Companies that adopt advanced analytics can better understand how their customers are navigating their web experiences. Simply put, they have a level of insight not open to those relying on simpler, packaged solutions. But with every opportunity, there are challenges to overcome.
The challenges with advanced web analytics
In order to undertake advanced web analytics, companies need to create an accurate representation of how users move through online experiences. These efforts can be limited by various challenges, such as privacy, tooling limitations, and the complexity of user journeys.
1. Browser restrictions
Changes such as ITP, browser cookie updates, and privacy software all lead to complications when making business decisions based on web analytics. With ITP, for example, cookies can be restricted to less than 7 days for Safari users, as well as those using other privacy-conscious browsers. This skews your data for what can often be your highest-value customers, often inflating figures and misleading your attribution efforts.
2. SaaS apps often use black-box logic which you don’t control
Many packaged web analytics tools are black boxes, meaning that organizations have no idea how their data is being used and transformed, or what assumptions are being made. Not being able to control how your data is defined can seriously limit your analysis. Take the length of a session, for example, or whether an event fires on click or page load – all these things can make a difference to companies serious about advanced analytics.
3. The complexity of user journeys
Cross-channel and multi-devive analytics pose a further challenge. When this is done incorrectly, it can cause data siloes across a variety of tools and storage destinations. This means effective analysis of your data is not possible and that you have to ask more limited questions of your data sets.
The opportunity of creating warehouse-first web analytics
Relying completely on a single SaaS application for your web analytics locks you into a tool that is not best-in-class for each function it provides, and may pose issues with black-box logic, as discussed above.
The warehouse is becoming the center of data gravity, with the global data warehousing market set to be worth $30 billion by 2025 (Global Market Insights).
When companies adopt a warehouse-first approach to behavioral event data, the structure of the pipeline might look something like this:
After the data warehouse, the data can be integrated with 3rd-party tools, such as your Martech stack.
With this warehouse-first approach, you can use the best tool for Data Creation, and data storage, as well as for integration with other tools (such as reverse ETL).
This can empower every department to answer more nuanced questions, as data analysts can slice and dice the data with SQL, Python and R in your storage location(s). This provides a level of granularity simply not possible with traditional packaged analytics solutions – such as Google Analytics.
Another key advantage of being warehouse-first is compliance. With a modular pipeline, you can choose the most compliant tool based on your context and store your data in the appropriate jurisdiction – for example, anyone capturing data on EU citizens generally needs to store this data in the EU.
What is a Data Product Accelerator for web analytics?
A data product is an actionable data set you can create through advanced user tracking.
It is the foundation of a data application, which could be a churn- prediction model or marketing-attribution model, for example.
A Data Product Accelerator, or DPA, is a guided recipe that helps data teams to undertake advanced analytics and set up a customizable dashboard by following a simple, step-by-step process.
Ultimately, you can build a deeper understanding of customer behavior on your mobile apps, so you can use data to influence business decisions.
Try a Data Product Accelerator (DPA) for web analytics
Why choose Snowplow for web analytics?
Snowplow was built for warehouse-first analytics from the ground up. Our data arrives in your storage location AI- and BI-ready, meaning your data team is freed from the endless data preparation which sees so many data projects fail.
Furthermore, Snowplow is a pioneer in terms of compliance and transparency – we even deploy the software infrastructure in your own cloud environment (a ‘private SaaS’ model).
Due to the ability to customize your data, as well as the extreme granularity offered (over 130 event contexts), you can create a true customer 360 – getting data on users across devices, sessions, and channels – avoiding many browser restrictions.
Ultimately, Snowplow data allows organizations to drive revenue out of in-app purchases and subscriptions, as well as gain a better understanding of customer lifetime value and user retention.
To test this for yourself, try Snowplow for free