AI analyzes sales, operations, and media data to identify price elasticity across product bands. Brands can then increase prices on premium items where consumers are less sensitive, while keeping prices flat on essentials, thus protecting margins without alienating the entire customer base.
AI uses shopper clickstream and sales data to segment customers and SKUs with precision. This allows brands to offer targeted discounts where needed, maintaining trust by avoiding deceptive practices like shrinkflation and being transparent about necessary price increases on less elastic products.
Rather than fully replacing humans, the optimal AI model acts as a teammate. It handles data crunching and generates recommendations, freeing teams from analysis to focus on strategic decision-making and approving AI's proposed actions, like halting ad spend on out-of-stock items.
With AI enabling precise control over media spend, key performance indicators are changing. Brands now move beyond simple Return on Ad Spend (ROAS) to more sophisticated metrics like incremental ROAS and contribution margin, reflecting a new emphasis on profitable growth rather than just volume.
As AI agents automate day-to-day e-commerce optimization, the primary role for humans evolves. Core competencies will shift from data analysis and execution to high-level decision-making and managing the complex, collaborative joint business planning process with retail partners.
The next frontier in e-commerce is inter-company AI collaboration. A brand's AI will detect an opportunity, like a needed digital shelf update, and generate a recommendation. After human approval, the request is sent directly to the retailer's AI agent for automatic execution.