Machine learning: Explore the ways to use it to boost sales in eCommerce

Machine learning ecommerce

Machine learning brings some robust applications in the eCommerce business. More and more eCommerce businesses are embracing this technology and deriving much value from it. With businesses seeking to automate tedious, labor-intensive, and costly manual processes, machine learning can be an enormous asset. They can help your online business create more clicks, convert prospects into customers, retain them, and develop strong customer relations. If you would want to evolve the efficiency of your e-commerce operations, you can’t ignore this article:

1. Personalized Customer Experience

Unlike offline physical stores, online stores have limited scope to offer an optimized customer experience that can boost sales and improve revenue. In order to offer an experience similar to a customer that would have offline-store, the owner store needs to gather huge amounts of data and make sense of it. This is where machine learning can help.

Machine learning is helping eCommerce stores take the customer experience to a whole new level. It can help the owner store run targeted campaigns that can convert potential customers into actual ones. Moreover, AI-based personalization for e-commerce gets the multi-channel approach. It monitors shopper activity on how they are interacting with your e-commerce store, utilizing the mobile application, website, or other referral platforms.

 2. Improve Product Search

Not all customers are great with keywords. Not all search is intelligent. To make a purchase, customers not just need to find what they need but also they must be able to find it easily. We may be too dependent on Google’s search engine to determine that not all search is intelligent. But often, product searches fall short of delivering results that absolutely answer the query. Factors like content, preferences, and similar products all play into offering optimal search results. Machine learning can show patterns in search, purchases, and preferences that enable optimal search results. Search results based on these factors can show shoppers correctly what they are seeking for and also recommend similar products.

3. Predictive Dynamic Pricing

Machine learning

Customers always looking for a discount, and great deals to find a product to value for their money. It certainly shows that pricing matters a lot for online customers and they always seek the least price for products. Prices for the products can fluctuate a great deal rely on dozens, if not hundreds of variables, it’s simpler than ever to compare prices from one website to another with just a few clicks.

Pricing is one of the main categories, where Machine Learning is offering a great deal of support in price optimization. A pricing engine can be generated to take into account a great deal of information about the customer’s profile, market trends, competitive prices, product abandonment rates, demand, time of day, and type of customer could all influence your price to decide what percentage discount. Machine learning technology makes it possible to adjust prices accordingly.

4. Product Recommendations

Amazon has proven that product suggestions increase revenue substantially. Their recommendation engine is responsible for 37% of its sales. But it takes a lot of energy computing to know the right patterns in product sales and shopping behavior. Machine learning allows you to do it. It is possible for a brilliant employee to write “if this, then that” rules, but this limits suggestion to only demonstrate the employee’s knowledge. Machine learning can easily identify buying behavior over and over again, each time digging deeper into trends to suggest suitable products to a shopper. Many eCommerce retailers have leveraged machine learning to successfully create a product recommendation engine.

5. Machine learning and Eliminate Fraud 

Machine learning

E-commerce businesses are susceptible to fraud. Chargebacks are just the beginning of the bad result of fraud. In some cases, a damaged reputation can forever destroy a company’s reputation. Detecting and averting this at scale is almost impossible without the support of machine learning. A machine learning system can process data about normal shopper behavior, such as registered devices, frequent login time, location, etc., and then utilize that to detect abnormal account activity.

6. Retargeting, Discount & Upsells

Unlike in an offline store where you can communicate with your customers to find out what they need, the e-store is hit with huge amounts of customer data. As a result, customer segmentation becomes extremely essential for e-commerce, as it supports companies to adjust their communication strategies for every customer

With the help of Machine Learning, your business can deploy retargeting campaigns that can be executed to reach out to previous customers who have purchased, abandoned a cart, or just browsing your online store. And these campaigns can be performed by ML algorithms by researching previous data of similar shopper profiles and how they may have been converted in the past with retargeting. It even comes with various discounts and limited deals which makes it promoting customers to purchase the products on huge discounts. With the help of machine learning, your business can easily retain their potential customers.

7. Manage Demand and Supply

All businesses based on forecasting to meet demand with supply. To predict well, owner store must base their determination mainly on data, among other things. To make reasonable data-backed decisions, eCommerce retails must handle large data. It is also crucial to make sure that the data is correct and that it is being processed properly.

Machine learning can handle comprehensive amounts of data correctly at the speech. Machine learning can also research data to offer as many insights as possible. This enables not just prediction about supply but also helps online businesses enhance their products and services.

8. Improve Omnichannel Marketing

Omnichannel marketing brings your business higher customer retention, higher purchase rate, and more engagement. However, omnichannel is centered around user data, more data can only boost the way it works for your eCommerce store. Because machine learning operates based on collecting data and enhance algorithms over time as more data is updated, your omnichannel marketing strategy can only be made more effective with this continually updating data.

9. Customer Support

Machine learning

Offering quality customer service in the e-store is challenging. Machine learning can once again bring you a proper solution. Intelligent chatbots allow to natural language for communicating with a customer, identify a problem, and resolve the problem. Chatbots exist in messaging apps and they can leverage natural language recognition to communicate smoothy with shoppers. What’s more, they can address multiple customers at once and they can operate 24/7. However, that’s not the most peculiar thing about chatbots. Their main benefit is their ability to cater to customer requirements by analyzing any relevant info about an individual customer. There’s a lot of creativity to how machine learning can be used to help customers, chatbots being just one example. It can enable the following assistance.

  • Speech recognition and natural language processing which removes the human reliance for any on-call assistance
  • Automated email assistant
  • Creation of service tickets
  • Appropriate priority flagging

E-commerce is changing the whole shopping concept for purchasers. All the e-commerce companies are investing a lot of money in technology to enhance their customer experience and bring them all the convenience that traditional sellers offer them in an offline store. AI is just one of the newest technological trends that will revolutionize the eCommerce world.

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