Menu bar and click on Analyze, then on Fit Model.
A pop up should appear, prompting for a JMP Data Table. Select the data table for running the regression model. The Fit Model Dialog will pop up.
Next, select the Role Variables. Y refers to the dependent variable(s) that we want to analyse. For Hypothesis 1, Y would be Sales/Customer and CustNo. By performs a separate analysis for each level of Y. This means that we can use By to account for the time of day and day of week effects. We add Day and Peak/Non-peak to the By option. Construct Model Effects is our X variables, the independent variables. We add a single cashier (eg. Staff 31) to this box, so as to identify the effect that this single cashier has on our dependent variables. Finally, we click on Run to run the analysis.
The resulting Fit Model Dialog before running the analysis should be like this:
Analysis Results.
The results show that on Saturdays during Lunch Peak, Staff 31 does not make a significant impact on sales/customer as it has t-value greater than 0.05. However, Staff 31 does make a significant impact on CustNo with F-value and t-value of 0.0233. The results show that when Staff 31 works on Saturdays during Lunch Peak hours, the number of customers will increase by 5.96.
Implications. Teppei Syokudo should assign Staff 31 to work on Saturdays during Lunch Peak hours so as to increase number of customers for that period.
Hypothesis 2
Hypothesis 2: The presence of managers can positively impact store productivity by increasing the staff’s ability to upsell and serve more customers.
We have identified three shop managers present in Teppei Syokudo and we want to test whether the shop would perform better when these managers are present as the staff are more motivated to upsell and serve customers faster.
Fit Model. We use the Fit Model with the dependent variables Y as Sales/Customer and CustNo, as well as independent variables Manager 1, Manager 2 and Manager 3. Manager-variables have a value from 0 to 1, indicating their presence for every hour. Since this hypothesis involves a multi-linear regression with three independent variables, we included two-way interactions between the managers to account for the scenarios where there are two managers in the shop. As there is no scenario where there are three managers in the shop, we excluded three-way interactions. The resulting Fit Model Dialog should look like this:
We ran the Fit Model with the independent variables so as to identify the effect that each manager has on the dependent variables.
Analysis Results.
The results show that on Mondays during Lunch Peak hours, all three managers do not have significant impacts on average sales per customer (Response = Sales/Customer No) and customer number (Response = CustNo), as the Prob>|t| values are greater than 0.05.
<Because there are multiple independent variables that might be correlated with each other, a multi-collinearity test should be done. The VIF function in JMP tests for multi-collinearity in a model. VIF values above 10 signal high multi-collinearity.
Right click on the Parameter Estimates of the Fit Model and go to Columns and then Click on VIF.
A VIF column should appear beside the Prob>|t| column. It seems that there is low multi-collinearity for this particular model because the VIF values are small.
Implications. Managers have no significant impact on the staff’s ability to upsell or to serve more customers on Monday lunch peaks. Teppei Syokudo can explore the feasibility of having managers to check into the store periodically since their continued presence do not motivate the staff to work harder.
