A recent study has estimated that by 2021 AI is expected to drive the business of $49 billion in eCommerce, putting it among industries that benefit from this technology the most. A subset of AI that often finds use in eCommerce is machine learning (ML) – the ability of systems to “learn” and improve their behavior based on data and experience rather than being explicitly programmed. In this article, we will explore various machine learning applications in eCommerce, and show how the use of ML can bring value to your business.

What can machine learning do for your business

Customer segmentation

Let’s begin with one of the most common machine learning applications in eCommerce – customer segmentation. This technique is widely used among businesses and does not necessarily require the involvement of AI.

However, regular customer segmentation can only result in larger generic customer groups that are segmented by something quite trivial, such as geographical location, age, or whether the customer has made a purchase in the online store before. These groups only offer basic insight into customer behavior and do not allow you to create highly targeted offers. In addition, regular customer segmentation often involves some amount of manual work, which can result in human errors and inaccuracies.

All of this changes when you include machine learning in the process. It offers multiple advantages over regular eCommerce customer segmentation:

  • Accurately predicts customer needs with highly targeted groups. An ML system can find patterns that may be unnoticeable to regular human marketers. As a result, they can segment your audience based on those specific shopping patterns rather than the age or geographical location, making it easier to accurately predict and satisfy customer needs.
  • Creates offers with a higher level of personalization. Another advantage of having an AI algorithm analyze your customer data is the ability to create truly personalized offers that are more likely to result in completed purchases. In fact, it was found that 80% of customers are more likely to complete their purchase from a personalized offer.
  • Eliminates bias and human error. As stated before, regular customer segmentation involves some degree of manual work and is not prone to human error. However, there is also another shortcoming to human involvement in customer segmentation – bias. A human marketer can often make decisions (sometimes without even knowing) based on presumptions rather than facts, while an AI algorithm can look at the actual data and tell you exactly who your customers are. A good example of such bias is the common assumption that only teenagers are interested in purchasing video games, when in fact the average age of a video game purchaser is 36.

As a recent study from an email marketing software company has found, the use of segmented marketing can boost revenue from campaigns by as much as 760%. By implementing machine learning you can make your customer segmentation a lot more effective, and increase your profits significantly.

Customer lifetime value calculation (CLV)

Customer lifetime value (CLV) is one of the most important metrics in eCommerce. Calculating it is not a very difficult process, however, predicting it is an entirely different beast. In the proper context, it can bring immense value to your business. On the other hand, if done carelessly it will provide little value and can sometimes even mislead marketers.

A machine learning model can analyze how a customer interacts with your store to make highly accurate (depending on the complexity of the model) predictions on how much the said customer will spend in your store over their lifetime. Marketers can use this information in a variety of ways:

  • Make well-informed decisions to drive long-term business growth
  • Obtain an additional type of customer segmentation based on the value
  • Mitigate expenses on customer acquisition
  • Increase customer retention

A study in the UK has found that only 34% of marketers are actually aware of what CLV is and what significance it holds to businesses. What does this mean? It means that by knowing and accurately predicting CLV you can quickly get ahead of your competition, resulting in significant gains to your bottom line.

Churn analysis

Customer churn (or attrition) rate is a health indicator of an eCommerce business. Even a slight increase in churn rate can have a major negative impact on your business, from raising customer acquisition costs to lowering customer retention and even decreasing revenue. Indeed, a study in the US has found that businesses lose approximately $138 billion yearly because of customer attrition. In addition, its tendency to compound over time can put a serious strain on your business growth.

No matter how much you try to outrun the churn rate by attracting new customers to your store, the only way to successfully reduce it is to improve the areas that cause your customers to leave. Although finding those areas is no easy feat, it can be made a lot more effective with the use of machine learning. By analyzing your data an ML model can quickly find patterns that point towards places where you lose customers, be it your pricing policy or ineffective product presentation.

Armed with this knowledge, you can quickly take steps towards improving those troublesome areas and, subsequently, reducing your abandonment rate. However, an even more important advantage of using machine learning for churn analysis is its ability to help predict its occurrence. Your AI algorithm can predict the most likely reasons for churn and identify customers that are at risk of leaving. This allows you to quickly react by putting strategies in place that will help you keep your clients from leaving.

Marketing campaign management

A machine learning model can have a significant impact on your marketing campaigns. 61% of marketers admit that AI holds the most important role in their data strategy. Here is why:

  • Analyze and improve campaign performance. Your algorithm can analyze your marketing strategies and make predictions on their outcome. It can then use this information to automatically generate suggestions on how to improve them based on your goals.
  • Reduce expenses. The ability to predict marketing campaign outcomes based on actual data helps you maximize their results and make sure that no dollar goes to waste.
  • Automatically generate reports and save time. An ML model can automatically generate reports that are easy to read and hold only the necessary information. This saves your employees time and lets them focus on other important tasks.

With the constant developments in AI, machine learning models are becoming even more effective at what they do, making them a reliable tool for your marketers.

Recommendation systems

There are numerous benefits a recommendation system can bring to a business, you can find out more in one of our recent articles on top eCommerce integrations. Combining it with a machine learning model will make those benefits even more substantial.

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Dynamic pricing

A dynamic pricing system implemented into an eCommerce business can boost its revenue by as much as 25% (on average). By introducing a machine learning model into the system, it can be made even more effective. Uber and Lyft – the leading transportation network companies – are great examples of how a company benefits from AI-driven dynamic pricing.

Here is how an ML model can help:

  • Further, improve customer segmentation by finding new patterns in customer shopping behaviors
  • Add more variables (industry trends, seasonality, geographical location, production costs, customer-related information, etc.) to optimize price changes and achieve better results
  • Align dynamic pricing strategies with your KPIs
  • Make well-informed pricing decisions based on predictions about price changes being acceptable to customers
  • Make automatic pricing adjustments based on the analysis of changes in the market conditions

It is important to keep in mind that, even though there are approaches to dynamic pricing that do not involve machine learning, they can never be made as effective.

While they are transparent and easy to understand, they can’t reach the performance of ML systems, with the exception of very simple problems.

Stylianos Kampakis, Data science specialist, author of “The Decision Maker’s Handbook to Data Science”

Take advantage of machine learning applications in eCommerce

Machine learning opens up almost endless possibilities for growth in eCommerce. If you want to expand your customer base and improve your sales – and ML model can be a trusty ally on your way to success. And, if you require assistance with the technical implementation – consider Forbytes, our experts are always ready to help.