Goodbye Price Tags, Hello Dynamic Gouging: How Grocers and Algorithms Are Quietly Taxing Your Life

TL;DR – You're Not Getting "Deals," You're Getting Modeled

• The classic price tag (one visible price for everyone) is being replaced by dynamic, personalized, algorithmic pricing.
• The largest traditional grocery chain in the U.S. runs a data science arm, 84.51°, that uses loyalty data from 60M+ U.S. households to optimize prices, promos, and what you see [and don't see].
• Every time you swipe a loyalty card or app, you're feeding a system that can infer your income, health, family status, stress level, and price sensitivity.
• Algorithms don't need smoke-filled rooms to act like cartels, they can "learn" to keep prices high and avoid real competition while looking totally legal.
• The endgame: personalized pain-based pricing, charging you more when you're more desperate, based on your data and behavior.
• Your defenses: Treat loyalty programs as surveillance programs, minimize shopping while logged in, be careful with "smart" devices, comparison shop as a habit, and push for laws that limit how personal data can be used to set prices.

There was a time when a price tag was a kind of truce.

You walked into a store, looked at the shelf, saw a number, and, crucially, everyone saw the same number. One price, one product, for every customer. It wasn't perfect, but it was at least fair-ish.

That era is dying.

And the replacement isn't just "dynamic pricing." It's data-driven, always-watching, algorithmic price steering, and if you're not careful, it's going to cost you far more than a few extra bucks at checkout.

As a privacy hawk and a former employee of the largest traditional grocery store chain in the U.S., who did early work on digital shelf tags / dynamic pricing systems ("ShelfEdge"-style tech) back when it was still a gleam in a VP's eye, I can tell you: It was scary then. It's much scarier now.

From One Fair Price to "What Can We Squeeze Out of You?"

The New York Times Opinion video, "Goodbye, Price Tags. Hello, Dynamic Pricing.", does a good job walking through how we got here:

• In the 1800s, shopping was basically live-action eBay. Clerks sized you up, how you looked, how long the line was, how desperate you seemed, and named a number. You haggled if you could.
• Then Quakers and others pushed for posted prices as a fairness reform: one visible price for everyone.
• Adhesive price tags took that idea mainstream. A tiny sticker became a public commitment: "This is the deal. You and the person next to you pay the same."

That shift did two big things:

1. Made it easier to compare prices between stores.
2. Put pressure on retailers to compete on price, not on how gullible each customer looked.

Fast forward to now:

• We've got digital shelf tags that can change prices in milliseconds.
• Pricing algorithms constantly monitor competitors and inventory.
• Loyalty programs and apps quietly vacuum up every detail of what you buy and how you behave.

We're drifting right back toward the 1800s "what can we get away with charging you?" model, except this time, it's not a clerk guessing. It's an algorithm with a dossier on your life.

The Three Ways Dynamic Pricing Screws You

The NYT video breaks the problem into three buckets:

1. The illegal stuff
2. The currently legal stuff
3. The "this should absolutely be illegal" stuff

Let's translate that into real-world English.

1. The Illegal: Algorithmic Cartels Without the Smoke-Filled Rooms

Old-school price fixing looked like this: Executives sit in a room, agree to raise prices together, and hope regulators aren't listening.

Now, thanks to software, you don't need the meeting.

The video highlights the RealPage case: landlords across the country fed data about their apartments into a shared pricing algorithm. That algorithm:

• Didn't try to find the "market price."
• Tried to find the maximum they could charge while still keeping units filled.
• Even encouraged leaving units empty rather than lowering rent.

Result:

• Over 3 million apartments affected.
• Americans allegedly overcharged by billions of dollars.
• A de facto cartel, coordinated by code instead of cigars.

The U.S. Department of Justice sued RealPage for facilitating price fixing. RealPage eventually settled and agreed to change some business practices.

So yes… some of this is probably illegal.

But here's the crucial part: you don't need a literal "let's fix prices" agreement anymore. You just need shared or similar algorithms, fed with massive, detailed behavioral data, all optimized for the same goal: extract more money from each customer.

84.51°: Algorithmic Cartels With Better Branding

The largest traditional grocery chain in the U.S. doesn't just operate supermarkets. It also owns a full-blown retail data science, insights, and media company called 84.51°, a wholly owned subsidiary of The Kroger Co.

84.51° describes itself as: "A retail data science, insights and media company that creates more personalized and valuable experiences for shoppers across the path to purchase and beyond."

Under the hood, that means:

• They use first-party retail data from over 60 million U.S. households,
• Sourced through the Kroger Plus loyalty program,
• To inform everything from pricing and promotions to assortment and advertising.

From their own content:

• "Our first-party retail data comes from over 60 million U.S. households and is sourced through the Kroger Plus loyalty card program."
• Every swipe "…provides a discount on the items they buy, and it lets us know what they like or dislike."

They openly talk about:

• Price optimization as a core use case of their optimization algorithms,
• All-around personalization, including the prices they pay, their experience on the Kroger app, the content and creative offers they receive, and the products they see.

Translated:

Every time you use a loyalty card, you're helping build a high-resolution behavioral model of your household: what you buy, when, where, how often, and at what price points.

Now stack that on top of:

• Digital shelf tags that can change prices in real time,
• Dynamic pricing algorithms tuned to maximize category performance and revenue,
• Retail media platforms that sell targeting against that same data to brands.

You don't need an explicit "let's collude" conversation between companies. You've effectively got a centralized intelligence layer optimizing for higher prices where you're less sensitive, "deals" that still maximize your total spend, and a system that learns how much each segment, or even each household, will tolerate.

Is 84.51° literally sitting in a dark room coordinating price hikes like a movie villain? No.

Is this functionally similar to cartel thinking, just executed by math and marketing instead of mobsters? That's a fair concern.

And this is what I mean when I say: This company (and others like it) knows more about you than you can possibly imagine, because of loyalty programs and the extensive purchase history you freely hand over every time you buy a product and identify yourself.

The scary part isn't just that they know you like oat milk and cat food. It's that they can infer:

• Income band
• Health indicators and conditions (or strong proxies)
• Family structure
• Religious or cultural patterns
• Life events and stressors (job loss, new baby, illness)
• Your price sensitivity and breaking points

Once you have that, price "optimization" stops being a neutral analytics problem and starts looking like: "How much can we ratchet up pressure on this exact household before they snap?"

That's algorithmic cartel thinking in practice, even if the legal system hasn't fully caught up.

2. The Legal: Independent Algorithms That Quietly Learn Not to Compete

Now imagine two gas stations across town.

Before algorithms:

• Station A drops its price to steal customers.
• Station B notices, undercuts them a bit.
• They hate the margin hit, but you benefit from the fight.

After both adopt dynamic pricing tools:

• Each algorithm monitors local competitor prices in real time.
• Any time one station cuts prices, the other instantly matches.
• Very quickly, both stations learn: "Dropping prices doesn't win us any extra volume, so why bother?"

A study of gas stations in Germany found that stations using pricing algorithms were able to charge about 15% higher prices than before, because neither side wanted to start a price war when the other would just auto-match.

No secret meeting. No explicit agreement. Just emergent behavior: "If my competitor has an algo and I have an algo, neither of us wants to be the first to blink on price."

Now apply that same logic to:

• Flights
• Hotels
• Rideshares
• Groceries
• Online retailers

Algorithms don't need to "collude" in the human sense to reach cartel-like outcomes. They just need aligned incentives and a constant firehose of data.

3. The "Should Be Illegal": Personalized Pain-Based Pricing

This is where my inner privacy hawk and my old grocery-tech experience both start screaming.

To really maximize dynamic pricing, companies don't just want to know:

• What competitors charge, or
• What their inventory looks like.

They want to know:

• How much you make
• How impulsive you are
• How stressed you are
• What your household needs right now
• How urgently you need it
• How likely you are to switch brands or stores

They get that from:

• Loyalty programs
• Store and fuel apps
• Location tracking
• Browsing and purchase history
• Device fingerprinting
• Cross-partner data sharing

We've already seen:

• Higher prices for users on premium devices
• Different offers or products shown based on browsing history and profiles

The NYT video walks through examples that are disturbingly plausible:

• A pharmacy chain charging you more for essential medication because your data screams "no alternative."
• A rental site hiking prices because it knows your move-in deadline is two weeks away.
• During a boil-water advisory, an algorithm quietly raises bottled water prices.
• A smart speaker or app noticing you're out of paper towels and bumping the price right before you click "reorder."

This is the future of the "price tag": not a sticker, but a shifting number calculated by a system that stalks your behavior and pokes at your pain points.

A Quick Insider View: Digital Shelf Tags in Their Infancy

When I worked on early digital shelf / dynamic pricing initiatives for the largest traditional grocery chain in the U.S., the official story was all upside:

• "We can update prices instantly without extra labor."
• "We'll reduce pricing errors at the shelf."
• "We can roll out promotions faster and more consistently."

All technically true.

But underneath that, anyone paying attention could see the other potential trajectory: If you can change prices instantly, and you're building deep behavioral profiles of tens of millions of households, the shelf price can become less of a public commitment and more of a real-time negotiation you don't know you're in.

Even before today's AI hype cycle, the direction of travel was obvious:

• Tie loyalty and purchase history to
• Dynamic, digital shelf infrastructure and
• Optimization engines focused on "category performance," "basket size," and "loyalty,"

…and you get a system that knows exactly when to offer you a coupon and when to quietly withhold one. The tech was "cool." The possible uses were, and are, deeply uncool.

Why Traditional Regulation Isn't Enough (Yet)

To be fair, governments and regulators aren't totally asleep:

• Antitrust agencies are investigating algorithmic price-fixing cases like RealPage.
• Some jurisdictions are tightening rules on data collection and ad targeting.

But these systems evolve much faster than laws.

The NYT video suggests a couple of straightforward policy ideas:

Limit when prices can change.

For example, require that physical retailers update prices only at fixed times (say, once a day at 6 a.m.), not continuously. That restores some genuine price competition and transparency.

Restrict how personal data can be used in pricing algorithms.

Your health, income, household situation, and emotional vulnerability should not be valid inputs to "how much can we charge this person?"

Those are good starting points.

The hard truth, though, is this: Until enough people change how they behave and spend, companies won't seriously change what they do. They listen to money, not outrage.

What You Can Actually Do (That Moves the Needle)

You can't fully opt out of this system, but you can starve it of the easiest fuel and push for better rules. Here's the practical playbook, prioritized by impact.

1. Treat Loyalty Programs as Surveillance Programs

That "free" loyalty account? You are paying in data.

Consider:

• Don't auto-enroll in every loyalty program on autopilot.
• When you do sign up:
  • Give only the minimum required info.
  • Avoid linking extra data sources (social logins, bank data, health info, etc.).
  • Opt out of "sharing with partners" and "personalized offers" where possible.

You might lose a few splashy coupons. In exchange, you reduce how precisely they can profile and price you.

2. Minimize Shopping While Logged In or Fully Identifiable

Online:

• Don't stay logged in just to browse or price-check.
• Use browser profiles or privacy-focused browsers with fewer trackers when shopping.
• Be aware that a newer / higher-end device can be a signal used against you.

In store:

• Realize that scanning your loyalty card or phone number is a data handshake: That specific transaction gets stitched into your long-term profile.
• If the "loyalty discount" is tiny, it may not be worth the surveillance.

3. Be Smart About "Smart" Devices

Assume that any always-on, always-connected device is a potential signal:

• Smart speakers
• Smart TVs
• Retail and grocery apps with broad permissions
• Voice assistants

Practical steps:

• Turn off one-click or voice purchasing where you don't truly need it.
• Review app permissions and data-sharing options.
• Keep purchase decisions off hot mics when you can.

Is it paranoid? Maybe a bit. Is it more paranoid to let a device quietly feed your life into a pricing engine? Also yes.

4. Make Comparison Shopping a Habit, Not a Hassle

Algorithms depend on:

• You being rushed,
• You being tired,
• You not checking alternatives.

Break that assumption:

• Compare prices across at least 2–3 retailers for recurring items and big purchases.
• Use tools or sites that show price history and variations over time.
• Don't assume "your" store is cheapest just because they shower you with "personalized deals."

Even small shifts in behavior, like walking away from absurd "dynamic" markups - send signals.

5. Push for Policy, Not Just Better Coupons

Behavior change matters, but we also need rules of the game to catch up.

You can:

• Support organizations and candidates that take data rights and algorithmic pricing seriously.
• Ask legislators (local, state, federal) to:
  • Limit the use of personal and sensitive data in pricing and offers.
  • Require transparency if prices are personalized: how and based on what.
  • Consider fixed pricing windows for essential goods like groceries, medicine, utilities, and housing.

One email won't fix this. But silence guarantees nothing changes.

6. Normalize Being Skeptical and Share What You Learn

Most people just feel like:

• "Everything is getting more expensive," and
• "Deals aren't what they used to be."

They don't necessarily see the machinery behind that.

You can help:

• Share resources like the NYT video.
• Share articles like this one.
• Talk honestly about what you see in pricing, loyalty programs, and "personalized" offers.

The more people treat loyalty programs, digital shelf tags, and hyper-personalization with healthy suspicion, the harder it becomes to keep creeping the line without pushback.

The Bottom Line: Don't Be an Easy Target

Dynamic pricing, by itself, isn't automatically evil. In theory, it can:

• Reduce waste
• Smooth out demand
• Offer genuine off-peak discounts

But combined with:

• Detailed personal and household-level data,
• Opaque algorithms optimized for extraction, and
• Weak or outdated regulation,

…it becomes something closer to a personalized tax on your urgency, your health, and your ignorance.

As someone who's seen pieces of this machinery from the inside and now lives as a loud privacy hawk on the outside, my advice is simple: Assume the system is trying to learn what you'll tolerate. Make that job as hard and unprofitable as possible.

Opt out where you can. Obfuscate where you can't. Spend intentionally. And push for laws that treat you as more than a data point on some revenue-optimization dashboard.

The old price tag may be dying… but we don't have to go quietly with it.