Both Ends of the Transaction
Why twenty-five years of selling and twelve years of driving is marketing analytics, not anecdote.
I have spent twenty-five years selling things on the internet, in a business I founded back in 2001, at the time of the famous dot-com bust. Since then, I have leveraged what I learned in that business to author 17 books — published by an actual publisher (not self-published). I am most of the way through a master's degree in digital marketing analytics, and I hold a graduate certificate in business analytics and a bachelor's degree in business from long before that. I am also a technical person, a systems developer for the past thirty years. Those are the credentials, and they are real.
They are not the reason you should read this.
The reason is that I have spent a quarter of a century on one side of a transaction, as a seller, and twelve years on the other, as a service provider — and almost nobody writing about marketing has done both.
On the selling side I have overseen tens of thousands of retail sales from an internet business going back to the early days of the internet, to customers in more than fifty countries. I also personally answered thousands of customer service inquiries. On the service side, driving for Uber and Lyft, I gave ten thousand rides over twelve years across half a dozen states, and talked to thousands of people.
I have only sporadically written about any of it publicly. I intend to more writing here in this blog, because I think these stories contribute something real to the understanding of marketing analytics — and to the subject matter of the business I have built to consult on it.
I want to explain that connection more clearly.
Why these are analytics and not just anecdotes
Marketing analytics has one hard problem sitting underneath all the others. Every number you get is a shadow of a decision you did not see.
A click. A conversion. A five-star rating. An abandoned cart. Together they amount to a customer journey. Each element is a compressed record of something a person did, for reasons nobody wrote down. The analyst's entire job is reasoning backward from the shadow to the decision. Attribution modeling, segmentation, cohort analysis, testing — all of it is an attempt to work out what happened in a room you were not standing in.
For twenty-five years I have had the shadow. Order records, traffic, catalog performance, marketplace reports, twenty-five holiday seasons of demand data.
For a lot of those same years, I was also in the room.
I answered the customer service email myself, so I know what the person who abandoned the cart was actually confused about — not what the funnel report guessed. I sat in a car with someone while they decided which of two apps to open over a twenty-five cent difference. I was rated thousands of times by people sitting three feet away from me, so I know exactly how little those stars recorded about what had just happened.
That is not a metaphor, and it is not a nice way to introduce a story. It is an unusual methodological position: having both the measurement and the event the measurement was supposed to capture, for the same transactions, over years. Almost nobody has that. Practitioners have the numbers and not the room. Researchers have careful method and a recalled account of the room. Big data sees the broad shape of millions, even billions, of data points, but not the individual stories. I have field notes from inside the room, and the reports for the same period.
Concretely, here is what that produces.
| The analytics problem | What being in the room adds |
|---|---|
| Construct validity — does the metric measure the thing you think it measures? | I lived under a five-point rating for twelve years. 10,000 rides. My Uber rating is 4.94 and my Lyft rating is 5.0. I have a good idea which rides produced which scores, and the score tracked the absence of friction, not the presence of value. That is a construct validity problem you can hold in your hand. |
| The customer journey — most maps are reconstructed from touchpoint logs | I watched complete journeys end to end. Someone comparing two prices on two phones, deciding, and riding. And on my side, the path from a phrase somebody typed into a search box to me taping a box shut. I can tell you which parts of a journey map are observed and which are inferred, because I have seen both halves of the same journey. |
| Attribution — which touch gets the credit | I sold to institutions where the purchase was the last step of an approval chain running a year long, and no analytics package saw any of it. If a model gave the credit to the final session, the model was wrong, and I can say why. |
| Selection and sampling — who is actually in your data | My riders are a convenience sample of people who hailed a car in one region. My customers are a self-selected group who already wanted a historical image. Neither generalizes, and knowing precisely how a sample is bent is more useful than pretending it is not. You can read what riders wrote. |
| Instrument design — you only get the answers your instrument allows | Two instruments, the same rides. A five-star tap produced almost nothing. An eleven-dollar notebook in the back seat produced hundreds of pages. The difference is not effort. It is design, and it is the most transferable lesson in this whole essay. |
The stories are not decoration on top of the analysis. Each one is a case where I can see the mechanism behind a number, and the mechanism is what a marketer actually needs. A number tells you that something moved. Only the mechanism tells you what to do next.
I estimate I have 2,000 – 3,000 thousand stories to tell in the coming weeks, months and years. I hope you will join me for some of them.
The problem with most marketing writing
Marketing advice tends to come from two kinds of people.
The first kind works at an agency or a consultancy. They see real accounts, real budgets and real outcomes. They also sign agreements that stop them describing any of it in a way you could learn from. So the writing comes out sanded down — a case study with the numbers removed, a lesson with the client's name replaced by "a mid-sized manufacturer." Useful sometimes. Never specific.
The second kind is academic. They can publish freely, and the method is usually sound. But the underlying data is almost always people reporting what they think they did, some time after they did it, on a panel they were paid to join. There is a well-known gap between what people say they do and what they actually do. Nobody in that world denies it. It is just very hard to get around. Other industries have the same problem.
And yet the gold standard these days for getting your message across — for marketing a business at all — is storytelling. Which requires having lived through something worth telling.
All these groups are missing the same thing. Not one of them has run a business where the customers arrive one at a time for twenty-five years, where the owner personally answered thousands of service inquiries. And few can also say they spent twelve years on a second job watching ordinary people make ordinary decisions in real time, with nobody performing for a researcher.