Analysis and Recommendations: A database can be used to analyse and make recommendations for a business.to show how this can be done using this database and the business, i am going to use some regular/casual business scenarios and the databse to try and analyse aswell as solve any scenarios/issues that may arrise. Scenario 1: The business wants to know how much money has been made so far off each individual performance. Using the query: Number, price (each) and total income of each performance type ordered across whole database, I am going to create a pivot table which will show how much money has been made from ticket sales. From the above screenshot it can be seen that in total income so far is 155,123.00. Scenario 2: It is necessary to keep a record of the total income each month I will use a pivot chart to display the query: How much money was made from ticket orders each day. Query 4.
it can be seen from the graph above where the most money was made was in january and you can also see that it made the most money compared to any other month by making at least a minimum income difference of 5000. In january the total income was over 22,500 compared to the month making the lowest income which was in july, where just 5000 was made. Most of the money was made from the first three months of the year, and the least income was between june and july. Between october and december as the total monthly incoming is decreasing however the reason the total income may have gone straight up without a trend because christmas was approaching as well as new years. Most of the income of the year came from the winter and autumn months which are january, february, march, september, october, november and december. This is probably because in these months due to it being cold people may decide to stay indoors and entertain themselves whilst being away from home. The two hottest months of summer which are june and july made the least amount of income meaning that people possibly preferred to stay outside then watch movies inside. Scenario Three: The business needs to cut its catalogue of performances by around 3 performances to make room for new performances. Earlier I created the query: Number, price (each) and total income of each performance ordered across whole database. I can now use this query to effectively decide which performances to drop from the catalogue. It would be most sensible to either drop: 1. The 2 least popular performances based on quantity of tickets sold 2. The 2 least money making performances based on the income they produced. I am going to find out which 2 performance would be dropped if either of these were to be used. 1. I know that there are 12 performances in the database. By having 2 less would bring the count down to 10. By using a pivot table on the query mentioned previously I can apply certain filters to decide which performances to keep and exclude.
This step by step guide shows how to solve this scenario: 1. Place the field names in their correct positions on the pivot table. 2. Select the entire Make column, as this is the column needed to exclude individual performances, and click the Show Top/Bottom Items icon along the top toolbar. 3.As we only require seeing the top 10 performances (therefore excluding the bottom 2) I selected:
4. Now this filter has been applied, the list has been shortened to 10 models of bike instead of 12. The list has gone from this: To this..
The solution using this method would be to drop the following performances: 2. To exclude the bottom 2 performances based on income they create, the method is very similar. Follow the instructions above but instead use the Sum of SumOfPrice field to apply the filter to. By doing this it results in the list going from this:
to this So these will be dropped based on the income they have produced. Scenario Four There are new performances being released every week so to be able to make room for new performances you need the get rid of the two least selling performances. From the above chart it can be seen that the performances to be dropped are as follows: Phantom of the opera and sound of music.
Analysis of information produced for Scenario 4 From the graph it is also possible to analyse further. For example the chart shows us which performances sold the most number of tickets. The performances that sold the most number of tickets are Chicago, les miserables and we will rock you. However the most selling performances was will rock which sold over 800 tickets. Whilst the second most selling performance was chicago selling just under 800 tickets. Since the database was created it was consistent that the phantom of the opera was underperforming considerably as it sold the least amount of tickets, made the less amount of income and it s ticket sales was so low that you can't even visualise it on the pivot chart. By going to the pivot table view, it is possible to view the performance sales as a percentage of the entire sales for the company. This is done by highlight the desired column (in this case the total tickets of each performance bought) and then selecting from the toolbar to display as Percentage of Grand Total. A view similar to this will be seen: We will Rock You 15.40% Chicago 14.22% Les Miserables 14.17% Mamma Mia 12.62% Chitty Chitty Bang Bang 12.23%
Jack and the Beanstalk 8.19% Blood Brothers 7.91% Evita 6.37% South Pacific 5.43% Guys and Dolls 2.17% Sound of Music 1.17% Phantom of the Opera 0.13% As you can clearly see that the maximum occupying single performance s we will rock you and and lest is the phantom of the opera. When the query is exported to Microsoft Excel it can be present in a different way. The data can be placed in a pie chart the following can be seen: