Case study- Dell

1. Case study- Dell Presented by: Siddhartha Sanghi (10714) Vaibhav Patni (10783) • Part I- Analytics for Marketing • Part II- Analytics for production strategy

2. Case I:Analytics for Marketing Strategy • Business Problem: To develop marketing strategies based on the customer purchase data • Data used : Daily per visit purchase a supermarket for the period 4/1/2010 to 6/13/2011 • Methodology: Cluster analysis and market segmentation based on the total purchases of the consumers • Derived variables: Frequency of visit, average purchase per consumer, 3 month aggregate purchase to check the seasonality, yearly spending of each customer

3. Macro-Data Trends

4. Data Segregation The data is being segregated in terms of consumer’s total spending in the store. Median, Lower Quartile, Upper quartile are the segregation points removing 31 consumers who were spending nothing in the store.

5. Marketing Strategy based on Segmentation • Daily Discounts on Weekdays (Mon-Thus): • Purchase below 30- 2% additional shopping voucher • Purchase [31, 75] - 5% additional shopping voucher • Purchase higher then 80 – 10% additional shopping voucher • Yearly Discounts: Divided into three categories • Platinum Card : If annual purchase is above 9000, 15% discount voucher on each purchase • Gold Card: If annual purchase is above 7000,7% discount on each purchase • Silver card: if annual purchase above 4500 gift hamper which is equivalent to 5% of the average amount of per visit purchase amount in the middle and middle high segment. • Problem of 31 consumers: • Clearance sale: Stock clearing sale with heavy discounts • Special Festival Discounts • Super Cheap Mondays • Active Online and SMS Purchase System for busy consumers: • Delivery in non peak hours of the day • Cash on delivery system • Purchase Points Rewarding System • Points would be given on each purchase and a leader board would be maintained top 10 consumers would avail a gift hamper equivalent to 5 % of the total purchase • Gift hamper equivalent to 1% of consumer total purchase

6. Case II: Production Strategy • Business Problem: To optimize future production based on forecasted sales • Data used: Sales volume from Jan’03 to Dec’05 • Methodology: Auto Regressive Moving Average or ARMA(p,q): where  and  are parameters,   is a constant, and the random variable   is white noise We select the ARMA process by checking the minimum squared residuals or minimum SIC (Schwarz Information Criterion) From table 2.1, the process is ARMA (2,1) Table 2.1 SIC values for ARMA processes

7. Additional variables to improve the forecast: Overall growth of the soap industry Industry Demand: The aggregate demand of the entire soap industry Per capital GDP of the economy: It will adjust for the overall trends in the economy. (It would explain the consumer behavior) Population growth of the country Sales of the substitutes and complementary goods (examples: Clothes) Herfindahl-Hirschman Index of the industry and the market share of the firm (less feasible)

8. Suggestions to meet the target of 3.25M profit • Products differentiation based on market segmentation: • Extensive advertisement to increase the sales • Introduction of super saver packs to promote bulk sales • Demographic segmentation: Introduction of a variety of products for different age groups such as “Crystal total” for regular use, “Crystal deo” for young adults, “Crystal Natural” as a herbal soap, “Crystal Care” for sensitive skin, “Crystal Neem” for pimple free skin • Diversifying the products by entering into new segments such as detergent, shampoo, utensil cleaner, electronic gadgets cleaner Premium(Priced at 25 or more) Popular (Price Rs. 21-24) Economy (Priced at Rs. 20)