The average time on site and bounce rates I have got from advertising my software inside ChatGPT and Reddit are so terrible that I can only assume that most of the clicks I am paying for are fraudulent, although it is not clear who is doing the fraud.
This has been known a long time. Freakanomics did a two part podcast on does advertising actually work. It is long but well worth a listen if you want to hear some science about advertising.
A lot of the time you are running experiments and seeing how the data changes, not looking at the data in a vacuum.
Many of the example critiques here don't apply so much when looking at the changes in data:
- if I got 50% more hits on my site this week vs last week, that's meaningful despite 36% of people blocking ads
- if my open rate doubled when I changed my email subject, also meaningful
The other examples are hard to pick holes in as they simply say "Z is a lie", but I can be looking at multiple data sources to decide how much of a lie Z is.
Some people are in business to get paid by creating actual real value all the way down the chain to the ultimate customer. Those people are the ones you'd rather work for, and those are the ones who will care about "science purposes".
sure, but generally most businesses do wish to pay for fraudulent activity.
It seems we have reached a point in time where many "business people" seem unable to discern a difference between fraud and not-fraud, but whichever businesses those people are responsible for will not be sustainable enterprises in the long term.
I mean isn't the business purpose for the company running the ad campaign to actually increase revenue? Sure for an outside ad company just making the client happy is sufficient but for internal teams and the customer themselves the data is still bad.
I dunno. There is a lot of valid and interesting criticism to write about digital marketing. Lots of people have attempted to study the efficacy of digital advertising, and I'd love to see a deep dive into the "MarTech" ecosystem and how shady much of it is. Anyone who has had to work on the go-to-market side of a company knows the general feeling of frustration the author is experiencing.
But this post reads like an aspiring "thought leader" posting a hot take to LinkedIn. It feels like lazy pandering to the "dumb marketers don't math good" crowd.
Here's the same author with a post titled "Marketing and The Modern Data Stack" where he gets very excited about the marketing automation and big data, kicking off the piece with line "There is a huge transformation happening in the data space." and really sells the data-driven future with "In short, you need data, lots of it and it needs to be tightly integrated across the entire customer lifecycle.": https://www.jacquescorbytuech.com/writing/marketing-modern-d...
Various dodgy bots and crawlers hammer my website continually. Consequently, my web logs and analytics are garbage. So bad data is all we have, but it's better no data at all.
> A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is where the light is".
The Red-Green-Refactor pattern is a coping mechanism to deal with the fact that if we want a change to work and the build process tells us we didn't break anything, we bowl right past any subtle hints that we are in the wrong, and our whole code change is a house of cards standing on a bad assumption that will immediately collapse when breathed on.
I have a love-hate relationship with negative tests because of this, and I wonder if there's some way with static analysis or maybe AI to validate that the test that is green because nothing happened isn't green now because I broke the API and the test is now testing nothing in, nothing out instead of something in, nothing out.
Sooner or later in some refactor someone finds a way to break the code without CI catching it.
But we are just people. And if you squint you can see how our relationship to green builds is the same drive that management, sales, and marketing, and scientists get with charts that Make the Numbers Go Up even when the data is just correlated and the proximate cause they were looking for is a hallucination.
Mark Twain knew. Lies, Damned Lies, and Statistics.
I believe this because Facebook continues to send spam to an e-mail address that was only used by me for my cat, and only once; and the cat has been dead for 15 years. The dead cat address received three spams from Facebook just yesterday.
There is also bad data out there about another cat that died ten years ago. He keeps getting snail mail from political candidates trying to convince him that they're deeply interested in the cares and concerns of people like him. A dead cat.
The average time on site and bounce rates I have got from advertising my software inside ChatGPT and Reddit are so terrible that I can only assume that most of the clicks I am paying for are fraudulent, although it is not clear who is doing the fraud.
https://successfulsoftware.net/2026/08/13/my-experience-buyi...
https://successfulsoftware.net/2025/08/11/what-i-learned-spe...
The answer is at every level, and each level is separated into multiple sublevels of fraud.
What is the motivation for someone (other than OpenAI) to fraudulently click on my ad in ChatGPT?
This has been known a long time. Freakanomics did a two part podcast on does advertising actually work. It is long but well worth a listen if you want to hear some science about advertising.
https://freakonomics.com/podcast/does-advertising-actually-w...
The second part goes into internet advertising.
https://freakonomics.com/podcast/does-advertising-actually-w...
There are transcripts of the episodes on the page if you want to read instead of listen.
> This has been known a long time.
This article predates that first Freakonomics episode by 11 days.
The Freakonomics podcast goes into events that happened before 2020. It mentions the following article, for example.
https://thecorrespondent.com/100/the-new-dot-com-bubble-is-h...
> Marketers are addicted to bad data
This article cites a random Statista page for the "36% percent of people in the UK use an adblocker" stat.
A lot of the time you are running experiments and seeing how the data changes, not looking at the data in a vacuum.
Many of the example critiques here don't apply so much when looking at the changes in data:
- if I got 50% more hits on my site this week vs last week, that's meaningful despite 36% of people blocking ads
- if my open rate doubled when I changed my email subject, also meaningful
The other examples are hard to pick holes in as they simply say "Z is a lie", but I can be looking at multiple data sources to decide how much of a lie Z is.
If it gets you paid, it's good data.
If it makes your clients happy, it's good data.
Business data is for business purposes, not for science purposes.
Some people are in business to get paid by creating actual real value all the way down the chain to the ultimate customer. Those people are the ones you'd rather work for, and those are the ones who will care about "science purposes".
sure, but generally most businesses do wish to pay for fraudulent activity.
It seems we have reached a point in time where many "business people" seem unable to discern a difference between fraud and not-fraud, but whichever businesses those people are responsible for will not be sustainable enterprises in the long term.
> Business data is for business purposes
I mean isn't the business purpose for the company running the ad campaign to actually increase revenue? Sure for an outside ad company just making the client happy is sufficient but for internal teams and the customer themselves the data is still bad.
I dunno. There is a lot of valid and interesting criticism to write about digital marketing. Lots of people have attempted to study the efficacy of digital advertising, and I'd love to see a deep dive into the "MarTech" ecosystem and how shady much of it is. Anyone who has had to work on the go-to-market side of a company knows the general feeling of frustration the author is experiencing.
But this post reads like an aspiring "thought leader" posting a hot take to LinkedIn. It feels like lazy pandering to the "dumb marketers don't math good" crowd.
Here's the same author with a post titled "Marketing and The Modern Data Stack" where he gets very excited about the marketing automation and big data, kicking off the piece with line "There is a huge transformation happening in the data space." and really sells the data-driven future with "In short, you need data, lots of it and it needs to be tightly integrated across the entire customer lifecycle.": https://www.jacquescorbytuech.com/writing/marketing-modern-d...
Various dodgy bots and crawlers hammer my website continually. Consequently, my web logs and analytics are garbage. So bad data is all we have, but it's better no data at all.
> A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is where the light is".
[1] https://en.wikipedia.org/wiki/Streetlight_effect
Everyone is addicted to bad data.
The Red-Green-Refactor pattern is a coping mechanism to deal with the fact that if we want a change to work and the build process tells us we didn't break anything, we bowl right past any subtle hints that we are in the wrong, and our whole code change is a house of cards standing on a bad assumption that will immediately collapse when breathed on.
I have a love-hate relationship with negative tests because of this, and I wonder if there's some way with static analysis or maybe AI to validate that the test that is green because nothing happened isn't green now because I broke the API and the test is now testing nothing in, nothing out instead of something in, nothing out.
Sooner or later in some refactor someone finds a way to break the code without CI catching it.
But we are just people. And if you squint you can see how our relationship to green builds is the same drive that management, sales, and marketing, and scientists get with charts that Make the Numbers Go Up even when the data is just correlated and the proximate cause they were looking for is a hallucination.
Mark Twain knew. Lies, Damned Lies, and Statistics.
What is good data and what is bad data?
Meta is most certainly addicted to bad data.
I believe this because Facebook continues to send spam to an e-mail address that was only used by me for my cat, and only once; and the cat has been dead for 15 years. The dead cat address received three spams from Facebook just yesterday.
There is also bad data out there about another cat that died ten years ago. He keeps getting snail mail from political candidates trying to convince him that they're deeply interested in the cares and concerns of people like him. A dead cat.