Article
Decision-Making in Marketing and Advertising Under Uncertainty
In the past, I judged my decisions by their outcomes. Over time, I recognised that many choices I once deemed "bad" only had unfavourable consequences. Today, I realise that what truly matters in a decision is not its outcome but its optimality given the circumstances and available information—or, more accurately, how closely it approximates the optimal decision.
Decision-making in marketing and advertising
How should we approach decision-making in marketing and advertising, especially under uncertainty? Can a decision with a negative outcome be more optimal than one with a positive outcome? Yes.
During my experience in decision-making in marketing and optimising budgets for online advertising, I have discovered that specific days within seasonal campaigns present exceptional opportunities. However, capitalising on these opportunities requires embracing a higher level of risk than on ordinary days. It is impossible to rely on data from the previous day with certainty, and waiting a few hours for data on a given day means the irreversible loss of many opportunities.
Without divulging sensitive details, the essential lesson I have gathered is that accepting a higher level of risk in these cases is advisable. Rather than fixating on individual situations, I focus on a 70% probability of a positive outcome in these cases. With this probability in mind, each decision of this type can be considered suitable, regardless of the impact of a single decision. What is important here is the high probability that the profit of decisions with a positive effect will cover the losses.
Continue reading
Enter your email to unlock this article and join the newsletter. You can unsubscribe anytime.
Summary
Common questions on this article's topic
Should a decision be judged by its outcome or by its quality?
What is the Weak Law of Large Numbers and how does it apply to business decisions?
How should you make decisions in marketing when data is incomplete?
What does it mean to be process-oriented rather than result-oriented?
Can a decision with a negative outcome still be the right decision?
Why do most people struggle with decision-making under uncertainty?
If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.
Related articles
He used words like “certainty” as if statistics were part of Newtonian physics.
Yesterday I could not tear myself away from the computer. When I lifted my head, it was half past eight in the evening. I had been sitting alone upstairs for about three hours.
Will AI take my job? A certified Google trainer told me in June 2024 that my profession would cease to exist. Twenty-two months later, my job title has not changed — but ninety percent of what I do during the day is different. I have delegated more of my thinking to AI agents than I thought possible. I am not afraid. This is why, and what it means for anyone asking the same question.
More articles
I am building an AI system to predict the S&P 500. It runs on my own machine, uses free public data — yfinance, FRED, the Shiller dataset — and grades every forecast against reality. This series documents the build itself: the decisions, the methodology, the mistakes. What I will eventually share from the running system is a separate question, and an honest one.
One hour. Fifty-five minutes. That is how long it took to build what a Czech software firm had quoted at over €50,000. I built it with Claude Code. Not a prototype. Not a proof of concept. A working tool — the one the company actually needed. By the evening of the same day, it was running on staging. This is not about Claude Code. It is about what Claude Code exposes.
I have conducted roughly one hundred and fifty practical interviews over the past four years. Fifty for data specialist roles. A hundred for advertising and performance marketing specialists. Almost every one of them involved sitting down with a candidate over a practical task — something close to a real problem we actually need to solve at the company. Not theory. Not trivia. Applied problem-solving. Over time, I started noticing a pattern.
Before you can teach AI to understand anything, you need to see what it is hiding from you.
The moment other people needed access to it, the problem changed completely. It was no longer about whether the agent could learn. It was about who gets to teach it.
I wanted to build an agent that doesn't just assist. One that acts.
This is what I learned about local vs cloud AI, and why I switched to Claude Code.
What happened — and how can it be reversed?