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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Google BigQuery
Score 8.8 out of 10
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Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Adobe is fairly robust and similar to Google Analytics however as a paid service it is not exceptionally better. It provides much the same data that Google Analytics does and in some areas is not as strong. Page-level reports are more difficult in Adobe. Selecting a group of …
BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function …
Maybe for a small company with small products for their thing, Adobe may be bit of an implementation too much for them, but when it comes to companies like us, like a life sciences or large enterprises and even small enterprises, but with more products, more analysis that they need to make their marketing experience better, maybe Adobe product is the best suitable.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
It summarizes large complex data better than any other analytics solution I've dealt with without the need for sampling, gives the right level of detail, does the right level of breakdowns, aggregation. I consistently not only use Adobe Analytics, but I use other data sets and compare against Adobe Analytics. And as I go into Adobe Analytics and compare, as long as I've done the query right and the other systems, they're very, very close. And if anything, with a lot of Adobe's newer products, they've gotten more accurate over time. So that's basically, you asked me what I liked about it. I like that it's accurate. I like that I don't have to do a lot of explaining. There's enough explaining in the world of web analytics to have to go back and explain why data's problematic. And so like I said, provided that the implementation is correct, it's a very easy conversation. Even if people may not like the answer.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Support. I mentioned this earlier and we don't know what we don't know. Researching the massive amounts of documentation isn't realistic with bandwidth constraints, and our rep getting frustrated with us when we go through what we are seeing is disappointing.
Education. More please, and designed more towards the "business side". I get with the many many many different implementations (every company is different!), that it's tough, but even a basic of the basics would be nice for situations that everyone is looking at, like the engagement with the merchandising on the home page (or any certain page).
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
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 have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
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.
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
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 have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
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 think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
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.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
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.
Google Analytics comes across more of a reporting tool whereas Adobe Analytics is more of an Enterprise level analytics tool. Contentsquare provides some traffic and flow capabilities but not to the same level as Adobe Analytics. However, Contentsquare's major advantage is its Zoning (Heatmapping), Impact Quantification and Find 'n' Fix modules; none of which are knowingly available in Adobe Analytics.
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
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.
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
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
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Adobe Analytics impacts nearly every aspect of a billion plus dollar revenue eCommerce business. From measuring the impact of new build features to marketing campaigns.
We are saving substantial money and resource effort by consolidating all of our properties to Adobe Analytics from alternative solutions, at which point we will finally be able to report on Total Digital, rather than disparate reports.
We support experimentation on every platform and the performance is only known through Adobe Analytics tagging.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
