How AI Drives Innovation in eCommerce & Retail

Companies across a range of industries are adopting machine learning technologies — and as early adopters, eCommerce and retail companies have seen the biggest wins from investing in machine learning. By applying artificial intelligence to key business problems, eCommerce companies are using machine learning models to drive higher sales, predict demand, and personalize the shopping experience through more relevant search results and real-time customer service. To train machine learning and AI algorithms to respond to high volumes of customers, retailers must collect large amounts of training data. We recently blogged about a 2018 report from McKinsey that analyzed hundreds of use cases for AI. While marketing and sales departments often see the quickest wins with AI, customer service teams can provide better experiences in real-time through smarter analytics and chatbots. McKinsey also recommends that retailers with physical stores invest in AI to optimize supply chains and improve inventory management. With technology that takes the entirety of an individual’s behaviors and preferences into consideration when generating a search result, pricing quote, or next best action, retailers can use AI to optimize product recommendations and personalize the customer experience. Read on to learn how leading companies are using ML and AI to deliver better eCommerce and retail experiences.

Boosting eCommerce sales using AI tools

Given their wealth of data around customer behavior and preferences, large online retailers are at the forefront of AI-driven personalization. Winning strategies include personalizing website content and product recommendations based on previous customer behavior, personalizing communications based on both behavior and learned preferences, using chatbots to help customers navigate the shopping experience, and connecting social media and programmatic ad buying to serve up the most relevant and high-converting ads to customers who are most likely to make a purchase. Retailers using AI to personalize the customer experience have seen increased profits and business value. A recent report from Boston Consulting Group found that retailers that adopted personalization strategies saw sales gains of 6-10% — a rate two to three times faster than retailers who did not. Using AI to personalize shopping will also boost profitability by 59% for wholesale and retail companies by 2035, according to Accenture. Italian fashion brand Cosabella, working with an AI start-up software firm called Sentient, is using AI to rapidly test multiple user experience designs for its website. Unlike traditional A/B testing, this multi-variant testing allows their product recommendation and communications engines to gain insight from and adapt to customer behavior. The brand claims the AI process immediately boosted sales by 35%. An AI-powered humanoid robot called Pepper that has been programmed to “perceive human emotions” has boosted sales and in-store customer interactions in both retail shops and cafes in California. After using Pepper in its retail outlets, retailer The Ave reported a 98% increase in customer interactions, a 20% increase in foot traffic, and a 300% increase in revenue.

Using AI to personalize the customer experience

Online retailers are investing in AI-powered personalization engines to bring together the human interaction of an in-store experience with the convenience of an online sale. These AI tools help shoppers search for products online by speaking, or even using images uploaded from their smartphone, to emulate the experience of interacting with a person. This strategy helps retailers differentiate themselves, boost word-of-mouth sales, and increase customer loyalty. Skechers uses AI for site search relevance for their lifestyle and sportswear brand. As online shoppers click on a product they’re interested in, AI-powered tools analyze the Skechers catalogue in real-time to serve up similar or related items. This provides a more seamless, intuitive shopping experience for customers, and helps surface the products they actually want — boosting both customer satisfaction and sales. A unified communications cloud provider, Star2Star, uses Conversica’s customizable sales assistant software bot to cross-sell or re-engage existing leads — in one case resulting in a 30% email response rate within hours. Bosch Automotive also uses Conversica, attributing an average increase of 60 sales per month at one Toyota dealership to the software.

Predicting demand and preferences with AI tools

AI algorithms are able to handle deep learning, statistical programming, and predictive analysis of huge amounts of data in a way that humans cannot. The popular Netflix series, House of Cards, resulted from AI pattern learning techniques. The company analyzed datasets of their most-watched TV shows to predict what kind of drama customers would most enjoy. Retail giants like Amazon have used machine learning algorithms since 2014 to forecast demand, drive stocking efficiencies across warehouses, and set prices based on analysis of how much consumers are willing to spend on certain items. As the Harvard Business Review states, “…this level of prediction requires detecting subtle patterns from massive data sets that are constantly in flux: consumers’ purchase histories, product preferences, and schedules; competitors’ pricing and inventory; and current and forecasted product demand.” AI and machine learning can help analyze these massive data sets. At Appen, we’ve helped leading retailers improve their machine learning programs through high-quality, human-annotated data. Whether you are looking to improve onsite search, provide personalization or improve your customer service, we can provide the high-quality data needed to enhance your machine learning models. — To find out more about the retail solutions that Appen’s high-quality data enables, click here.