Algorithmic price discrimination does not just affect one group. It is a systemic failure that reaches every level of the American economy — extracting more from those with less, disadvantaging the businesses least able to fight back, and leaving the institutions designed to protect people without the data they need to act. Clarita Analytics provides the infrastructure that addresses all of it.
Consumers
You are paying a price no one else can see — and you have no way to know
When you open a delivery app, you see one price. Someone else — same item, same store, same moment — may see another. The algorithm knows your location, your device, your browsing history, your loyalty patterns, and your likelihood to switch. You know none of that. That information asymmetry is not a side effect. It is the product.
Lower-income households spend up to 33% of before-tax income on food, versus 6.4% for the highest earners. Rural consumers already pay more for delivery with fewer alternatives. People in distressed communities have less ability to comparison-shop across platforms. These are not abstract harms — they are weekly costs, compounding silently, with no mechanism for the person paying to ever verify what they’re actually being charged relative to anyone else.
Small Businesses
The platform sees everything. The seller sees a bill they cannot explain.
36.2 million small businesses in the United States depend on digital platforms for advertising, fulfillment, marketplace access, and customer reach. The cost of that access is not fixed. It is determined by algorithms — auction-based, behavior-driven, opaque by design.
The FTC, in its case against Amazon, found that total platform fees were approaching 50% of seller revenue. One seller described paying a share of product price, storage fees, Prime delivery fees, and advertising spend — then having to raise consumer prices just to survive. A small restaurant on DoorDash or Uber Eats faces delivery fees that vary by demand, location, and courier availability, with no benchmark to know whether they’re being charged more than comparable merchants nearby.
Without independent data, a small business cannot tell whether rising costs are a market reality or discriminatory treatment. It cannot build a legal case. It cannot even ask the question with evidence in hand.
Attorneys & Researchers
Every investigation starts from zero. Every case builds the same evidence that was never preserved.
Price discrimination litigation under the Robinson-Patman Act requires showing goods of like grade and quality, sales to at least two purchasers, and a reasonable possibility of competitive injury. Antitrust class actions under Rule 23(b)(3) require common proof that the challenged practice affected the entire class — not just one buyer at one moment.
A screenshot proves nothing. A collection of screenshots assembled after the dispute began proves very little more. What courts and economists require is matched observations: same product, same seller, same time window, different profiles — collected continuously before any specific case existed, preserved with full chain of custody, and reproducible under expert scrutiny.
That infrastructure does not exist. Every investigation currently hires experts to build a narrow custom dataset after the fact, at enormous cost, under evidentiary standards that were not designed for the collection method used. Clarita Analytics builds that infrastructure once, so no one has to rebuild it for every case.
Federal & State Regulators
The law now requires disclosure. Enforcement requires something that does not yet exist.
The FTC’s 2025 surveillance pricing study confirmed that companies use precise location, browser history, demographics, shopping patterns, abandoned-cart behavior, and even mouse movements to set individualized consumer prices. The study documented this across major retailers and intermediaries. The FTC named it. The OECD analyzed it. Seven state-level algorithmic pricing bills are currently active.
What comes next — enforcement, rulemaking, litigation — will require independent, preexisting datasets showing what prices were actually shown to which profiles, over time, across platforms. Defendants’ internal records are the only current alternative. Discovery is expensive, contested, and incomplete.
Regulators need a source that nobody owns, nobody can suppress, and that was collecting data before the case began. That is what Clarita Analytics provides.
Policymakers & Legislators
You cannot write policy about a practice you cannot measure. The measurement does not exist yet.
Disclosure laws tell companies to say an algorithm exists. They do not require any reporting on what that algorithm does, to whom, by how much, or with what economic effect. Lawmakers across seven states have introduced bills addressing algorithmic pricing — but none have access to empirical data showing what is actually happening in the market they are trying to regulate.
How much more does a rural consumer pay for the same grocery order than an urban subscriber? Are low-income households paying higher delivery premiums as a share of their basket? Are small businesses on the same platform being charged structurally different fees without knowable justification? These are answerable questions — but only if someone is collecting the data.
Clarita Analytics produces the structured, publicly documented findings that turn legislative intent into measurable, evaluable policy. We give policymakers something to point to that is not a company’s own disclosure.
Distressed Communities
The people paying the most in algorithmic premiums are the least equipped to prove they are being overcharged
According to the Economic Innovation Group’s Distressed Communities Index, over 50 million Americans live in economically distressed communities — and tens of millions more live in at-risk or mid-tier areas with limited economic mobility. These are households with the thinnest financial margins, the fewest digital alternatives, and the least time and resources to investigate why their app showed them a higher price.
Digital platforms are not neutral infrastructure. They are data collection systems that observe which users have fewer choices, less flexibility, and greater need — and price accordingly. A family in a food desert using a delivery app because there is no grocery store nearby is, by the logic of algorithmic pricing, a high-value extraction target. They have low substitutability. They face high urgency. They show repeat behavior patterns. The algorithm reads all of that.
This is not a regional issue. It is a national one. Clarita Analytics provides the public-interest transparency layer that these communities cannot build themselves and that platforms have every financial incentive to withhold.
36.2M
small businesses in the U.S. — the majority depend on digital platforms whose pricing they cannot audit (SBA, 2025)
33%
of before-tax income spent on food by lowest-income households vs. 6.4% for the highest quintile (USDA ERS, 2024)
51
algorithmic pricing bills introduced across 24 states in the first 7 months of 2025 alone — up from 10 in all of 2024 (Consumer Reports, 2025)
23%
grocery bill inflation documented by Consumer Reports when Instacart showed different prices to different users for identical products (2026)
