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…
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Looker
Score 8.2 out of 10
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Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.
Adobe analytics has less flexibility in terms of joining data sources and creating visualizations. It does well at creating custom analytics, but so do the other platforms. Workspace does tend to be better at creating segmentation.
Adobe Analytics has better features for finding problems and highlighting areas of improvement on a website, but the Google products seem to integrate better into third-party tools, which can be very valuable in a complex business environment. All of the listed tools take care …
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.
Quick dashboards from Google Sheets - Easier to do the graphs than in Google Sheets - Operational dashboards to be used in the day-to-day work - It is good both for retrospective data and to do a pulse check of the current status - Better for not giant amounts of data and not multiple data sources. - If you need a lot of graphs to be displayed on the same page, it can be a bit glitchy during configuration (then the use works fine).
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.
Filtering - you can filter across different dimensions and metrics to get a more specific "cut" of data
Refreshing - data automatically ingests into Looker which allows reports to be updated and backfilled in real time
Conditional Reporting - you can leverage Looker's reporting features to flag when a given metric or KPI falls below or above a specified threshold. For example, if you had a daily sales benchmark in a SAAS organization, you could use Looker to flag whenever daily sales falls above or below the benchmark
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.
Looker is less graphical or pictorial which makes it less attractive
Consumes a lot of memory when there are multiple rows and columns, impacts performance too
At times when we download huge chunks of raw data from Looker dashbords, the time taken to prepare the file is enormous - The user fails to understand if Looker has frozen or if the data is getting prepared in the background for downloading. In turn, user ends up triggering multiple downloads
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.
We've been very happy with Looker so far, and all teams in the organization are starting to see its value, and use it on a frequent basis. It has quickly become our accessible "source of truth" for all data/metrics.
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.
Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.
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.
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 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.
Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.
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.
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.
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.
Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.
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.
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.
Allowing others to self-serve their own analytics and connect it to Looker simply and easily has helped unblock the central data team so they can instead focus on validated dashboards whilst stakeholders manage their day-to-day analysis themselves. Countless engineering hours have been freed up by not having to manage every user permission for each BI tool; we have a BYOBI approach; Bring Your Own BI
Creation and management of a semantic layer (LookML =Looker Modeling Language ) allows peoples sandboxes and production databases to become clutter free. Minor adjustments, conditional fields, and even some modelling can all be done in LookML which doesn't need oversight or governance from the central data team.
LookML, specifying drilldown fields and their sub-queries, as well as generally creating dynamic parameters with Liquid are all great features, but can have a steep learning curve. it may take some time to understand how to create this middle layer correctly, or even pose a risk of inheriting complex code from another source which can be unmaintainable if it becomes too big. Some level of governance is recommended if Looker is used by a large number of editors.
