Machine Learning for Ecommerce

Ecommerce brands have a tremendous amount of information about their customers — so much, in fact, that it can be difficult to understand without the powerful tools offered by machine learning. 

Here’s what I mean. If those brands are using platforms like Google Ads and Google Search Console, they know what types of marketing tactics and messages are leading to their websites. 

Web analytics tools like Google Analytics or Adobe Analytics can provide insight into how visitors are using those sites, what content and products are most engaging, and where shoppers are dropping out before buying. 

Combine that with data from Shopify or another ecommerce platform, and you have detailed order and inventory information, too. 

But due to the number of variables, analyzing that data can be extremely time-consuming. Most companies are limited to surface-level findings simply because the analytics process is so time-consuming. 

Fortunately, machine learning (ML) is becoming more and more accessible to online retailers, helping them to generate insights from extremely large datasets at scale. It can help uncover patterns from multiple data sources that would be almost impossible for a single expert — or even a team of experts.

How Machine Learning Can Benefit Ecommerce Brands

At its most basic level, machine learning applies an algorithm to a dataset in order to make decisions and predictions. 

What makes ML unique is that its algorithms can “learn” or change based on the datapoints that it encounters, as opposed to traditional programs that can only follow the steps they were originally programmed with. 

So, for example, you could create a machine learning algorithm that sorts your customer base into groups (or segments) based on their associated characteristics. As the algorithm is exposed to more customers, it “learns” which characteristics are most relevant and uses that knowledge to create refined customer profiles.

The algorithm can do all of this faster than a human being can. It can also discover patterns that a human being would probably overlook because they are hidden in levels of variables and the interactions between those variables.

Examples of Machine Learning in Ecommerce

So how can ecommerce companies use machine learning in their businesses? Almost more than you can imagine. Here are a few projects that our team has worked on: 

ML can help you understand the impact of external forces on your product sales.

Weather, economic changes and the time of year are a few examples of external factors that might have big impacts on your business. Machine learning can help you find the relationship between those forces and your sales, revenue or some other desired outcomes. 

Once you know that if Variable A increases by 2%, then Outcome B will decrease by 10%, you can make more informed plans for the future.  

As an example, we had a client that sells home improvement products. We built a model that incorporated weather and historical sales data. The model showed there was a major sales increase that coincided with eight days of above-freezing temperatures. 

As a result, the next time that temperatures were forecasted to rise, the brand knew that it should increase marketing spend and ensure inventory levels were suitable for increased demand.  

ML can help you figure out how to turn customers into repeat customers. 

One of our clients had a large number of shoppers who did business with them once, but never came back. So our team applied a machine learning model to customer data to see what separated one-and-done shoppers from people who returned again and again. 

The model looked at a huge number of variables as it created the two groups, including how many items they had in their virtual cart and the time of year, among many, many others. 

One insight that we found? If a user didn’t have to return an item and their order total was above a certain price point, they were much more likely to become not just a repeat customer, but a brand ambassador. 

It was a complete surprise — and a good example of what makes machine learning useful. A single person working on their own with a spreadsheet would probably never have been able to find those combinations of variables that identified the group split. 

ML can also help forecast inventory demand.

It’s one of the most important questions for online retailers: How much merchandise should you order to meet demand? Order too much, and you’re left with product you can’t sell. Order too little, and not only are you missing sales, you’re also creating a lot of unhappy customers. 

For one client, we built a model that — in addition to previous order history, general economic conditions and other factors —- examined how the company’s catalog impacted customer demand. If a product was featured on the catalog’s cover, how did that affect orders? Did the product’s color matter? What if there was no other special advertising or promotion for it? 

And those are just a couple of examples. Machine learning can also help ecommerce companies …

• Personalize the customer experience so that shoppers are more likely to buy. Recommendation engines — “Customers who bought this also bought” — are one of the most common tactics.

• Predict customer churn. Machine learning can help identify the customers most at risk of falling away, so retailers can offer a special promotion to get them re-engaged before it’s too late.

• Protect against fraud. Online thieves are constantly changing how they scam their victims. Machine learning can spot purchases that diverge from a customer’s normal orders and even quickly adapt to identify new types of scams. 

You might already be using machine learning and not even know it. There are several off-the-shelf technology solutions powered by ML. For example, it’s possible to buy a tool that offers built-in recommendation engines like those mentioned above. 

Those tools have one big drawback: They’re usually built around one type of data, and it’s almost always data created and captured by their solution. 

There’s nothing wrong with that, but you’ll get some of your most useful — and most unexpected insights when you combine a larger array of data sources, like augmenting your sales data with weather data, information on the larger economy, customer geography and more. 

The same way that a tailored suit usually looks and fits better than something off-the-rack, a custom model will usually be a better investment. 

Why You Should Work With Our Team

Machine learning — and all the useful insights that can come with it — are within your reach, even if you don’t have a great deal of experience with data science. 

For much less than the cost of a full-time employee, our team can help you set up the necessary data and build and maintain your models — the same way we’ve helped a long list of ecommerce brands. 

Not only can we help you figure out what you really want to accomplish with machine learning, we can track the necessary data and set it up for analysis, without much oversight necessary. 

(One caveat: Machine learning requires a certain amount of data. If your company just started, it might take a year or two until you have enough for machine learning. But our team can still assist you. We can help set up your data collection and storage so that, when you are ready for machine learning, you’ll be ready to go right away.)

We work with companies of all sizes, but our services can be especially beneficial to midsize ecommerce brands. Most of those companies don’t have their own analytics team or data science expertise. Or if they do, that staff doesn’t always have the bandwidth to launch a machine learning project. 

What questions would you answer if you had access to machine learning models? Let’s talk — and see how our team can help you get started.