Welcome to our Vision for Personalized AI Shopping Agents!
You can find Part 3 here on our website.
In the same way digital internet advertising opened a cost-effective path for participation from a broad range of advertisers, AI sales agents open a path for a broad range of retail ecosystem stakeholders to influence shopper switching behaviors in retail.
For example, a brand provides a budget and a target objective for a given campaign to its sales agent. The sales agent then generates emotionally potent shopper-level offers that motivate brand-switching. With another campaign, a brand allocates budget to drive pantry loading (AKA multiples and exhaust market demand ahead of a holiday. Outcomes are validated and commissions paid based on retailer transaction data.
Or a health insurer provides a budget and target objective for a diabetes awareness campaign. The sales agent provides information and drives nutrient and ingredient switching toward diabetic-friendly choices across all food and beverage categories. KPIs such as A1C levels are monitored across both control and focus groups relative to the target objective.
With both examples above, expectations are based on behaviorally informed shopper-level forecasts. They include closed-loop learning models that continuously monitor focus vs control to quantify outcomes.
Over time, positive feedback loops help the ecosystem optimize assortments and new product innovation within the evolving values of a retailer’s shopper base. With fast feedback loops and insight and quantifiable outcomes, stakeholders are empowered to take calculated strategic risks. With this, the marketplace paradigm shifts from products to people and from prices toward values.
The emotional potency of price is key to unlocking this trillion-dollar $ paradigm shift. First, as a thread that interprets the why behind the buy (AI), then as a lever to align ecosystem resources with shopper values (optimization). With this thread, the entire ecosystem AND its long tail can motivate shopper-level switching behavior at scale. Since switching is based on shopper choice, the outcome is a mutual win.
For a retailer, embedding AI agents into their shopping apps enables ecosystem partners to channel a new flow of monetary incentives into a retailer’s stores and e-commerce channels. The retailer’s shopper base, trusted relationships and expertise driving loyalty program adoption provide a marketplace. Through the flow of monetary incentives, AI agents help a retailer offer lower prices, increase trips and basket size while maintaining or improving margins. As shopper-specific models learn, agents deliver more relevancy, more savings, and a hard-to-replicate relationship.
SUMMARY: THE RISE OF AI-BASED SHOPPING AGENTS
As we enter the era of prices, the business models of retailers, brands and health care companies are already under downward margin pressure. Economic conditions, the growth of lifestyle diseases, and product proliferation are further increasing marketplace complexity. This complexity is outpacing the capacity for reasonably informed decisions on both the buy-side and the sell-side.
Power has not yet shifted to the shopper. The cognitive effort and time needed to find, choose, and afford emotionally compelling marketplace options for routine household purchasing still falls on the shopper. Today’s emerging data foundations, AI and mathematical optimization enable values-based personalization. Most of the elements needed are already operational at scale. Soon, buy-side AI agents will automate this effort in support of a shopper’s ever-changing values and context. With over $25 trillion in annual global sales, this promises to create a tectonic shift in knowledge, outcomes, and power. This time, to shoppers.
The opportunity is to place the predictive accuracy of a shopper’s voice front and center and align the ecosystem on this single version of truth. With this, sell-side agents will compose bespoke digital experiences. With predictability, these bespoke experiences will align with shopper values to simplify cognitive load while minimizing the discount needed to close a sale. Closed-loop learning models will continuously monitor focus vs control to quantify outcomes and optimization will tighten the gap between stakeholder business models and shopper values (supply and demand) by shifting the marketplace paradigm from products to people and from prices toward values.
For stakeholders across the retail ecosystem, their alignment toward a race-to-the-top (differentiating on values) would serve as a meaningful point of mutual relief.
CONCLUSION
However, the journey is far from over. We are now entering a critical phase of market validation and testing, with the aim of demonstrating how personalized offers can incentivize shoppers to make better purchasing decisions. We are also actively seeking to collaborate with insurers, retailers, and ecosystem partners to make this vision a reality.
The future of retail is here, and it is personalized, data-driven, and values-oriented. With the right collaboration and innovation, we can create a marketplace that truly serves the needs and values of individual shoppers, while also driving positive outcomes for the broader ecosystem. The rise of personalized AI sales agents at scale is not just a possibility, but an inevitability. And at Engage3, we are excited to be leading the charge.
