Redefining The Retail Ecosystem

By Florian Quarré, Exponential AI

The way consumers engage with retailers has drastically changed over the last decade, not only because of transient changes in the infrastructure and societal fabric in which we live but also due to the way retailers have curated their engagement with customers. Retailers have transformed their business models to meet the needs of hyper-connected shoppers who want a seamless, unique experience that fits their digitally-driven lifestyles.

The retail industry remains a leader in adopting the continuous cycle of action-feedback-insights-and-experience, constantly refining its knowledge of the wants and needs of consumers.

The availability of large-scale data stores, along with the use of AI for finding consumers’ patterns and high-speed decision making techniques, has significantly increased the precision of customers’ insights and exponentially accelerated the speed of engaging with customers by creating a highly targeted experience.

The next phase of the industry’s evolution will focus on transformative experiences that not only personally engage with each consumer, but also drastically redesign every part of the shopping value chain across marketers, designers, suppliers, manufacturers, retailers, payment brokers, etc. AI coupled with edge computing and other emerging technologies will be one of the main accelerants of the future.

From Mass To Targeted Engagement, And Generally Curated Experience

Retailers have already made a significant foray into AI, developing models able to aggregate large volumes of data obtained from highly complementary yet heterogeneous sources, and generating a growing number of data points about consumers to engage with targeted consumer pools with a high probability of interest in the retailer’s offerings.

Amongst many other AI concepts successfully applied, reinforcement learning dynamically suggests prices that match supply to demand for non-staple offerings. Natural Language Processing coupled with Computer Vision and Deep Learning has helped organize the vast amount of unstructured data on the likes and dislikes of consumers into insights. They also have  provided mass retailers with the ability to precisely identify groups exhibiting characteristics relevant to their offerings and allowed advanced retailers to move from mass to targeted engagement by linking consumers’ actions to intents and leveraging this understanding to nudge customers toward a desired outcome – typically a purchase.

From Targeted To Personalized, Holistic Experience

As AI agents are developed with self-awareness of purpose and goal, they will have the ability to continuously monitor signals, coordinate actions and make decisions on behalf of the consumers to create a seamless, tailored experience. Retailers, designers, manufacturers, suppliers and all other actors of the shopping value chain will be able to use their customer insights to streamline the orchestration of their capabilities and become highly responsive to market trends and demands.

In an evolved ecosystem where hub and spoke-style malls have made way for hyper-focused neighborhoods, micro-communities, and other “15-minute cities,” consumer engagement will be local and hyper tailored toward true personalization, delivering a non-obtrusive yet omnipresent experience. Nuanced by the preferences and unique behavior of customers, this will enable a fluid, continuous evolution between the digital and the physical worlds. Spatiotemporal Multi-View-Based learning will fill in the gap of missing geospatial information, while federated and transfer learning along with edge computing will allow for highly contextual, continuous aggregation of learning data points about customers’ behavior. Finally, reinforcement learning applied to all of the independent agents will allow them to learn by interacting with each other, as well as with their environment, and grow their expertise via a reward mechanism.

It is not all that far-fetched to imagine a scenario where digital experience permeates into the physical world. For example, a customer books a vacation. A series of agents spring to action to coordinate preparation for the trip. A local AI agent at their destination places a customized order for 3D printed clothes that meet not only the precise measurements of the customer but also their need for hypoallergenic materials and their taste in colors and patterns, along with providing function for the trip itself – in this case, light rain and heavy winds during hiking activities. The AI is already aware of local suppliers and uses additive manufacturing augmented with machine learning to create the customized order, optimizing freight weight and keeping waste to a minimum.

For all these channels to come together, AI agents will have to have a compute ability to analyze text, images, voice, etc. right where the consumers are. They also will need to be refined enough to ignore the large amount of irrelevant noise to only filter in what is relevant to the consumer to prevent going into hyperdrive and negatively impacting experience. Finally, they will have to grow their collective knowledge about the consumer to achieve a greater level of success in serving them.

Towards An AI-Enabled Hyper-Personalized Continuous Shopping Ecosystem

Retail is already on a transformative journey to redefine the industry within the fourth industrial revolution, delivering on the vision of radical interconnection between digital and physical, between robots and humans.

AI agents are the force multipliers that make sense of the ever-growing consumer dataflows that seem more like noise than insights – and can deliver recommendations of actions in a matter of milliseconds before consumers move on to the next widget competing for their attention.

Retail companies building their AI foundations to deliver interconnected, omnipresent and highly personalized experiences already face some of the early challenges – how can we increase the breadth and depth of data to quickly and accurately interact with consumers? As a society, what data access and insight creation do we think is acceptable and ethical? Where is the line between accelerating an action versus incepting a need that didn’t exist?

AI adoption at scale will transport the industry from the era of mass engagement to targeted outreach, and from targeted to personalized engagement. From M to N to…1.

About The Author

Florian Quarré is CRO at Exponential AI.