Descriptive Analytics Help AustraliaDescriptive Analytics Help Australia

  • Here we use the Descriptive Analytics: The use of data to understand past and current business performance and so make informed decisions. (introduction)

The purpose of this report is to analyze and investigate a spreadsheet that contains bookings and calculations of Porpoise Swim School in order to improve probability and timetable in term four. Measurements like teacher performance, type of class and day and time will all be considered as evidence of how the swim school can be more profitable and manageable. A discussion of each measurement will provide important points that can also be used as recommendations by the end of this report. These recommendations can be implemented to improve each factor ().

  • Here we use the Prescriptive Analytics: Using a model where the output of the model can be optimised (maximised) by adjusting the input alternatives, so “prescribing” what the best decision is. (body)

Study of spreadsheet:

  • Instructors performance:

So that, teacher performance can be highly important as he/she will keep customers satisfied. So that, we will look at the spreadsheet to identify how each instructor performs. There are eight instructors with different rates based on their qualifications and experience. Even though Claire, Jamie and Maryanne all have same rate, they have different performance. Firstly, Claire generates 14.81% of total cost per term,

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whereas Jamie has only 8.82% and Maryanne has 10.62% of total cost. Secondly, Tom, a higher rate instructor, has the most performance with 23.42%. Thirdly, with 17$ rate per session, Steven and Joanne have both a total of 27.66% of total cost. Finally, Rachael, who has the highest rate with 22$ per session, generates only 7.68%. It is being noted the most qualified and experience instructor (Racheal) generates less than the other instructors. Looking at each instructor performance, we can notice that Tom generates more than what Jamie, Rachael and the owner (Regina) all generate.

  • Type of class linked with Early Payment discount:

Penguin and Seal have the lowest demand with 10.40% combined, so we can hire the most qualified and experienced staff to increase the demand for it, and we also could do a discount to drive customers into taking these classes with the best staff available. Or we can just cancel them and focus on the other classes.

  • Day and Time:

On Saturdays, with only four sessions from 9:30 to 11:00 am, we get 9730$ per term, whereas in full working days like Wednesdays and Thursdays we generate less. So we can introduce more sessions on weekends with different timetable because we have high demand. However, we only have one class at 8:30 on Friday so there is no point of keeping this session. There is also different working peaks for each day, so we can re-organize the timetable according to the demand of classes.

In conclusion, we write our findings as evidence to use in the recommendations list

Recommendations, we use Predictive Analytics: Analysis of past performance in an effort to predict the future by examining historical data, detecting patterns or relationships in these data (i.e. creating a “model”), and then extrapolating these relationships forward in time

It could be as bullet points.