This is an 8-part series
Click below to navigate to the next chapter:
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Chapter 1 The state of web analytics in 2021
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Chapter 2 Privacy updates, ad blockers, and the need for 1st-party tracking
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Chapter 3 Building a web analytics stack: packaged vs modular
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Chapter 4 The best-in-class tools for web analytics
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Chapter 5 Redefining web analytics metrics
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Chapter 6 Data modeling for web analytics
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Chapter 7 Snowplow for web analytics
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Chapter 8 How Welcome to the Jungle took ownership of their web data with Snowplow
Download the full eBook Rethinking modern web analytics
Web analytics has become a more vibrant, fractured and challenging industry in recent years. From humble beginnings, websites have evolved out of static web pages into compelling web experiences. They can now host game changing features such as personalization, dynamic pricing and content recommendations to make browsing a richer, more rewarding experience. And the teams behind them: developers, product teams, data teams and engineers are laser-focused on understanding the user experience at a granular level, in order to make incremental improvements on a constant basis.
This is far from easy. Building a website to drive competitive advantage involves a deep understanding of your users and customers. It means diving into the intricacies of how they explore and interact with your website, examining how their needs are met (or not) throughout their journey and identifying where their overall experience can be improved. Underpinning all of this investigative work is the need for complete, reliable behavioral data. Ideally high-quality data; data that is complete, accurate and well-structured so it can be easily worked with and understood.
And getting this data is another huge challenge. In part, this challenge is a logistical one. It requires a data team to establish a successful data management practice that will make the most of the data. It requires a suite of tools that will take the data on a journey from the point of capture, to enrichment, modeling, storage, to visualization and reporting. It also requires a significant investment, not just in terms of cost and effort, but also a unified internal effort to align data objectives with the wider business and forge a culture of data excellence across the organization.
Keeping pace with the web industry
The gist is that the challenges involved in modern web analytics have now outgrown the packaged analytics solutions that got us this far. So much thought and innovation – driven by consumer demand – has gone into creating rich digital experiences, and rightly so. But as a consequence, the data practices in many organizations have been left behind, struggling to keep up. As the web industry has evolved, so must our processes, our approach and our tools for web analytics and data management.
In part, this is because our tooling has not evolved at the same pace. Packaged tools helped us get started with web analytics, and at their best, they can help us get off the ground at the start of our data journey. But as businesses grow and our reliance on data increases, the limitations of these tools prove costly and frustrating.
This is because:
- Packaged analytics don’t provide the flexibility and control over your data in how it’s captured or structured.
- Privacy updates such as ITP mean that tracking with third-party cookies is increasingly unreliable.
- Relying on packaged tools forces you to outsource your data collection approach to a third party. For example, you don’t get to decide what counts as a ‘conversion’ or ‘bounce rate’, the tool decides it for you.
- Packaged tools are ‘black-boxes’ – it isn’t possible to see what happens to your data under the hood.
- Third-party tools that model your data do not take your unique business model or logic into account. Data is aggregated according to a standard approach based around the ‘page view’, ‘session’ and ‘user’.
- Packaged tools don’t provide access to your raw data, limiting your ability to leverage data beyond basic reporting.
We know that companies winning today are the ones who use behavioral data to cultivate a strong understanding of their users and their needs. To get there, modern organizations should look to move from ad-hoc data functions, siloed off in their marketing, product and BI teams, to a centralized strategic capability that can empower the whole business.
Building a strategic data capability
As we mentioned in chapter 3, organizations looking to drive more value from their behavioral data should consider the advantages of breaking free from packaged analytics solutions.
Breaking out towards a more modular stack, made up of best-in-class tools makes it possible to build a strategic data capability that can sit centrally at the heart of the organization, empowering multiple teams and use cases. With this approach, your data is no longer in the hands of a third party. Your data, your data infrastructure and your overall data strategy belong to you and your organization. It’s this level of control and oversight that opens the door to new possibilities – bringing data closer to the user experience and the potential to use behavioral data, not just to generate insights, but to enhance products.
Moving towards building a strategic data capability is as much a cultural shift, a change in mindset of an organization. It involves a transition from perceiving the data team as a cost center or IT department, to a strategic resource who can empower every aspect of the company.
While there is too much to be said on this subject to cover it sufficiently here, the goal of the strategic data capability is to create a centralized, high-quality data asset that can provide insights, power use cases and inform decisions for all internal teams.
The first step for companies embarking on this path is to take full control of their data. Built from the ground up with ownership and flexibility in mind, Snowplow is a solution that can help data teams make this crucial step on their data journey.
Why Snowplow belongs in the modern web analytics stack
Snowplow is the preeminent behavioral data platform, built to put data teams back in the driving seat of their web data. With Snowplow, data teams can capture and manage rich, high-quality web data in a way that makes it easy for analysts and other data consumers to use and understand. Snowplow treats behavioral data differently to packaged analytics solutions because it was designed to handle data as a company’s most important asset.
There are multiple reasons why Snowplow is the solution of choice for modern web analytics. The following examples are just the beginning.
Total control and flexibility
Snowplow puts you in control of your data. It’s up to you how to collect your data, with multiple trackers at your disposal for web, mobile, server, IoT and more. Then you have complete flexibility over how you structure, model and store your data.
It’s your choice how the data is used – for whatever use case or company goal you are striving for. Snowplow data is flexible and does not prescribe a particular approach or assumption on how your data should be utilized. You decide how the data should be modeled, and ultimately used, to grow your business.
“Thanks to the unlimited, real-time data points Snowplow lets us gather, we can calculate individual user footprints, and will soon offer users a more personalized content space when they come to La Presse sites.”
– Hervé Mensah, Director – Data Science & Integration, La Presse
The best behavioral data set
Snowplow data is made up of events that register user interactions. Snowplow events automatically capture 130 properties, making the data uniquely rich. When it comes to web data, Snowplow lets you capture events with first-party, server-side tracking. This means your data collection isn’t affected by the restrictions of browser privacy measures or ad blockers, since you don’t have to rely on third-party cookies.
“With Snowplow we are focused on extracting and centralizing data from everywhere, ensuring data quality to be able to stitch everything we need together to get a complete picture.”
– Kevin James Parks, Data Engineer, Tourlane
Snowplow data arrives clean, well structured and ready to use in your data warehouse. All data collected by Snowplow is validated by JSON schemas, set up according to the requirements of your unique tracking plan. The result is that behavioral data delivered by Snowplow requires little cleaning or reformatting before your data consumers can put it to work.
Complete ownership of your data and data infrastructure
Snowplow data never leaves your own cloud environment, giving you total control over your data and data infrastructure. Your raw data is completely at your disposal – it’s never concealed or difficult to obtain.
And because Snowplow infrastructure is yours, you can configure your data pipeline in a way that makes sense for your business, with no vendor lock-in or preference for certain tools.
With total ownership of your data and freedom over your end-to-end infrastructure, you can choose how you’d prefer to work with your web data asset.
“The gist is that once you have all the relevant data for each event, which is possible with Snowplow, you can do whatever you want with it. Snowplow’s importance will only continue to grow as we customize our pipeline.”
Rahul Jain, Principal Engineering Manager at Omio
Every organization will take a different approach to web data management. But we believe it boils down to treating your web data as a strategic asset that can (and should) be owned by you, opening the door to limitless possibilities and use cases, far beyond basic reporting.
