The Notebook in the Back Seat
Two instruments measured the same rides. Then the dead ends, and what I do with all of it.
The drivers notes and the in-car journal
At some point I began taking notes. To date I have a hundred pages of them — single-spaced bullet points of things I saw and things people told me.
Later I put a blank journal in the back seat with an arrow and two words: WRITE HERE. That is the entire mechanism. No app, no incentive, no follow-up email, no request. Hundreds of people filled it in over the following years. They wrote about where they were going and why. They left recipes — a startling number of recipes. They left restaurant recommendations in nineteen states and four countries. They drew pictures. They wrote to whoever would read the page next, which is not something I asked for and not something I expected.
One rider called it a mobile recipe wagon. Another took a second ride with me weeks later and said the best part was reading what had been added since.
Why the journal is the most useful thing in this essay
Two instruments measured the same rides. A five-star tap at the end, and a blank page in the back seat.
The star rating is a well-designed metric by every conventional standard. It is universal, cheap, comparable, near-total in coverage — I have thousands of them — and it fed an algorithm that determined whether I kept working. It also told me almost nothing. It recorded whether anything had gone wrong. It could not record what had happened.
The journal has none of those virtues. Coverage is partial. The sample is self-selected toward people who felt like writing. Nothing in it is comparable to anything else. You could not build a dashboard from it.
And it produced hundreds of pages of what people were actually carrying around.
It is worth being precise about why, because the reasons transfer to any feedback mechanism you own.
- It asked for nothing specific, so it did not constrain the answer to a scale somebody else designed.
- It was anonymous, and anonymity raised the quality of what people wrote rather than lowering it.
- It was physical, and it sat in a shared space where a bored person would pick it up. Friction near zero, at the exact moment of idleness.
- Crucially, the audience was not me. People were writing to each other. That one design choice changed what got written more than anything else did.
I have spent a lot of professional time on feedback mechanisms — surveys, review prompts, post-purchase emails, star ratings. The ten-dollar journal outperformed all of them. Not because it was more rigorous, but because the instrument was designed for the answer rather than for the report.
That is the lesson I would hand to anyone running a business. Before you ask what your numbers say, ask what your instrument is capable of recording. Most of the time the honest answer is: less than you have been assuming.
The parts that did not work
I would rather put these near the front than bury them.
I once had an excellent conversation on the way to the airport with a vice president of data science at a large job-search company — Monster.com, to be exact. This was at a point when the company that old-timers remember as the leading job search site of the early internet had been failing and was being rebooted. We talked the whole ride about why her company was losing ground to two competitors, LinkedIn and Indeed.
As I had just studied machine learning in a graduate program, I told her I could put together a study on how it could benefit Monster. She was excited to receive it. So I built her a deck laying out what I thought the answer was. She was complimentary. Then nothing. No rejection, no follow-up, no explanation. That is the single most common outcome of a great meeting and almost nobody writes about it.
And yet there I was, the Uber driver, probably the first person to advise Monster.com on how machine learning could help their business. This was around 2017, which I have since learned is the year Google created the Transformer ML architecture that became the backbone of the AI systems everybody uses today.
On the business side there are equivalents. Proposals that went unanswered. Institutional relationships that took three years and produced one order. A catalog of three hundred thousand items where a meaningful share has never sold at all. And fifteen years of data I did not analyze until after I had closed the business.
I am telling you this because I am about to publish a lot of material drawn from these twenty-five years, and its value depends entirely on whether you believe I am reporting straight. A version containing only the good parts would not be worth reading.
What I actually do with it
None of the above is evidence on its own, and I want to be exact about that.
The driving is a convenience sample of people who happened to hail a car in one part of the country. It skews toward travelers, toward people heading to an airport, and toward people willing to talk to a stranger. It has no control group. The customer record is one business in one niche. Neither generalizes by itself.
What they produce is hypotheses — and hypotheses that almost nobody else has access to, which is the point. A hypothesis you can get from an industry report is one your competitor already has. The work is then to take it somewhere it can be checked. Against server logs. Against public archives of how websites are actually built. Against a structured assessment run the same way across a hundred and eighty-seven organizations. Against numbers that do generalize.
That is the method, and it is the same one I use on client work. Notice something in the field. Form a specific claim. Go and see whether the data supports it. Report what you find, including when it does not.
It is also why the storytelling and the analytics are not two separate things here. The story is where the hypothesis comes from. The analysis is what decides whether it survives. A story without the check is an anecdote. A number without the story behind it is a chart nobody knows what to do with.
What this blog is going to be
A series.
Some of it will be about pricing, because I have watched thousands of people decide what something was worth to them at the moment they decided it — and set prices myself, early on without proper research, and later with the benefit of full data analysis. Some of it will be about reputation systems, because I lived under one for twelve years and sold under another for thirteen. Some of it will be about catalogs, description and findability, which is the least fashionable subject in marketing and the one that built my business.
Some of it will be about how holiday demand actually behaves, because I have run through over two dozen Christmas seasons online and the pattern held every time. Some of it will be about the segments nobody serves, because I drove them. Some of it will be about what people believe is happening to their data, which is wildly different from what is actually happening and matters more to your marketing than the truth does.
Every one of them will follow the same shape. Here is what I saw. Here is what I think it means. Here is what would have to be true for me to be right. Where I have checked it, I will show you the check. Where I have not, I will say so.
And some of it will just be a story about a person in a car, or an order that arrived from Greenland on an ordinary Tuesday, because that is where all of it came from.
One rule before I start. Most of the people in these stories will not be named. Organizations that bought from me are a matter of business record and appear as themselves. People who told me about their own business, named it, and left me their details extended an invitation, and I will treat it as one. Everyone else appears as a role — a banker, a teacher, an engineer. The personal things people wrote in that notebook because it felt like a safe place to put them stay exactly where they are.
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