Machine learning will develop more in the e-commerce industry in the future. Currently, e-commerce retailers are abundantly adopting this technology and deriving advantages from it. Below are a few advantages of ML in e-commerce.
Machine learning technology is helping e-commerce companies transform the customer experience. It makes them agile and assists them in generating revenues in new ways.
According to Statista, the revenue from marketing e-commerce sales globally will amount to $4.88 trillion in the upcoming time. It means that e-commerce expands at a rate of 20% every year. It makes them agile and assists them in generating revenues in ways that were not used before.
The power of machine learning can benefit the e-commerce industry in several different ways. This post covers seven ways to show how ML benefits the e-commerce sector. Let’s read on.
Benefits of machine learning in e-commerce
Below are the top 7 advantages of machine learning in the e-commerce industry:
1. Improved customer service
Chatbots are becoming prevalent on e-commerce websites. Customers expect businesses to reply to them quickly while answering their queries, making product recommendations, and reacting to feedback. It is sometimes not easy to have a customer support service team 24/7, mainly when you are new in the market.
However, this is when you need to attract most of the customers. Chatbots that utilize machine learning-based systems are the best solution to overcome this situation. It should be made clear that the purpose of chatbots is not to risk the customer service department’s job but to boost the existing customer support strategy. Secondly, it was also found that 80% of e-commerce businesses use chatbot software.
Chatbots handle repetitive questions that businesses encounter almost every day. Think about how often you receive customer messages asking for help resetting their passwords, when their order gets delivered, the shipping cost, and much more. Use chatbots to address consumers’ concerns across all industries efficiently.
As a result, customer support team members can allocate more time to handle more complex problems. Secondly, adding a chatbot gives higher customer satisfaction, a boost in traffic, and more sales.
2. Improved search results
Improved search results give massive payoffs to retailers. Machine learning enhances the search results whenever a buyer purchases on the website, taking into account personal preferences and buying history. Rather than using traditional search methods such as keyword matching, machine learning algorithms create a search ranking based on the relevance for that particular user.
It is crucial for tech giants like eBay. With over 800 million items listed, the retailer uses AI-based systems and data to predict, analyze, and show the most relevant search results.
3. Personalized customer experience
Every client has different behavioral habits. For ages, retailers have been trying to recognize the consumers buying patterns and later use them to boost sales. The process turned out to be not only time-consuming but also ineffective.
In this digitalized age, companies fail to personalize their marketing campaigns. For example, customers receive unwanted emails after buying a product that doesn’t reflect their taste or need for a year. It is where machine learning technology comes into effect.
Machine learning technology enables companies to use algorithms that monitor clients’ activity carefully and from the start. The software creates a personalized customer experience by displaying relevant ads, sending relevant emails, and maintaining a dialogue with every customer in a way that shows their preferences.
4. Price optimization
Online buyers are very price-conscious. If any product costs them as much as what it does in the stores, they don’t feel the need to shop online. They are more satisfied going to the outlet and inspecting it first-hand before purchasing. Most people first compare the prices of products from other e-commerce websites to find the best deal.
The price difference negatively impacts the business. Now, the e-commerce business has found a way to overcome this issue. They are now using the technique of dynamic pricing, and it is all possible because of advancements in machine learning.
As per the buyer’s demand, machine learning-based systems alter and readjust the prices by considering different aspects simultaneously. These factors include the type of customer, day and time of the week, product demand, and competitor pricing.
5. Protection against fraud
While running an online business, nothing is more important than keeping your website secure from cyber-crooks. Numerous risks are associated with the site, ranging from hackers and scammers who aim to steal customers’ personal information. It has been retailers’ worst nightmare because most buyers fear that the e-commerce platforms are unsafe and lead to identity theft.
There have been many occasions when e-commerce sites have been compromised. Merchants should be vigilant because this is common with an online business. To prevent falling victim, companies should know ways to secure their e-commerce website and prepare to detect and prevent fraudulent activities; here, machine learning also comes into play.
With the help of its algorithms, ML technology eliminates the scope of fraud, proactively processes a massive amount of detailed and repetitive data, and identifies any abnormal activity within the bud.
6. Managing demand and supply
Businesses make specific predictions to match demand and supply. To forecast, the e-commerce sellers focus their decisions mainly on the data instead of anything else. Businesses must process as much as possible to make more profound decisions. It is essential to ensure the data is accurate and processed correctly.
With machine learning technology, e-commerce companies can process exhaustive data faster and accurately. It also examines data to provide as much insight as possible. Furthermore, it not only enables forecasts but also helps online businesses to improve their products and services.
7. Using predictive analytics for better ROI
Predictive analytics allows business tycoons to choose the most helpful information sets from the company’s database and make structured insights regarding customers’ buying behavior, patterns, and emerging trends.
This way, companies adapt their business strategy more quickly and attract potential clients. If anyone is not interested in imposing their decisions entirely on the suggestions given by the software, machine learning tools are valid. The predictive models are a great way to gain valuable suggestions, but it doesn’t mean you stop checking the results yourself and later decide the best approach.
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About the Author
Rebecca James is an IT consultant with forward thinking approach toward developing IT infrastructures of SMEs. She writes to engage with individuals and raise awareness of digital security, privacy, and better IT infrastructure.
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