Optimizing E-commerce With Machine Learning

Machine learning

E-commerce industry has garnered a lot of popularity among consumers since 2010. Consumers are getting more comfortable with to shop online. Looking back from 2014, the e-commerce global market has grown from USD1.3 trillion to USD 4.9 trillion in 2021; partly due to induced demand as a result of Covid – 19 restrictions. E-commerce platforms are facing cutthroat competition among themselves. Numerous existing platforms offer quite similar products or services with very little differentiation. This has led these platforms to look into innovative ways to attract and retain customers.

Over the past 10 years, technology has been advancing at a neck-breaking speed. The field of machine learning (ML), a subset of artificial intelligence (AI), has grown in leaps and bounds. ML leverages machines to be able to recognize, learn and predict patterns on their own. With a given data set, ML algorithms are able to learn and predict trends and outcomes, and make connections. Even a small e-commerce platform is able to generate millions of useful data points, that a team of human experts could never fully understand and utilize. The introduction of ML into e-commerce platforms is not only a natural step to keep up with the competition but also a more cost-effective way to make business decisions.

To keep up with the ever-changing customer preferences and their behaviour, e-commerce platforms can use ML in the following ways to optimize their actions:

Dynamic price optimization

To begin with, ML can be used in price optimization. Bearing in mind that 46% of online shoppers are price-sensitive, having your price slightly higher than your competitor can increase cart abandonment rates. Online shoppers have the laxity of visiting various online platforms to find out who has the best deal for the same product or service. For instance, if you wanted to take a cab, you would open two or three cab hailing applications just to find the best deal for the same service. Most likely than not, you would end up picking the most affordable ride cetris peribus. With machine learning, the vendor is able to combine competitor pricing, product’s level of demand, as well as the day and time of the week to come up with the most optimal price, in real-time. Some of the useful tools that can be deployed to aid in dynamic pricing are Granify and Personali.

Inventory management

Another key application of machine learning in e-commerce platforms is inventory management and forecasting. Imagine a scenario where your anticipation for demand and supply is purely on intuition. Often, you will end up with either a demand surplus or a supply deficit. With ML you are able to anticipate these situations and take corrective measures. For example, a taxi-hailing app will be able to predict what location will have the most ride requests and hence mobilize available taxis into that location to keep up with the demand. On the contrary, it can advise drivers to keep off certain locations as they won’t be any sufficient orders coming in. Also, a restaurant will be able to anticipate the kind of food orders that are most frequent and hence stock more on those ingredients.

Product recommendation

One of the reasons why Amazon has continuously dominated the e-commerce space is due to fine-tuned product recommendations. This is characterized by about 36% of the purchases on the platform being recommended products, which is basically machine learning on steroids. During check-out, Amazon is able to show you even more products related to what is in your cart and gives you the choice to make if they will be worth the purchase. Another powerful recommender system vastly appreciated is the Netflix recommendation system. It is able to display content based on your specific likes. At no point will your homepage match with another person. The recommendation engine is able to learn and analyze a person’s behavior from millions of data points, and then present you with choices. Whether the recommendations were unsuccessful or not, the algorithm considers the outcome and improves even further in the next selection.

Predictive maintenance

With vast amounts of data generated in e-commerce platforms, it is easy to detect anomalies with machine learning. Fraud protection is as useful in banks as it is in e-commerce. Based on preset conditions, machine learning is able to identify and categorize behavior as either “normal” or “suspicious”. This is highly useful when dealing with customers who make purchases using stolen cards or even those who file for a charge-back soon after a product or service has been delivered.

As echoed by Havard Business Review in their publication on 400 uses of AI, the top two beneficiaries of this technology are companies in sales and marketing, and supply chain management. As a result, e-commerce platforms are in a privileged position to use this technology to their advantage.

Author: Dickson Ndoro