In our noisy, ravenous tech market, most organizations see speed as critical to successful growth. This puts software teams under extreme pressure to move quickly. But while these teams might be shipping products faster than ever, their confidence in the success of new features can sadly lag behind.
Features often go live without a structured way for teams to measure how well they’re working. The result is a precarious loop: teams ship a release, observe a handful of delayed metrics, and hope that upward trends indicate progress. However, when signals are mixed (or absent), these teams can be forced to rely on instinct, which isn't necessarily enough to build great software.
The problem with guessing
A redesigned homepage can increase conversions, but it might also push some prospective users away; a revised onboarding flow might simplify activation, but it could also introduce a new source of friction.
These aren’t theoretical risks; they’re common outcomes that can happen silently when there’s no way to detect what’s changed. Without experimentation, teams are often left to guess which changes are beneficial, which are detrimental, and why.
In addition to degrading product performance, this uncertainty can affect team dynamics and relationships. Disagreements are more difficult to resolve without data. Confidence among team members can gradually erode if no one can clearly identify what’s working.
The limits of intuition
Without structured feedback, teams fall back on what they know: intuition, experience, and anecdotal evidence. This approach isn’t always without value! In fact, successful teams develop strong instincts over time. But instincts are often shaped by personal context and unique experiences, and don’t always scale well across different user groups, markets, and product surfaces.
Research indicates that even experienced professionals are wrong more often than they expect. One of the best-known references to this finding is from a large-scale A/B testing program conducted at Microsoft Bing. In the study, researchers found that only about one-third of the ideas that teams believed would improve their chosen metrics did actually end up improving them. They also learned that in simpler or less mature domains (where teams don’t already have ample product experience or strong intuitions about user behavior), the success rate is even lower. (Note: If you're a behavioral science or human-computer interaction nerd, the study cited above is a delightful read.)
Despite this limitation, some teams still treat product development as a matter of opinion. A new feature might be prioritized because it “looks right,” or a design may be shipped because it has tested well in a limited user interview. These judgments are well-intentioned, but not definitive.
The challenge of measuring what matters
Even teams that want to experiment often struggle to do it. Experimentation requires clarity around what’s being tested, how success is defined, and what metrics to observe. It also requires a solid technical foundation, including instrumentation, data infrastructure, and analytical support.
In many organizations, these elements exist, but they're fragmented. For example, metrics may live in one system, while releases live in another. Experimentation tools are often disconnected from day-to-day development workflows (if those tools are used at all). As a result, experimentation becomes harder to trust and easy to deprioritize.
When experiment results do arise, they’re sometimes too technical to interpret or too shallow to be useful. Teams need data, but they need the right data, at the right time, and in the right format. Most importantly, they need that data to be trustworthy across disciplines, including engineering, product, design, and beyond.
The case for experimentation
Experimentation is a decision-making framework. It allows teams to ask clear questions, define measurable outcomes, and rigorously evaluate impact. Done well, it can turn uncertainty into insight. Experimentation helps teams:
- Understand how real users behave in real environments
- Detect unintended consequences early
- Compare multiple ideas without committing prematurely
- Iterate quickly based on observable impact
Maybe most critically, it helps build trust between individuals, across functions, and among users. Decisions grounded in data are easier to defend and explain, and more likely to lead to meaningful outcomes.
Moving from observation to action
Integrating experimentation into your development lifecycle requires aligning it with the tools and data that teams already use. It also mandates designing experiments that align with a product team's agreed-upon goals rather than with generic KPIs.
The value of experimentation lies within both what it reveals and in how it accelerates iteration. Faster feedback leads to faster learning, and faster learning (ideally) leads to better products.
Taking a path to more informed development
Integrating experimentation into the way you build products, using the data you already trust, is the best way to stop guessing.
LaunchDarkly can help you embed experimentation into feature flags and engineering workflows. It can also set you up to support warehouse-native experimentation powered by metrics your team already uses.
You don’t need to build a lab to test your ideas. You just need the infrastructure to measure what matters and the tools to act on what you find. LaunchDarkly can help. To see what that looks like in practice, request a demo.
Like what you read?
Get a demo
