Making your eCommerce Data Analysis More Effective, by Keeping it Basic!

Trust your gut….

It’s one of the most common pieces of advice you get when making a big decision. While trusting your gut can be helpful in some situations, your gut can’t infer insight or pull trends from data.

Your intuition can’t look at the month-over-month trends in your caffeine consumption, and tell you that your second coffee in the morning boosts your productivity exponentially. Or delve even deeper, and tell you that you crave more coffee on Monday mornings because you tend to sleep-in more on the weekend.

If your brain could have looked at the trends, it would have made the decision easier or at least shed light on the reason for you craving that second cup of joe.

eCommerce Data Analysis

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There are basic analyses that any person with a copy of MS Excel can perform, and for eCommerce companies these are super handy.

When you hear about data analysis these days, phrases like big data, machine learning and neural networks might pop in your head. However, data analysis can and should be trivial for the most part. You don’t need fancy systems to predict your sales next season.

To keep things simple, by looking at what we can understand from one data set that every eCommerce store accumulates.

Looking at Your Historical Sales Data.

There are many different insights you can extract from sales data. For the purpose of this post, let’s see which of your customers are bringing in the most revenue.

Most often, we witness the presence of the Pareto principle where 80% of results are as a result of 20% of the causes. The numbers 80 and 20 might differ in the real world and eCommerce, but the general premise still stands.

Before that we should understand where your business is at. To achieve this, we should get a year’s worth of transactions with their associated customer’s, basket value, number of items in the basket.

Here is a sample sales data we’re going to work with:

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By simple analysis of the raw order data, we can get deeper with overall business health metrics, we’ll cover these metrics in more detail in another post. The best metrics are the ones at are simple to calculate and directly represent real business activity. The table below illustrates some of the metrics we calculated from the raw order data.

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Get to know your customers better. To do so, look to see how many orders they’ve had, how many items they’ve purchase from us in total, and how much revenue they’ve brought us.

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We’re almost done. Now you should create a table with total revenues per customer and how much of the total revenue that represents; let’s call it “Percent of Total Revenue”. Then you should sort the table in and descending order by the Percent of Total Revenue column and calculate the “Cumulative Percent of Total Revenue” which represents how much of the total revenue this customer and the ones above it represent.

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Keeping it Basic isn’t Bad!

From this basic exercise, we learn that customers 1, 2, 3, and 5 bring in 76% of our revenue! This means 40% of our customers are responsible for 76% of total revenue. Also, you’d based on the data is the Analysis 2 table, the all have ordered 3 or more times with basket sizes of 8 items and more.

Based on this simple calculation of a small amount of data, you can conclude customers that buy more than 3 times from you are more valuable and you can focus your efforts on either building features that help them more and/or encouraging your customers to pass the 3 order mark.

We’ll cover some other simple calculations in future posts. Feel free to contact us and tell us what simple calculations you’ve been using or what you’d like to be efficiently tracking through eCommerce analysis.