monday dev vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
monday dev
Score 8.9 out of 10
N/A
monday dev is a collaboration tool for development teams from Monday.comN/A
Optimizely Feature Experimentation
Score 8.7 out of 10
N/A
Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.N/A
Pricing
monday devOptimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
monday devOptimizely Feature Experimentation
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
monday devOptimizely Feature Experimentation
Features
monday devOptimizely Feature Experimentation
Project Management
Comparison of Project Management features of Product A and Product B
monday dev
8.6
Ratings
12% above category average
Optimizely Feature Experimentation
-
Ratings
Task Management8.80 Ratings00 Ratings
Resource Management8.60 Ratings00 Ratings
Gantt Charts9.10 Ratings00 Ratings
Scheduling8.70 Ratings00 Ratings
Support for Agile Methodology9.10 Ratings00 Ratings
Support for Waterfall Methodology9.10 Ratings00 Ratings
Document Management8.70 Ratings00 Ratings
Email integration8.00 Ratings00 Ratings
Mobile Access8.80 Ratings00 Ratings
Timesheet Tracking8.40 Ratings00 Ratings
Change request and Case Management7.80 Ratings00 Ratings
Budget and Expense Management7.90 Ratings00 Ratings
Search8.70 Ratings00 Ratings
Visual planning tools8.80 Ratings00 Ratings
Agile Development
Comparison of Agile Development features of Product A and Product B
monday dev
8.2
Ratings
0% below category average
Optimizely Feature Experimentation
-
Ratings
DevOps Tool Integrations8.30 Ratings00 Ratings
Code Review8.00 Ratings00 Ratings
Code Collaboration8.40 Ratings00 Ratings
Velocity Calculation8.20 Ratings00 Ratings
Dependencies and Blockers8.30 Ratings00 Ratings
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Score 9.9 out of 10
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Score 8.7 out of 10
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Score 9.9 out of 10
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Score 8.7 out of 10
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User Ratings
monday devOptimizely Feature Experimentation
Likelihood to Recommend
8.7
(0 ratings)
8.9
(0 ratings)
Likelihood to Renew
-
(0 ratings)
4.5
(0 ratings)
Usability
8.7
(0 ratings)
7.3
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
Product Scalability
-
(0 ratings)
5.0
(0 ratings)
User Testimonials
monday devOptimizely Feature Experimentation
Likelihood to Recommend
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.
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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 -
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Pros
  • Tracking
  • Changing Status
  • Backlog
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  • 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
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Cons
  • More customization in automated tasks
  • Ability to send visual snapshots of reports and dashboards
  • Integrated reports from dashboards in dev and work management
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  • 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.
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Likelihood to Renew
We are very likely to renew, because it has been really useful
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Competitive landscape
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Usability
While monday dev is an excellent ally to organize and work in harmony with your team, there are still certain important aspects that need to be improved. They are minor, but if corrected, they will help improve the user experience when using it.
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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
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Support Rating
Our experience with Monday dev support has been good
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Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams
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Implementation Rating
No answers on this topic
It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
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Alternatives Considered
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.
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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.
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Scalability
No answers on this topic
had troubles with performance for SSR and the React SDK
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Return on Investment
  • We can more easily grab the KPIs for our quarterly reviews.
  • There are no automatic charts or reports I can pull for the quarter. Everything is manually gathered.
  • Doesn't give us job costing.
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  • 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.
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ScreenShots

Optimizely Feature Experimentation Screenshots

Screenshot of Feature Flag Setup. Here users can run flexible A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methodsScreenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groupsScreenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test dataScreenshot of a Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategiesScreenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimizationScreenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes