You are the Chief Analytics Officer & Business Strategy Head at an online shopping store called DressMart Inc. The company wants to improve the conversion rate of the campaigns, i.e. the number of customers buying products from the product catalog. Their campaigns include price reductions, advertisements with celebrities, gifts for buyers or large quantities, and a loyalty card.
They are also eager to find out what the best selling product categories are. Their catalog features male wear, female wear, children wear, and household items.
They also want to know the average amount of money spent at one go and see if there are differences between customers living in large cities, mid-sized cities, and small towns. This data is used to help improve the profit generated through the converted customers.
To develop a regression model, you will use the data for 4,200 customers out of the hundred thousand solicited customers who have responded to previous campaigns. All these 4,200 customers live in different locations that can be grouped into the above three categories.
Incidentally, these customers are evenly divided into these three categories, with 1,400 customers in each group. The first thing you checked is the average value of profit generated from these three categories of cities.
Draw the causal model.
What methods would you use for data collection?
Who would be the sampling frame?
Make 10 questions to find out about the problem.
What kind of ANOVA test could you do with the collected data?
What kind of regression model could you write for the problem?
Y=K+b1x1+b2x2+b3x3+b4x4+c
(Y) Improved conversion rate= b1(IV1: price reduction)+b2(IV2: advertisement)+b3(IV3: gift)+b4(IV4: loyalty card)
More sale: price, design, convenience
7. Could there be correlations? Which ones?
8. How would you present the findings?