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.
