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.

Answer 1A) In the snapshot here, one big thing to notice is that sales takes a severe drop of almost $27,000 from weeks 23-25. However, this is not due to there actually being a drop in sales, rather, the drop is due to missing data from the data source. The data has sales values for two years, and they number the weeks ordinally until 52, then repeat counting from 1 to 52 for the second year. Because of this, each week has two values for sales, and the graph plots the sum of sales values for each of the two years, and there is data missing for weeks 23, 24, and 25. Because of this, instead of there being a sum of two values, there is only one instance of sales for each week, leading to these sales points being drastically less than their added counterparts.

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).

This new graph no longer shows the drop in sales that was seen in 1A because it is not adding the two sales values, and is now just taking the average. And if there is only one value, like in weeks 23-25, then it just posts that one value, which should run pretty consistent with the other data points. From the graph, we can see that higher sales occur in the middle of the year, from weeks 20-30.

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.

Based on the given graph, we can see that increased sales occur when temperatures are warmer. The highest sales point is in the warm summer months, and sales decline later in the year when temperatures are lower.

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?

There seems to be a grouping of the points in two areas that break off near the $90,000 range of spending. Spending more money for TV advertisements does lead to an increase in sales, however, it is not worth increasing the investment past about 90,000 because sales don’t increase as much afterwards.

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.

When overlaying Radio ads, we can see that there is a grouping of higher sales from radio ads as well, and that the pattern with Radio and sales is similar to the one with TV and sales. Spending more money on radio ads does seem to correlate with higher sales, but after the $90,000 mark, spending more money for radio ads does not have much of an impact.

In a separate Tableau sheet

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

In this graph, we can see that there is a positive correlation between the volume of fuel sold for the week and sales in the convenience store. The more fuel that they sell, the higher their store’s sales are. This does not mean that one necessarily causes the other, but they are positivly correlated.

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

When overlaying temperature, we can see that the days of highest convenience store sales and highest fuel volume sales are correlated with higher temperatures, and cooler temperatures correlate with low fuel volume and c-store sales.

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

When there is a zero, this signifies non-holidays, and 1 means that it is a holiday. We can also tell that there are higher c-store sales and higher volumes of fuel sold on holidays, which also happen to be mostly warm days. However, some of the lowest sales and fuel volume days are on holidays as well, so this does not mean that holidays lead to higher sales necessarily.

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 my opinion, I believe that Sales is most affected by higher temperatures, but is also marginally increased by tv and radio advertisements, but should spend no more than $100,000 on each. Additionally, if more TV and radio ads are run through the warm summer season, then they will benefit more than if they spent more money on advertisements through the winter, because regardless of the amount spent on ads, temperature does seem to be the biggest driver of co-store sales.