# Big Mess Analysis

Topics: The Table Pages: 9 (2266 words) Published: June 23, 2009

Management Summary3
Research method used4
Observations6
Recommendations8
Appendixes11

MANAGEMENT SUMMARY

We were asked to analyze the customer data that the Big Mess theatre collected during the past three years. With this data we could analyze what the most valuable customers are. We analyzed the data from different perspectives.

Our main findings are that the Big Mess theatre is dealing with loyalty problems. Also we found out that the theatre is having problems getting young people to watch their shows.

In the first part of this analysis we explain the method we used and the steps we took to process the data. In the next chapter we write about the conclusions you can draw from these findings. One of the most important findings is that the 80/20 principle is not applicable at the Big Mess theatre. According to this principle 80 percent of your profit should come from 20 percent of your customers.

In the last chapter we made some recommendations on how to improve their customer relation management. In short these recommendations are:

-improve the working atmosphere
-promotional activities to acquire and retain customers

RESEARCH METHOD USED

In order to analyze the data we received from the theatre we took a couple of different steps. We have used the programs SPSS and Excel to find the answers for this case assignment.

Question 1
First we had to add up the number of visits from three different years. In order to do this we used ‘compute variable’ under ‘transform’ to create a new variable, which we named total visits. Then we used the function ‘frequencies’ under ‘analyze’ and ‘descriptive statistics’ to create a frequency table (appendix table 1.1) and a bar chart (chart 1.1). This way we could clearly see what the frequency is of a number of visits. We copied the results from the table to Microsoft Excel in order to be able to easily divide the group in five parts. We left a blank row after every 20 percent. Then we created a new column in which we calculated the product of the frequency with the number of visits. After that the total number of visits for each group. From these numbers we could then calculate size of each group in percentages (see table 1.2). Group% of visitorsVisitsTotal visitsPercentage total visits 120%1-1077466 %

220%11-151336311 %
320%16-212148518 %
420%22-303086026 %
520%31-964727639 %
120730100 %
Table 1.2

Question 2
The first thing for this question that had to be done was dividing everyone up in age categories. To do this we first used ‘recode into different variables’ under ‘transform’ to create different age categories. We made groups from 0 – 9, 10 – 19, 20 – 29 and so on. The last group is from 90 – 100. We decided to include the one person who is 100 years old in the last group, because this is only one case. Because some age fields were left empty in the data fields we had to put them into a separate group. To do that we used ‘recode into same variable’ under ‘transform’. There we selected ‘system- or user missing’ and gave that group number 10. Our next step was to separate the different programs with the totals of three years. To do that we used ‘compute variable’ under ‘transform’. To create a table which sets out to ages categories against the different show categories we used ‘custom table’ under ‘analyze’ and ‘tables’. Here we dragged the ages categories in the horizontal field and the different genres in the vertical field. Instead of the standard setting we used ‘sum’ and for age category we selected ‘nominal scale’. The result you can find below in table 1.3.

Age category
012345678910
SumSumSumSumSumSumSumSumSumSumSum
youth_family01810052638976197254257171510093
drama_cabaret738140923773012565360401132715849
dance_balley2211086295679926345023502211083
classical_music020503715163539520621130365361...