This document was written by a small independent research company in Finland. We do not disclose any information about us in this document, if you want to know more, there is a newsletter subscription in the end. We have no investors controlling our direction. We have no political affiliations. We have no financial positions in any company or industry we discuss. We wrote this because we believe the information we produce changes how companies, banks, and governments operate and we wanted to explain why in plain language.

We do not want anything from you. We are not asking you to join anything, buy anything, sign anything, or believe anything. We are presenting an argument. If the argument makes sense to you, use it however you want. Share it. Challenge it. Ignore it. Apply it to your own situation.

The only assumption this entire document rests on is that people, when given clear verified information about their own situation, act on it. Not because someone told them to. Because it would be irrational not to. This is not a movement that requires followers. It is a conclusion that anyone can verify with simple mathematics and common sense. We are betting on rationality. Everything else in this document is a logical consequence of that single bet.

We follow this philosophy because we believe it is the right thing to do. You do not need to follow it. Its purpose, among other things, is to ensure that we must deliver a good service and treat our colleagues and the society around us fairly. Every claim we make about how institutions should operate applies to us first. If our data is inaccurate, our philosophy is wrong. If we treat our people poorly, our philosophy is hypocrisy. If we overcharge, our philosophy is a lie. We published this document knowing that it holds us to a standard we can never walk back from. This is so our own clients and people in our society can hold us responsible to act in a way that avoids short sighted decisions making.

Institutions often preserve and justify power through information ordinary people can’t access, verify, or challenge.

Your bank knows your risk profile but you don’t know how they calculated it. Your government makes policy about businesses without measuring what those businesses actually need. Your employer knows what the company earns per employee but you don’t know your actual contribution. Every consulting firm, rating agency, and regulatory body operates on the same principle: they know something you don’t and they charge you for the difference.

This is not a conspiracy. It’s a structure. The structure exists because until now there was no practical way to give everyone the same quality of information. That has changed.

What now exists

An independent Finnish research institution has built a method for measuring what any organisation’s customers, employees, or citizens actually think. Not through online surveys with 4% response rates that nobody takes seriously. Through structured phone conversations with trained interviewers. Five minutes per call. Every call recorded and timestamped. Every finding traceable to a specific person on a specific day.

Response rates consistently exceed 50% and in some cases go up to 80%. The statistical model explains above 50% of why people recommend or don’t recommend an organisation. For context, most existing measurement tools are happy with 10-15%. The methodology is grounded in two academic theses. The audit trail is complete.

After roughly two years of projects across industries, the finding is always the same: basic service quality predicts everything. Does someone answer when you call. Does the same person show up. Do they do what they promised. Do they tell you when something changes. Organisations that do these things consistently score 9 out of 10. Organisations that don’t score 5 or 6 and spend millions trying to figure out why. The answer was always basic. Nobody measured it properly until now.

How it works

An accounting company has 300 customers. The managing partner believes customers stay because of technical expertise. We call all 300. 160 answer. 53% response rate. Every call recorded and timestamped. This is not cold calling, it’s professional outreach in a controlled and courteous manner and the value proposition for the person answering is explained later.

The call sounds like this: “Hi, this is a researcher calling on behalf of [company]. Their managing partner sent you an email about this call. We’d like to ask a few questions about the service you receive. It takes about five minutes.” Also an SMS is sent when we call explaining the purpose and length of the call. This straightforward approach has led to extremely high response and completion rates.

We measure four service areas on a scale of 1-5 disagree/agree or bad/good. Response time when you need them. Problem-solving ability. Quality of interaction with your contact person. And errorlessness of the bookkeeping, stated as: “Errors in accounting haven’t caused unnecessary extra work during the last 12 months.”

Then open questions. What do you like about the service? “Anna. She knows our business. I don’t have to explain our cost structure every quarter.” What would you like to see changed? “When I send an email about a tax question, sometimes it takes a week to hear back. I’ve started calling instead because at least someone picks up.” Finally: on a scale of 0-10, how likely would you recommend them? 7.

Multiply that by 160 conversations. The data across all respondents shows: interaction with contact person scores 4.5 out of 5. Errorlessness scores 4.3. Problem-solving scores 4.1. Response time scores 2.8.

The statistical analysis shows that response time has the strongest weight on whether customers recommend the company. Not errorlessness. Not expertise. Not the personal relationship. It explains more of the variation in recommendation scores than all other drivers combined. Recommendation score (sometimes also named NPS) is one of the most widely used customer metrics in the world and its ability to predict retention is supported by research and is widely adopted in many industries.

The managing partner thought customers stayed because of technical expertise. The data says customers stay despite slow response time because Anna is excellent. But three customers in the open feedback mentioned they were already talking to other firms. Not because of errors. Because nobody got back to them fast enough.

The fix: a policy that every email gets acknowledged within 4 hours and answered within 48 hours. Cost: zero. It’s a decision, not an investment. The three customers considering leaving represent roughly 45,000 euros in annual revenue. The measurement cost 14,000 euros.

One measurement. One finding. One fix. Zero cost to implement. Verified at the next annual measurement. That is what the data produces. Not opinions about strategy. A specific, actionable, verifiable finding from the people whose money keeps the company alive.

To understand why this matters, consider what a typical company actually spends money on. An accounting firm with 300 customers and 3 million euros in annual revenue might spend 40,000 euros per year on marketing to attract new clients, 25,000 on software licenses, 15,000 on compliance and regulatory reporting, 8,000 on a website nobody visits, and occasionally 50,000 to 200,000 on a consulting engagement to figure out why growth has stalled. Meanwhile acquiring a single new customer might cost 2,000 to 5,000 euros in sales effort and onboarding time. Losing a customer that pays 15,000 per year means you need to spend 2,000 to 5,000 just to replace the revenue you already had. Most companies spend more money trying to find new customers than understanding why existing ones stay or leave. A 14,000 euro measurement that prevents three customers from leaving protects 45,000 euros in annual revenue and eliminates the 6,000 to 15,000 it would cost to replace them. That is not a cost. The return on investment is pretty much obvious. Savings can be then directly distributed into things like employee salaries and healthcare. Last thing where they should be spent on is leadership compensation packages because they argued a year ago that salary increase is not possible at the moment (while the CEO drives a leased 95000€ Mercedes). This sounds like a joke but anyone in business knows that this happens all the time.

How do we know the analysis is accurate? Because every input is auditable. The customer list is documented. The contact attempts are logged. The response rate is published. Every conversation is recorded with a timestamp. The questions are standardised across all projects. The statistical model is applied the same way every time regardless of what the data shows. We do not search for patterns that support a preferred conclusion. We run the analysis once and report what it says. If the dominant driver is something embarrassing or unexpected, that is what the report says. The methodology does not have an opinion. It has a process. The process is the same whether the client is a cleaning company or an accounting firm. The findings are replicable because the method is fixed. Anyone with access to the recordings, the data, and the model can reproduce the result. That is what separates measurement from guessing.

Pricing model

The cost of a measurement is proportional to the size of the customer base and the value it protects. For any company, it represents a fraction of the annual revenue. The methodology is the same regardless of company size. The rigour is the same. The audit trail is the same. We believe that if the most important business information a company can receive costs more than a fraction of a percent of their revenue, someone in the chain is overcharging. This applies to us as much as it applies to anyone else.

What to look for if someone offers you customer measurement

Are the interviews conducted by phone with a live person or is it an online form? Phone conversations produce honest, detailed responses. Online forms produce checkbox data with single-digit response rates that nobody takes seriously.

Is every conversation recorded and timestamped? If you cannot listen to the conversation that produced a specific finding, the finding is not verifiable. It is an interpretation, not evidence.

Did the company’s leadership send an advance email to customers before the calls began? Without this step, the respondent has no way to verify who is calling or why. With it, the customer knows the call is legitimate and the response quality improves dramatically.

What is the response rate? Below 40% the data represents a self-selected minority, not the actual customer base. Above 60% you are hearing from the majority. The difference between the two is the difference between anecdote and evidence.

Does the analysis identify which specific driver predicts the outcome and with what weight? Or does it just report average scores? A satisfaction score without a weighted driver analysis tells you the temperature but not which window is open. The driver and its weight are where the actionable value is.

Is there a guarantee? If the provider is confident in their methodology, they should be willing to not charge if the data quality is insufficient for reliable analysis. If they are not willing, ask yourself why.

Who benefits and why

The customer who answers the call.

For possibly the first time, someone with no agenda asks them how the service is going and genuinely listens. Five minutes where their experience matters. Not a feedback form that disappears. A recorded conversation that becomes part of an auditable dataset. Their words directly influence how the company operates. They said “nobody calls me back” and that becomes the number one priority. That is dignity through being heard.

The company being measured.

The chaos gets sorted into a neat pile. One dominant driver. Clear priorities. No more guessing what matters. The CEO reads the report and knows exactly what to fix. The fix almost always costs nothing. Investment goes to the right place. Prices can be set with confidence. Risk becomes visible before it becomes a problem. The company gets calmer because uncertainty is replaced by knowledge.

The employees of that company.

Their contribution becomes visible. If the dominant driver is response time, the person answering phones is now visibly the most important employee in the company. If it’s staff continuity, the person who has been there for ten years finally has data proving why they matter. Compensation, recognition, and career development can be based on verified contribution instead of subjective opinion. And when the company knows what to focus on, the employees stop doing unnecessary work. They go home at 16 knowing they did the thing that actually matters.

Our interviewers.

They are not telemarketers. They are researchers conducting structured conversations that produce institutional-grade evidence. Every call they make directly changes how a company operates. They hear the truth before anyone else and they know their work has impact because the report proves it. That is meaningful work.

Our analysts.

They work with data that is cleaner and more honest than anything available in their field. Response rates that academics dream of. Verified identities. Recorded sources. They build tools and models against data that standard software was not designed for because the data is too good. That is the kind of problem a researcher wants to have.

The bank.

The bank gets the one variable its risk model is missing: verified evidence about whether the borrower’s customers are staying. Better risk data means fewer defaults. Fewer defaults means better portfolio performance. The bank that uses this data prices risk more accurately than any bank that doesn’t. That is a competitive advantage built on truth rather than financial engineering.

The government.

For the first time, policy makers can see the actual health of the economy at the level where it is produced: individual companies serving individual customers. Not lagging indicators. Not aggregated averages. Verified data from verified conversations showing which sectors are healthy, which are deteriorating, and why. Policy designed from this data is surgical instead of ideological.

The citizen.

The person who never had access to institutional-grade economic information now has it. They can see whether their employer’s customers are happy. They can see whether their government’s services work. They can ask their bank why their rate doesn’t reflect their company’s verified performance. They have the same quality of evidence that was previously available only to institutions with large budgets. That is information equality. That is the point.

The logical conclusions

If verified data shows what citizens and businesses actually need, then policy decisions should be based on that data. Any public official who refuses to base decisions on verified evidence while claiming to serve the public interest is either incompetent or corrupt. Both deserve scrutiny.

If the job of a public official becomes executing against verified evidence rather than making judgment calls under uncertainty, then the compensation for that job should reflect what it actually is: senior operational management. Not visionary leadership. Operational management has a market rate. It’s approximately 150,000 euros per year with reasonable benefits. Any compensation above that is a premium for uncertainty that data has removed.

If banks can access verified customer data for their borrowers, then risk can be priced accurately for the first time. The well-run business gets better terms because the data proves it deserves them. The poorly-run business gets honest pricing instead of being hidden in an industry average. Banking becomes what it should be: a utility that holds money, moves money, and lends money at fair rates. Not a gatekeeper that profits from knowing more than the people it serves.

If a company’s customers have already said what matters, then compliance departments designed to ensure good behaviour are redundant for companies already demonstrating good behaviour through verified measurement. The measurement is the compliance. Exempt verified companies. Scrutinise unverified ones.

If consulting firms charge hundreds of thousands of euros to diagnose what a 14,000 euro measurement reveals in six weeks directly from the people who actually know, then the consulting industry’s value proposition has expired for every problem that customer data addresses. Which is most of them.

Industries that information equality makes obsolete or reforms

Sales.

Nobody wants to be sold to. The entire sales profession exists because the buyer lacks information and the salesperson fills the gap with persuasion. Remove the gap and the persuasion has no function. In a world where verified customer data is available, selling becomes presenting. The company with a 9.4 score from 160 verified customers does not need a salesperson to convince you. The data convinces you. Stop telling prospects what you can do. Show them what your customers said you already do. Ask yourself one question: what do you really want your prospects to know about your company? Whatever your answer is, say that. Nothing else. If it is true, the measurement will confirm it.

Consulting.

A consulting engagement exists to diagnose a problem the client cannot see from the inside. When verified customer data reveals the same diagnosis in six weeks for a fraction of the cost, the traditional engagement loses its purpose. Consulting will not disappear entirely. Genuine strategic problems that customer data cannot address will still require expertise. But the diagnostic phase, which is where most consulting revenue comes from, is replaced by a phone call. The companies still hiring consultants to tell them what their customers think are paying for an intermediary between themselves and a truth that is one conversation away.

Compliance.

Compliance departments exist because regulators cannot tell which companies behave well and which do not. So they impose rules on everyone and hire people to check whether the rules are followed. The rules produce paperwork. The paperwork produces overhead. The overhead produces cost. The cost is passed to the customer. Meanwhile, Volkswagen had ten compliance officers when it committed the largest corporate fraud in automotive history. Compliance does not prevent bad behaviour. It documents the appearance of good behaviour. Verified customer and employee data actually measures whether the company operates with integrity. The measurement is the compliance. Exempt verified companies. Scrutinise unverified ones.

Rating agencies.

A rating agency sells an opinion about an organisation’s quality or creditworthiness. The opinion is based on the agency’s analysis, not on verified data from the people affected. When verified measurement data exists with a complete audit trail, the opinion becomes redundant. The measurement is more accurate, more transparent, and more accountable than any rating. The rating agency’s business model depends on being the only source of quality assessment. When the assessed organisation’s own customers can provide a verified assessment, the monopoly on judgment disappears.

Market research.

Traditional market research surveys thousands of people with low response rates, self-selected samples, and no audit trail. The findings are statistical generalisations that cannot be traced to any specific person or conversation. Verified phone-based measurement with high response rates, recorded conversations, and traceable findings produces more accurate data from fewer people at lower cost. Market research will not disappear but its methodology will be forced to meet a standard of accountability it has never been held to.

Corporate training and HR.

Companies spend billions on employee development programmes, leadership training, team building, and performance management systems. Most of these exist because nobody measured what actually drives employee performance from the customer’s perspective. When the data shows that customer retention depends on one person answering the phone consistently, the company does not need a leadership workshop. It needs to make sure that person is paid well, supported, and not pulled into meetings that prevent them from doing the one thing that matters. Training budgets should follow verified findings, not generic competency frameworks designed by people who have never spoken to a customer.

Why this is different and why now

Most customer surveys measure an outcome. Your satisfaction score is 7.2. That number tells you the temperature. It does not tell you which window is open. Some surveys measure drivers, but the drivers are chosen by whoever designed the survey. If the actual driver is something nobody thought to include, it stays invisible. The company asks about digital experience because that is the current trend. The data shows digital experience scores 4.5. Everyone feels good. Meanwhile the actual reason customers are leaving — nobody returns their calls — was never measured because it was not on the survey.

Our statistical model does not assume which drivers matter. It identifies which drivers predict the outcome and with what weight, derived from the data itself. The result is not a score. It is a specific finding: this one factor explains more of the variation than everything else combined. Fix this first.

And the outcome that everyone else measures is already in the past. Financial statements, satisfaction scores, churn rates — all of them tell you what already happened. By the time a problem appears in the financials, the customers who caused it have already left.

Here is what that looks like in practice. A bank analyst opens a company’s balance sheet. Revenue 3 million euros, up from 2.8 million. Margins stable. Cash flow positive. No missed payments. The analyst checks ratios against industry benchmarks. Everything looks healthy. Loan approved at standard terms.

What the analyst cannot see: three of the company’s biggest customers scored them 5 out of 10 last month. They account for 40% of revenue. Two of them mentioned in a recorded phone call that they are already talking to competitors. Not because of price or quality. Because nobody returns their calls.

None of this appears in any financial document. The balance sheet will look healthy for another 6 to 12 months while those customers finish their contracts and leave. By the time the revenue decline shows up in next year’s statements, the customers are gone and the bank’s healthy loan is at risk. The bank approved the loan looking at yesterday’s numbers while tomorrow’s numbers were already deteriorating. Not because the bank is incompetent. Because the information about tomorrow does not exist in any financial document. It exists only in the heads of the customers who have already decided to leave. We measure what is in their heads. That is the difference.

If this is so obvious, why hasn’t it been done before? The tools have always existed. Phones exist. Statistics exist. Academic research on survey methodology exists. But what passes for customer research even at the largest companies in the world is remarkably poor. We have seen major corporations ask their customers “would you continue with this vendor next year” and in the same interview ask the recommendation question. Both produce nearly identical percentages. Then someone has to explain who the 3% are who would not recommend the company but plan to keep paying them. Nobody can explain it because the questions were not designed to produce insight. They were designed to produce a number for a slide deck. The tools exist. The rigour never did. Not for large corporations. Not for small ones. Not anywhere. We built the rigour from the ground up using academic foundations that existed but were never applied to this specific problem with this level of discipline.

We made the economics work by keeping the operation lean and the methodology focused. We do not need 50 consultants and an office in London. We need trained interviewers, a good analyst, and a phone.

And to the question of why anyone should believe data we have not published: we cannot publish client data because the data belongs to the client. Averages and aggregated data are always inspectable to anyone and we heavily encourage our clients to publish main findings and numerical averages on their website somewhere. That creates information equality as we call it. Their customer relationships are their business. But the methodology and scores are inspectable. The statistical approach is grounded in published academic work. The audit trail exists for every project. We guarantee results, meaning we will call your customers and record each conversation so you can be 100% certain that contract was honored. We will publish aggregate data and methodology in coming months, but right now any competent statistician can extrapolate our methods from this document and verify them. We are not asking you to believe our data. We are asking you to evaluate the logic. If the logic holds, try it once. The data will convince you. That is what it is for.

Example: the bank that information equality makes possible

A local mobile-only bank that does three things. Holds money. Moves money. Lends money. No trading desk. No advisory services. No complex financial products. No branches with marble floors. An app, a compliance framework, and a lending model built on verified customer data. Call it a utility bank. Because that is what banking should be. A utility.

A traditional bank prices your loan from financial statements that show what already happened. The utility bank prices your loan from verified customer data that shows what is going to happen. You provide an independently verified measurement of your customer base. The bank sees your customer stability score, your dominant retention driver, and your risk profile as described by the people whose decisions determine your future revenue. The lending decision takes hours, not weeks. Because the data is already verified and auditable.

The cost to run this bank is a fraction of a traditional one. No trading desk means no traders. No advisory means no advisors. No branches means no high street rent. No complex products means no product development team. The entire operation is a compliance team that pre-clears customers at onboarding, a development team that maintains the app, a lending team that reads verified data and approves loans, and a support team that answers the phone. The CEO earns around 150,000 euros per year and works six hours a day. Because that is how long it takes to run a bank that only does three things well.

For thousands of SME owners across Europe this is not even a decision. Their current bank charges fees that fund activities they never asked for. Their current bank prices loans from incomplete data and calls it risk management. Their current bank’s CEO earns millions from the complexity that the SME owner’s fees fund. The utility bank charges a flat monthly fee, prices lending from verified data, and does not risk deposits on anything. The SME owner pays less, gets better terms, and sleeps better knowing their operating capital is not funding a trading desk or a CEO’s compensation package.

This can happen fast. A banking license in Finland doesn’t take that long. The technology for a mobile-only bank is off the shelf. The compliance framework is well documented. The lending model is built on data that can be produced in four to eight weeks per company. A team of 15 people could launch this bank within a year. The first customers are companies that already have verified customer data ready to present on day one.

The comparison is simple. A traditional bank has 10,000 employees, a CEO earning 5 million euros, a trading desk gambling with deposits, lending priced from backward-looking data, unexplainable fees, a building in the city center, and a compliance department with 200 people monitoring complexity the bank created for itself.

The utility bank has 15 employees, a CEO earning 150,000 euros, deposits that are never touched, lending priced from verified customer data, a flat monthly fee, a normal office, and compliance handled at onboarding because the bank does not do anything that requires ongoing monitoring. Same three functions. Hold money. Move money. Lend money. One costs billions to operate. The other costs a few million. The difference is everything the SME owner was paying for without knowing it.

The utility bank also serves private citizens. The cost per customer in a mobile-only bank is so low that basic personal banking can be offered for a few euros per month. Instant transfers. A debit card. A savings account. Simple mortgage. A support line that someone answers. No investment products pushed at you. No insurance cross-sells. No premium account upsells. For the vast majority of people who just want their money held safely and moved easily, this is everything they need and nothing they don’t. And the connection to corporate clients creates a natural advantage: if your employer is a verified corporate client with stable customer data, the bank already has evidence that your job is secure. That means cheaper personal credit for you. Not because of your employer’s brand or size. Because verified data shows the company’s customers are staying, the revenue is stable, and your position is supported by measured demand. Traditional banks give better terms to employees of large corporations based on name recognition. The utility bank gives better terms based on evidence. That is the difference between assumption-based banking and information-based banking.

Investing is handled separately. On purpose. The institution that holds your operating capital should never be the same institution that takes risk with other money. A utility bank holds and moves and lends. If you want to invest, you go to an independent investment advisor or asset manager who earns fees based on performance, not on selling you their own products. This separation protects you twice. Your deposits are never exposed to investment risk because the bank does not invest. Your investment advice is never conflicted because the advisor does not hold your deposits and has no incentive to push products that benefit their balance sheet. The traditional bank bundles these functions because bundling creates cross-selling opportunities and makes it harder for you to leave. The utility bank unbundles them because unbundling is safer for you and cheaper for everyone. Each vendor does one thing. Each vendor is accountable for that one thing. Each vendor is replaceable if they stop performing. That is how every other industry works. Banking should be no different.

THIS IS NOT A HARD THING TO DO. Start a bank and you’ll get a queue of customers waiting to buy from you because they are fed up paying for nothing to some foreign investment bank that competes with local banks.

Opportunities in other industries

The same logic applies everywhere a service provider charges more than the service costs because the client lacks information to challenge the pricing. Accounting firms that charge by the hour instead of a flat percentage of turnover. Insurance companies that price premiums from industry averages instead of verified company-specific data. Recruitment firms that match candidates without measuring whether existing employees are satisfied and why. Legal services that bill for complexity instead of fixed fees per outcome. IT service providers whose response times are measured by internal metrics that nobody compares to what the customer actually experiences. In every case the pattern is the same: add verified measurement, price from the data, publish the results, and let the market reward the companies that perform. Anyone can start one of these businesses. The methodology is public domain. The data is producible in weeks. The competitive advantage is simply being willing to be measured when your competitors are not.

What anyone can do today

Ask questions at work and political forums. Strong arm politicians and irresponsible business leaders into admitting that they are basically guessing and misallocating resources leading to current unequal society where people pollute the environment with 300 million euro boats when at the same time a family in Hamburg has to think about every purchase they make so they can pay their rent to those who drive the boats.

For your boss:

“How do we know our customers are happy? Not what sales says. What the customers say. Do we have that data? Have we ever asked them directly?”

“We’re spending 200,000 euros on this CRM system. What evidence do we have that it improves customer retention? Did anyone ask the customers whether the thing we’re fixing is even a problem for them?”

“We lost three clients last year. Do we know why? Not why sales thinks we lost them. Did anyone call them and ask?”

“We’re hiring a consultant to review our strategy. What will they know about our customers that we couldn’t find out by calling 50 of them ourselves?”

“Our compliance department has 6 people. What measurable outcome do they produce? If we removed them tomorrow, what specifically would go wrong that isn’t already going wrong?”

“We’re about to raise prices. Do we know whether price is the reason customers stay with us? Or are we assuming?”

For your politician:

“You say you support small businesses. What is the average customer satisfaction score of SMEs in this country? You don’t know? Why not? You’re making policy about them.”

“This employment office has had a budget of 5 million euros for ten years. Has anyone ever asked the people who use it whether it works? Not a feedback form. Actually called them and asked.”

“You spent 2 billion euros on SME support programs last year. How many of those companies measured whether their customers are staying? Zero? So you’re supporting companies without knowing if they have customers who will keep paying them?”

“The library in my district works brilliantly. The employment office doesn’t. Both are publicly funded. Have you ever measured why one works and the other doesn’t? Using the same method?”

“You’re proposing new regulation for my industry. Did you ask anyone in my industry what their actual problem is? Not the trade association. The actual companies. Their actual customers.”

“You say this policy is evidence-based. Show me the evidence. Not a report written by the ministry about itself. Independent evidence from the people the policy affects.”

What any company can do right now without us

Before any measurement happens, there is one test you can do today that costs nothing and tells you more than you expect. Try to produce a complete list of your customers with contact persons, phone numbers, and a rough indication of how important each customer is to your revenue. If you can do this in an hour, you are better organized than most companies we have worked with. If you cannot, that is your first finding. A company that does not know who its customers are, how to reach them, and who is responsible for each relationship has already identified its biggest structural risk. No measurement needed. Fix that first.

Before you pay anyone to measure your customer experience, look at your own operations honestly. Do you answer the phone when customers call? Does the same person handle the same accounts consistently? When something changes, do you tell the customer before they find out themselves? When something goes wrong, do you fix it and communicate the fix? These are not strategic questions. They are operational basics. In every company we have measured, these basics explain the majority of why customers stay or leave. If you know any of these are broken, fix them now. You do not need data to tell you to answer your phone.

If you have people in roles like customer experience director, customer success manager, or chief customer officer, ask yourself honestly what they produce that your frontline employees and one annual measurement would not. In most companies these roles exist because nobody had direct customer data, so someone was hired to guess what customers think and build programs around those guesses. When verified data exists, the guessing role becomes unnecessary. Your frontline people are not stupid. They know what customers want because they talk to them every day. They just need the data to confirm what they already sense and the authority to act on it. Give them both. Remove the layers between the customer’s voice and the person who can do something about it.

When your customer list is clean, your basics are working, and your organization is simple enough that findings can be acted on without passing through five layers of management, you are ready for a measurement. Not before. Fix the obvious problems for free rather than paying someone to point them out. When you want to see the things you cannot see from the inside, subscribe below. We will share contact information and updates as the methodology becomes available in more markets.

Would you like to live in a world that works like this?

Independent organizations ask people directly how they feel about the services they use, the companies they work for, and the institutions that govern them. The answers are verified, published, and available to everyone equally.

Here is what changes for you as a regular citizen:

Your bank can no longer charge you more than your verified business performance justifies. You walk in with data. They price accordingly. The negotiation is based on evidence, not on their assessment of your risk that you were never allowed to see.

Your employer can no longer claim your contribution is worth less than it is. The data shows which roles drive customer retention. If your work is the reason customers stay, that is now visible and documented.

Your government can no longer spend your taxes on programs that don’t work without that failure being measured and published. The employment office that scores 3.8 while the library scores 9.1 has to explain why.

The companies around you get better because they know what their customers actually want. Prices drop because waste disappears. Service improves because effort goes to the right place. Local businesses become stronger because verified quality beats marketing budgets.

You stop paying for institutions that exist only because nobody measured whether they were needed. The consultant who charges 500,000 euros to tell a company what its customers would say for free. The compliance department that documents documentation. The rating agency that sells opinions when measurements exist.

Your park is nice. Your bus runs on time. Your health center answers when you call. Not because a politician promised it. Because someone measured it, published the results, and the institution had to respond. That is information equality. That is the world we are building. One phone call at a time.

As this methodology becomes familiar, something important happens. Response rates climb and data quality improves because people understand the context. The phone call stops being an interruption and becomes a five-minute contribution to a system that directly benefits everyone involved. You answer because you know your answers shaped the service last year and will shape it again. You are specific because you know someone will actually read what you said. You are honest because you have seen what honesty produces. Most companies have perhaps ten to twenty service relationships. Five minutes per call once a year means less than two hours annually to participate in a system that keeps every vendor around you accountable and improving. That is not a survey. That is maintenance of the invisible infrastructure that makes your professional life work.

The roadmap

This does not require billions. It does not require political power. It does not require anyone’s permission. It requires a few years, a couple of million euros, and maybe a good civil rights attorney or civil rights organization.

Year one: prove it beyond question.

Scale from initial projects to hundrends across industries and countries. Same methodology. Same findings. Same audit trail. At 200 verified measurements with consistent results, the evidence base becomes impossible to dismiss. Publish aggregate data showing sector-level patterns in SME customer health. This dataset will be the first of its kind in Europe.

Year two: spread the methodology.

Train 20 independent operators across Europe through a free academy. Each one measures local companies using the same methodology. The network produces comparable data across countries. A company in Portugal is measured by the same standard as one in Finland. The methodology is public domain. Anyone can practice it. We train people who want to do it with rigour.

Year three: measure the institutions.

Apply the same methodology to government services, public institutions, and banking. Measure citizen satisfaction with the same rigour used for private companies. Publish everything. The employment office scored 3.8. The library scored 9.1. The data is public. The question asks itself.

Year four: enforce accountability.

Partner with a civil rights attorney or civil rights organization if needed. Identify one government service that has been funded for years with measurably poor outcomes verified by citizen data. File the first case establishing that verified measurement data creates accountability obligations for public institutions. The legal precedent changes everything because it means ignoring verified evidence is no longer just bad policy. It is actionable.

Year five: the data speaks for itself.

Thousands of companies measured. Hundreds of government services evaluated. Aggregate data covering multiple countries. Banks incorporating verified customer data into lending decisions. Policy makers citing the dataset. Citizens asking “how do you know” as a matter of habit. The philosophy is no longer a proposal. It is infrastructure. At that point I will move away from the operational side and live a fairly modest life somewhere in Europe and you won’t hear much from me. Because a system built on verified truth does not need a leader, it just needs to repeat forever.

The falsification challenge

We openly state the conditions under which this philosophy is wrong. If any of the following can be demonstrated, the entire argument collapses and we will say so publicly.

That customers systematically lie when asked simple questions about their service experience by a neutral third party. Not that individuals sometimes exaggerate or forget. That the aggregate pattern across hundreds of conversations is so unreliable that it cannot be acted on. This would contradict the foundational assumption of every court testimony, every medical diagnosis, and every journalistic interview in history.

That a company which identifies its weakest service driver and fixes it does not improve at the next measurement. That acting on verified customer feedback produces no measurable change. This would mean that customers report problems that don’t exist or that fixing reported problems doesn’t affect satisfaction. Neither is plausible.

That better information about customer relationships does not improve business decisions. That a CEO who knows exactly what keeps their customers makes the same decisions as one who guesses. This would contradict the entire basis of evidence-based decision making in every field from medicine to engineering to finance.

That institutions should maintain power and pricing advantages based on information their clients do not have access to, even when that information is producible at minimal cost. This is not an empirical claim. It is a values position. If someone holds it, we simply disagree.

Why this system sustains itself: a note on incentives

An interesting property of this system is that no participant benefits from leaving it or undermining it. This is worth explaining because most systems require enforcement to keep participants honest.

The company benefits from measuring because the data improves their decisions. If they stop measuring, they go blind again. There is no advantage to stopping.

The customer benefits from answering honestly because their answers directly improve the service they receive. If they stop answering or answer dishonestly, the service stops improving. There is no advantage to dishonesty.

The measurement provider benefits from accuracy because accuracy is the only thing they sell. If the data is corrupted, the product is worthless. There is no advantage to corruption.

Each participant’s best move is to participate honestly, regardless of what the other participants do. This is true for the company even if some customers are dishonest. It is true for the customer even if the company is slow to act. It is true for the measurement provider regardless of either.

In game theory, a situation where every participant’s best individual strategy also produces the best collective outcome is considered the strongest form of what is called a Nash equilibrium, named after mathematician John Nash. These situations are rare because most systems create at least one incentive to cheat, free-ride, or defect.

We believe this system has this property. We are not presenting a formal mathematical proof. We are observing that after roughly two years of operation, no participant has had an incentive to defect, and the logical structure suggests none will. We invite anyone with expertise in game theory to examine this claim and challenge it.

This document was written with Claude, an AI made by Anthropic. I’m stating this because I believe in transparency and because it doesn’t matter. The argument is the argument regardless of who or what typed it. If you disagree, point to the specific claim you think is wrong. The falsification section is there for exactly that purpose.

I’m a 40 years old startup founder. I live in a small apartment in Finland. I started a research company that measures what organisations’ customers actually think. The data is real. The findings are consistent. The philosophy is public domain. Anyone can do what we do. We just happen to do it with academic rigour and a money-back guarantee.

I’m exhausted dealing with institutions and people that can’t justify their own existence or position with data. EU policies for example should be reformed immediately because the math doesn’t support the current arrangement and public funds deserve the same accountability as private ones. If this doesn’t happen, we can deem the whole union basically corrupt and I’m sure some civil liberties lawyer might be interested in that. I personally do not have any investments in any company and I am not affiliated with any known political party or government organization or agency. I see many current models and institutions extremely inefficient, misaligned with what common people want and refuse to engage with them. I just want things to be efficient and follow common sense and be left alone and live my life in solitude without the society and world around me going down the drain because of uninformed, short sighted and ego driven decision making.

Thank you for reading.

PS: private jets are the epitome of poor decision making and inefficiency and they need to go. If your company is so badly optimized that your leadership needs to fly around the world in an airplane that is nothing more than a comically huge drain on resources to save few hours, you are doing a lot of things wrong. Most business is simple if you just ask what your customers want. You are not solving some vexing business problem that requires your CEO to be physically present on three continents this week and present himself as visionary leader. Most meetings can be done remotely. Most decisions can be made from data. Most travel exists because the information to make the decision was never available in the first place.

If your CEO has to work 80 hours a week and shave off hours by flying in a private jet, no rational customer should be paying you anything because your resource allocation is so fundamentally broken that the cost of that jet is being passed to people who never asked for it and never benefit from it. A company whose leadership needs a private jet to function is a company that has not asked its customers what they actually want. If it had, most of that travel would not exist. Companies justifying private jets literally sell stuff like sugar water and toothpaste to consumers or basic equipment to businesses and their finance department has to make a complex calculation if they raise their prices by 1 cent.

A corporate jet is not a tool. It is not a time saver. It is a symptom. A symptom of a company operating on poor information and massive egos. A private jet emits roughly 2 tonnes of CO2 per flight hour. At 100 hours per year that’s 200 tonnes. The average European citizen produces about 6 tonnes per year. One corporate jet produces the annual emissions of roughly 33 people. For one person’s travel convenience.

Here is the math. A corporate jet costs roughly 1.5 million euros per year to operate. It flies on average less than 100 hours annually, meaning it sits idle 97% of the time. A round trip from Helsinki to London costs approximately 40,000 euros by private jet. The same trip in first class for five executives costs approximately 11,000 euros, including lounge access, flat bed seats, priority boarding, and a car at the gate. The jet saved maybe three hours. The company paid 29,000 euros for those three hours. That is roughly 10,000 euros per hour saved. For that to make business sense, each executive would need to produce 2,000 euros of value per hour that they could not produce from a first class seat with wifi. Nobody does. And if someone in your company is so critical that three hours of their time is worth 29,000 euros, then your company has a personnel dependency so severe that our measurement would flag it as a structural risk on page one of the report.

Just be honest. If the jet exists because your leadership enjoys flying privately, say so. Do not call it a business tool. Do not call it efficiency. Do not wrap personal preference in financial justification that falls apart under basic arithmetic. Your customers are paying for that jet through prices that include overhead they never asked for. The data will show the difference between companies that spend on what customers value and companies that spend on what leadership values. We already know which ones score higher. And then these people argue stuff like that minimum wage shouldn’t be raised by 5 dollars or so because some ideological macroeconomic factor from 50 years ago they learned in Harvard tells them so, I feel this is incredibly close minded and narrow thinking which tells something about a person.