Business Analytics & Data Visualization are two faces of the same coin. You need the ability to chart, graph, and plot your data. Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to understand what’s going on is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe.
Before you decide to create any chart or graph, you need to decide what you want to show or convey. Charts convey one of the following types of information: Key Performance Indicators (KPI), Relationships, Comparisons, Distributions, and Compositions. Click on the sections below to learn more about each.
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Key Performance Indicator (KPI)
A KPI is usually a single value that relates to a particular area or function and is a reflection of how well you are doing in that area or function. This varies from business to business and function to function. Here are some popular KPIs that companies like to track:
Net Promoter Score (NPS): How likely is it for a customer to recommend your product or service to a friend?
Customer Profitability Score (CPS): How much profit does a customer bring to your business after deducting customer acquisition and customer retention costs?
Conversion Rate: How many leads get converted to customers?
Relative Market Share: How big is your slice of the pie compared to your competitors in the market?
Net Profit Margin: The percent of your revenue which is net profit.
KPIs are best represented using KPI charts.
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Relationships
Here you are trying to either establish or prove whether a relationship exists between 2 or more variables. As shown in the table below, the chart type that is best suited for your data would depend on the number of variables you need to use.
Relating Marketing Spend to Sales Revenue. Here you could show Marketing Spend on the X-axis and Sales Revenue on the Y-axis.
Relating ROI, Investment Time, and Investment Size. Here the X-axis would be the Investment Time, the Y-axis would be ROI, and the size of the Bubble would be Investment Size.
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Comparisons
Here you are trying to show or examine how different variables change over time or provide a static snapshot of how different variables compare. The type of chart you choose would depend on the number of variables you need to use.
Comparing Revenue Per Employee, Revenue Growth, and Territory Size. Here two of the dimensions would be the Row and Column of the table while the third dimension would be the cell value.
Showing month-over-month Sales and Web Traffic. You would have two columns for each time period. One representing Sales and the other Web Traffic. The X-axis represents Time in months while the Y-axis represents Sales and Web Traffic.
Showing month-over-month Sales, Web Traffic, Webinar Registrations, White Paper Downloads.
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Distributions
As the name suggest, here you are trying to show how your data is distributed over certain intervals. Here intervals implies clustering or grouping of data, and not time.
Showing how many customers have made one transaction, two transactions, three transactions, etc. Generally you are counting something and putting them into ‘buckets’ or ‘bins’ of a measurement like amount, frequency, duration, etc.
Relating ROI, Investment Time, and Investment Size. Here the X-axis would be the Investment Time, the Y-axis would be ROI, and the size of the Bubble would be Investment Size.
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Compositions
This is when you want to highlight the various elements that make up your data - in other words, its composition. Your first choice here is whether your data is static or if it is changing over time.
Showing the composition of how much of your sales come from each region; what percentage of marketing leads come from each lead source; or percentage of responses to a survey (sliced by gender or ethnicity or the response of the question itself). Generally you should keep the number of slices in your pie to less than 10.
Same as above (for Pie Chart). Advantages are that it is easier to compare multiple sets of stacked 100% bar charts side by side vs comparing the relative sizes of a given slice of the pie in multiple pies charts that are next to one another.
Showing the composition of your entire customer base by how much revenue they each contribute to the whole. The benefit of a Tree Map is that you can display dimensions that have a very long tail (vs Pie charts and Stacked Bars which are best with less than 10 slices).
Showing the composition of how the number leads which came from each lead source have changed on a weekly basis.
Showing the composition of how the % mix of marketing lead sources have changed on a weekly basis.
Showing composition of quarterly revenue totals broken out by each region (quarters being the x-axis and regions being the colors of the stacked bar).
Showing composition of the quarterly % mix of gender (or any demographic) for our new hires; or our quarterly win rates vs a competitor (with the colors in the stack being % won vs lost).
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