Experimentation-done-right is too expensive and too ambiguous to sell as a product. Every product experiment requires a complex set up, a lengthy running period across a huge base of users, and then heavy analysis in order to achieve statistical confidence over a specific feature's impact on a business metric. That "confidence" is often represented by a subpercentage point that may or may not be statistically significant. Fun problem for the data scientist, plain hell for the PM.
In my company which uses experimentation for everything, each A/B test requires two weeks before the Product Manager can even see the results. Two weeks of waiting for a confusing, contradictory dashboard that can't be taken at face value, that needs careful, human analysis before it can be called a "win".
That slowness is fine for high-traffic, high-risk & high-value lines of business. But it's not fine when you're releasing feature that aren't just optimizations.
Competitors like LaunchDarkly and Split.io have recognized that critical difference, I think. They know that causality is expensive, and are particularly aware that the fine line between feature release and metrics impact is tied too heavily with a company's politics, i.e. it chafes against the intuition of executives.
Instead, they offer experimentation as an add-on to their developer tools. You can experiment if you need to, but it doesn't obligate you to do so.
That goal is much more realistic than the Optimizely's current goal: "helping our customers win in a digital-first world".