AI for eCommerce

Revenue in the eCommerce market if projected to reach $3.1 billion in 2023. And the trend continues with an estimated annual growth rate that is projected by many to reach $5 billion by 2028. The number of eCommerce sellers and the range of product sku’s will continue to increase allowing customers to purchase virtually any product or service on line. That is good news for anyone looking for the most obscure, hard to find item. At the same time, this growth will make it more challenging for search engines to churn through the offerings to help customers find what they are looking for. The words Artificial Intelligence are showing up everywhere. Companies are having many serious conversations, to find a way to use Artificial Intelligence to harness this growth.
eCommerce has accelerated the past few years due to the pandemic and the need for customers to buy products and services safely. With more people shopping online than ever, eCommerce companies have the opportunity to attract many more potential customers while facing many new challenges. Growing customer demands and increasing numbers of support queries to managing a growing number of products and services being offered, anyone who manages their online store has a lot on their plate. Nobody wants to cut back so to handle all of the tasks, many companies are now turning to artificial intelligence to learn more about their customers, what products they prefer and anticipating their wants and needs. All the while providing the best possible customer experience that customers are demanding. Find out more about some of the ways you can use Artificial Intelligence by clicking here.

AI Powered eCommerce use cases

Machine learning helps AI devices get “smarter” the more they interact with customers and the more data they have to process and learn from. Using this data, from customer purchases and interactions, you can start to predict what they want and return messages back to them in real time. Not only that, but AI can help generate a customized email with a discount to incentivize the final purchase decision. These and other tools can do so much more. Read on here to see some examples of how to use data gathered by AI to further enhance your eCommerce customer experience.

Product Descriptions

Writing product descriptions that are both persuasive and SEO-friendly is not an easy task, even for the most skilled copywriters. The challenge becomes magnified with categories that are sku-intensive. It will take much longer to writer a description that is unique and sets itself apart from other similar sku’s. Here’s where AI-powered tools can help. Especially for simple product descriptions, AI can very quickly generate engaging, unique and customer friendly content based on product specifications. Most AI tools such as copywriting principles like AIDA (attention, interest, desire and action) to write like a human being and use its intelligence to add keywords to fit with the product. AI-generated product descriptions are still very new and not commonly used yet inn various industries. Like most everything else, its just a matter of time considering the speed at which tools armed with natural language processing continue to make significant improvements.

Category Descriptions

Creating Category descriptions allows you to directly create you meaningful descriptors at a higher level than the product descriptions. Many of the category descriptions can be automatically generated. If the content generated by the AI does not meet your expectation, you can easily rewrite it by generating a new version. Once you are satisfied with your result you can copy it where required. Once a category description is generated, you can also generate a Title and a meta description to option the SEO. One word of caution is that you should always proofread and adapt your text. Using AI does not completely prevent mistakes from happening. AI can be a solid base that develops over time but it also needs the human eye for proofing and correcting.

Product Recommendations

With personalized product recommendations, companies can personalize customer interactions and provide more relevant online shopping experiences that will improve conversion rates, average order values and above all, customer loyalty. AI powered product recommendations can help immensely here. They are a powerful marketing tool that you can use to increase conversions, boost revenue, and stimulate shopper engagement. eComchain’s commerce product recommendations use artificial intelligence and machine-learning algorithms to perform a deep analysis of aggregated visitor data. This data, when combined with your online commerce catalog, results in a highly engaging, relevant, and personalized experience. You can optimize and scale experiences with real-time intelligence. Predict customer behavior based on attributes, differences, and conversion factors.
Product recommendations allow you to choose from recommendation types based on areas such as: shopper-based, item-based, popularity-based, trending, and similarity-based. You can use behavioral data to personalize recommendations throughout the shopper’s storefront journey and measure key metrics relevant to each recommendation to help you understand the impact of your recommendations.

Sales and demand forecasting / Stock risk

eComchain has the ability to use forecasting to manage inventories, plan logistics and warehouse space, and determine pricing strategies. Sales demand is only getting more challenging because historical sales data are no longer enough, even when combined with seasonal data. eComchain does not use historical data only. Instead, AI makes sales and demand predictions using real-time data, including demographics, weather, the performance of similar items, and online reviews or social media. Machine learning also improves forecasts over time with more available data. In addition to creating more accurate estimates for short-life products, eComchain’s machine learning system also improves planning between departments such as sales, supply chain, finance, and marketing. This system also improved efficiency and inventory balance to achieve its targeted service levels at the channel and store levels.

Abandoned Cart

Given that the average cart abandonment rate is a staggering 70%, there continues to be significant opportunities to increase sales simply by addressing this issue. eComchain’s AI has the ability to sense when a shopper is about to leave the shopping cart behind. One solution is the use of personalized messaging and timings to change the mind of the customer to prevent a potential loss of revenue. An AI powered shopping cart tool can recommend visually similar products and styling suggestion due to its ability to understand each shopper’s visual style preference and auto create compelling cart abandonment emails to save the sale.
Understand why shoppers abandon. Use analytics to explore this segment, looking at their preferences, behavior, and history. With eComchain, you can do onsite search, page, and form abandonment, as well as cart recovery emails after potential shoppers have left. A true cart abandonment marketing solution. You can tailor this by monitoring the impact of your cart and fine-tune parameters like filtering conditions; or time between adding to basket and emails.