Dynamic Yield is presented as an AI-powered Experience Optimization platform that delivers individualized experiences at every customer touchpoint: web, apps, email, kiosks, IoT, and call centers. The platform’s data management capabilities provide for a unified view of the customer, to allow the rapid and scalable creation of highly targeted digital interactions. Marketers, product managers, and engineers use Dynamic Yield for: Launching new personalization…
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Optimizely Feature Experimentation
Score 7.6 out of 10
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Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.
Dynamic Yield is great for just about any sized organization, though to get the best bang for your buck, I recommend having a front-end web developer well-versed in JavaScript. Additionally, a front-end web designer would be advisable as well as their templates have great functions but some have lackluster UI's that can't be tweaked without developer assistance. Were it not for the above + the occassional slowness on the console/admin-side of the platform, I'd give it a 10. If you have a front-end dev/designer, then it's closer to a 9.5. Ideal utilization scenarios could include: Personalization, CRO/UX/UI testing, and audience or user-level tailored digital experience.
Optimizely Feature Experimentation is good if you have a clear release process that incorporates It into your current product release cycle. However, It requires a lot of resources and time, especially at the start when teams are learning how to use and deploy it. Therefore, it may not be the best Experimentation to go for if you are starting in your Experimentation journey.
Provide fantastic support, both in relation to strategy/best practice and troubleshooting.
An easy to use interface, as a user who is relatively new to Dynamic Yield I find that it is an intuitive platform to use.
The ability to segment and drill down on data allows for really specific insights which, whilst not necessarily being leveraged on a testing basis, can be super valuable from a greater marketing perspective.
Its ability to run A/B tests and multivariate experiments simultaneously allows us to identify the best-performing options quickly.
Optimizely blends into our analytics tools, giving us immediate feedback on how our experiments are performing. This tool helps us avoid interruptions. With this pairing, we can arrive at informed decisions quickly.
Additionally, feature toggles enable us to introduce new features or modifications to specific user groups, guaranteeing a smooth and controlled user experience. This tool helps us avoid interruptions.
The impact (either positive or negative) of potentially overlapping campaigns, especially the UX personalization or custom code campaigns, may not be easily identifiable.
It would make more sense for the new deep-learning and machine learning (ML) driven strategies be made part of the standard offering, as opposed to positioning them as add-on subscription, given that many other completing services are baking in ML as part of their platform evolution.
The documentation on the API and custom code implementation can be fleshed out further.
Extremely confusing, complicated, and unintuitive webapp. It's hard to figure out if a feature flag is active and what it will evaluate to for a given user, organization, or audience. The app has many different toggles for enabling, disabling, and targeting a flag, and they don't follow a consistent design.
Slow and buggy login process
Difficult to use human-readable aliases for user IDs and organization IDs when defining audiences. We maintain spreadsheets to understand our Optimizely configurations
implementation took a long time but also, DY has really proven that they are transforming and adapting their platform to be more user friendly and the right technology choice for their brand or company
Setting up strategies, audiences, and experiences is simple and fast. It is incredibly easy to modify the appearance of your site and optimize every aspect with the Dynamic Yield Personalizations. However, while the data visualization on an experience level is easy to modify and analyze, exporting the data in meaningful ways is time consuming.
Overall, the support is very good. If you are a partner (my case), they assign you a customer success manager, that helps a lot. Also, there is a technical person to provide support to the partners, again a great help.
My only "complain" is that with some complex issues, the support may delay in providing you with a solution. Sometimes that can cause some tension with your client.
Oracle Maxymiser is very clunky and hard to code with. Previewing changes was a challenge and development for fixes were slow
Optimizely - Great for coding. Fast and efficient. Everything worked great. They were limited at personalization triggers though and their costs were expensive.
Monetate - Evaluated but their UI was hard to use.
Google Tag Manager was less flexible for the business and required the Google Analytics tool for analysis and metric tracking. Optimizely allows the building of use cases. Optimizely provides real-time data and metrics that are easier to use. GTM provides tracking capabilities on marketing campaigns, however, Google Analytics could potentially store user personal data.
Most tests have had a positive impact on either revenue or conversion rate - quite often in double digits.
Dynamic Yield has also helped us to stop some particular initiatives through direct interaction with the customer base via questionnaires or by a test proving negative quicker than rolling out a permanent feature.
