v

Store * Service satisfaction * Contact with employee Crosstabulation

Contact with employee Service satisfaction

Strongly

Negative

Somewhat

Negative Neutral

Somewhat

Positive

Strongly

Positive

No Store Store 1 Count 16 9 18 17 19

% within Store 20.3% 11.4% 22.8% 21.5% 24.1%

Store 2 Count 2 15 16 13 12

% within Store 3.4% 25.9% 27.6% 22.4% 20.7%

Store 3 Count 9 14 23 22 14

% within Store 11.0% 17.1% 28.0% 26.8% 17.1%

Store 4 Count 17 14 19 10 10

% within Store 24.3% 20.0% 27.1% 14.3% 14.3%

Total Count 44 52 76 62 55

% within Store 15.2% 18.0% 26.3% 21.5% 19.0%

Yes Store Store 1 Count 9 11 20 13 14

% within Store 13.4% 16.4% 29.9% 19.4% 20.9%

Store 2 Count 24 15 18 14

% within Store 30.8% 19.2% 23.1% 17.9% 9.0%

Store 3 Count 6 6 18 11 15

% within Store 10.7% 10.7% 32.1% 19.6% 26.8%

Store 4 Count 10 21 25 12 24

% within Store 10.9% 22.8% 27.2% 13.0% 26.1%

Total Count 49 53 81 50 60

% within Store 16.7% 18.1% 27.6% 17.1% 20.5%

How many ppl r buying across departments

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

Primary department * Made

purchase

582 100.0% 0 .0% 582 100.0%

To find relation by seeing at the table:

Add strongly negative n somewhat negative same for positive

-ve store 2 - 50%

+ve store 3 –46%

To find relation before contact (yes n no)

Before contact after contact

-ve store 2- 41% 50%

+ve store 3 – 44% 46%

For store 2 –ve satisfaction is increasing after contact so the contact should not b increased

How to isolate 39 ppl from store 2

1) Ppl who went to store 22) Who had contact with employees3) Were negatively satisfied4)

Data view – rows r crossed out except for 39 rows

Conclusion: train d employees in clothing department coz 59% are not satisfied

How many males n females went to each n every department

This is data mining

Method of payment v service satisfaction

As .595>.05 we accept the null hypothesis that there is no relation between method of payment n service satisfaction

We go for 99% level of significance when we need to decide whether to close d store or not

Cluster analysis: the way we group d data

Hierarchical clustering and k- means clustering methods

Hierarchical – divisive clustering, agglomerative clustering

Clustering process – 1) selection of variables – ask what is the purpose of making groups

2) Distance between objects – physical dist, no of people between them etc, try to b innovative as possible

3) Clustering criteria’s – from where d dist is measured between cluster n object, and between clusters

Hierarchical is used when no of objects that u want to grp r less than 50, more than 50 k mean

similarity – 1 – more dist, less proximity

disSimilarity – 0 - less dist., more proximity, higher dist better relation

Euclidean distance property –

1) It is a straight line distance2) AB- same from A to B and B to A

The dendogram – critical element, it tells how the combinations happened and how many clusters we should have- graphical depiction of how clusters form

Distance measurement

Interval var r scale variables – we use Euclidean dist

Count – we use chi or phi square

For binary – jakard or simple

Nearest neighbour: no of objects = n(n-1)/2

Farthest neighbour: find dist between neighbour that r farthest,combine the least dist n then..

Centroid clustering:

We use between group linkages as clustering criteria

File – cell inter

How consumer feels about service and cell phones

q) which features r concumers using in cell phones

Proximities

We see variables and not cases as in cases we check for all the data and it’s very difficult to infer anything

To chk no of clusters

Chk the no of lines cutting vertical lines

In above case 5 line

But only 1 line is coming from a group therefore 1 cluster

Point of cut off: the pt at which next obj to join the cluster does so at a longer distance

In above eg 2 and 8 joined at a small dist and 4 joined at a higher dist therefore above is cut off point and there are 4 objects which are funused 0,5,1 and 7(sms,games,alarm,time and date)

Next cross tab between camera and scheduler

Jaccard – 35/(206-62) = 0.24

jaccard Yes matches/(total – no matches)

jaccard index inc if yes matches or no matches inc.

Cluster above can help in giving bundle offers

Eg buy a game and get 50 sms

0r buy games , sms ,alarm ,time n date n get internet free – gv the buyer something that they r not using

Aglomeration schedule (between groups – avg is taken

– 1 and 6 combine and distance is 862

Stage 2 sms to alarm 835

Alarm to game 807 therefore avg 821

(dist average of 1 to 6 and 1 to 2)

Stage 3(dist avg of 1to 6, 1 to 2,and 1 to 8)

Cut off point – coefficient between stage 3 and stage 4 is high therefore vll have cut off oint over here and we will have 4 objects 1,6,2,8

Russel and Rao – only yes matches

Yes/2

Output same 1,3,5,7

K means

3 cluster solution

Monthly exp of cluster 1 is 734.69 ie avg 13 ppl in cluster 1 is 734.69

4 cluster solution

Not evenly distributed

5 cluster solution

No of cases not equally distributed

Now in cluster 1 inly 1 case with monthy exp 2000 so outlier is monthly exp now do monthly exp

Black line is the median..50% cases above n below the line inside the green box

In the green box above n below line 25% each, and 25% in the t –lines(whisker), rest r outliers, astrix extreme cases and circles outliers

Outlier more than 1.5 times length of whisker, extreme case 3 times length of whisker

Data select cases

f

Now monthly expenditure is considerably well distributed if compared to previous cluster analysis

Save k means

We get another variable

Qcl_1 selects all the ppl in cluster 1

Now all the freq in one

Compare groups is best as we can come to know differences between groups

We use qcl_1

Now we do frequency, all 3 together,

We do frequency for category variables

Scaled r continous rest mostly category

Age comparison

3 group is oldest with 88.9% less than 18, 2nd youngest with 96.3% less 18

Gender of respondent

Case 1 more females

Case 3 more males

Level of education:

3rd group more educated,group 1 less educated

Name of current service providerlook at differences in all the cases

1 – less reliance 2 – more bsnl , more reliance 3- more tata indicom

Connection type:

1- More prepaid2- More postpAID

(in above egs see differences between cases)

So now profile of group

Summarize 1,2 and 3

1- Morefemales, less education, less reliance, high prepaid, 2- Relatively younger, less bsnl,more rel, more postpaid, less freq, less cash and more credit

cARD3- 3- relatively older, more males, more edu

So what is the strategy that co should adopt to target groups 1 ,2 and 3

Group 1 – come up with cheaper phones as less educated, lower education, females watch more tv so advertise more on tv and less on radio, advertise during drama serials

Trp – 8% out of all 8% ppl r watching that serial

Based on services

All groups r different

Cluster 3 only sms – ppl r not tech savvy, relatively older

Cluster 2 – tech savvy

Now save this

Now split file to build profile

permap