Day 6 - Team E - Ashok Sasidharan

   Today initially a questionnaire regarding various music download services was shown. This was used to design the music download service of Nokia Ovi. This survey was done to assess the customers’ association with music downloads in order to identify the best music download service as per the perception of customers. As the distance measure, we used probability. The questionnaire contained 18 popular music services. When these brands were plotted in PERMAP, they formed 3 concentric circles with almost equal distances between each brand in a given circle.

  Then we returned to the ‘retail.sav’ file.  We were introduced to ‘custom tables’ using the retail file. Store variable’s scale was set as ‘nominal’ and then it was taken rows. 4 stores were considered in the study. In columns, we took 6 attributes - price satisfaction, variety satisfaction, organization satisfaction, service satisfaction, item quality satisfaction and overall satisfaction. The following data was generated regarding the various satisfaction levels for each store.

		Price satisfaction	Variety satisfaction	Organization satisfaction	Service satisfaction	Item quality satisfaction	Overall satisfaction
		Mean	Mean	Mean	Mean	Mean	Mean
Store	Store 1	3.007	3.082	3.253	3.178	3.171	2.986
	Store 2	3.206	3.096	2.941	2.875	3.309	3.000
	Store 3	3.159	3.094	3.232	3.297	3.080	3.312
	Store 4	2.969	3.037	3.315	3.006	3.080	3.068

 Then a permap format text file was created with this data and it was opened in PERMAP. Then map evaluation was done by checking ‘all active vectors’.  The following plot was obtained.

 The arrowheads represented each service attribute. The stores which were closer to the arrowhead of an attribute was found to be rated higher on that particular service parameters. It was found that ‘overall satisfaction’ was highest for store 3. To further narrow down our analysis, we first tried by removing the ‘Price’ attribute from the active vectors list. But there was no significant change in the mapping. Thus we could conclude that price was not a big determinant in terms of customer satisfaction.  After that different individual attributes were chosen and ‘vector tie lines’ was checked to measure which store is actually closer to which attribute. This was done separately for each attribute. 

   Finally an exercise was given in which each group had to pick up one variable for analysis of store satisfaction. Our team picked the variable ‘Distance from home’. Generated output in SPSS, created permap text file, opened it  in PERMAP and finally checked the option to view all the active vectors. Seeing the plot, we could conclude that people living very close to the store (<1 km) came there because of price, variety and overall satisfaction. On the other hand, people who lived far from the store (>30 km) came to the particular store because of the organization of items in the store.

                        

Each team was asked to come up with 2 strategies based on the mappings and crosstab evaluations.