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Aug 9, 2023 | 6 minute read
written by Elastic Path
In the dynamic world of eCommerce, brands can stay ahead of the pack by offering customers exceptional and meaningful shopping journeys. Personalization is a powerful tool proven to enhance customer engagement, satisfaction, and sales. But as technology continues to evolve, what exciting opportunities are on the horizon for eCommerce personalization?
One promising advancement in eCommerce personalization is the use of machine learning. Machine learning has the potential to revolutionize the way eCommerce businesses personalize their offerings to their customers by providing faster, smarter, and more targeted personalization in almost any market. Learn about the benefits of machine learning and how this approach to eCommerce personalization is shaping the future of online shopping.
eCommerce personalization strategies are important for leading brands. A study from McKinsey shows that 71% of customers expect personalized shopping experiences and 76% of those who don’t get them report feeling frustrated with the brand in question. The study also found that faster-growing brands garner 40% more revenue from personalization strategies than slower-growing brands, indicating that personalization delivers real-world results.
A current eCommerce personalization trend is using social media and mobile apps to gather information about a customer's preferences and behavior and then using that to personalize their shopping experience. Personalized marketing campaigns through email and other channels are also becoming more popular in the eCommerce industry. Also, there is a growing emphasis on creating a seamless, “omnichannel” personalized customer journey across all touchpoints, from browsing to checkout to post-purchase follow-up.
Apart from these commerce trends, machine learning is becoming increasingly popular for its ability to provide tailored product suggestions and individualized promotional strategies via data-driven algorithms. Each of these capabilities contributes to machine learning now being widely regarded as the future of eCommerce personalization. But what exactly is machine learning? and How can commerce brands benefit from using machine learning for personalization?
Machine learning is a subset of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. It involves using algorithms that allow computers to recognize patterns in data and make predictions or decisions based on those patterns. According to a Statista report, the global machine-learning market is expected to grow from around $140 billion to $2 trillion by 2030.
In eCommerce, machine learning allows retailers to understand the preferences and needs of individual customers and then tailor their marketing efforts, product recommendations, and website design to meet those needs. Machine learning algorithms can also be used to optimize pricing strategies, inventory management, and supply chain operations.
Machine learning is widespread but comes in different forms. See the variety of methods and techniques employed to train machines to recognize patterns and make accurate predictions:
Supervised learning is a process in which algorithms are trained to make predictions or recommendations based on a set of labeled data. It involves using previously classified data to teach a machine-learning model how to do the same.
In unsupervised learning, the machine learning system is trained to independently discover patterns and relationships within the data, rather than being told what to look for. This approach is especially useful for digital commerce because it identifies customer segments and behaviors that may not be immediately apparent through traditional market research methods.
Reinforcement machine learning involves the algorithm learning through a trial-and-error process, receiving rewards or punishments for certain actions, and adjusting its behavior accordingly.
Semi-supervised learning is a combination of labeled and unlabeled data, allowing the algorithm to learn from both simultaneously and more accurately predict new data and outcomes.
From improving customer satisfaction to increasing sales, there are several reasons why businesses should consider implementing machine learning algorithms. The benefits of using machine learning to personalize your eCommerce offerings include:
Machine learning algorithms can analyze customer data such as purchase history, search queries, and click behavior to determine their preferences and make personalized product recommendations, leading to a more efficient and enjoyable shopping experience for customers.
Using machine learning for personalization is efficient and saves time because it allows for automated analysis of large amounts of data to identify patterns and trends. Recommendations and personalized content can be generated quickly and accurately, without the need for manual analysis or intervention. Machine learning algorithms can also learn and improve continuously based on user input, reducing the time maintenance and management teams need to spend on upkeep.
By analyzing data such as customer demographics, machine learning algorithms can identify patterns and trends that inform pricing decisions. For example, if a particular demographic purchases more during certain sales or discounts, the algorithm can suggest offering more, and more specific, promotions to that group. This technology can also help businesses quickly adjust prices in response to shifts in demand or competitor pricing.
Machine learning can also be used to improve inventory management, as it can predict demand for specific products and identify which items will likely sell out quickly. By leveraging machine learning for personalization, your brand can improve its inventory management practices, reduce missed sales opportunities, and provide a better shopping experience for its customers.
Machine learning technology has made it possible for many iconic brands to provide personalized shopping experiences for their customers, including:
Global eCommerce giant Amazon has made significant strides in machine learning-powered personalization. The company uses predictive analytics to analyze customer behavior and personalize recommendations, product suggestions, and search results. Amazon's powerful machine learning algorithms use data points, such as past purchases, browsing history, and search queries, to offer personalized product recommendations to its customers and enhance their shopping experience.
The "Sephora Virtual Artist" feature uses augmented reality to allow customers to try on virtual makeup and hair products from the comfort of their device screen. Machine learning algorithms analyze the customer's facial features, skin tone, and hair color to provide personalized product recommendations that match their appearance and preferences, improving customer satisfaction and driving down return rates.
Walmart’s machine learning algorithm helps automate the processing of over 4 million customer orders every day. Using computer vision software, Walmart accurately identifies products ordered, optimizes inventory levels, and reduces costs associated with manual ordering processes. In addition, Walmart leverages big data analysis through its Intelligent Retail Lab initiative to create a smart store experience by tracking customer movements in physical stores and predicting demand in specific areas of the store.
With the online commerce landscape in a continuous state of evolution, personalization is key to attracting and retaining customers. Yet, many brands still rely on outdated personalization methods, or worse yet, don’t personalize at all, feeling stymied by all the options available and the rapid pace of market change.
Fortunately, Elastic Path is here to help. Discovering the perfect eCommerce personalization solution can be challenging, but our team of experts is ready to guide the way. Get in touch with us today and take the first step toward optimizing your brand’s personalization strategies, your customer’s experiences, and your business’s growth and bottom-line results.