AI Is Watching You Shop—And Changing the Prices
Fri Feb 06 2026
AI is rapidly turning modern marketing into a surveillance-and-optimization machine. What started with loyalty cards and basic customer databases has evolved into always-on tracking across apps, websites, and devices—feeding models that learn what people want, when they're vulnerable to buying, and how to push them toward a decision. In this video, we break down how "surveillance marketing" works in plain language: companies collect massive amounts of behavioral data, stitch it together with identity graphs and third-party sources, and use AI to target messages in real time.
Then comes the next step: dynamic pricing. Instead of one price for everyone, algorithms can adjust prices on the fly based on demand, timing, channel, device signals, and past behavior—essentially guessing what you're willing to pay. That may boost revenue, but it also creates real risks: bias, unfair outcomes, privacy exposure, and a growing "trust debt" when customers realize the system is opaque.
We'll also cover why the vendor ecosystem matters—data brokers, ad platforms, CDPs, and personalization engines—and why governance is lagging behind. The takeaway: this isn't going away, but it must be architected responsibly, with limits, audits, fairness testing, and transparency.
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AI is rapidly turning modern marketing into a surveillance-and-optimization machine. What started with loyalty cards and basic customer databases has evolved into always-on tracking across apps, websites, and devices—feeding models that learn what people want, when they're vulnerable to buying, and how to push them toward a decision. In this video, we break down how "surveillance marketing" works in plain language: companies collect massive amounts of behavioral data, stitch it together with identity graphs and third-party sources, and use AI to target messages in real time. Then comes the next step: dynamic pricing. Instead of one price for everyone, algorithms can adjust prices on the fly based on demand, timing, channel, device signals, and past behavior—essentially guessing what you're willing to pay. That may boost revenue, but it also creates real risks: bias, unfair outcomes, privacy exposure, and a growing "trust debt" when customers realize the system is opaque. We'll also cover why the vendor ecosystem matters—data brokers, ad platforms, CDPs, and personalization engines—and why governance is lagging behind. The takeaway: this isn't going away, but it must be architected responsibly, with limits, audits, fairness testing, and transparency.