S3E7 – Is Your Grocery App Spying on Your Wallet?

Key takeaways

• Surveillance pricing uses customer data to show different prices or offers to different people.
• Grocery apps make the practice more visible because customers may compare prices across platforms.
• Dynamic pricing is already common in airlines, hotels, and B2B software.
• Convenience, loyalty, and willingness to pay can all influence the price a customer sees.
• AI shopping agents may give buyers more power to compare prices and reduce sellers’ advantage.
• The best pricing decisions balance commercial opportunity with transparency and customer trust.

What surveillance pricing really means

The term sounds alarming. In simple terms, surveillance pricing means using information about a customer to set or adjust a price. That information might include purchase history, app activity, loyalty status, location, or the products someone regularly buys.

It is a form of individualized price discrimination, sometimes supported by AI. A retailer may decide that one customer is more likely to accept a higher price, while another is more likely to respond to a discount.

That does not always mean a seller is secretly charging more. Customer data has long been used to create targeted promotions, increase shopping frequency, or encourage someone to buy more. The concern becomes stronger when the same product appears at different prices for different people.

For pricing leaders, this is where discipline matters. AI can help detect patterns and identify opportunities, but it should work inside a clear pricing strategy built around the company’s market, data quality, customer segments, and commercial goals. It is not a replacement for judgment.

Why grocery apps feel different

Imagine seeing bananas for $3.00 on one grocery app and $2.75 on another. You might wonder whether the difference comes from your purchase history, the app you use, or the retailer’s own pricing.

There is another possibility: the bananas may cost $2.00 in the physical store. In that case, the customer is already paying for convenience, delivery, and saved time.

That distinction is important. Customers may accept paying more for:

1. Home delivery.
2. A faster or easier shopping experience.
3. Access to a wider selection.
4. A service that saves them a trip to the store.

They may react very differently if two people standing at the same checkout are charged different prices for the same item. The more visible and difficult to justify the difference, the greater the risk to trust.

Revenue Management Labs often sees this issue in practical pricing work: customers do not judge a price in isolation. They judge the reason behind it, the alternatives available, and whether the exchange feels fair.

Dynamic pricing is already everywhere

Airlines and hotels have used dynamic pricing for years. Prices change based on timing, demand, capacity, route, season, and booking behavior. Most customers accept this because the differences are tied to a recognizable service choice.

A nonstop flight can cost more than a flight with two connections. A hotel room can cost more during a major event. In both cases, buyers understand that availability and convenience affect the price.

The debate changes when a customer sees a higher price for the exact same flight at the exact same time simply because the seller believes that customer is less likely to shop around.

That is the dividing line between pricing for a different value proposition and pricing based on a customer’s perceived willingness to pay. Both can be commercially rational, but they do not carry the same customer or reputational risk.

B2B pricing has its own version of surveillance pricing

In B2B markets, individualized pricing is already common. A software company may publish a list price per user, but few customers pay the exact same net price.

The final price may depend on:

• Number of seats or locations.
• Contract length.
• Products purchased together.
• Sales targets for the quarter.
• Account potential and strategic value.
• The customer’s negotiating position.

A discretionary sales discount can look like a friendly commercial decision: “I like this customer, so I’ll give them another 5%.” In practice, it may be a data-informed estimate of how much discount is needed to close the deal.

B2B customers tend to challenge this less because there is less public visibility. Buyers expect negotiation. Still, inconsistent discounting can quietly erode margins and create fairness concerns across accounts.

This is why a strong pricing program needs more than a list price. It needs clear discount guardrails, deal analysis, approval rules, and ongoing measurement. At Revenue Management Labs, AI can support faster analysis of these patterns, while pricing experts help turn the findings into decisions sales teams can actually use.

Will the market or regulation decide?

The free-market argument is straightforward: customers can compare apps, shop in stores, use incognito mode, switch suppliers, or refuse to buy. If enough people dislike personalized pricing, sellers may lose loyalty and volume.

That logic assumes customers have the time, information, and ability to compare. Many do not. A parent ordering groceries with a child crying in the background may not spend an hour searching for the lowest banana price. Convenience has real value, but limited choice can make the exchange feel less voluntary.

Regulation could improve transparency, but rules that are too broad may also prevent useful personalization, targeted discounts, or pricing that reflects genuine differences in service and demand.

A practical approach is to focus on clear boundaries:

• Do not use sensitive personal information unfairly.
• Make material price differences explainable.
• Avoid misleading customers about the price they should expect.
• Monitor outcomes across customer groups.
• Give customers reasonable ways to compare or opt out.

The next shift: AI shopping agents

The balance may soon change. AI shopping agents could compare retailers, search for deals, and complete purchases on a customer’s behalf. That would make it harder for sellers to rely on customer inertia or limited search behavior.

Retailers and suppliers are also using AI in procurement and demand analysis. Both sides are becoming faster at finding price differences. Sellers may gain sharper insight into willingness to pay, while buyers gain tools to identify alternatives.

The companies that perform best will not simply charge every customer the maximum possible price. They will build pricing systems that connect value, data, customer behavior, and execution. That requires customized models, experienced judgment, and close attention to how pricing works in the field.

Surveillance pricing is not going away. The real work is deciding where personalization creates value, where it damages trust, and how to capture profitable opportunities without losing the customer in the process.