ETEAM Blog A Comprehensive Guide to Customer Retention for E-commerce with Machine Learning

It is a frustrating experience for any business to lose its trusted customers. Despite the revenue loss that a business records when trusted customers leave, it is expensive and difficult to gain the trust of new customers.

Online retailers are also among the business practitioners who are experiencing difficulties in retaining current customers. Luckily, machine learning can help them predict which client is going to abandon their products or services. This technology can also help them to gain insights on why the client is quitting their offerings.


Machine Learning in E-commerce

Machine learning (ML) technology works as a discipline of artificial intelligence (AI) focused on enabling computers to discover trends or patterns in data sets. The exciting thing about ML is that it also enables managers of e-commerce enterprises to effectively and quickly evaluate their customer data for complex patterns. Most common ML algorithms include Generalized Linear Machines and Gradient Boost Trees. These ML algorithms can help them in understanding dimensional data and the complex patterns that drive customers’ purchasing decisions.


How Machine Learning Can Drive Customer Retention in an E-commerce Business

Machine learning focuses on generating rules for analyzing particular data sets. One thing that stands out in ML is that its rules can’t make sense to our minds. When running an e-commerce business, online retailers may never understand the logarithmic link between customer service and customer satisfaction. In this case, machine learning can enable them to personalize customer experience, optimize prices and prevent fraud as explained below.

Provide Optimized Customer Experiences

The main reason why most e-commerce businesses find it hard to retain customers is that their websites fail to segment, target and personalize buyers’ experience. For you to get people to buy your products or services, your sales team should understand the behavior, choice of language and body language of potential customers. The sales team should use this information to know exactly what the customers want.

It is difficult for the sales experts to master all these consumer behaviors at once hence the need to use machine learning algorithms. Using ML algorithms, it is easier to create and run powerful product campaigns for targeted consumers to drive them into buying your products or services. In turn, as an online retailer, one can not only drive sales using these algorithms but also provide targeted consumers with optimized experiences.



Predict Demands and Supplies

In the past, e-commerce companies spent lots of resources when trying to make demand and supply predictions. After the invention of artificial intelligence, these companies are now relying on machines to make the projections. The advantage of using machine learning algorithms in this context is that they can offer unique insights on certain data sets. According to Gartner, by 2020, ML algorithms will be instrumental in managing 85 percent of customer interactions.

Using the demand and supply predictions, online retailers can also rely on ML algorithms to set analytical goals. These retailers have the opportunity to make data-backed decisions that will help them improve their services and products. They can only achieve these goals by using the accurate data analyses that machine learning provides.

Optimize Prices

Pricing is crucial to the success of any online business. Online retailers should note that for them to drive sales, they shouldn’t rely on local market prices or set markup rates. These days, retailers can easily check and compare the prices that their competitors set for certain products or services. Furthermore, online buyers are always on the lookout for better deals.

With machine learning technology, managers of online stores can conveniently set product or service prices by considering various factors. These factors include the type of customers, time of day, competitors’ prices and demand. ML technology also gives them the convenience of adjusting the prices accordingly to drive sales.

Provide Quality Customer Self-service and Support

It can be quite challenging to provide customers with quality self-service or support when running an e-commerce business. The good news for managers of e-commerce enterprises is that ML technology offers intelligent chatbots to handle this daunting task. The chatbots rely on natural language to initiate conversations aimed at identifying and solving issues that customers have. When e-commerce enterprises automate their customer self-service and support initiatives, it is easier for them and their clients to experience satisfaction. These automated processes also help them to take their service delivery mechanisms a notch higher.

Detect and Prevent Fraud

Just like other businesses, e-commerce enterprises are prone to fraud. At times, online enterprises experience chargebacks when fraudsters gain unauthorized access to financial systems. They are also at risk of having a tarnished reputation after being a victim of fraud. It is close to impossible detecting and preventing fraud at scale without the implementation of ML technology. This means that online enterprises can rely on machine learning to counter fraudulent transactions in time.



Offer Personalized Product Suggestions

E-commerce giants such as Amazon can attest to the fact that personalized product suggestions work. To be precise, Amazon relies on a product suggestion tool known as the Recommendation Engine to drive 35 percent of its sales. It took the engineers of Recommendation Engine lots of effort to come up with a tool that can help in finding the right patterns in shopping behavior and product sales. Thanks to machine learning technology, e-commerce companies can effortlessly get quantified results of the buying behavior of their customers. There are ML algorithms that can dig deeper into trends in products sales and customers’ behavior to offer them with personalized product suggestions.


Rank Searches

Shoppers are unlikely to purchase a product or service from an e-commerce site if they can’t find it there. Online retailers rely too much on Google’s search engines to rank their product pages. They should note that not all product searches on Google can deliver results based on consumers’ queries.

Machine learning technology offers online retailers a powerful way of showing their customers the exact products or services they want. This technology helps to provide optimal search results on e-commerce sites by considering factors such as similar items, preferences, and content. It also has the capacity of pulling information from the patterns of customers’ purchases and searches.


Final Thoughts

Machine learning technology takes an iterative form and is effective in driving customer retention to e-commerce companies. The longer these companies rely on ML applications to retain customers, the more effective the applications will be. With time, these applications become smarter based on the data that they handle. They are also able to handle ever-growing, changing and shifting data.

If you’re interested in more posts about e-commerce and retail, check out our complete report detailing how technology is transforming the both brick-and-mortar and online stores.

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