Everyday businesses collect data in different forms and variety. Many of these datasets stay buried within various organization systems without any further action. Forward thinking organization don’t just collect these information's, but rather process it to extract insights hidden in them. While many institutions are seeking to understand the data they collect, coming up with a suitable guide can be difficult, and therefore having some case studies to follow through can make the process simple and painless. Therefore in this series, ‘data in use’. We’ll explore few cases on use of Tableau Level of detail (LODs) to respond to different business scenarios.
In this first case, we'll assume the role of Marketing Manager of ‘Brand A’, for sure as 'Brand A' we’ve been collecting tones of data, which is a good thing. But something is not right because we never bothered to question this data. Therefore, this time as a marketing manager I want to run some adverts, my goal is to target a certain customer segment. I do not know for sure which segment, but I know after interrogating my data I will have some ideas, as a manager I know we’ve been profitable in the past four years, and therefore my first idea is to evaluate who are the most profitable customers by date of acquisition? This would help me understand which customer segment I should target with a certain advert and what message should be communicated.
Assuming my dataset partially resembles Superstores dataset packaged with Tableau app, the following simple procedure would help me as a marketing manager of 'Brand A' make this decision.
Step 1: Compute customer acquisition date
Here, we’ll seek the power of Tableau LOD to compute the minimum date for every 'Customer ID'.
Step 2: Build a simple column chart of profit for different years
Drag dimension field Order Date at Year level to the Column(s) shelf.
Drag measure field Profit to the Row(s) shelf.
Drag the calculated field ‘Customer acquisition date’ to the color shelf. (Note; You can specify the date part you’re targeting, could be yearly, monthly etc.)
Add some Table calculations by computing the percentage of the total, Table (down).
Step 3: Lets re-build our story by reworking on the tool-tip details
Open the tool-tip tab and rework it as shown below;
Using this simple chart, it makes it easier to view where my profits are coming from. As a marketing manager, it makes my work easier to target customers based on different metrics e.g Profit for this case.
For instance, from this analysis. My data tells me, of the total profit generated in the year 2015, 64.88% and 22.00% came from customers acquired in the year 2012 and 2013 respectively. With such understanding of my business based on the data collected, decision making becomes easier. I can decide who to target with an advert and what should be in the message. It also helps me respond to business challenges revealed by the data. For example in this case. There is a huge problem with customers acquired recently, and therefore this could prompt me to dig dipper on this customer segment.
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