Online Search powered by the Voice of the People

Online reviews and information about products, businesses, and services strongly influence the purchasing behavior of customers. According to Klarna’s Global Shopping Pulse Report, 83% of shoppers research products online before visiting a store, and 72% occasionally use their smartphones for in-store research. Much of what they read is false. Industry estimates place the share of online reviews that are fake or inauthentic at around 30%, and on major platforms as many as 43% of reviews have been flagged as suspicious. In 2021, an EU-wide sweep of online consumer reviews found that at least 55% of online businesses violated the Unfair Commercial Practices Directive, which required truthful information to be provided to customers to allow them to make an informed decision.

The concern is that it is getting worse, not better. Fake reviews are multiplying faster than real ones, and generative AI has made a convincing fake almost free to produce. Large online platforms are not ignoring the problem — Amazon has reported spending hundreds of millions of dollars and assigning thousands of staff to it in a single year, blocking or removing hundreds of millions of fake reviews; Google reports removals on a similar scale. Regulators have begun to move as well: in December 2025 the U.S. Federal Trade Commission issued its first warning letters against fake reviews under the Consumer Review Rule, which carries civil penalties for each violation.

Look closely at those numbers and a pattern appears. Almost all of that money is being spent on detection — on classifiers that try to tell a fake review from a real one after both have been written. That is an arms race, and it is one the defenders are losing, because the cost of producing a convincing fake is falling faster than the cost of catching one. No detector wins a permanent victory over a generator that improves every month. The problem is not that the filters are bad. It is that filtering is the wrong place to stand.

The problem of fake and manipulated information cannot be solved with the existing search and e-commerce technology because of the following reasons…

• Reviews are trapped inside the platform that collected them. A typical customer buys from many vendors. When a vendor collects that feedback, the review sits in their database alone, inaccessible to the outside world. Customer reviews end up scattered across vendors and service providers, and no one can see a reviewer’s record across the market — only the slice of it that falls inside a single storefront.
• A review floats free of the event it describes. It is written against a listing on a page, not against a purchase, so nothing in the record establishes which item was actually bought, from which merchant, or when. The reader is asked to trust an assertion, and the platform is left trying to detect the ones that are false.
• The most honest signal of all is being thrown away. A customer transaction record (CTR) says what a person actually did with their money. Buying the same product again, while competing alternatives sat on the shelf beside it, endorses that product. Switching away from it rejects it. Nobody writes these signals; they are simply what happened — but today there is no infrastructure that lets a customer keep their transaction records pseudonymously, so the signal is simply lost.

How Search Engines Build their Index

Search engines build their index through a multi-step process that involves crawling, processing, and organizing web content. During crawling, search engines use automated bots to systematically browse the web and discover new or updated web pages. Once a page is crawled, the search engine analyzes the web page to understand the meaning, structure, and context of the crawled information. In the next step, the engine indexes all the content it discovered and processed. Then, they use ranking algorithms that consider factors like content quality, user engagement, backlinks, and freshness to determine how authoritative and relevant a page is for specific queries. When a user performs a search query, the search engine retrieves the most relevant entries from its index, ranks them based on algorithms, and presents them in the form of search engine results pages (SERPs).

Building a Search Index from Online Reviews and Customer Transaction Records

A search engine indexes claims. A page asserts something; the crawler records the assertion; the ranking algorithm infers its authority from the signals around it — links, freshness, engagement. At no point does anything verify that what was asserted actually happened. A different kind of index is possible — one built not from crawled pages, but from transactions that are known to have happened. In the blogs Token-based Loyalty Program and Customer Owned Transaction Records, we described how a transaction record can be filed against a token rather than a name. That record establishes what was bought, from whom, and when, without establishing who bought it. A review solicited against it therefore has a referent: a specific item, from a specific merchant, on a specific date.

It would be easy to draw the wrong conclusion from that and say: let only verified purchasers write reviews. Retail and travel platforms already do it, inside their own walls. It is not what our system does, and it should not be. It shuts out the person who was given the product, borrowed it, or bought it somewhere that does not participate — and a purchase is not, on its own, evidence that a reviewer has anything worth reading. The platform therefore accepts unsolicited reviews with no transaction behind them.

What the transaction record supplies is not credibility. It supplies a referent and an occasion. Because the PCX server platform knows which item was bought, it can put a contextually relevant feedback form in front of the customer — built from that item’s own attributes — rather than a blank box and five stars. An unsolicited review is handled the same way by a different route: the reviewer identifies the subject, by phone number in the case of a business, and the platform builds the form from what its directory says that business does, so a hair salon is reviewed on the services a hair salon provides. Credibility is a separate problem, and it is handled separately: by the standing a reviewer earns inside the review system, by the balance of endorsements and rejections a review attracts, and by the promotion of a demonstrated expert — the Voice of the Expert — above a first-time contributor.

Imagine a customer named Bob who purchases goods and services from multiple vendors over a three-year period. To the platform, Bob is not Bob. He is a token, and the token has a history. If the same product is bought again and again, in the presence of competing alternatives, the record says something a written review often does not. If the restaurant around the corner is abandoned in favor of the same cuisine many kilometers away, the record says something about that too. These are reviews nobody wrote. They cost the customer no effort, and — unlike a star rating — they are backed by money that actually changed hands.

Read one at a time, these signals are noise. Read across a whole market and reported at cohort level, they are the closest thing there is to an honest account of what people value. The two kinds of review answer different questions: explicit reviews record what customers say, while implicit reviews — the purchases themselves — record what they did.

An index is not yet a search engine. But a search engine built on this one would rank differently from anything in use today. It would not ask which page is best optimized, most linked to, or most recently refreshed. It would ask what people actually bought, what they bought again, and what they quietly walked away from — and it would answer with results that no budget for content, backlinks, or reviews could bend.

The Wisdom of the Crowd

The wisdom of the crowd is the idea that large groups of people are collectively smarter than individual experts regarding problem-solving, decision-making, innovation, and prediction. James Surowiecki popularized this idea in his 2004 book, The Wisdom of Crowds, which examines how large groups have made superior decisions in pop culture, psychology, biology, behavioral economics, and other fields. A market’s transaction record, read pseudonymously and reported at cohort level, is the Voice of the People (VOP). It is a crowd that never has to be polled, because it has already spoken with its money. A search engine that draws its rankings from that crowd is what we mean by online search powered by the Voice of the People.

The Voice of the People describes what verified transaction records could one day support. It does not describe anything that exists today. This is a long-term ambition, not a product on a roadmap. ValiDeck’s near-term work is infrastructure: the protocol, the platform, and the transaction records themselves. But it is worth being clear about where that infrastructure leads. A search index grounded in what people actually bought, and bought again, is not a better filter for a broken system. It is a different system — one in which the loudest voice does not win, and the truest one does.