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.

With the image taken from Tableau, we notice a sharp drop in sales from weeks 23 - 25. Looking further into the data that we are using, one would notice that we have some missing data. This sheet tracks 2 years of data and from weeks 23 - 25 there is only one week of sales recorded. The graph is taking the sum these two years’ sales. Since there is only 1 years worth of sales recorded, that is why there is a massive dip in the data.

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).

Relative to the picture under 1A, we can see that this graph better represents the sales throughout the 2 years. Not only are the sales averaged, the scale of the Avg-Sales has been adjustes to show a more dynamic vew of sales rather a more flat view under 1A. When we averaged the sales, the weeks with only 1 revenue recorded better fit the graph. The sales total are all mostly around 22,000 euros. From the scaled view, sales seem to be the highest from weeks 20 - 32.

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.

When adding the temperature dynamic to the graph, we can see that as the temperature increases, the sales increase also. The middle of the year seems to be the summer time. We can also see that fall of sales when it gets 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?

From this graph we can see that the more we spend on TV advertisements the more sales goes up. This is only true up until 90,000 euros. No more than 90,000 euros should be spent, because there aren’t as much increases in sales from advertisement. There is a big grouping of the data in the upper right corner of this graph that portrays this.

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.

By adding the radio ads dynamic to the graph, we can the relationship. From this graph we can see that the trend is pretty similar to that of tv and sales. When spending more money on ads for TV and Radio, sales seem to increase, but one will notice that spending 90,000 or more euros there won’t be that great of an impact.

In a separate Tableau sheet

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

When looking at this graph, we can see that fuel volume and sales is positively correlated. Sales seem to increase when more fuel is sold. Not being correlated doesn’t always mean it is causation.

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

From this graph we can see that the sales in volume go up during warmer temperatures. When it is colder sales decreased.

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

From looking at this graph, being either holiday or nonholiday seems to have no affect on the sales or higher volume of fuel. We can see this by looking at the number 1 next to the points. There is a number 1 next to warm and cold days. This is also the same for number 0.

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.

Sales seems to be mostly affected by temperature and to an extent, advertisement. My suggestion to this business it to heavily advertise during the summer when temperatures are usually warmer. Another suggestion i would include is that is does not really matter if there is a holiday or not. People seem to buy randomly when taking holidays vs nonholidays into account. From this treemap we can see the that the hotter days (red) have higher sales volume (bigger boxes). Another suggestion would be to advertise during the summer to get the most out of sales. The expenditure on sales would be capped around 90,000 euros, noted from the previous tasks.