The field of eCommerce continues to be a hotbed for emerging technologies. On one level, online stores are competing with other online stores. But on another level, online stores have to compete with the conventional marketplace of physical stores. One of the key differentiators is artificial intelligence which is making significant inroads into addressing the inherent flaws of eCommerce.
Earlier prototypes of artificial intelligence were known to be brittle and prone to wide margins of error. But developments in the field have come a long way; in 2016, most programmatic ads are run by AI. With the adoption of AI tech for eCommerce applications, higher levels of accuracy and intelligent predictions are observed. This is possible because feedback and rectification of information are key features of modern AI systems. One of the best innovations that has come out of AI is decision-tree formation for shoppers, which has greatly improved the user experience.
Wondering how AI can change things for your store? Then, read on.
Humanizing digital shopping experience
One of the biggest problems of eCommerce is the cold, mechanical experience of shopping. Despite the allure of online shopping with deep discounts, multiple options for payment and the convenience of home delivery, consumers still continue to flock to brick-and-mortar stores.
With artificial intelligence, The North Face online store is providing human-like interactive experiences to their customers. By engaging online shoppers more effectively, the advantage that offline stores have enjoyed traditionally is within the ambit of online stores as well.
Making searches more flexible
Searching for products on online stores is a tedious task. Lack of context about the user, irrelevant and rigid filters and issues with understanding keywords correctly are some of the issues that plague the tepid search bar.
For AI, search keywords are of great significance. Search keywords are instant actionables and so they are an integral input to the comprehensive analysis that AI performs. Macy’s, Shoes.com and others have started using technologies like reverse image search and image recognition, the scope of search can be expanded in each and every store’s inventory. By taking visual cues of the customer’s preferences, searches can be fine-tuned to relate to specific products more effectively.
Most recommender systems use collaborative filtering to recommend products to the consumer. This means they rely on data on best sellers, most viewed history and other general parameters of aggregation. The best recommender systems might even remember what your customer likes (at best!).
But with AI systems, the insights into customer yays and nays are holistic. Deep Learning enables the system to gather more information about the customer by analyzing different aspects of browsing behavior. Dynamic learning and unlearning of preferences results in accurate predictions and product recommendations.
Enhancing product discovery
Artificial intelligence helps users dive deeper into online store catalogs to find the perfect item that would otherwise go undiscovered. Because it is aware of the customer’s needs, systems can use that knowledge to leverage smarter and more relevant up sells and cross-sells.
Without AI, there is a high probability of discouraging customers by just dropping things dead at zero results at times or by recommending some unrelated items.
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Choice bridges the gulf between online and offline stores by acting as a personal shopping assistant for e-tail stores. By nature of its non-intrusive and highly customizable presence, Choice is a perfect fit with businesses of all shapes and sizes. Sign up to try Choice.ai for free and validate the future experience today.