Which companies are built for speed? We are about to find out.
Change is happening so fast that companies are finally addressing the structures—silos, layers, long planning processes—that gum up the works. It’s messy and painful and hard to get right. And it’s genuinely terrible for those being laid off as companies sort themselves out.
AI is part of it, but it’s not the only driver. Company operating models have been shifting at the edges for years. Collabs, limited-time offers, and drops, for example, aren't just short-term marketing tactics—they're signs of a shift to shorter everything cycles. That need for speed is the wrecking ball that is collapsing old corporate architectures.rch: panels, focus groups, discrete choice analysis, customer interviews, and synthetic personas are all about posing questions rather than observing actions and behavior.
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It's Really About Risk
Underneath it all, it’s really about risk. Shorter cycles are a smart way to manage risk—a way to avoid missing a window or ending up with too much inventory. And there’s so much risk: AI, tariffs, geopolitical shifts, and rapid-fire cultural change mean that nimbler is better.
Some companies have already moved that nimbleness from the edges to the core. As more meaningful innovation (like, not just limited-edition sneakers) happens in faster cycles, the organizations that have the capability to experiment continuously will have an advantage over those where old decision-making structures are still in place.
Experimentation is one way to speed up decision-making, by bringing real-world evidence to forks in the road. Collabs, LTOs, and other short-cycle offerings can be a form of experimentation. They’re the vanguard, a symptom of what’s coming organizationally.
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Down to a Science
Everyone runs experiments. But not everyone runs experiments the way a scientist would.
First, testing is not the same as experimenting. Experiments have structures that allow a hypothesis to be proved—or disproved. Asking respondents to rate package designs or creative styles on a scale from one to five doesn’t qualify. There’s no control group and no way to prove or disprove anything.
But lots of market research does qualify as an experiment. A|B testing website components, email subject line testing, and cold email sequence testing are all experiments because they prove that one thing works better or worse than some other thing. They do it by creating a structure that isolates status quo and then measures the effect of a single change.
Those types of marketing experiments are usually sequential: you test a single variable, read the result, then move to the next test. The process is usually slow and focused on small questions. But it’s a fantastic way to optimize something that is working pretty well to begin with.
Other experiments test a bundle of variables all at once. Minimum viable products (MVPs), for example, test one product at a time, sequentially, so each test is a binary test. Did it work or not? If it didn’t, you change something and test again.
Changing one variable within an MVP bundle may improve results the next time, but it makes for a long, slow process. And market conditions may be different for each iteration, further clouding the drivers of success or failure.
There is another kind of experiment that does something fundamentally different: factorial testing.
Instead of one variable at a time, you test multiple variables simultaneously—different concepts or strategies, different messages, different audiences, different offers—in all possible combinations, and you measure the interaction effects:
...Not just which message works, but which message works with which audience, across all concepts, at the same time
…Not just which product concept works, but which features with which messaging and so on.
Factorial testing is not faster sequential testing. It’s a higher-level system of learning. And it means that you can test a real strategic question like ‘how do we reposition our brand to open up new audiences?’ very, very quickly and with statistical confidence. The result is decision-grade evidence built on behavioral data. CFOs love it.
Factorial testing exists in corners of marketing research, but it’s rarely applied to strategy, marketing, and innovation. It should be: it’s a way to test big, discrete ideas about how to move your business forward.
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With No Further Ado
Experiments build confidence about direction, and it’s not a surprise that we’re hearing more about them at a time where risk is high.
To make experiments more valuable, however, means making them faster, more flexible, even more relevant to strategy, and—most important—earlier in the decision making process, before too much investment has taken place.
We’ve built something that makes that happen. It’s called Lab No. 9 and we’ve been using it with clients for several months.
Here’s how Lab No. 9 works:
- Hypothesis Development. We use our new Sparky tool to collect your inputs—brand or product attributes, attitudes and behaviors, potential audience targeting factors, creative elements. We use them to build a multivariate test plan. It’s factorial, which means every audience sees every combination of factors. The way all the variables interact is where the insights are generated.
- Test in Live Environment. Then we run the test in the real world, with real audiences, generating real behavioral data. (This is not a survey or a focus group, where people say they'll do one thing and then do something entirely different.) The test takes about a week, since we like to see behaviors across weekdays and weekends.
- Real-World Evidence. Then, we analyze results and pull out a wealth of insights. And by insights, we don’t mean attitude or opinion. We mean statistically valid data about how each audience responded to the same positioning for different product concepts or whether a creative style drove performance no matter what brand strategy it was applied to.
Experiments address the speed of change by managing risk with real-life data. They are part of an emerging operating model that we are seeing play out in real time as companies restructure for a very different business environment.
Lab No. 9 is designed to support that shift . It’s experimentation on demand for important strategic questions. It’s also fast—exactly what’s needed as companies compress decision-making into shorter cycles.
Some of you reading this may be feeling the need for speed. We would love for you to take Lab No. 9 for a spin. Reply to this email or reach out to one of us.