The e-commerce market is vast and only expected to get bigger with time. Not only has the global e-commerce market been valued at $9.09 trillion, but it is also expected to grow at a compound annual growth rate of 14.7 per cent over the next seven years.
As a result of this enormous size, companies have been flocking to the e-commerce space. In fact, the number of e-commerce websites worldwide ranges from 12 to 24 million, with more being built each day.
“It is fair to say that the e-commerce market is a competitive space, where very few companies make it big. Additionally, any startup that ventures into the field will have to compete with behemoths like Amazon, eBay, and Alibaba.”
With this in mind, e-commerce companies need to take advantage of every possible tactic and technology to give them a leg up over the competition. One such technology is machine learning, which is an application of AI.
The Benefits of Machine Learning for E-Commerce Companies
Machine learning and AI offer several advantages to companies:
- Enhancing features functions and the performance products, or the website itself (for e-commerce sites)
- Optimizing business operations and automating more mundane tasks (thus freeing up employees’ time for more complex ones)
- Enabling more informed decisions (AI systems are good at both analyzing data and creating predictive models)
In this article, we will look into these benefits in more detail.
Different Use Cases for Machine Learning Within E-Commerce Companies
Machine learning improves a wide variety of aspects of doing business in the e-commerce space. Here are some of the basic ones.
Enhancement of Features of the Site
Perhaps the most fundamental parts of the website that can be enhanced with machine learning are the search function and product recommendations.
Based on the data collected from previous use, systems that use machine learning algorithms can significantly enhance their user experience.
The Search Function
When it comes to search functions, Google has set the gold standard. This is why the verb “Google” has become part of our lexicon: if you don’t know the answer to a question, just google it.
However, when it comes to e-commerce sites, the search function tends to be less than stellar.
It demands accurate typing, no spelling mistakes, and clear knowledge of the product in question, all of which can make finding anything maddeningly difficult. As a result, many would-be shoppers end up turning away from the site in frustration.
Machine learning is set to change that.
“Intelligent algorithms will make finding anything on e-commerce sites easy and straightforward by drawing from similar searchers by current and previous customers.”
Hence, they will be able to serve those customers who have a vague idea of what they are looking for but remain unsure of the name of the product that fits their needs.
Whenever a customer walks into a brick and mortar store, a sales clerk assists them with their purchase.
An excellent sales clerk will go a step further and recommend other products that complement the initial purchase, which can go a long way towards fattening a company’s bottom line.
Unfortunately, with an e-commerce site, there is no salesperson who can upsell the customer that way.
Systems powered by machine learning can take on the role of the salesperson.
Artificial intelligence can recognize patterns and trends in purchasing behaviour, enabling the website to offer relevant suggestions and recommendations.
For instance, think of Netflix suggestions or Amazon’s function that shows you what other shoppers tend to buy with the product you’re viewing. They are powered by powerful recommendation engines and help generate sales.
The Optimization of Business Operations
Managing inventory is among the many operational problems e-commerce companies deal with while doing business, and it can be a real hurdle.
One of the most common mistakes companies make is failing to track their inventory, which results in overselling, slow deliveries, or making wrong forecasts on a shipment’s status.
Unfortunately, promising something, then failing to deliver is one of the fastest ways to lose customers in e-commerce.
So, why don’t such companies just track their inventory?
Well, it is an arduous task if done manually, especially if the site carries a large number of products.
Fortunately, machine learning proves useful here, as well. The automatization of inventory tracking also facilitates supply chain management by predicting future demand.
After all, e-commerce sites need to foresee fluctuations in demand and prepare for them ahead of time. This enables a company to replenish its stocks automatically and in a timely manner.
Machine learning enables the automation of operations, freeing up the employees’ time. What’s more, as machines never tire or get emotional, they are sometimes better suited for certain tasks than people.
Exceptional customer service is a cornerstone of the e-commerce business. This translates to supporting customers whenever they need it and doing it through different channels, including the company website, social media, and the phone.
Companies would have to hire enormous support teams and have them work around the clock to achieve this. Not only is this expensive, but it is also unsustainable in the long run.
Consequently, e-commerce companies frequently turn to AI and machine learning to solve this problem.
For instance, they rely on chatbots powered by advanced machine learning algorithms to conduct conversations with the customers. These bots are so advanced that it is sometimes difficult to tell whether one is conversing with a bot or with an actual human being.
To reach the customer, companies have to rely on more than one marketing channel. This increases customer retention, improves purchase rates, and enhances engagement.
That said, omnichannel marketing is also a challenging endeavour, especially since it relies on so much data. It also requires being able to personalize communication and reach out to each customer through their preferred channel.
This is another area where machine learning proves effective.
“Through an automated workflow, e-commerce companies can use an advanced machine learning algorithm to choose which marketing channel to employ to reach out to a customer, based on past experience.”
Moreover, this workflow can reorder itself to ensure that the message being sent to the customer resonates as profoundly as possible.
As machine learning algorithms develop, they will automatically incorporate data from other sources as they attempt to understand the consumer better. This development will obviously be accompanied by other developments, such as IoT, 5G networks, and cloud computing.
The general idea is that even when a customer walks into a brick and mortar store, the cameras there, using facial recognition software, should be able to not only identify the customer but also record all their shopping patterns.
Adding this data to its existing database and analyzing it later will enable the company to further develop its offer.
Making More Informed Decisions
Seeing as machine learning algorithms are adept at finding patterns and trends in gargantuan amounts of data, they are unsurprisingly well-suited to help executives make informed decisions.
Determining the price is one of the trickiest decisions for a business. If it’s too high, you risk losing customers, but setting it too low will shrink the profit margin, perhaps to an anaemic degree.
When the competition is taken into consideration, it becomes all the more imperative to get this decision right.
Again, machine learning can help this area in more than one way. For instance, the right algorithms facilitate dynamic pricing.
This means that they’ll automatically change a product’s price based on such factors as competitor’s prices, the demand for the product, the day of the week, the type of customer, and even the time of the day.
On the other hand, machine learning allows companies to conduct experimenting and AB testing.
It also lets companies know which prices performed better and under which conditions.
With customer profiles on hand, businesses can predict how customers would respond to certain prices and specific offers, then test these hypotheses.
For many e-commerce enterprises, security is a constant concern. Companies will invest millions in fraud detection and protection, and machine learning has many uses in this area.
“Machine learning algorithms crunch enormous amounts of data, allowing them to differentiate between a genuine transaction and a fraudulent one.”
They learn to distinguish the telltale signs of a typical transaction, and they’ll flag anything that deviates from this norm.
Technological Trends in E-Commerce
Alongside mobile technology broadening the possibilities of doing business and AR and VR allowing customers to inspect products as if they were in a physical store, machine learning is one of the key trends that are transforming the e-commerce space.
What all these technologies boil down to is this: to succeed in e-commerce, it is essential to offer customers an exceptional experience, one that is memorable, smooth, and enjoyable.
Machine learning allows businesses to make more informed decisions based on data collected from their customers and predictive analysis, optimize their business operations, and even improve their search engines.
— Joe Peters is a Baltimore-based freelance writer and an ultimate techie. When he is not working his magic as a marketing consultant, this incurable tech junkie devours the news on the latest gadgets and binge-watches his favourite TV shows. Follow him on @bmorepeters
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