monday dev vs. Optimizely Feature Experimentation

If you do web programming, code integration with your remote team, and/or software development that requires real-time monitoring of development progress, monday dev is an excellent tool for this. Like its base platform, Monday.com, monday dev is developed and attempts to integrate into a very "new era" organizational system of digital whiteboards, only now focused more on productivity and helping developers to be comfortable in remote work.

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

• Tracking
• Changing Status
• Backlog

Incentivized

• Splitting traffic between variants and enabling you to scale up or down the amount of traffic in each one
• Giving a standardised report that you can share with a huge number of users
• Showing a large variety of results/metrics you can then dive into

Incentivized

• Sometimes, it's a little technical to set up. Someone who isn't particularly savvy will have a hard time setting it up.
• Sometimes there are glitches in the automations.
• The comments areas can be confusing- how they nest. Things can get lost in there.

Incentivized

• Difficult integration if your data is not front end
• Costly MAU model needs to be based on experiments not on site visits
• It's not easy to understand how to build an Experiment
• Onboarding team is more focused on punching through their slides and not focused on your needs or understanding.

Incentivized

We are very likely to renew, because it has been really useful

Incentivized

Competitive landscape

Incentivized

This has been one of the better software implementations and acquisitions that I have been a part of in the last 20 years. The ease of setup, use, and training make this a simple and quick tool to implement in any organization. The usability is so simple that the uptime to start using it is nearly 0.

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

Good engagement from account manager

Incentivized

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

Incentivized

No I wasn't involved

Incentivized

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

Incentivized

Monday is better than Jobber, as it gives you a place to see where all the jobs are and what the current status is. Everyone in the company can go to and see that view. It's not dependent on the status of the employee. Excel is much more technical and requires much more work to set up.

Incentivized

In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my experience. Obviously Monetate may have improved since when I lost use it, but from what I can see, yeah.

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• Although the initial operational cost is usually somewhat high, it is really worth considering in an environment with more than 10 employees working together.
• In project costing (web development) it is quite acceptable and falls within the scope of the final product quote.
• I don't have exact numbers or percentages (because I'm not in charge of that section) but I can say that it falls within a good to acceptable ROI range.

Incentivized

• We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.

Incentivized