Consumers’ desire for personalized shopping experiences is by now well documented. In response to these expectations, merchants are increasingly using tools that present products and content, which reflect shoppers’ preferences, and past picks. These investments are likely to pay off, as merchants report personalization improves results throughout the customer life cycle.
Product recommendation and customer profiling tools increasingly integrate past brand interactions with “big data” insights that predict shoppers’ likely needs and paths to purchase. Artificial intelligence, known as AI, is the logical next step in this progression, enabling machines to respond to shoppers’ input with relevant content and products. Usage of AI-enhanced services is on the rise with Forrester reporting that more than a third of U.S. online consumers say they are using a web site’s virtual agent or a smartphone-based virtual assistant, such as Apple’s Siri or Google’s Assistant, to seek customer service help.
On the e-commerce front, merchants are using AI to transform their businesses through what’s been termed “conversational commerce,” whereby natural-language dialogue replaces explicit searching and browsing activities, such as keyword-searching for products or navigating through the customer service section to find shipping information. From Facebook chatbots to on-site product discovery tools, merchants are using AI to give shoppers a user-friendly entry point into their offerings. Whatever the platform, these AI implementations are built on a common foundation: solid knowledge of the customer experience and the purchase life cycle. Therefore, merchants contemplating their own AI-enhanced shopping experiences would do well if they have a wealth of data and best practices to draw upon.
To gather information relevant to future AI-driven tools and services, merchants should:
Document successful store interactions. Nothing can replace the interplay of staff expertise and one-to-one product guidance that occurs during a face-to-face interaction at a physical store. But merchants should attempt to capture store shoppers’ most common questions and build their AI routines to accommodate those needs. Similarly, top associates’ sales techniques and scripts can be woven into AI techniques.
Expand product discovery tools. As an interim step en route to AI-facilitated product suggestions, merchants can offer an expanded range of product search tools. To start, implement faceted search tools that can surface which product attributes matter most or suggested search terms that guide shoppers to categories and even individual products directly from a drop-down box that evolves and learns as new terms are typed in real-time. In addition, visual search gives shoppers the means to select a particular color or icon to see matching products, or to input photos from their smartphones to find similar items, match color swatches, or find products that fit within the pictured dimensions.
Use proactive chat for customer service. Merchants should not only offer a live chat link alongside their customer-service phone number; they should also experiment with proactive chat triggered by specific actions — or non-actions — along the path to purchase, such as stalling on the cart page or backing out of checkout. Not only do such prompts highlight chat as a customer service option; they help merchants understand the context of shoppers’ on-site behaviors, fueling richer interactions in the future.
Harnessing AI may seem like too futuristic of a goal to add to many merchants’ 2017 priority lists. But while there’s plenty of hype swirling around such implementations as chatbots and IoT, ever-more-sophisticated personalization and recommendation technologies are already verging on the first phases of AI. Merchants should lay the groundwork now for future shopping experiences that are both automated and satisfying.
Tushar Patel is chief marketing officer of Kibo.
More business and technology news from WWD: