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How to Boost E-commerce Personalization With Machine Learning

E-commerce personalization

Buzzwords like ‘Big data’, ‘Machine learning’, and ‘Artificial Intelligence’, are now being used on a day to day basis and there’s a reason for it. The solutions offered by Artificial Intelligence makes a big impact on organizations, especially industries like E-commerce. Online stores have access to a huge amount of customer data and can use it to optimize their services. Machine learning helps in that by using digital data at a quicker rate.

E-commerce websites cannot interact like a salesperson with customers. But they can use machine learning techniques to understand consumer needs and offer them personalized products like a salesperson does. Similar to a salesperson, machine learning helps to nudge a customer closer to a purchase.

There’s a lot for companies to gain by improving e-commerce personalization on their online stores. Product personalization through machine learning is a modern strategy that enables companies to anticipate what consumers want and offer them products accordingly.

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E-commerce personalization enhances online experience

Personalization is expected by consumers as it saves time and improves their search experience. It anticipates customer needs and offers a variety of products similar to what they are looking for. With the help of this technique, products with the highest proof of performance, i.e. which have not been returned by other customers previously, can be offered. This reduces the abandonment of shopping carts and helps in improving sales.

Following image shows how after implementing ecommerce personalization products are suggested according to the search history of a customer.

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Effect of product personalization on e-commerce stores

Product personalization results in long term benefits like improving customer experience. Most online customers will leave the store immediately if they don’t see the products of their interest. It removes the frustration of looking through many products or having recommendations that are completely useless.

Personalized product recommendation leads to impulse decisions, which results in higher average order value (AOV). People respond positively to product recommendations such as clever placements of products at the checkout stage. It's about knowing what your customers want before they do and suggest products accordingly.

Personalization plays a big role in driving loyalty. The chances of creating repeat buyers increases as 44% of online customers become loyal due to a personalized shopping experience.

Following image shows personalization being used for product recommendation, which may lead to an impulse purchase.

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Example of machine learning getting smarter at personalization

Google is a great example of how machine learning improves its search capabilities. Google understands the searcher’s intent and provides the most accurate results in milliseconds. If e-commerce personalization can act like Google, it can result in great success for online stores.

E-commerce websites record user information such as their preferences and purchase history. The search query results can be improved with machine learning. Instead of relying on traditional methods, machine learning can help with anticipating user preferences better. Whether it is a big company like eBay that has more than 800 million items listed, or a startup e-commerce store that wants to make it big in the market, using machine learning to predict customer expectations is beneficial for both.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

How machine learning application benefits e-commerce

Personalized product recommendation results in purchases by 49% of online shoppers. This is one of the most important aspects of e-commerce experience, so, companies need to invest greatly in them.

Product personalization needs to be accurate and machine learning helps with that. A great recommendation engine can result in higher conversion rates. Machines keep on learning and with increased data, they get smarter and provide more accurate recommendations.

Data is constantly collected and updated with consumer on-site behavior, including what products they click on, how long they spend on every page, etc. Information is analyzed through data records such as emails, paid advertisements, and social media channels. E-commerce personalization improves with time as the machine learning models keep on learning. The technology is continuously evolving. Always improving.

This is an age when people don’t want to waste their time on things that don’t interest them. It’s an indication for organizations to start understanding their customers and provide exactly what they’re looking for. Personalization offers them the tools to provide what their customers are expecting, helping optimize consumer purchase experience and driving sales.

Optimize your business and create a differentiating factor by using machine learning models for product recommendation. Use Skyl.ai’s powerful Natural Language Processing platform that helps e-commerce companies create an engaging shopping experience for their customers.

Check out the various solutions that can be built using Sky Platform here.

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