Want to enable AI in your e-commerce portal? Here's two tips to start the ball rolling.
The role of AI in retail was borne out of chaos in a rapidly evolving world. Once brands were able to sell products directly to consumers; like how you can buy your jeans directly from H&M, third-party vendors began slashing prices to keep up. This led to retailers losing sight of the customer experience, value proposition and consumer loyalty while scrambling to stay afloat in this new price war. This may be where businesses have the most to gain from AI. AI has an unrivalled ability to process data with far more speed and accuracy than any human being, giving retailers far better insight into things like pricing structures, inventory management, target prospective customers and more. The data is also rich with actionable insight on how to improve service to customers. Chatbots are becoming a recognizable use of AI in commerce and also one of the better use cases to show its power to learn consumer behaviour and supplement human intervention. Commerce interactions generate a never-ending stream of data and feedback, the perfect fuel for AI technologies. The more data they have at their disposal, the better they perform. While implementing a chatbot however, companies need to know how to meet consumers’ expectations on all fronts; one wrong move, and they’re off to a competitor. Two out of three respondents of a New Voice Media survey (and 80 percent of 25- to 34-year-olds) reported leaving a business due to inadequate customer service. In 2017, that meant businesses lost out on $75 billion because of poor customer service. AI enables putting customer service at the forefront both realistic and easy to implement. Another important aspect of customer service is predictive preferences, wherein businesses try to deliver products and services based on the needs of the customer without the customer explicitly stating said needs. As per a 2020 report, 35% of Amazon's total sales are delivered through their recommendation engine which uses data and analytics to identify complementary, similar, recently viewed and previously bought products to create your unique buyer profile and offer a host of recommendations with different labels for you to browse and ultimately click the "Add to cart" button. We at Brandbuddiez are working on a similar engine which will learn not only from users, but also take into account inputs from stakeholders at various points in the value chain. That means channelling data from the marketing and sales team, the logistics team, POS team and site visitors to deliver a holistic and personalized user experience. An engine which is capable of learning from its mistakes and improve the quality of recommendations day by day. Sounds exciting, doesn't it?