LaunchDarkly vs. Lookback vs. Optimizely Feature Experimentation

If a new feature should be added but unsure of how it will actually work or how users will accept the new enhancement or change, this tool allows you test and measure initial results. This saves so much time and energy knowing the results before it is deployed and might have low user adoption or acceptance.

Incentivized

Best suited to conduct remote interviews that are moderated and facilitated by the interviewer/researcher.

Not the best if you want to do it unmoderated, there are much more sophisticated tools out there. Unfortunately, for a design research team that does both these kids of research, it can be hard to get budgets to get two softwares and hence the Unmoderated Feature can seem super undercooked and doesn’t really do the job.

Otherwise it’s a great tool

Incentivized

Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -

Incentivized

• A/B or Multi Variant Testing as a methodology to gather insight from customer usage. Experimentation as a feature within LaunchDarkly offers information around the success of one variant over another and whether the experiment has reached statistical significance.
• Being able to decouple deployment of code from the release of a feature is hugely valuable.
• Development teams are empowered to manage features within their production applications for reliability or testing purposes.

Incentivized

• Organization of user interviews
• Sharing of interviews across the team
• Creating highlights of insights

Incentivized

• It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.

Incentivized

• Limited number of users on cheaper plans that is limiting our ability to audit log who is making changes.
• Some of our engineers are confused between flags and segments and have set up items incorrectly.
• Better documented support for React with Typescript.

Incentivized

• Unmoderated interviews is still under cooked as a feature
• The process of how participants have to download an app to start an interview is a large friction point for us

Incentivized

• Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
• Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
• Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test

Incentivized

It fits out business case

Incentivized

Competitive landscape

Incentivized

It's very easy to create new feature flags and set them properly. It is more difficult to get LaunchDarkly integrated within a distributed system so that flags can be used. Especially on stateless servers where gating features by user is not easy. Overall though, it is very easy to get started and I like how simple it is to use.

Incentivized

Once you understand how the interface works, it works great, but there is a learning curve

Incentivized

Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code

Incentivized

No issue with availability at all

Incentivized

From what I have seen, LaunchDarkly integrates well with your code and also services you might have in your tech ecosystem. We use Jenkins for automation and we were able to use it to build pipelines to automate the control of LaunchDarkly toggles in our code.

Incentivized

The overall support is very responsive

Incentivized

Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams

Incentivized

Yes I do.

Incentivized

It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it

Incentivized

LaunchDarkly stood out to us because it put control of the application within the hands of our engineers. We didn't want to allow business users to manipulate the production site via a third-party tool. Instead, our focus was on delivering faster as an engineering team.

Incentivized

Zoom was way more expensive and it o is designed to other things apart from just running qualitative interviews. It also requires a different kind of approval and different approval processes to go through when trying to get it simply for qualitative research purposes.

Lookback records, scribes, helps observe and provides a sentiment check as well in the price that it does

Incentivized

When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years

Incentivized

The platform didn't go down since we implemented it

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• Improved developer experience with some teams moving to Trunk-based Development.
• Increased deployment frequency due to smaller code releases.
• Validation of the technical and business value of work is achieved more quickly through smaller pieces of work and through experimenting with a small group of users before a feature gets to 100% of customers.

Incentivized

• It allows us to understand our customers’ problems in a very team compatible way.

Incentivized

• We have a huge, noteworthy ROI case study of how we did a SaaS onboarding revamp early this year. Our A/B test on a guided setup flow improved activation rates by 20 percent, which translated to over $1.2m in retained ARR.

Incentivized