Richard Golian

1995-born. Charles University alum. Head of Performance at Mixit. 10+ years in marketing and data.

#myjourney #myfamily #health #cognition #philosophy #digital #artificialintelligence #darkness #security #finance #politics #banskabystrica #carpathians

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AI sales forecast: 9 traps so far

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.
25 April 2026 292

Will AI take my job?

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.
23 April 2026 168

€50,000 Quote vs. Two Hours with Claude Code

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.
18 April 2026 484
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Performance Marketing, BI & AI Automation

Head of Performance, BI, AI agents, e-commerce

Contact: X LinkedIn mail@richardgolian.com

My name is Richard Golian. I have spent the past ten years working in marketing and data — most of that in e-commerce. Here is what I do.

Richard Golian
Me and my colleague at the summit of Slovak e-commerce leaders. 6.6.2024

Business Intelligence

I lead BI projects — data quality, reporting, making sure the numbers people rely on are actually reliable. I have written about what happens when they are not: messy data, misinterpretations, and the nonsensical actions that follow.

I do development too. I have built my own analytics system, and I have written about how AI changed my coding in ways I did not expect.

I work with applied statistics. I try to be careful with words like certainty and causality — they get thrown around too loosely.

Corporate data protection is part of the work too.

Marketing & Advertising

I analyse advertising accounts — looking for errors, wasted spend, and opportunities. ROI optimisation, growth management, problem-solving.

Summary

Head of Performance in e-commerce. I lead data-driven marketing, build BI systems, train AI agents, and apply phenomenology where analytics falls short. I also build teams — hiring for common sense, motivation, and chemistry over experience.
Richard Golian

If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.

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Common questions on this article's topic

What does a Head of Performance Marketing do?
A Head of Performance Marketing leads the strategy, execution, and optimisation of paid advertising campaigns across digital channels. This includes managing advertising budgets, analysing campaign performance through metrics like ROAS, CPA, and conversion rates, and making data-driven decisions about where to allocate spend. The role also involves team leadership, cross-functional collaboration with product and data teams, and staying ahead of platform changes across Google, Meta, and emerging channels.
How can business intelligence improve marketing ROI?
Business intelligence connects marketing performance data with broader business metrics — revenue, customer lifetime value, margin, and retention. Without BI, marketing teams often optimise for surface-level metrics like clicks or conversions without understanding their actual impact on profitability. BI also improves data quality, which prevents costly decisions based on flawed or incomplete information. The most impactful BI work often involves making bad news visible faster — catching underperforming campaigns, data errors, or attribution problems before they compound.
What is applied phenomenology in marketing?
Applied phenomenology is a qualitative research discipline that examines how people perceive and understand the world — not through surveys or data, but through the structures of experience itself. In marketing, it helps answer questions that analytics dashboards cannot: Why did a customer scroll past an offer they would have benefited from? How is the market interpreting a situation before rational analysis begins? It bridges the gap between what data says happened and what people actually experienced.
What should companies look for when hiring a data-driven marketer?
Three things matter more than technical skills: common sense, internal motivation, and the ability to question assumptions rather than just optimise within a given frame. Technical skills — running campaigns, analysing data, building dashboards — can be taught. What cannot be taught is the instinct to ask whether the data itself is reliable, whether the right questions are being asked, and whether the current approach makes sense from the customer perspective.
How are AI agents used in marketing and business automation?
AI agents automate workflows that would otherwise require hours of manual work — data analysis, content production, code development, and decision support. Unlike simple automation scripts, well-trained agents can handle ambiguity, learn from previous sessions, and adapt their approach based on context. The key challenge is not the technical setup but designing how the agent should interpret instructions and communicate results — which requires understanding how language and meaning actually work.
How do you assess advertising account performance?
Start with data quality — are the tracking and attribution systems actually reliable? Then examine cost efficiency across channels and campaigns, identify where budget is being wasted on low-intent audiences, and look for structural problems in campaign architecture. The most common issues are not dramatic failures but quiet inefficiencies: campaigns that perform well enough to avoid scrutiny but far below their potential.