Why You Should Be Running Regression Analysis Against Your Ecommerce Data

As the internet becomes more central to most marketplaces, eCommerce and online sales are following suit. One of the internet’s many functions is as a giant mall, where consumers are searching for goods and services, and businesses are competing for their clicks. The competitive nature of eCommerce means that finding any sort of edge or competitive advantage is paramount. Luckily, optimising your online business through data can give you such an edge, and it’s not all that difficult to start.

One of these edges can be found through regression analysis, a statistical method that allows businesses to discover the relationship between different variables in their business, and to eventually forecast future outcomes based on past data. You can read our previous article explaining regression here. Regression models have a wide range of applications in business, sport, and other areas, but they can be particularly beneficial for the data-heavy space of eCommerce and online business.

Huge entities such as Google and Walmart already use regression and machine learning methods to predict search behaviour, but SME’s with an online element can implement simple yet effective analytical measures to improve their business. Essentially, regression analysis can provide the ‘perfect mix’ of marketing ingredients to drive growth.

Using data effectively in this way comes down to a simple principle: asking the right questions. Once you have the data in front of you, whether that’s weekly online sales, daily traffic on your website/store, or another aspect, you have to organise and frame it so that it answers the questions you have. Here are some examples of how regression can be implemented in eCommerce:

The effect of social media engagement

There is plenty of accessible data around how your social media presence is linked to your online business. This can include the quantity of visits to your social media channels, the breadth of engagement, and which social media channels are providing traffic to your online store. By running multiple regression with any of these variables, you can analyse how they correlate to online sales, and how you might be able to forecast based on your social media strategy. For example, regression might show that 76% of your Facebook posts lead to a hit on your store, whereas only 24% of your Instagram posts lead to a hit. Or, regression might show you that the number of Facebook posts you make in a week is extremely correlated to the number of online sales you make. These insights might prompt you to focus on Facebook, coming up with an effective strategy to make plenty of good quality Facebook posts. You may even see the poor correlation of Instagram to sales and realise that there’s work to be done with your Instagram page. 

Paid, organic, and direct traffic

It’s easy to break down a website based on paid, organic, and direct traffic. Paid traffic is any traffic that comes from an ad, organic is traffic from search engines like Google, and direct is where the user has typed in your URL directly or via any untracked source such as being linked by a friend.

This data is helpful on its own, but with regression analysis, an eCommerce business can measure how each of these data points impacts sales, signups, or any other dependent variable you might want to check. You might find that high paid traffic leads to high online sales whereas high organic traffic doesn’t impact sales, and that would lead you to increase ad spend or widen your advertising approach. You might even check your paid/organic/direct data against your social media activity, and find relationships between them, such as high numbers of certain Facebook posts leading to high direct traffic. There’s a myriad of ways to frame your questions, but regression can provide a much better answer than instinct or guesswork based on your own observations.  In summary, regression analysis is an effective way to learn what drives your sales, traffic, engagement, or any metric that might be significant to an online business. With the incredible amount of data available from online activity, businesses are only staying on the back foot by failing to analyse and learn from the data on-hand.