Interrogation

Project 2

1 Replication of a Research Question

• Initial Question: Is Employee Efficiency an effective predictor of Monthly Sales Revenue?

• New Extended Version: Is Customer Footfall a better predictor of Monthly Sales Revenue?

2 Justification

This extension of the initial question continues to explore factors directing monthly sales revenue. The new investigation explores customer footfall (number of customers entering/ exiting a business) as a potentially stronger predictor - honing in on the customers rather than the employees themselves. Both conditions influence revenue—efficiency affects service speed and transaction volume, whereas footfall impacts sales opportunities (Kenton, 2022). Comparing these helps define whether workforce productivity or customer volume better predicts revenue. Thus, this refined question enhances our initial analysis.

3 Linear Regression Analysis

## `geom_smooth()` using formula = 'y ~ x'

## 
## Call:
## lm(formula = MonthlySalesRevenue ~ CustomerFootfall, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -192.034  -45.969   -1.414   44.724  236.017 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      307.864887   6.925352  44.455   <2e-16 ***
## CustomerFootfall  -0.004263   0.003334  -1.279    0.201    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 65.53 on 1648 degrees of freedom
## Multiple R-squared:  0.0009911,  Adjusted R-squared:  0.0003849 
## F-statistic: 1.635 on 1 and 1648 DF,  p-value: 0.2012

A p-value of 0.2012 is found, indicating a weak correlation between Customer Footfall and Monthly Sales Revenue. Thus, we accept the null hypothesis that Customer Footfall lacks a significant effect on Revenue.

4 Comparing predictor efficacy via adjusted R-Squared Value

5 Conclusion

This analysis investigates whether customer footfall is a more robust predictor of sales revenue than employee efficiency. The regression results suggests which factor has the higher adjusted R² value, indicating a better model fit. This steers businesses to prioritise factors that accumulate the most revenue growth (CFI, 2023). Evidently, both values present low. For example for Employee Efficiency the adjusted R-squared value is very close to zero (4.135585e-05), indicating a weak predictor capacity. As for Customer Footfall the value is also very low (3.849278e-04), suggesting that customer footfall also shares little explanatory power for the dependent variable (revenue) in this model. A possible explanation for this is the various other factors including product desirability and customer experience which have been observed to collectively contribute to company growth (Kenton, 2022).

6 References

CFI. (2023, November 21). Adjusted R-squared. Corporate Finance Institute. https://corporatefinanceinstitute.com/resources/data-science/adjusted-r-squared/?

Kenton, W. (2022). Foot Traffic: Definition, Tracking, Ways to Increase. Investopedia. https://www.investopedia.com/terms/f/foot-traffic.asp?