Interviews, insight & analysis on digital media & marketing

AI’s role in revolutionising interactive commerce

By Ori Bauer, CEO of Dynamic Yield by Mastercard

In today’s rapidly evolving technological landscape, social media shapes more than just fashion trends; it also transforms consumer shopping behaviour and expectations. No matter the generation, consumers want specific and relevant experiences—and for the past decade, studies have consistently shown that when they get these personalised experiences, they’ll become repeat customers. In short, shoppers want brands to interact with their wants, needs, feelings and opinions as individual humans, and they want that relationship to deepen with every purchase. And while personalisation engines have allowed brands to deliver tailored content at scale, new artificial intelligence (AI)-driven advances now make it possible to interact with consumers, creating a valuable, useful, and enjoyable commerce experience for every customer, every time. 

The Limitations of Interactive Commerce

Up until recently, interactive commerce has been defined by pre-programmed quizzes and chatbots, which have largely helped brands respond to customer inquiries, provide recommendations, offer assistance with order tracking, and facilitate transactions with 24/7 availability. While they’ve optimised the customer experience, they haven’t been able to replicate online the in-store experience that really drives long-term loyalty.

However, new AI-driven technologies can help retailers close this gap. Machine learning technology can help create a unified view of the consumer across touchpoints and at scale. This granular understanding enables businesses to then tailor their marketing strategies, product offerings, and pricing models in line with dynamic consumer demands.

For example, consider how complex it can be to decipher contemporary wedding dress codes, often presented as enigmatic blends such as “garden party.” While an in-store associate could always pull from past customer connections and other ineffable experience to deliver the right attire for a shopper, the digital experience has been limited to typing “garden party” into search bar and yielding tagged products, other pre-curated experiences, or –more often than not—nothing, as search engines are not generally equipped to respond to colloquialisms. That is until now: a blend of natural language processing and image recognition tools have recently yielded revolutionary conversational search tools such as Shopping Muse, which leapfrog over traditional keyword-based search constraints and empower consumers to engage in real-time. Through specific inquiries, consumers receive personalised outfit recommendations that mirror the essence of in-person shopping, even down to accessory suggestions. 

AI can now also help retailers extend useful interactions beyond the first purchase. While product recommendation widgets have led to frustrating post-purchase experiences in the past by recommending similar products right after a consumer has bought something, AI can comprehend past consumer behaviour and preferences to accurately forecast future intent. Instead of redundant suggestions, it should recommend complementary items informed by collective behaviour, aligning with a consumer’s real-life wants and needs. For example, a consumer who purchases a couch is not likely not to buy another couch for another decade. AI can help ensure that the next recommendation is not extra couches, but instead couch extras – pillows and throw blankets – that are more relevant to where the consumer is post-purchase. 

Redesigning the Commerce Landscape

AI’s potential to transform interactive commerce extends far beyond convenience and personalisation for shoppers and even customer service, revolutionising what’s possible in retail. The new frontiers of predictive personalization driven by these customer interactions can enable businesses to forecast trends, inventory requirements, and customer purchasing behaviours with higher accuracy. This foresight allows for proactive adjustments in stock levels, personalised marketing campaigns, and strategic decision-making, thereby optimising operational efficiency and revenue generation.

When effectively nurtured and harnessed, AI can both solve unique commerce challenges while upholding stringent data privacy principles. While numerous retailers remain fixated on engagement-centric personalisation, the trailblazers harness AI throughout interactive commerce experiences, synchronising them with context, perception, and expectations, thereby forging more meaningful connections with consumers. In this paradigm of dynamic trends and advancing technology, loyalty emerges as the ultimate barometer of success, transcending mere cost or speed of delivery. The brands that adeptly harness these capabilities to accurately predict consumer desires and tailor experiences to individual behaviour are poised to thrive amidst shifting consumer expectations and preferences.