Explore or Exploit? Marketers: Know When To Do Each.

Marketers constantly face an impossible choice. Double down on what works, or take a chance on something new?

This is the essence of the explore-exploit tradeoff. In machine learning, computers have to choose between one of two options: exploitation, or choosing the best option based on currently available information. The alternative is exploration, which means trying new things that might work out better…but also might not.

In marketing, you have to make choices like this all the time. Do you explore by testing new channels, creatives, or campaigns? Or do you simply scale up proven tactics to maximize ROI now?

Exploit too early and you stagnate as competitors pass you by. Over-explore and you burn your budget without anything to show for it.

I’m not just using this as a metaphor. Getting the balance right matters because marketing budgets are finite, growth cycles are unpredictable, and consumer behavior changes quickly.

From ad testing to brand-building strategy, you have to be able to balance exploration and exploitation. And that’s what I’ll be talking about in this post.

But first, let’s talk about multi-armed bandits.

There’s an old statistics problem called the “multi-armed bandit” problem. In it, a gambler walks into a casino full of slot machines. They know that some of the machines are going to be more generous than others.

So the gambler has to make a decision: keep playing machines that pay out, or test ones that might pay more? Exploit the most generous machines you can find, or keep looking for better?

Statisticians have an odd sense of humor.

But take note all the same: this won’t be the last time we talk about multi-armed bandits.

When I first heard the explore-exploit tradeoff in Algorithms to Live By by Brian Christian and Tom Griffiths, it immediately clicked for me. Sure, the idea comes from computer science, but we all know people who have spent way too much time—or way too little time—before making important decisions.

In marketing, it’s tempting to start throwing dollars at the first ad campaign that returns a good ROI or to double down on the first brand messaging that gets a good response. But premature optimization is risky, in much the same way that indecision is risky.

We have extremely powerful tools right now. We can cut or double our ad spend in 2 minutes. We can scrape huge databases of contacts and load them into a cold email program in about 30 minutes.

But that kind of speed is a blessing and a curse. Back when marketing relied on direct mail, print media, television, and radio, multiple people had to get involved in every single campaign. Now, a single cowboy can log onto Google Ads at 2 in the morning and change things on a whim.

That means organizations and the professionals working in them need clear rules on:

Being aware of the fundamental tradeoff at the core of these decisions—explore-exploit—is the bedrock foundation on which these rules can be built.

There’s no single correct balance between exploring and exploiting—but there are patterns that show up over time. One of the most reliable is campaign maturity.

Early-stage campaigns: You don’t have data yet and you don’t know what works.

This is fine. The goal isn’t efficiency—it’s learning what works.

At this stage, you need to run broad tests. You need to try a bunch of slot machines. Give your experiments time to breathe. Don’t obsess over attribution because you won’t get clean reads anyway. You’re still mapping the territory.

Mid-stage campaigns: Now you’re narrowing in. You’ve got a few things that look promising.

Keep exploring, but with more precision. Isolate specific creative angles or offers. Use A/B tests when you need statistical clarity, but don’t be afraid to use bandits if timelines are tight or the environment’s changing quickly around you.

Mature campaigns: You’ve got winners—use them. This is where exploitation makes sense.

But keep a probe running. Set aside 10% of budget to test against your reigning champion.

Markets change and so do people. You don’t want to be caught coasting, so it’s important to always keep feelers out there for a better method.

That’s the mindset. The math will follow.

The core problem marketers run into when trying to decide whether to double down on what works or try something new isn’t a technical one. It’s more about math and logic than, say, conversion tracking.

You want to test fast and you want to scale winners. And ideally, along the way, you want to learn something new too.

This is where smarter bandit testing comes in.

You need to build a system that adjusts based on feedback loops. That’s why bandit testing shines—when implemented well, it will help you balance exploration and exploitation, and it can adapt on the fly.

And before we go any further, let’s be clear on terms: the bandit is the slot machine itself. Each arm is a different option with an unknown payout. The marketer is the gambler.

So when I refer to a “bandit”—I mean a novel test.

And you should know: bandits aren’t magic. That’s why it’s so important to control the structure of the test. Otherwise, you could fall prey to noise, false signals, and overfitting—all of which are subtle money-wasters.

Less experienced marketers run bandits the way they run slot machines. It’s tempting to feed the computer 5 headlines, get a “winner” in 3 days, then copy–paste that headline across everything.

And while that’s better than roulette, it’s still not ideal. The sharpest marketers treat bandit testing as a continuous feedback model that evolves in stages:

There are different bandit types. Pick one based on your goals and constraints.

In practice, though, I’ll be frank: unless you have a data science team, use a platform that implements Thompson Sampling under the hood (e.g., Facebook’s conversion-optimized creative rotation does something similar).

But don’t blindly trust it—algorithmic “best” is still subject to your settings. And the biggest lever is what you feed it. Garbage in, garbage out, as computer scientists say.

Just because the bandit favors a variant of what you’re testing doesn’t mean it’s a true winner.

Watch out for:

Fix this with structure:

Bandits optimize for local maxima—the best thing they can think to do based on what they’ve already tried.

You have to step back and decide if the local maxima found is really the best you can do.

Smarter bandit tests also require smarter budgeting.

Don’t lump all tests into one campaign. Run distinct bandits for each stage and each audience.

So for example, if you were testing for a subscription box, you might do something like this:

Each test gets its own budget, own timer, and own judgment criteria.

Every bandit test should come with explicit kill criteria:

This prevents you from being misled by noise or letting poor variants soak up budget.

Better to fail fast and run a new test than let an algorithm spin its wheels on junk data.

Bandits only show you what performs. But you still have to use your instincts and better judgment to figure out why.

So don’t just look at CTR and CPA. Use:

Your goal is to figure out the reason why a variant won—and how you can reproduce that win elsewhere.

As you try new things, make sure you’re writing everything down. For each test, document:

Store this in a shared tracker or Notion doc. If you can keep up this simple and tremendously helpful habit, it will pay you compound interest with time. Over time, you’ll build a proprietary library of what works for your brand—something no platform algorithm can match.

Bandit testing is great for helping you balance exploration and exploitation. But it’s a subtle art and one that can go wrong pretty fast. So keep an eye out for these four common traps:

In short, write down everything, be a little skeptical, and do everything in your power to make a failure—in small doses—acceptable in the workplace. That’s how you get all the data you need in order to cash in while still have the time to, well, cash in!

The difference between a gambler and a strategist is what they do with uncertainty.

Gamblers guess and hope. Strategists test and adapt.

That’s the lesson of the explore/exploit tradeoff.

If you only exploit, you’ll coast until the market changes and you find yourself playing catch-up.

If you only explore, you’ll burn through your budget without real gains.

But if you learn as you go—really learn—you’ll outpace competitors who are stuck in either extreme.

Bandit algorithms are just the math. The real advantage is in how you use them: to stay curious, stay alert, and build systems that evolve with your customers.

This isn’t about being clever. It’s about being deliberate.

And the marketers who get that? They compound wins, outlearn rivals, and adapt before others even realize the rules have changed.