Did you ever anticipate that computers might be able to recognize and learn patterns in order to make their own decisions? If your answer was no, you are in the same boat as plenty of experts in the e-commerce industry; no one could have predicted its current state.
However, machine learning has played a significant role in the evolution of e-commerce over the last few decades. Let’s take a look at where e-commerce is right now and how machine learning service providers will shape it in the not-too-distant future.
Some may believe that e-commerce is a relatively new phenomenon that has fundamentally transformed the way we shop, owing to technological advancements in the field. That isn’t entirely the case, however.
Even though technology plays a large role in how we engage with shops today, e-commerce has been around for more than 40 years and it is bigger now than ever.
Retail e-commerce sales worldwide reached 4.28 trillion dollars in 2020, with e-retail revenues expected to reach 5.4 trillion dollars in 2022.
But if technology has always been around, how is machine learning changing the industry now? It’s simple. Artificial intelligence is doing away with the image of simple analysis systems to show just how powerful, and transformative, it can truly be.
In earlier years, artificial intelligence and machine learning were too undeveloped and simple in their execution to truly shine in terms of their possible applications. However, that is no longer the case.
Brands may use concepts like voice search to promote their products in front of customers as technologies like machine learning and chatbots become more prevalent. AI can also aid with inventory forecasting and backend support.
There are multiple major applications of this technology in e-commerce. On a global scale, recommendation engines are one of the hottest trends. You can thoroughly evaluate the online activity of hundreds of millions of people using machine learning algorithms and processing enormous amounts of data with ease. You can use it to produce product recommendations for a specific customer or group of customers (auto-segmentation) based on their interests.
You can figure out which sub-pages a client used by evaluating acquired large data on current website traffic. You could tell what he was after and where he spent the majority of his time. Furthermore, results will be provided on a personalized page with suggested items based on multiple sources of information: profile of previous customer activities, interests (e.g., hobbies), weather, location, and social media data.
By analyzing structured data, chatbots powered by machine learning can create a more “human” conversation with users. Chatbots can be programmed with generic information to reply to consumer inquiries using machine learning. Essentially, the more people the bot interacts with, the better it will understand the products/services of an e-commerce site. By asking questions, chatbots can give personalized coupons, uncover potential upsell possibilities, and address the customer’s long-term needs. The cost of designing, building, and integrating a custom chatbot for a website is roughly $28,000. A small business loan can readily be used to pay for this.
Users can utilize machine learning to find precisely what they are looking for based on their search query. Customers currently search for products on an e-commerce site using keywords, so the site owner must guarantee that those keywords have been assigned to the products that users are looking for.
Machine learning can help by looking for synonyms of commonly used keywords, as well as comparable phrases people use for the same question. The capacity of this technology to achieve this stems from its ability to evaluate a website and its analytics. As a result, e-commerce sites can place high-rated products at the top of the page while prioritizing click rates and previous conversions.
Today, giants like eBay have realized the importance of this. With over 800 million items displayed, the company is able to forecast and offer the most relevant search results using artificial intelligence and analytics.
Unlike a physical store, where you can speak with customers to learn what they want or need, online stores are bombarded with massive amounts of client data.
As a result, client segmentation is critical for the e-commerce industry, as it allows businesses to tailor their communication methods to each individual customer. Machine learning can help you understand your customers’ wants and provide them with a more tailored purchasing experience.
Ecommerce companies can use machine learning to provide a more personalized experience for their customers. Customers today not only prefer but also demand to communicate with their favorite brands in a personal manner. Retailers can tailor each connection with their customers using artificial intelligence and machine learning, resulting in a better customer experience.
Furthermore, they can prevent customer care problems from occurring by using machine learning. With machine learning, cart abandonment rates would no doubt decrease and sales would increase eventually. Customer support bots, unlike humans, can deliver unbiased answers at any time of day or night.
Anomalies are easier to spot when you have more data. Thus, you can use machine learning to see trends in data, understand what is ‘normal’ and what isn’t, and receive alerts when something goes wrong.
‘Fraud detection’ is the most prevalent application for this. Customers who buy huge amounts of merchandise with stolen credit cards or who cancel their orders after the items have been delivered are common problems for retailers. This is where machine learning comes in.
In the case of dynamic pricing, machine learning in e-commerce can be extremely beneficial and can help you enhance your KPIs. The ability of the algorithms to learn new patterns from data is the source of this usefulness. As a result, those algorithms are constantly learning and detecting new requests and trends. Instead of relying on simple price reductions, e-commerce businesses could benefit from predictive models that can help them figure out the ideal price for each product. You can pick the best offer, the best pricing, and show real-time discounts, all the while considering the best strategy to increase sales and inventory optimization.
The ways that machine learning is shaping the e-commerce industry are countless. The applications of this technology have a direct impact on customer service and business growth in the e-commerce industry. Your company would improve customer service, customer support, efficiency, and production, as well as make better HR decisions. Machine learning algorithms for e-commerce will continue to be of significant service to the e-commerce business as they evolve.