COMBINED ASSIGNMENT DATA ANALYTICS & R

TEAM MEMBERS

Alexandra Aedo

Umar Farooq

Ryutei Kaguragi

Adiza Ojei

Nivedita Venkatramanan

Marketing Campaign Analysis

Factors driving Web Purchases

Income vs Number of Web Purchases taking into account Education

Education and Income are main drivers for web purchases. Most web purchases are made by people who have completed Graduate and Masters degrees.

Age vs Number of Web Purchases taking into account Number of kids at home

Most web purchases are done with homes with one kid or none.

Analysis of Web Purchases vs Education shows that PhDs and Graduates do maximum web purchases

Analysis of Web Purchases vs Marital Status shows that maximum Web Purchases come from Married Graduates and Single PhDs

According to the analysis, there is no correlation between web visits and web purchases.

## 
## Call:
## lm(formula = NumWebPurchases ~ NumWebVisitsMonth, data = mktg_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4248 -2.0411 -0.3609  1.7670 22.6391 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.42481    0.14123  31.330   <2e-16 ***
## NumWebVisitsMonth -0.06395    0.02417  -2.646   0.0082 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.775 on 2238 degrees of freedom
## Multiple R-squared:  0.003119,   Adjusted R-squared:  0.002673 
## F-statistic: 7.002 on 1 and 2238 DF,  p-value: 0.0082
##       (Intercept) NumWebVisitsMonth 
##        4.42480627       -0.06394878

Scatter plot of Number of Web Visits to Web Purchases

The geographical region is not statistically significant to the success of the campaign

## 
## Call:
## lm(formula = NumWebPurchases ~ Country, data = mktg_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4404 -2.0018 -0.4404  1.9125 22.9982 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.08750    0.21970  18.605   <2e-16 ***
## CountryCA    0.21847    0.27764   0.787    0.431    
## CountryGER  -0.11250    0.33559  -0.335    0.737    
## CountryIND  -0.14155    0.31693  -0.447    0.655    
## CountryME    1.91250    1.61941   1.181    0.238    
## CountrySA    0.05790    0.26680   0.217    0.828    
## CountrySP   -0.08567    0.23520  -0.364    0.716    
## CountryUS    0.35287    0.34513   1.022    0.307    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.779 on 2232 degrees of freedom
## Multiple R-squared:  0.002948,   Adjusted R-squared:  -0.0001785 
## F-statistic: 0.9429 on 7 and 2232 DF,  p-value: 0.4719
## (Intercept)   CountryCA  CountryGER  CountryIND   CountryME   CountrySA 
##  4.08750000  0.21847015 -0.11250000 -0.14155405  1.91250000  0.05790059 
##   CountrySP   CountryUS 
## -0.08567352  0.35286697

Average amount spent by a customer on fruits in the last two years is $26.36

avg_amount_fruits <- mean(mktg_data$MntFruits)
avg_amount_fruits
## [1] 26.30223

Complain is a qualitative factor that would affect the amount spent on fish and meat

## 
## Call:
## lm(formula = Complain ~ MntMeatProducts + MntFishProducts, data = mktg_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.01133 -0.01112 -0.01056 -0.00813  0.99372 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.134e-02  2.618e-03   4.330 1.55e-05 ***
## MntMeatProducts -7.301e-06  1.097e-05  -0.666    0.506    
## MntFishProducts -1.982e-05  4.533e-05  -0.437    0.662    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0964 on 2237 degrees of freedom
## Multiple R-squared:  0.0006369,  Adjusted R-squared:  -0.0002566 
## F-statistic: 0.7128 on 2 and 2237 DF,  p-value: 0.4904

However, according to the analysis there is no correlation between amount spent on fish and meat products.

According to the analysis,people with advanced degrees purchase more fish than others

Fish has Omega 3 fatty acids which is good for brain and so educated people tend to buy more fish.

Food preferences of family with teenagers

Families with one and two teenagers purchase more wines followed by fruits

Most successful campaign is CAMPIGN 4

Campaigns 3, 4 and 5 have performed equally well, However campaign 4 being slightly better. Campaign 2 has been the least successful.

The most complaining customers seem to be the Graduates.

Average age of customers in the given sample is 52 years

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.00   44.00   51.00   52.19   62.00  128.00
## 
##  Pearson's product-moment correlation
## 
## data:  mktg_data$Income and mktg_data$NumWebPurchases
## t = 19.801, df = 2214, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3519222 0.4226905
## sample estimates:
##       cor 
## 0.3878778
## 
##  Pearson's product-moment correlation
## 
## data:  mktg_data$age and mktg_data$NumWebPurchases
## t = 6.9348, df = 2238, p-value = 5.303e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1042505 0.1853426
## sample estimates:
##       cor 
## 0.1450401
## 
##  Pearson's product-moment correlation
## 
## data:  mktg_data$NumWebVisitsMonth and mktg_data$NumWebPurchases
## t = -2.6461, df = 2238, p-value = 0.0082
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09703774 -0.01446393
## sample estimates:
##         cor 
## -0.05584633