Applications of Machine Learning in the E-commerce Industry
fArtificial Intelligence (AI) and Machine Learning (ML) have become buzzwords in the E-commerce industry. The way the E-commerce industry has embraced Machine Learning and Artificial Intelligence to optimize its operations is an example for other industries. Before diving deeper into the machine learning E-commerce applications, first, we will discern the difference between machine learning and artificial intelligence.
Artificial intelligence refers to computers performing tasks which traditionally required human intelligence. In other words, we can say that computer systems are trained to perform functions by imitating human cognition. On the other hand, machine learning is a branch of AI, which involves designing computer algorithms that have the capability of enhancing their performance by learning through experience automatically. In this article, we will specifically discuss the ways the E-commerce industry is leveraging Machine Learning to optimize its operations and generate more revenue.
Better Search Results and Product Recommendations
Some buyers do not know how to employ relevant keywords to search for an item. These buyers visit an E-commerce site with the intent of purchasing something, however, if their desired product is not displayed in the search results, they leave and continue their product hunt on another site. Most of the time, the website has the product they are looking for, but it is not displayed in the search results, due to the incorrect combination of keywords. Machine learning optimizes the search results by looking for the synonyms of the keywords and the related phrases people have used in the past when searching for similar items. It keeps a close eye on site usage and past metrics. The past metrics reveal important information about the customers' search queries, clicks, and conversion rates. Based on these metrics, the site displays the top-rated or trending products at the top of the webpage, so that the customers can find those products conveniently. Hence, in this way, Machine learning helps to reshape the customer experience.
In a traditional brick and mortar store, a salesperson can not only anticipate the needs of the customers but also suggest the relevant items. A customer may have entered the store to buy a single product, but an excellent salesperson convinces him to buy multiple products and thus increasing the revenue of the store. Online stores do not have this opportunity to convince buyers to purchase multiple items, however, with machine learning coming into the picture, online stores can also recommend similar products to the customers. Many bigger E-commerce stores like Amazon are leveraging machine learning for recommending products to shoppers. A report by McKinsey and Company reveals that 35% of the purchases on Amazon are attributed to the product recommendations.
A multitude of customers order products online because of convenience and pricing. If the product in an online store has the same price as the same product in the offline store, then the customers prefers to order online, for saving time. However, if the price of the product in an offline store is lesser than the online store, then the customers will prefer to visit the store themselves, instead of ordering online. Hence, the price of the products in the E-commerce website plays a fundamental role in customer’s buying decisions. In the past, E-commerce stores struggled with setting the right price of the product based on the market activity. Fortunately, machine learning solved the woes of E-commerce websites by helping them to optimize their pricing structure. Machine learning algorithms learn from experience about the customers' responses towards different price points, thus they suggest the best prices for the products and fuel the conversion rates. These algorithms consider multiple factors to suggest the dynamic pricing of the products. These factors include product demand, desired price according to the customers, day of the week, month, competitor pricing, and customer type.
Superior customer support along with a quality product warrants optimal customer experience with the E-commerce website. The increased competition in the business world has also enhanced the customers’ expectations with the businesses. Majority of customers do not want to go through the pains of calling the toll-free numbers to hear endless menu options and wait in a queue to talk to the customer service representatives. Automated customer support is a dream of every business because it not only ensures enhanced customer satisfaction, but also helps in cost reduction associated with maintaining a huge customer service department. Machine learning has helped the companies to realize this dream through chatbots. The chatbot stimulates a conversation with the customer and resolves issues like humans. Many E-commerce stores have integrated chatbots with their websites to handle the complaints and queries of the customers. Sometimes, many customers have the same general issue and chatbots can be programmed using machine learning to solve this general issue at a massive scale. The encouraging aspect of these chatbots is that they learn continuously, hence their efficiency increases as more customers interact with them.
Machine learning can analyze patterns in the data and identify anomalies to prevent fraud. When machine learning algorithms are exposed to large amounts of data, they can learn what the normal and abnormal behavior looks like. Hence, they can detect even the smallest anomaly in the data and help the website to take proactive action to prevent fraud. The most common types of frauds that are prevalent in the E-commerce industry are buying from the stolen cards and withdrawing money after the product has been received.
Identifying these fraudulent activities accurately is impossible without machine learning algorithms. This is because of the existence of false positives, which means that when companies do not leverage machine learning for detecting frauds, there is a probability that their system rejects the transaction of the real customer. Machine learning reduces the false positives by analyzing multiple factors, for instance, the price paid, geographic location of the buyer, and how many cards the buyer has used and many others. Thus, by combining all these factors, the system can analyze the transaction in detail and block the transaction if the activity looks suspicious. As the amount of data increases, the system increases its capability to detect the frauds accurately.
Another way an E-commerce store can leverage machine learning algorithms for detecting frauds is by auto flogging the suspicious transactions. The system learns the fraudulent behavior by analyzing manually flogged transactions. As the patterns in frauds also change over time, therefore human employees should continue flogging the potential fraud transaction manually, so that the system learns about such transactions, and auto flog them in the future. For example, a user may be using multiple cards in a short time. Someone working in the fraud prevention department can detect this activity and flog it as a potential fraudulent transaction. The machine learning system learns from this and the next time when someone tries to use multiple cards at once, it can auto flog the transaction and eventually block it.
All E-commerce stores struggle with stock replenishment. Sometimes sellers can sell more products than they have, thus resulting in shipment delays and customer dissatisfaction. Machine learning algorithms analyze historical and current sales and help the companies to achieve automated stock replenishment. E-commerce businesses are operating in a highly competitive environment; therefore, they cannot afford to have such problems at a larger scale because customer dissatisfaction means losing existing and potential new customers to their competitors. When the website oversells a product, it needs some time to replenish its stock and ship it to the customers who have already paid for the purchases. The delay in shipment can cause a distress in customers, and they may cancel the transaction or leave a negative review online. Manually tracking the demand and supply of the products is quite a time-consuming task and is also prone to error. However, employing machine learning for executing this task can give actionable insights about the demand and supply picture.
As the world is transitioning from a traditional to a digital economy, new challenges are arising, which require new solutions. Technology has changed the behavioral patterns of the customers and online shopping is in its full swing now. In this era of a digitized economy, customers want everything at their doorstep without taking the pains of visiting the market. Millions of people order products online every day. Trust in online stores and convenience are the major factors behind the popularity of online shopping. To maintain this trust and facilitate the customers, the E-commerce industry is embracing Machine Learning at a faster pace. Without a speck of doubt, Machine Learning is offering some mind-blowing applications to the E-commerce industry. Besides the machine learning E-commerce applications discussed above, it is anticipated that, in the future, some additional applications of machine learning in the E-commerce industry, will reshape the online buyer and seller journey altogether.