eCommerce is all about differentiating a company and staying on top of the latest trends. They don't get any more significant at the moment than artificial intelligence and machine learning, which could redefine the way online companies do business.
On the machine learning side, one of the most enormous potential benefits for businesses involves using the vast amounts of data produced. Every piece of content posted to a website, social media post, marketing email, and even completed sale provides information about audiences, and that can prove immensely valuable. However, this information is so plentiful that it can quickly become overwhelming when monitored manually – and that's before a business even takes any action on it.
Machine learning has the capabilities to carry out both tasks at the same time. Not only does it scan, understand and arrange information, but it can either act directly or provide plans for the business going forward. It can interpret patterns and act on them, carry out experiment management on a company's behalf and ensure that companies not only hold lots of information but have the tools required to act on it too.
Machine learning continues to evolve, just as the name suggests, but there are already numerous practical applications for businesses. Crucially, they apply to companies of all sizes too. So while more prominent names like AWS, Azure, and Google Cloud provide the centerpiece for most machine learning technologies, there's already software and addons for existing applications that ensure that even the smallest companies aren't left behind.
If the idea of seamlessly and instantly harnessing data and acting on it without significant overhead appeals, here are ten great ways to make machine learning an indispensable part of any eCommerce strategy.
1. Increased Personalization
Personalization has long been one of the most vital sales and marketing components. While the debate over the value of personalizing emails continues, machine learning enables businesses to personalize much more of their process.
It's possible to view this as bringing eCommerce more closely in line with the bricks-and-mortar experience. Online retailers don't have the benefit of salespeople on the shop floor, on hand to answer questions and make recommendations. Instead, most customers land on a site already with a specific product in mind, often through search engines, and the sales opportunity may often come down to whether they decide to buy or not.
However, machine learning has the potential to provide the kind of sales support expected from a regular store. It can understand how people arrived at the site, learn their shopping habits and potentially answer questions, recommend additional products and even upsell, all without any further human workload.
2. Flexible Pricing
Amazon famously updates its prices dynamically. In 2018, it made 2.5 million price changes every day. At the time, that meant that a single product's price might change every ten minutes on average. This is in response to margin, demand, competitor pricing, and various other factors.
Price is one of the most critical factors in any purchasing decision, and the tools Amazon uses have long been out of the hands of small businesses. However, machine learning can make all the difference when it all comes down to it. In this specific case, it monitors abandoned carts, conversion rates, and, once again, potentially even competitor pricing to ensure that every product appears to be a great deal.
There are few limits to what the technology can achieve in this regard. Prices can change between new and existing customers, the time of year, and even the time of day. The only crucial consideration businesses need to keep in mind is to ensure they add parameters to ensure profitability. After all, the growth of machine learning could see artificial intelligence from different sites undercutting each other to the point that the respective businesses are practically giving their products away!
3. Business Integrity and Fraud Detection
Fraud and chargebacks remain two of the biggest concerns for online businesses. Consumer protections are in place, and rightly so, but they're still open to abuse.
This is where machine learning's ability to spot patterns can come in. They can monitor transactions and quickly discover the characteristics shared by ultimately fraudulent transactions. But, most importantly, it can act quickly on its findings, potentially referring suspicious transactions to a human for review or blocking them altogether if they cross a certain doubt threshold.
4. More Intelligent Search Results
Google is the most prominent search engine going. The time and money that goes into making it as effective as it is remain far beyond the resources of most businesses that can't prioritize search functionality. Most on-site search engines can't keep up for obvious reasons, but machine learning can work to bring them closer.
Many basic search functions on eCommerce websites are truly simple. If they cannot match something specifically, they won't return any results. However, an excellent eCommerce search facility is able to spot trends, understand the context and identify common misspellings. Done manually, this can quickly become a mammoth task, which is why most search options are so generic compared to Google.
However, machine learning can take the lead, gradually coming to understand syntax and context and learning that another name may refer to a specific product in different regions.
It can cross-reference sales that involve a customer buying something other than what they searched for initially. If it happens more than once, it can identify a pattern and learn to display another product as part of the same search results.
Ultimately, machine learning can discover what someone is looking for and ensure that they find it, even if that particular journey wouldn't make sense to everyone.
5. Greater Inventory Management
If you sell digital products and services, you don't need to worry about inventory. However, if your business relies on physical products, spending as little time displaying an 'out of stock' sign as possible makes sense.
Online shoppers don't tend to backorder, especially when they can head to a competitor that does have something in stock in just a few seconds. Therefore, inventory management is vital in maximizing sales opportunities.
It's difficult to predict when a product may enter high demand. It may be seasonal, or it could go viral for whatever reason. But, crucially, machine learning can identify these trends as soon as they happen and indicate to businesses when they might need to increase or reduce their product orders to meet demand.
Having popular products in stock can significantly impact any business's bottom line for obvious reasons. Machine learning reduces risk by indicating when to slow those orders down once demand shows signs of slowing down.
6. AI Customer Service
Most eCommerce have defined customer service metrics. This could be a set wait time for phone contacts or email replies, or it might involve ensuring that a customer receives at least some kind of response within a minute of initiating contact.
In some ways, machine learning is the next evolution of automated emails. When someone fills in a contact form, registers for a mailing list, or buys a product, it's customary to send them an automatic confirmation. It demonstrates that whatever they've done, the business knows about it and will address it as soon as possible.
Machine learning can be just as automated but can also deal with interactions as they arise. It could, for example, spot questions in emails and formulate an answer. In doing so, that's another satisfied customer without any additional demands on human operators.
It's also becoming increasingly popular with chatbots. Of course, not all prospective customers are happy with chatbots and want to speak to a person out of principle as much as anything else. Nevertheless, those that just want information can benefit from a bot that not only answers questions but constantly refines itself to ensure that its answers become better and better over time.
7. Dynamic On-Site Marketing
Many eCommerce sites rely on reasonably basic tools for upselling and cross-promotion. For example, it might be a related posts plugin or a manually formatted list of products that people interested in one item might also be interested in.
Machine learning can take it to the next level, quickly emulating the 'People Also Buy' section on Amazon without any extra effort and potentially even surpassing it. It can discover what people want to buy when they arrive on an eCommerce site, what they ultimately end up buying and what other products they tend to buy with it. From there, they can make recommendations to others that might not know those complementary products are there.
The technology can also reach beyond recommendations. If a site has ad space, it can dynamically adjust what's shown. If a visitor arrives at the site through specific keywords, they can be directed towards articles and additional content that may reinforce the initial buying decision.
8. Timed Discounts
Machine learning is excellent for flexible pricing, but the functionality can also extend to complete discount management. For example, it might learn that a specific visitor has reached a product page on multiple occasions without ultimately making a purchase. Thus, a timely discount might be just the extra nudge someone needs to commit. Crucially, machine learning can handle this on a case-by-case basis, so there's no need to offer sitewide discounts for those that may be perfectly happy to purchase at the standard price.
9. Customer Value Predictions
While past performance doesn't always indicate what might happen in the future, most businesses need an idea of how they'll perform going forward to inform what they do in the present. There are few better ways to do this reliably than with machine learning.
The technology can profile and segment customers based on the amount spent with you already and how much they're likely to spend in the future. In addition, it can automatically predict the signs of a long-term customer based on their purchase patterns, reviews left on products, and willingness to sign up for a mailing list.
While this process may well take slightly longer than others as it requires a broader data pool, it will soon provide some of the most accurate indications yet of what a business can expect from every visitor to their site.
10. More Effective Omnichannel Marketing
Certain components of the marketing mix apply to virtually all businesses. The chances are they're not into eCommerce if they don't have a website, and a mailing list and social profiles are practically a given for drumming up business.
However, there are limitless marketing opportunities out there. Some work better than others, and many businesses simply don't have the resources to take the time to experiment and guess at other possibilities.
As with so many of the cases in this feature, machine learning can work to eliminate that guesswork altogether. It can understand where audiences come from, what they do, and the best way to target them next. It can gather data on where to find that audience and the best ways to target them with something of interest.
For example, your business might pay for ads on Facebook. Machine learning may indicate that a significant number of shoppers on the site share your images on Pinterest, leading to further clicks. Armed with that knowledge, a business can decide to scale back their Facebook marketing efforts, at least temporarily, to focus on getting more of their products in front of Pinterest's eyes. Naturally, they'll still have machine learning behind them to interpret just how well that shift in focus performs.
Machine learning is part of the future of eCommerce, but even smaller businesses must understand that it's a viable part of the present too. So, for example, even if you merely carry out search engine optimization on your website, you're already connected to the machine learning world, as you're trying to make your site as appealing as possible to Google's automated crawlers. Similarly, if you use any kind of AI text generation for product descriptions, the chances are you're using a tool that improves as it goes.
There will inevitably come a time of critical mass in the machine learning industry, where everyone is doing it, and those that don't have been left behind. But, for now, it represents an attractive competitive advantage and one that shouldn't be overlooked by any business that wants to optimize processes, boost marketing and, above all, take sales to another level.
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