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.

Analyzing the graph above, you can notice that there is a huge drop in sales in weeks 23-25. Everything else looks relatively flat.

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).

Now that we changed from sum to average, the drop in weeks 23-25 went away. This is due to the fact that nearly every week has two data entries but week 23-25 only had one. Averaging the data shows results that are much more accurate.

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.

Looking at this graph, you can see that average sales increase in the middle of the year when the temperature is higher.

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?

Looking at the graph above, it doesn’t look like there is much of a correlation between tv and sales. This is determined by looking at majority of the dots being on the y axis, showing that when tv changes it doesnt affect sales that much. This result would lead me to invest very little in tv.

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.

This graph shows that radio seems to be a better investment for sales.

In a separate Tableau sheet

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

When comparing fuel volume vs sales, you can easily notice that when fuel volume increases, sales also tend to increase.

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

Already knowing that high fuel volume leads to sales, this graph gives more information showing that the high level of fuel volume that result in the highest sales is when it is hot out rather than cold.

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

This graph also shows that when there is a holiday, sales will be higher.

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.

In conclusion, I think it is fair to say that the largest contributor to total sales is an increase in fuel volume. In addition, temp and holidays lead to increase in sales. With that being said, the week with the highest sales will be in a week that is hot, has a holiday, and when fuel volume is at a high.