We are often asked about the differences between CoolaData and Google Analytics (GA). GA is an excellent tool – its free version is used by millions of website managers to track pageviews and website trends all over the world. Google’s Premium version is also useful and popular, although available only in a handful of countries. We are huge fans of Google in general and we partner with Google Cloud Platform, so our focus here is on what makes CoolaData a unique and different tool.
Unified Data & Behavioral Analysis – A focus on page views is often times not enough. Tracking the number of pageviews is basic, as is the analysis of a path users take between two points. However this info is only helpful if it is viewed in a relevant context that allows answers to questions about analyzed data. CoolaData focuses on unifying data from a multitude of external and internal sources, and inspects all of your data as one unit in order to provide comprehensive business insights.
By putting data into context we help you get deep insights into your users’ behavior, and analyze what they do and how they do it rather than simply tracking what they do. This approach is what we call behavioral analytics.
Power Cohorts on CoolaData can perform analysis using many different attributes, not only dates. This grouping by a wide variety of attributes enables the analysis of row-level data for events and users in order to help analyze, visualize, predict and act on it. Google Analytics’ cohort analysis is limited to certain pre-set attributes, like date or location, but CoolaData allows for these to be customized. The ability to create and analyze custom properties is at the core of CoolaData’s behavioral analytics. More on our Power Cohorts here.
CQL – Another way that CoolaData allows our customers to perform analyses which differs from platforms like Google Analytics is with CoolaSQL (CQL). CQL allows for custom queries within a database that can be layered. This means that a company is able to ask the exact questions it wants answered, for example a CQL query can ask how many users from the United States played three levels between the hours of 10:00pm and 1:00am on January 15th. This high level of customization enabled by CQL is one of the greatest assets CoolaData offers.
Path Analysis is a great tool offered by many companies, including Google Analytics. In addition to the traditional sankey path analysis offered by GA, CoolaData’s path widget displays paths which incorporate the duration between events and are comprised of multiple properties. For example, rather than just tracking the path of a user from login to purchase, we can track the duration of each step in that path in order to identify potential UX design flaws. For more info on CoolaData’s path analysis tools make sure to check out our latest path analysis blog post here, as well as a great definitional post here.
User ID Groups – It is often difficult to track individual users, as users might log into an eCommerce site from a laptop, then view an item on a smartphone and complete the purchase on a tablet. Most analytics tools will count each of these actions as coming from separate users, rather than one person with three devices. CoolaData creates User ID groups that identify individual users from multiple sources into individual profiles. This allows for more accurate analysis and a better understanding of user behavior. Additionally, it becomes much easier to analyze users on a more granular level with these individual user profiles .
Relevant Information – When Google Analytics is installed on a platform, it is installed on the entire website, tracking every page, gif and crawler that is on it. A website with hundreds of pages, many of which are back-end and have no relevance to the understanding of user behavior, are all tracked and counted. CoolaData allows for the installation of our SDK only on pages that are relevant. So, a company can track every page, if it wants to, but it also has the option of only tracking the ones that provide it with behavioral insights.
Sessions – Session length is something that changes both from platform to platform and user to user. An avid gamer may spend days playing a single game, while a more casual player will only play for a few minutes on the bus heading to work. Someone who is trying to decide between purchasing two items in their shopping cart may leave the items there for a number of days before finally deciding which one they want, while another shopper will make that same decision in a matter of minutes. It is therefore important to be able to set custom session lengths that can be analyzed per user and platform. This is something that can be done with CoolaData. Google Analytics, by default, cuts session lengths off after one day (24 hours).
Virtual Dimensions – It can be very useful to group channels together in order to analyze them. For example, you may want to group your three smallest channels together and compare them to your largest channel. In Google Analytics this must be done by analyzing each individual channel and then comparing them on their own, but CoolaData allows for the grouping of channels and other data groups into Virtual Dimensions. These dimensions can be customized and compared, allowing for more flexibility.
Calculated Columns – Another great feature that is unique to CoolaData. A database may have one column for yearly revenue figures and another for expenses. Using Calculated Columns, one can define custom metrics based on these two originals columns, without having to actually add them into the database. For example, one could calculate the percentage of costs out of revenues and use that new calculated column as if it was its own “real” column. This can, in turn, be used to set targets and determine Key Performance Indicators (KPI’s).
Contact CoolaData to find out more about these differences and how your company can benefit from CoolaData’s unique and powerful analytics platform.