Playing with Tableau

Getting Started, Loading Data and Creating Bubble Charts

We have used the Sales dataset for this exercise. The data has 3 categories- 1) Furniture 2) Office Supplies 3) Technology

All these categories have various sub-categories. We visualize the profits across the sub-categories. We color code the circles as per the categories.
The size of the circle indicates the amount of profit for the particular sub-category.

We can see that the Technology category has the highest profits while Furniture has the lowest profits with losses in Bookcases and Tables sub-categories. In the next exercise, we will be analyzing region wise trends in Profit and Discount from our Sales data. Since we are interested in geographical comparisons, our natural choice will be a map.

As seen above, the maps illustrate profit and discount ranges by region. We can determine that losses (red) are incurred on the coasts with profits (green) in the midwest and north. We also ascertain that large discounts were given in the coastal areas with lower values in the midwest and south. Based on our visualization, it becomes evident that large discounts did not translate to profits.

Now, we are trying to visualize Difference in Average Profit over months from 2014 till 2017. We also filter based on the day of the week the order has been placed.

It is clear that profits made from Office Supplies orders are reduced or non-existent over the weekends. Technology profits are high during the weekends whereas Furniture profits are low or even negative.

Let us try to visualize a Scatter plot between Profits and Discounts. Here, we observe that there is an inverse relationship between discount and profits and that there is high probability of a correlation between these two variables. Let’s plot the Profits and Discounts w.r.t Order Date.

Now, we shall incorporate all these charts into a dashboard.

Our dashboard currently shows the profits and discounts over time, by profit / loss and drilled down by Ship Mode. We add interactivity to view individual Ship modes by hovering on a single value of Ship Mode. This helps us see how a particular class of delivery attributed to revenue.

It also shows how all transactions related to a specific Ship Mode fared over time, what kind of profits or loss it accrued and so on. As observed here, our dashboard gives us some interesting insights. By being able to drill down to single elements, we can see that First Class had the highest profit and the greatest fluctuations while Standard class, with least profits also was the most stable source of revenue. We also get a holistic view of the company's financial health.

Profits show a steady upward trend with the period around Jan 2014 showing slight fluctuations for Same Day ship Mode and wide fluctuations for all the other Ship Modes. We join our dataset with Returned Orders table. Now we plot Persons vs. Profit.

We will now blend data from two sources and compare the profits-our Sales dataset and the Coffee chain dataset.

We also create a new calculated field for Aggregate Profit=Profit*100/Sales Then we plot a Histogram the see the count of Sales. We can see that within 0k-15k the sales have happened the most.

Let’s look at the tree map for the profits of Categories and Sub-Categories.

As we can see, Technology is the most profit-making category while the Furniture department incurs the most losses. Along with departments, geographic spans are also a key metric when gauging profits and measuring performance. As we have city level data, we shall look into what kind of profits were made on the sales orders by city.

As evident here, First class was the most favored and popular on the West coast and Midwest. Same day ship mode is sporadic and Second class is popular in the south.

In this sales data, our key performance indicators or KPIs are Profit, Sales and Discount. Our goal is to scrutinize these parameters by looking at each Measure on an overall basis and then drilled down by the Ship Modes. For this, we will create bar plots for the holistic look and area charts to make observations on the aforementioned KPIs, categorized by the shipment modes.