About

In this section, we will be using Tableau to learn concepts on data outliers, seasonality effect, and the relationships and impacts. There is no R coding in this lab session.

Setup

This worksheet will be used to capture your images from Tableau and to share your observations. Example of capturing and including an image is included at the end of this sheet for your reference. You will need to log onto Tableau and Connect/Import the file EuroStore.xls found in the ‘bsad_lab10’ folder.

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Note

For your assignment you may be using different data sets than what is included here. Always read carefully the instructions on Sakai. Tasks/questions to be completed/answered are highlighted in larger bolded fonts and numbered according to their particular placement in the task section.

Task 1: Data Outliers and Seasonality Effect

First get familiar with the data and what each columns represent. A description of the data is provided in a seperate sheet called ‘Desc’ in the same Excel file. Refer to Lab05 for early exercise using Tableau.

In a new Tableau sheet

1A) Plot Sales (Rows) versus Week (Columns). Include a snapshot here. Analyse the data source and explain in clear words the behavior you observe.

The Data shows a line graph that illustrates the sum of sales for specific weeks over the course of a little under 2 years. Initially, the data shows that sales were increasing, but at about week 22/23, sales plummeted to around 25K. This stayed like so for a couple more weeks. Sales then increased again to being over 50K, but then gradually decreased as more weeks went by.

1B) Switch from SUM(Sales) to Average AVG(Sales). Change the Sales scale to be more reflective of the data. Include a snapshot here. Explain the new behavior relative to 1A).

The Data shows a line graph that illustrates the Avg. of sales for specific weeks over the course of a little under 2 years. The new data shows that Avg. sales over the weeks. The data is at a constant level with slight rise and fall at around 20-25K. This shows that the company is steady in sales, and does not show the huge decline as in graph 1.

1C) Add Temp to the Color scale found in Marks. Change SUM(Temp) to AVG(Temp). Edit the color legend to be more reflective of hot and cold temperatures. Include a snapshot here. Explain the combined behavior of sales and temperature.

With the addition of Temp, we can see a slight relationship between temperature and sales. The warmer it is, the better the sales are, and vice versa. This can be associated with the belief that people are more receptive on going outside and shopping while it is warmer, and will tend to stay inside when it is colder.

Task 2: Relationships and Impacts

In a seperate Tableau sheet

2A) Plot Sales (Rows) versus TV (Columns). Switch both measures from SUM() to Dimension. The plot should look more like a scatter plot. Include a snapshot here. Explain the behavior of Sales versus TV. How much you think is the upper limit amount that should be invested in TV ads?

The scatter plot correlates the sales with the amount of advertising spent on tv during that period. While it can possibly be correlated that if you spend around 90K, then you will get varying amounts of sale, the graph shows that spending no money will still result in sales. Based on the chart, it can be said that there is very little, or no correlation between spending on Advertising and sales. The upper limit should be around 90K, otherwise the amount of investment does not match how much money is made from sales.

2B) Overlay Radio to the previous plot using the Size. scale found in Marks. Include a snapshot here. Explain how the additional Radio ads to Tv ads is impacting Sales.

With the Overlay of Radio, it can be seen that there is a greater correlation between amount spent on Radio ad and sales. The greater amount of money that is spent on Radio ad, the greater potential for sales is seen.

In a separate Tableau sheet

2C) Plot Sales versus Fuel Volume. Explain behavior.

The behavior between sales and Fuel Volume shows the correlation between how much fuel is sold and the amount of sales that generates. There is a clear rise with the amount of fuel sold and increasing amounts of sales between 60-66K amount of fuel sold, we have rising sales ranging from 20-28K.

2D) Overlay Temperature using the Color scale. Follow 1C) for temperature settings. Explain the new combined behavior and the impact of temperature.

With temperature, it can be seen that the colder it is outside, the less sales are occurring, and the higher the temp is, the higher the sales are. The colder Temp groups the sales around 20-24K, and the higher temp has a range of 26-28K.

2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.

With the addition of Holiday, we can see that when there is a holiday, there is a higher correlation with more sales of Fuel and higher sales revenue. This can be because of Road trips that people take to visit family during the holidays.

In a separate sheet

2F) Use a Tree Map to best show the combined effect of Sales, Fuel Volume, Temp, and Holiday. A sample view is shown below. Consider using the Quick Filter on Holiday and Temp to isolate and better view the impact of each. You can have more than one filter at a time. Include a snapshot here.
2G) Write a small paragraph summarizing your final conclusions on what you think most affect Sales and under what conditions.

I believe that sales is most effected by Temp, Holiday, and amount of money spent on Radio ad. These seemed to have the strongest correlation and seemed to express the best trends.