With the sheer amount of data that e-commerce companies have at their disposal, it is no wonder that the use of AI in online commerce is bringing in 20% extra revenue for businesses. Be it through AI-powered search engines, predictive analytics, or chat bots (the use of which has been explosive in the last few years), advanced AI algorithms are transforming the e-commerce game.
This is particularly valuable for businesses that operate in fast-changing industries, such as fashion and electronics. AI can help these companies keep up with the latest trends and adjust their product offerings accordingly. In some cases, AI can even identify new trends before they become mainstream, helping businesses better serve their customers. Let’s take a deeper look at how e-commerce businesses are making the best use of this technology to predict consumer trends and inform their business strategy for optimal performance-
1. Machine learning algorithms are able to segment large amounts of data (such as products customers have purchased, websites visited, & keywords searched for), analyze them, and identify patterns and trends that would be impossible or too resource-consuming for humans to spot. For example, a machine learning algorithm could examine past purchase data to identify which products are often bought together. This information could then be used to recommend additional products to customers or even create new product bundles.
Similarly, machine learning can be used to segment customers into groups based on their purchasing behavior. This information can then be used to tailor marketing messages and even customize the shopping experience for each group.
This approach is already being used by some e-commerce companies to improve their product recommendations and target marketing messages. For example, Amazon uses machine learning algorithms to personalize the products that are shown to each individual user on its website. E-commerce giant eBay is using these algorithms to predict which items will sell well and when they should be listed for sale. This allows eBay to optimize its inventory and better match supply with demand.
2. Natural Language Processing (NLP) allows AI algorithms to understand and interpret human language, allowing companies to analyze customer reviews, social media posts, common site queries, forums, and other sources of customer feedback in order to gain a better understanding of what consumers are looking for. By understanding the language that consumers use to describe their wants and needs or while searching for products, NLP can provide valuable insights into future trends.This information can be used to make decisions about product development, marketing strategies, and customer service improvements.
Additionally, NLP can also be used to understand what customers are looking for when they search for products on an e-commerce website. By understanding the intention behind customer queries, businesses can provide relevant results and improve the customer experience.
NLP can also help e-commerce businesses personalize their marketing efforts. By understanding the specific language that each customer uses, businesses can tailor their marketing messages to appeal to individual customers. This allows businesses to create a more personal connection with their customers, while offering them a smooth browsing and shopping experience.
3. Predictive social intelligence can be used to identify the types of products and services that are likely to be popular with consumers based on the language and sentiments expressed in social media posts. For instance, Amazon uses it to analyze customer reviews and feedback to identify positive and negative sentiments. This information can be used to create incredibly fast and effective targeted marketing campaigns that are more likely to resonate with consumers.
Predictive social intelligence can be used to forecast demand for certain products, understand customer needs and wants through real feedback, and anticipate changes in customer behavior. This type of AI algorithms are particularly useful for ecommerce businesses that need to constantly adapt their offerings to meet the ever-evolving needs of consumers. For example, a clothing retailer might use PSI to predict that a certain style of dress will become popular in the next season.
4. Predictive search works by analyzing the user’s past search history, as well as the search histories of other users, to identify common patterns and trends. This information is then used to make predictions about what the user is likely to search for next.
For example, if a user has previously searched for “walnut cake recipes,” the predictive search technology may suggest related search terms such as “cake frosting” or “cake flour” as the user is typing their query. This can save the user time and effort by providing them with relevant suggestions, and can help them find the information they are looking for more quickly.
To offer accurate predictions, predictive search algorithms need to be constantly learning. E-commerce businesses that use this technology typically collect data on shopper behavior, including what terms are being searched for and which products are being viewed and purchased. This data is then used to train the predictive search algorithm, so it can make more accurate predictions in the future.
By harnessing the power of your existing data combined with powerful AI algorithms, businesses can finetune their selling and marketing strategies. And gain insights into your customers that would otherwise be impossible to obtain.
What’s more, the self-learning characteristic of AI and machine learning is expected to deliver increasingly accurate predictions of consumer behavior. As a result, e-commerce businesses that start early can create highly effective and targeted campaigns, offers, personalized recommendations, and a lot more. That’s why it makes sense for you to explore these new technologies as soon as possible!
To Read More Tech Blogs Visit: Technical Nick