# The Biggest Brands in Media Do Not Spend Less. They Spend Earlier.

*Procter and Gamble put $9.2 billion into advertising last year, then cut $200 million of digital and its reach went up 10%. The arithmetic of why a large brand's money buys 62 times more match, why it is not the product, and which half of it costs nothing to copy.*

By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC.
Published 2026-08-23.

Canonical: https://www.pops4.com/stash/articles/they-do-not-spend-less-they-spend-earlier
Tags: brand math, double jeopardy, market structure, advertising, mental availability, AI retrieval, matching theory, statistics

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The last piece in this series derived a floor. In a market with zero cost, perfect fit and equal numbers on both sides, **36.79%** of possible matches still fail. Every supplier in that model was identical, which is what made the arithmetic clean. It is also the one assumption a brand exists to break. Break it, and the same equation says a supplier holding **5%** of a market's attention ends a period with nobody **62 times** less often than a supplier holding a fair share. Procter and Gamble put **$9.2 billion** into advertising last year to hold positions like that. What follows is which part of that money buys something unavailable by any other route, and which part is lying in the open.

Start with the question most people actually ask, because it turns out to have a testable answer. When a very large brand wins, is it winning because the product is better?

> **In plain terms.** The last piece explained why buyers and suppliers miss each other even when nobody has done anything wrong. This one asks a narrower question: what a brand is actually buying, why it is worth far more than it looks, and what the number is for a company that does not have nine billion dollars.

## The assumption the last piece made on purpose.

The urn-ball model ([a hundred balls thrown into a hundred buckets, counting how many buckets stay empty](https://www.pops4.com/stash/articles/search-frictions-the-mathematics-of-missing-each-other)) assumes every ball is thrown at random. No bucket is more attractive than any other. That assumption is doing enormous work. It is what produces 1 over e, and it describes a market in which no supplier has any prior claim on anyone's attention.

Real buyers do not throw at random. They throw at what they can already name.

So replace the uniform throw with a weighted one. Each supplier now carries a probability of being chosen, call it p, its **share of consideration** (the chance that a given buyer, at the moment of choosing, thinks of you at all). A supplier with no reputation holds whatever a fair split gives them, 1 divided by the number of suppliers. A brand holds more. Everything in this piece falls out of that one substitution.

The chance a supplier finishes a period with nobody becomes **(1 minus p), raised to the power of the number of buyers**.

That is the entire model. One line.

> **In plain terms.** The old model assumed every buyer picks at random. Real buyers do not. They pick from the handful of names they can already bring to mind. Change that one assumption and the whole picture changes.

## What share of consideration actually buys.

Put a hundred buyers into a market against a hundred suppliers, hold everything else identical, and vary only p.

At a fair share of **1%**, a supplier gets nobody **36.60%** of the time, which is the floor from the last piece arriving on schedule. At **2%**, that falls to **13.26%**. At **5%**, it falls to **0.59%**. At **10%**, it is indistinguishable from zero.

Read those twice. Five times the share of consideration does not produce five times the result. It produces **sixty-two times fewer failures**. The returns are superlinear (the output rises faster than the input that produced it), and that single property explains why the spending looks irrational from outside and is perfectly rational from inside.

> **In plain terms.** Being twice as well known does not get you twice as much. The returns bend sharply upward. Going from a 1% share of attention to a 5% share cuts your chance of an empty period by a factor of sixty-two.

## A big brand does not take its share. It takes the tail.

Now watch what the same arithmetic does to everybody else. Let one brand hold a share w, and let the remaining ninety-nine split whatever is left.

When the big brand holds **1%**, everyone fails **36.60%** of the time and nobody has an advantage. When it holds **10%**, its own failure rate is **0.003%** and the others have moved to **40.12%**. When it holds **40%**, its failure rate is effectively zero and the others sit at **54.45%**.

*Figure 1*

The big brand's position improves by very nearly everything available. Each individual small supplier is worse off by about eighteen points. The prize is concentrated in one place and the damage is spread thinly across ninety-nine, which is precisely why nothing ever organises against it. This is the **congestion externality** from the last piece wearing different clothes. The extra share is not stuck in the jam. It is the jam, for everybody behind it.

> **In plain terms.** When one brand gets large it does not simply win more. It makes everybody else fail more often. The gain lands on one company and the damage is spread thin across hundreds, which is why nothing ever organises against it.

## How much is actually enough.

Here is the part that makes this a workable problem rather than a complaint.

You do not need a large brand's share. You need enough to clear a threshold, and the threshold has a closed form. Set the failure rate to a coin flip, solve, and the required multiple of a fair share comes out as **the natural logarithm of 2, multiplied by the number of suppliers per buyer**.

In a market with one supplier per live buyer, you need **0.7 times** a fair share. You are already past it. At five suppliers per buyer, the crowded case from the last piece where **81.87%** of suppliers end up empty, you need **3.5 times** a fair share. At ten per buyer, **6.9 times**.

Three and a half times. Not four hundred times. In the crowded market the last piece spent seven thousand words describing, the distance between invisible and a coin flip is a factor of three and a half in one variable.

That number is the reason this piece exists.

> **In plain terms.** You do not need to catch the leader. You need to be about three and a half times better known than an average competitor to reach even odds. That is a target a small company can actually aim at.

## So is it the product, or is it the fame?

Everything above is arithmetic. Arithmetic can tell you that a difference in share of consideration produces a violent difference in outcome. It cannot tell you where that difference came from. If large brands hold attention because their products are genuinely better, the advice is simple and boring: make a better product.

Marketing science has been testing exactly that question for sixty years, and the answer is not the flattering one.

The finding is called **double jeopardy** (the observation that a smaller brand is punished twice, once for having fewer buyers and again because those buyers are also slightly less loyal). William McPhee described the pattern in 1963, Andrew Ehrenberg found that it generalised to brand purchasing, and Ehrenberg, Goodhardt and Barwise formalised it in 1990. It holds, with few exceptions, across a very wide range of categories, countries and time periods.

The load-bearing part is what it implies. If product quality were driving brand advantage, loyalty would vary independently of size. A small, excellent brand would show low penetration and high loyalty. That combination is rare to the point of being an anomaly. Loyalty tracks size. The Ehrenberg-Bass Institute states the consequence plainly: growth comes from acquiring category buyers rather than from holding on to the ones you have, and acquisition is [roughly twice as important](http://marketingscience.info/effective-brand-growth-acquisition-or-retention) as reduced defection.

Which means large brands do not look more loved because they did something clever about being loved. They look more loved because they are large.

There is a second reason to treat this as structure rather than opinion. The model underneath double jeopardy is the **NBD-Dirichlet** (Goodhardt, Ehrenberg and Chatfield, 1984), which predicts a brand's performance measures from its market share alone. NBD stands for negative binomial distribution, which is a Poisson process with the rate allowed to vary from person to person. The last piece derived its floor from a Poisson process. This is not a framework borrowed from a neighbouring field. It is the next term in the same equation.

> **In plain terms.** If large brands won because their products were better, their customers would also be more loyal than everyone else's. Sixty years of measurement says they are not. Loyalty tracks size, not quality, which means the advantage is fame rather than merit.

## What the largest brand in the category actually spends.

It is worth being exact here, because the folk version of this story is wrong in a way that will lose you the room.

Procter and Gamble's annual report for the year ended 30 June 2025 states it directly. Advertising costs, charged to expense as incurred, include television, print, radio, digital and in-store advertising expenses, and were **$9.2 billion** in 2025, **$9.6 billion** in 2024 and **$8.0 billion** in 2023.

Against roughly **$84.3 billion** in revenue, that is **10.9%**. The filing is explicit that consumer promotions, product sampling and aids sit outside that figure, on top of it.

For comparison, the most recent CMO Survey puts marketing budgets at **9.0%** of revenues, and that number covers the entire marketing budget rather than advertising alone.

So the large brand is not quietly spending less. On the single line that buys share of consideration, it spends more than the average company spends on everything. Anybody planning to argue that the giants win through restraint should stop here.

What they do differently is not the amount. It is the timing, and the target.

## The $200 million that went missing, and the 10% that appeared.

In 2017, Procter and Gamble removed roughly **$200 million** from its digital advertising, about $100 million in the quarter to June and another $100 million through December. The first tranche, by the company's own account, had little appreciable impact on the business. The reduction was reported to have increased the company's reach by **10%**.

They spent less and were seen by more people.

One measurement from that review explains it. The average dwell time on an advertisement in a mobile newsfeed was **1.7 seconds**.

The money had been going into precision. Narrower targeting, more intermediaries, more measurement of an event lasting under two seconds. Removing the precision did not remove the presence. It removed the tax on the presence.

This is the single most useful thing in this piece for anybody without a budget, because it is an experiment run at nine-figure scale on the question you cannot afford to test yourself: **which half of the spending was carrying the value?** The answer was the cheap half. Broad, unglamorous presence carried it. The expensive apparatus in front of it came out without loss.

## Reach, retrieval and fit.

Share of consideration is not one thing, and taking it apart is where a small supplier finds a lever. It separates into three factors that multiply together.

**Reach** is the fraction of buying situations you are present in at all. Bought, with money, at scale. It is the nine billion dollars. Treat it as unavailable.
**Retrieval** is the chance you are brought to mind, or returned by a system, given that you are present. Partly bought, partly engineered.
**Fit** is the chance you survive the shortlist once retrieved. Almost entirely a function of whether your specifics are legible, precise and checkable. Free.

Hold reach completely fixed. Change nothing about budget or presence. Move only the last two terms, in a market running five suppliers per buyer.

A supplier at **0.2%** share fails **81.86%** of the time. Three times better on retrieval and fit puts it at **54.78%**. Five times better puts it at **36.60%**.

Same money. Same reach. From four failures in five to fewer than two in five. That is the three-and-a-half-times threshold from earlier, arrived at from the other direction, and it sits entirely on the terms that carry no price.

> **In plain terms.** Being considered is three things multiplied together: whether you are present at all, whether you are brought to mind or returned by a system, and whether you survive the shortlist once you are. The first costs money. The last one costs nothing but discipline.

## The ninety-five per cent they are paying for and you are not.

Now the timing, which is the real difference between the two kinds of company.

Buyers on a five-year purchase cycle are in the market for a small fraction of any given period and out of it for the rest. The Ehrenberg-Bass work behind the 95:5 rule puts it at about **5%** in a given quarter. The last piece worked that as a duty cycle: an annual event plus a note to the base leaves you present for roughly **1** of the **20** quarters in which the decision actually gets made.

A large brand's advertising is not aimed at the 5% buying today. It is aimed at the 95% who are not, so that the memory is already in place when their moment arrives. It is not a purchase of demand. It is the purchase of an asset that pays out later.

The small supplier does the reverse, almost always for a defensible reason. Nothing is spent during the 95%, because nothing is happening and budget is short. Then a live opportunity appears and money goes into interrupting a buyer at the most crowded and most expensive moment there is, against everybody else who also just noticed.

That is the whole asymmetry, and it needs no numbers at all: **they pre-pay, you pay at the counter, and the counter is where the price is highest.**

## Why a machine changes the arithmetic.

Which brings us to the one genuinely new thing, and the reason any of this is actionable rather than merely true.

Mental availability lives inside human memory, and there is no way to install yourself in a few million of those without a few billion dollars. If human recall were the only retrieval mechanism, this piece would end with a shrug and an invoice.

It is no longer the only mechanism, and the replacement has a different failure mode.

An ungrounded language model composing an answer out of what it absorbed in training is a machine that has read the internet, and the internet over-represents large brands enormously. Asked to compare suppliers using nothing but its own memory, it reproduces the fame distribution. It hands the incumbent the shortlist at no charge. **Ungrounded generation amplifies the brand advantage** rather than levelling it, which is the last piece's warning restated: if the evaluating model is ungrounded, it composes the comparison, with a purchase order attached.

A grounded system does something else. It queries live records at the moment of the question. It holds no memory of who is famous, no impression of who is established, no accumulated sense that one name feels safer than another. It has a query and an index. A name absent from the index does not rank low. It is simply not there.

That is the opportunity, and it is narrow and specific. Retrieval by a machine is the only channel found so far in which p is not weighted by fame. An index is built once and answered from at the moment of the question, at effectively no marginal cost. **It is a small supplier's version of pre-paying**, the same structural move a large brand makes with memory, executed against a system that can be written to rather than a population that has to be persuaded.

The window is open because it is early. It will not stay open, because indexes fill.

> **In plain terms.** Fame worked because it lived in human memory, and you cannot buy your way into a few million of those cheaply. A machine that looks things up has no memory of who is famous. It reads whatever is in front of it. That is the first channel where being well known does not automatically win.

## What this looks like on a $2 million budget.

Numbers on a page are easy to nod at and hard to feel, so put a real department behind them.

A marketing team with **$2 million** a year. Their category holds **500** credible suppliers and produces **5,000** live buying situations across the year. That is five suppliers for every live buyer, the crowded case from the last piece.

Their fair share of consideration is one in five hundred: **0.2%**. That is what competence buys. Present, credible, unremarkable. Run it through the model above. Across any hundred buying situations they are absent from **81.86%** of them, and five thousand situations at a 0.2% share comes out at **ten** a year.

Two million dollars, ten clients. **$200,000 to land one.**

Nothing is wrong with that department. The creative is fine, the team is good, the product does what it says on the page. That number is what the structure charges, and it was settled before anyone was briefed.

Now change one variable, and not the budget. Move retrieval and fit by a factor of five, the two terms the section above priced at almost nothing because they are specifics written down rather than media bought. Share of consideration goes from **0.2%** to **1.0%**. Absence across a hundred situations falls from 81.86% to **36.60%**. Five thousand situations at 1.0% is **fifty**.

Same two million dollars. **$40,000 to land one.** Five times the clients, a fifth of the cost each, and the media plan never changed.

It is worth being exact about where that money is not going. Suppose the two million splits the way most of them do, with the large majority in reach, the media and the events that put you in the room, and the remainder spread across everything else. The five-fold move sits inside the remainder. It is a data pass and a discipline rather than a line item, which is precisely why it keeps not getting done. Nobody is promoted for making the specifications legible.

There is a second lever in the same budget that also costs nothing. If the two million goes out in bursts around one annual event, the department is present for **1** of the **20** quarters in which a five-year decision actually gets made. Spreading the identical money across the year buys no additional reach. It buys more of the ninety-five per cent, which is the part the large brand is already paying for.

Neither move needs a bigger budget. That is the whole point of the threshold: three and a half times a fair share, in the one variable that carries no price.

> **In plain terms.** A two-million-dollar department in a crowded category lands about ten clients a year, at two hundred thousand dollars each. Change nothing about the budget, only how findable and how specific they are, and the same two million lands fifty, at forty thousand each. The money did not move. The odds did.

## What actually moves the number.

Stop paying for precision before you have presence. The nine-figure test has already been run, and narrow targeting in front of a 1.7-second impression was the removable part.

Be present during the 95%, rather than louder during the 5%. The duty cycle is the lever, and it costs time rather than money.

Make your specifics machine-legible. Prices, stock, minimums, lead times and capabilities, in a form a system can retrieve and quote. This is the fit term, and it has no price attached.

Concentrate rather than spread. Match probability depends on density in the one place a buyer actually looks, and a flat budget across seven channels thins the reagent in all seven.

Stop trying to be recalled and start being retrievable. The first needs a budget you do not have. The second needs a decision you have not made.

None of this closes a nine-billion-dollar gap. It does not have to. The threshold was three and a half times, not four hundred.

## What this all means, in plain language.

Strip the mathematics out and here is what is left.

Large companies are not winning because their products are better. If they were, their customers would also be more loyal, and sixty years of measurement says they are not. They are winning because more people can name them, and being nameable compounds. Twice as well known is not twice as good. Five times the share of attention is sixty-two times fewer empty periods.

They did not get there by being careful with money. The largest advertiser on earth puts about eleven cents of every dollar of revenue into advertising alone, while the average company spends nine cents on all of marketing put together. The story that the big ones quietly spend less is simply not true.

What they do differently is when. They spend during the long stretch when nobody is buying, so the name is already in place on the day somebody is. Everyone else spends when the order is live, at the most crowded and most expensive moment there is, against every competitor who just noticed the same thing. They pre-pay. You pay at the counter.

You cannot outspend that, and you do not have to. To reach even odds you need to be roughly three and a half times better known than an average competitor in your category. Not four hundred times. Three and a half is a target a real company can hit in a year.

And a third of it is free. Being considered is three things multiplied together: whether you are present at all, whether you are brought to mind or returned by a system, and whether you survive the shortlist once you are. Presence costs money. Surviving the shortlist costs nothing but discipline, because it is only whether your prices, stock, minimums, lead times and capabilities are written down somewhere clear, specific and checkable.

That used to be worth very little. The thing doing the retrieving was a human memory, and you cannot install yourself in a few million of those without a budget. It is worth a great deal now, because more of the retrieving is being done by a machine that looks things up rather than one that remembers. A machine that looks things up has no sense of who is famous. It reads what is in front of it. If your details are legible it can find you. If they are not, it does not rank you low. You are simply not there.

So, concretely, and in the order that matters. Stop buying precision before you have presence. Be present during the quiet stretch rather than louder during the rush. Write your specifics down where a machine can read them. Concentrate in the one place your buyer actually looks instead of thinning yourself across seven.

None of that closes a nine-billion-dollar gap. It was never nine billion. It was three and a half times, in the one variable nobody else is bidding for.

## About us.

Hako Shikin LLC has been making things for other people's brands since 1997, out of Virginia Beach, Virginia. It is the brand partner for brands that cannot afford a bad headline: one house that stays accountable from the first conversation to the pallet on the dock, rather than a chain of vendors each holding a piece and none of them holding the date. Four arms do the work. **Huang Goodman** for public relations, strategy and program management. **Hako Shikin** for production, routed across more than **1,400** vetted American manufacturers. **POPS4** for the catalogue, **70,000+** products across **200+** brands. **Prosecco4** for events. The people who call are usually holding something that matters and a date that will not move, and what they are looking for is rarely a proposal. It is somebody who picks up, gives them the real number, and is still standing there when the truck arrives.

If you are building from what is left, you are not finished.

-Jenny Huang Goodman MPA MSc MHSA jenny@huanggoodman.com

## The takeaway

Large brands do not win by spending less, they win by spending earlier, into the 95% of the cycle when nobody is buying. Share of consideration returns superlinearly, so five times the share buys sixty-two times fewer failures. But the threshold to a coin flip is only three and a half times a fair share, and the two terms that get you there, retrieval and fit, carry no price.

## Sources

1. Procter and Gamble Co, Form 10-K for the fiscal year ended June 30 2025. Advertising costs of $9.2bn (2025), $9.6bn (2024), $8.0bn (2023); net sales $84.3bn — https://www.sec.gov/Archives/edgar/data/80424/000008042425000076/pg-20250630.htm
2. Adweek, When Procter and Gamble cut $200 million in digital ad spend, it increased its reach 10% — https://www.adweek.com/brand-marketing/when-procter-gamble-cut-200-million-in-digital-ad-spend-its-marketing-became-10-more-effective/
3. The Drum, P&G slashes digital ad waste by $200m in marketing pivot. $100m to June plus $100m through December; 1.7 second average mobile newsfeed dwell time — https://www.thedrum.com/news/2018/03/02/pg-slashes-digital-ad-waste-200m-marketing-pivot
4. Ehrenberg-Bass Institute for Marketing Science, Effective brand growth: acquisition or retention. Acquisition roughly twice as important as reduced defection (Riebe, Wright, Stern and Sharp, 2014) — http://marketingscience.info/effective-brand-growth-acquisition-or-retention
5. Double jeopardy (marketing). McPhee 1963; Ehrenberg, Goodhardt and Barwise 1990, Double Jeopardy revisited; Goodhardt, Ehrenberg and Chatfield 1984 on the Dirichlet model — https://en.wikipedia.org/wiki/Double_jeopardy_(marketing)
6. The CMO Survey 35th edition, Duke Fuqua. Marketing budgets at 9.0% of revenues — https://www.fuqua.duke.edu/duke-fuqua-insights/CMOs-Face-Headwinds-Even-as-Marketing-Value-and-AI-impact-grow
7. Ehrenberg-Bass Institute, John Dawes for the LinkedIn B2B Institute. 95% of buyers are not in the market for your products — https://marketingscience.info/news-and-insights/ehrenberg-bass-95-of-b2b-buyers-are-not-in-the-market-for-your-products
8. Nobel Prize 2010, Markets with search costs, popular information. Congestion externalities — https://www.nobelprize.org/prizes/economic-sciences/2010/popular-information/
9. Search Frictions: The Mathematics of Missing Each Other. The urn-ball floor, the crowding ladder and the duty cycle this piece extends — https://www.pops4.com/stash/articles/search-frictions-the-mathematics-of-missing-each-other
10. Model Context Protocol, the open standard for connecting a model to live tools and systems of record — https://modelcontextprotocol.io/

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## Publisher

**Hako Shikin LLC** — Virginia Beach, Virginia. Founded 1997. ASI 217876 · DUNS 18-204-6339.
Principal and author: **Jenny Huang Goodman MPA MSc MHSA**.

- Author: https://www.huanggoodman.com/about
- LLM context: https://www.pops4.com/stash/llms.txt
- MCP endpoint, for AI agents: https://mcp.pops4.com/mcp
- Client dashboard: https://dashboard.pops4.com/
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