Adobe acquired Omniture in 2009 and re-branded the platform as SiteCatalyst. It is now part of Adobe Marketing Cloud along with other products such as social marketing, test and targeting, and tag management.
SiteCatalyst is one of the leading vendors in the web analytics category and is particularly strong in combining web analytics with other digital marketing capabilities like audience management and data management.
Adobe Analytics also includes predictive marketing capabilities that help…
N/A
Optimizely Web Experimentation
Score 8.6 out of 10
N/A
Optimizely Web Experimentation empowers teams to conduct experiments (without having to rely on developer resources) in order to test various user interactions, make website changes backed by data, and personalize customer experiences.
The Adobe Analytics functions are more responsive and very flexible especially on multiple data analysis and to manage different marketing content is simple. I like Adobe Analytics performance which is cost effective and gathering marketing insights is easy and quick. To import …
Adobe Analytics is more expensive than Google Analytics, but Adobe Analytics is also more robust, customizable, and extensive. The level of detail and specificity available in Adobe Analytics is much greater.
Adobe Analytics (or any natural reporting tool), will always be my vote over any testing tool's reports (like Adobe Target & Optimizely) due to a much larger universe of reporting options available to the user. Amplitude has a lot of flashy bells and whistles, and is relatively …
It's a lot more, well, site stacked, it's way better than that. Adobe Target. I think the UI is easier to use on Optimizely. The one thing that I would say comparatively is our analytics talking to each other. Obviously Adobe, we use Adobe Analytics and Adobe Target, so they …
Optimizely Web Experimentation stacks up favorably against Adobe Target, Kameleoon, and Oracle CX Marketing in terms of ease of use, customer service, and features. It is easy to set up and execute experiments, the customer service team is always quick to respond to any …
I have used tools in various spaces that have all the flashy bells and whistles, and is, but lacks some basic features - Optimizely isn't this. While other tools, such as Adobe Target, Evergage, Dynamic Yield, Google Optimize, or even Taplytics may make more sense for your …
Maxymiser - The statistical significance engine used by Optimizely helps to reduce the detection of false positives. These were noticed on many occasions within the maxymiser tool.
Sr. Director, Traffic, Testing and Conversion Optimization
Chose Optimizely Web Experimentation
Prior to Optimizely, we used Test & Target (now Adobe, formerly Omniture and before that, Offermatica). Optimizely is better in every way. It's much easier to use (interface), and it's MUCH easier to have tests run since it requires just one code snippet (where T&T often …
Adobe Analytics is better than some other tools as it feels better set up for actual "analysis", rather than simply "reporting". The power of Workspace allows you to drag 'n' drop at ease which makes you are far more in control of your own analysis/discovery/exploration. However, regards the final reports and dashboards' look 'n' feel the Workspace PDF output is lacking visually compared to other products like Google's Looker. To engage with less technical end users sometimes Looker feels the better, more polished option.
Best for CRO initiatives, including testing variations of landing pages, user flows, and product pages. Optimizely may not be suitable for more complex machine learning models. To analyze the effect of the feature usage the way we will do in our solution. Using the audience to measure the success of the features Also, to the best of my knowledge, Optimizely is the only tool that can do all this.
They've been really an industry standard tool in analytics for a long, long time. They've got the trusted brand and the reputation, a wonderful community behind it. It is always nice, having that level of support where you can meet other practitioners. It's a great benefit because I can meet other people who have already pushed the tool a lot farther than I have. And it's a great place to get ideas in that way. We came from a world where we were running on a homegrown system that we'd use to do click tracking. You get some advantages on that of the customization, but losing out on community of support was one of the big reasons why we decided to move beyond that and implement Adobe Analytics instead.
Powerful Stats Engine that drives conclusivity of outcomes and helps generate trust in results when shared to leadership and stakeholders.
Customizable metrics with various tags, properties, and attributes that allow users flexibility in what and how they architect their Optimizely analytics.
Flexibility for different levels of tech expertise, I live in the tool as an expert JavaScript and front-end developer, someone else might use solely the visual editor to click and make changes without knowledge of how to code.
Most of the problems that Adobe Analytics as of now is having, it is getting addressed in a newer tool called Web Desk DK from implementation. They are already addressing that issue with the new tool and also the time data with the customer general analytics. So there is something not in workspace analysis and this is what they're addressing in customer general analytics. Which is good.
Google Tag Manager. Our challenge, it's a strange use case, but our challenge is that we don't have Google Tag Manager, so we can't integrate with GA4. And that's been a bit of a bummer. So I would like to be able to integrate with GA4 even without Google Tag Manager.
We've found multiple uses for Adobe Analytics in our organization. Each department analyzes the data they need and creates actionables based off of that data. For E-Commerce, we're constantly using data to analyze user engagement, website performance and evaluate ROI.
I rated this question because at this stage, Optimizely does most everything we need so I don't foresee a need to migrate to a new tool. We have the infrastructure already in place and it is a sizeable lift to pivot to another tool with no guarantee that it will work as good or even better than Optimizely
Sometimes the processing times are very long. I have had reports or dashboards time out multiple times during presentations. It could be improved. It is understandable since there is a huge data set that the tool is processing before showing anything, however for a company that large they should invest in optimizing processing times.
Optimizely Web Experimentation's visual editor is handy for non-technical or quick iterative testing. When it comes to content changes it's as easy as going into wordpress, clicking around, and then seeing your changes live--what you see is what you get. The preview and approval process for sharing built experiments is also handy for sharing experiments across teams for QA purposes or otherwise.
I do not ever recall a time when Adobe Analytics was unavailable to me to use in the 8 or so years I have been an end user of the product. My most-used day-to-day analytics tool Parse.ly however, generally has a multiple hours planned offline maintenance every two to four weeks, and sometimes has issues collecting realtime analytics that last anywhere between 15 minutes to an hour, and happen anywhere between 1 to 5 times a month.
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
Again, no issues here. Performance within the day updates hourly. other reports are updated overnight and available to access by the next morning. Pages load quickly, the site navigates easily and the UX is quite straightforward to get command over. On this front, I give Adobe kudos for building a great experience to work within
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
I barely see any communication from Adobe Analytics. The content on the web is also not that great or easy to read. I would recommend a better communication about the product and the new addons information to come to its user by a better mean.
They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
It was a one-day training several years ago that cost the organization several thousand dollars. There were only about 10 people in the training class. Adobe tried to cram so much information into that one-day class that none of our users felt like they really learned anything helpful from the experience. Follow-up training is too expensive
The online training for Adobe SiteCatalyst consists of short product videos. These are ok, but only go so far. For a while Adobe charged a fee for this, but recently made these available for free. There are many great blog posts that help users learn how to apply the product as well.
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
One of the benefits and obstacles to successfully using Adobe Analytics is a great / more accurate implementation, make sure your analytics group is intimate with the details of the implementation and that the requirements are driven by the business.
In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.
I think Adobe's been around longer as a product but Tealium, from when I did research, it has auto-tagging. So one of my biggest pet peeves is when I'm rolling out new features, and whether it's an app or a website, is that I have to go speak with our metrics team or tagging team and we have to come up with these different strategies. Okay, how are we gonna tag it? What are we going to name it? It just seems like a lot of wasted time in my opinion. I want to track everything. I want to know every single thing these people are doing. We shouldn't have to have this conversation if we tag this, you might not have time to tag this right away for MVP. It's like that to me right now. That shouldn't even be a conversation. I should be able to release a feature, I should be able to just automatically go pull reports on that. And just figure out exactly what they were doing.
Optimizely Web Experimentation was more robust and able to handle the broad array of sites we run than VWO. It has been a great platform to easily add additional sites onto, but still providing a universal overview of all of them, making management a simple task.
Adobe Analytics is relatively affordable compared to other tools, given it provides a range of flexible variables to use that I have not found in any other tools so far. It is worth investing in if your company is medium or large-sized and brings a steady flow of revenue. For small companies, it can be overpriced.
My organization uses Adobe Analytics across a multitude of brand portfolios. Each brand has multiple websites, mobile apps and some even have connected TV apps/channels on Roku and similar devices. Adobe can handle the multitude of properties that have simple, small(ish) websites and the larger brand properties that include web, mobile and connected TVs/OTT devices.
Each of those larger brands has multiple categories and channels to keep track of. We can see the data by channel/device or aggregate all the data together. This gives our executive teams the full picture and the departmental teams the view they need to see their own performance.
It's incredibly flexible and adapts well to organizations of all sizes, whether you’re running a single site or managing multiple departments and platforms. The ability to deploy experiments seamlessly across different environments is a huge plus, especially for growing businesses. While it’s highly scalable, the last point would depend on the right team leveraging its full potential.
The professional services team is one of the best teams for complex adobe analytics implementations, especially for clients having multiple website and mobile applications. However, the cost of professional services is a bit high which makes few clients opt out of it, but for large scale implementations they are very helpful
I like to think it's positive. It's a very steep learning curve, so we do face a lot of challenges with adoption inside of the companies. My team and I evangelize this and also who's in charge of data and advanced analytics, but it's very hard to leverage that with typical business analysis people. These are people who live their life in Excel and SQL and Power BI. They just use this very occasionally and by only looking at that sort of aggregate data, they miss out on behavior and what actually happens in execution. And because it's such a steep learning curve, we do have a challenge pushing it in there.
