Why analyzing Marketing feels impossible

👋 Hey, it’s Sundar! Welcome to a 🔓️ paid 🔓️  article of experiMENTAL: a weekly newsletter on B2C Marketing & data science how-to guides, frameworks, and stories.

This article is brought to you by The Marketing Millennials

A free newsletter with the marketing ideas you need

The best marketing ideas come from marketers who live it. That’s what The Marketing Millennials delivers: real insights, fresh takes, and no fluff. Written by Daniel Murray, a marketer who knows what works, this newsletter cuts through the noise so you can stop guessing and start winning. Subscribe and level up your marketing game.

Sign Up Free

Why analyzing Marketing feels impossible

I’ve been both a marketing and product data scientist and if I could go back and start my career I would 1000 times over choose to be a product data scientist. Only those that are a bit masochistic would choose to be in Marketing Data Science.

And every so often, I hear a question that completely stumps me and it’s what prompted me to write this article: “Why don’t we understand how our Marketing is doing?” My usual response would be to just drop my jaw and ask if they were serious, but I realized that it’s a very valid question. WHY is Marketing data never as simple as it looks?

Here are 10 reasons we’ll cover today

1. Attribution

2. Seasonality

3. Data pipelines

4. Product errors

5. Channel biases

6. Algorithm updates

7. Campaign changes

8. Stakeholder opinions

9. Cross channel impact

10. Experimentation costs

Attribution

Just look at the image above and you’ll immediately know why attribution alone makes analyzing Marketing feel impossible. 6 different approaches. Attribution is the bane of all of our existence but it’s a necessary evil. Humans want certainty and attribution solves that but it adds an insane amount of complexity.

First, no one can agree on attribution but also everyone knows it’s wrong. Only in Marketing Data Science do you set something up that you know is wrong but still use it because it’s better than having nothing. Absurdity. Taking it one step further though is that attribution rarely stays consistent. Many of you have probably experienced broken attribution or changes to attribution or some other catalyst to the marketing data that makes you revisit all of your assumptions.

Attribution makes analyzing Marketing feel impossible.

Seasonality

Seasonality is the world’s greatest excuse for an answer for 95% of questions. And the reason is because 95% of the time it’s true. From the hundreds of analyses I’ve done, seasonality has been the primary root cause for most answers. But seasonality doesn’t impact JUST the behavior of users. Oh no. It’s more than that.

If seasonality around Thanksgiving was just isolated to knowing shoppers will stop buying around early November to save up for the last week that would be one thing. But you also have to deal with the seasonality of Marketing itself.

Picture this Marketing scenario that you have to contend with:

1. Shoppers buy less around early November but also…

2. CPMs start to go throughout the month of November because…

3. Everyone in the world wants to advertise for Black Friday which means…

4. Your competitive landscape has seasonality too

So, not only do your customers have seasonality but so do your CPMs, your conversion rates, your competitive landscape, and most excitingly your CMO also has a seasonality to their mood. No one loves November CMO.

Seasonality makes analyzing Marketing feel impossible.

Data Pipelines

The product analytics pipeline is fairly simple. Track a bunch of events and ingest as 1st party data. I know I’m simplifying but let’s compare it to the complexity of the MarTech stack:

There are over 14,000 MarTech vendors for any piece of the Marketing Journey. Want a vendor that just sends good morning email to your users every day? There’s probably a vendor for that.

Now, having that many vendors sounds great BUT the problem is how little companies invest in Marketing vs Product. Look at the # of product engineers in your company vs marketing engineers. It’s probably an absurd ratio of like 5:1 . Is product really driving 5x the value especially when almost all of the variable cost of a company is under Marketing? No.

So, you bring in a bunch of engineers to solve a “small” problem of tracking 3rd party data across multiple websites to try and perfectly attribute and lo and behold your data pipelines suck. And to fix it? “Ah, we’ll try and do it on the 2026 q3 roadmap”. Cool cool cool.

Data pipelines makes analyzing Marketing feel impossible.

Get access to the rest of this article...