How can the firm manage valuable customers and expand the loyalty program?

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QUESTION: How can the firm manage valuable customers and expand the loyalty program? 

Need to conduct data analysis using RStudio.

We have been provided a dataset of a company’s customers and their purchasing data. In particular, we are analysing the effectiveness of their loyalty program (so the customers who have earned and redeemed points). My job is to conduct multiple regressions on a particular cluster of customers (RFM Cluster 3 in Q1 Data spreadsheet) to find out which descriptor factors (gender, age, race, home town) correlate to a higher transaction amount, volume and where the most profitable group (Cluster 3) spend.

So we need to conduct multiple regressions like these:

lrn(formula Amount Gender + Race + OwnCar + OwnCreditCard +

HomeCity, data = dta.cluster3)

Residuals:

Min 1Q Median 3Q Max

-3075.9 -1091.3 -510.3 201.9 26156.5

Coefficients:

Estimate Std. Error t value Pr(>ItI)

(Intercept) 2303.30 403.39 5.710 2.22e-08 e”

Gender 275.25 297.18 0.926 0.3549

Race 455.60 225.16 2.023 00437 *

OwnCar -595.35 289.49 -2.057 0.0404 “

OwnCreditCard -301.58 296.88 -1.016 0.3103

HomeCity -98.18 64.92 -1.512 0.1312

Signif. codes: 0 ‘‘‘ 0.001 ‘‘ 0.01 “‘ 0.05 ‘.‘ 0.1 ‘ ‘ 1

Residual standard error: 2878 on 396 degrees of freedom

Multiple R-squared: 0.0308, Adjusted R-squared: 0.01857

F-statistic: 2.517 on 5 and 396 DF, p-value: 0.02927

Figure 13. Mulliple Regression 2 Results

lm(formula — No..of_transactlons — Gender + Race • OwnCar • nCredltCard .

HomeCity, doto - dto.RFM)

Residuals:

P4n 1Q Median 3Q Max

-78.10 -24.08 -9.20 9.62 44714

Coeffctents:

Estimate Std. Error t value Pr(>ItI)

(Intercept) 53.9674 6.7349 8.013 1.27e-14

Gender 11.5583 4.9617 2.330 0.020334 ‘

Roce 12.8072 3.7592 3.407 0.000724 ‘

.nCar -7.8328 4.8332 -1.621 0.105896

nCredttCard 1.8528 4.9566 0.374 0.708754

HomeCity -0.6809 1.0839 -0.628 0.530208

Signif. codes: 0 ‘••‘‘ 0.001 ‘“‘ 0.01 ‘‘ 0.05 ‘. 0.1 ‘ 1

Residual standard error: 48.05 on 396 degrees of freedom

Iltiple R-squared: 0.05109, Adjusted R-squared: 0.03911

F-statistic: 4.264 on S and 396 DF, p-value: 0.0008664

Attachments:

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