AI predicts customer max price, worker minimum wage: FTC investigating
The FTC is consulting on enforcement policy for personalised pricing, the practice of using AI to tailor prices to individual customers based on their data. Consumer groups warn loyalty programmes give retailers detailed shopping habits, including clues about maximum willingness to pay. The same technology is moving into compensation. beqom markets Pay Intelligence, which uses machine learning to predict pay trends and generate salary recommendations based on job, skills, location, and company pay scales. It models turnover risk from pay gaps. Compa says its AI agents give a 10-person team the reach of 100. Pave markets itself as AI-native compensation management and says its agent can auto-price salary ranges and equity targets in minutes. Bain research says AI pricing can monitor market prices, analyse customer response, and improve willingness-to-pay modeling. Examples show 4% to 8% incremental revenue growth and churn reductions of 5% to 10% in dynamic pricing use cases. The competitive set spans startups and incumbents. Salary.com says its AI is built on 25+ years of compensation data. Payscale remains an established benchmarking business. DynamicPricing.ai, a smaller ecommerce vendor, claims to map each customer's willingness to pay and price to the ceiling. For sales teams, the implications are direct. If your product uses dynamic pricing, your comp plan needs to account for variable deal values. If your company uses AI compensation tools, your OTE may be based on machine learning predictions of what you will accept, not just market rate. Worth noting: there is no evidence ANZ supermarkets are individually pricing products this way. Yet. But Consumer NZ argues the data is there, and the tools exist. The question is not if this comes to ANZ B2B sales, but when, and whether comp transparency survives it.