Difference between revisions of "Teppei Syokudo - Improving Store Performance: ESK Findings"
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<p>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.</p> | <p>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.</p> | ||
<p>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.</p> | <p>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.</p> | ||
| − | <i><b>Fit Model.</b></i>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. | + | <i><b>Fit Model.</b></i> 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. |
| − | <p>[[File:Reg-figure16.png| | + | <p>[[File:Reg-figure16.png|500px]]</p> |
<p>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.</p> | <p>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.</p> | ||
| − | <p>[[File:Reg-figure17.png| | + | <p>[[File:Reg-figure17.png|500px]]</p> |
<p>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.</p> | <p>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.</p> | ||
<p>The resulting Fit Model Dialog before running the analysis should be like this:</p> | <p>The resulting Fit Model Dialog before running the analysis should be like this:</p> | ||
| − | <p>[[File:Reg-figure18.png| | + | <p>[[File:Reg-figure18.png|500px]]</p> |
<i><b>Analysis Results.</b></i> | <i><b>Analysis Results.</b></i> | ||
| − | <p>[[File:Reg-figure19.png| | + | <p>[[File:Reg-figure19.png|300px]]</p> |
| − | <p> | + | <p>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.</p> |
| + | <i><b>Implications.</b></i> Teppei Syokudo should assign Staff 31 to work on Saturdays during Lunch Peak hours so as to increase number of customers for that period. | ||
| + | |||
</font> | </font> | ||
</div> | </div> | ||
Revision as of 13:50, 17 April 2016
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.
