Google Analytics alternatives - An overview
Given the recent developments surrounding Google Analytics within the EU, it's not unrealistic to think that organizations are wondering what else is out there. Competitors in the analytics space are bashing Google Analytics every chance they get, but I would advise to look beyond that.
Don't get me wrong, I think Google Analytics is an fantastic product and I hope they will sort it out. But it's always good to broaden your view, especially when talking about Google Analytics, which was / is a no-brainer for a lot of companies.
Instead of giving you an exhaustive list with all the alternative or complementary analytics vendors and/or frameworks available, I will try to identify common analytics functionality and what types of alternatives are out there that enable you to build a data driven organization:
- An overview of common functionalities within analytics tools and frameworks.
- Relevant developments within the analytics and data space (not only on technical).
Approaches & types of alternatives.
I'm aware that the recent developments is potentially impacting more than Google Analytics alone, but I'm not diving into the legal aspect of things in this article.
Google Analytics, a brief history
Yes, Google Analytics, 2005, Urchin and all that. Bottom line is, Google Analytics is the leading analytics platform for years. From my experience, Google Analytics has the following benefits:
- Adoption & maturity
- Familiarity of the (reporting) interface
- Documentation & community
- Ease of implementation
- Reporting API
- Integration with other products. Not only GA integrating with other Google products but also out-of-the-box integration of GA in numerous tools (think of CMS systems, plugins etc).
- Advanced capabilities (like raw data exports to Google BigQuery)
It's not that other tools are lacking most of these capabilities, but making your organization data driven is mainly about people and process. Data analysts and technical marketers are scarce, so it really helps that there is a tool most users are already familiar with, even when they switch jobs (especially for the less tech savvy users).
Common features
So let's dig into the (high-level) features of (web) analytics tools to give some context. In reality, there are countless features and every tool has it's own approach. But overall, you can argue that the main focus is to measure and report on behavioral data, mostly related to web and app activity. Common features:
| Functionality | Description |
|---|---|
| Data collection client | Think of Javascript libraries (e.g. gtag.js) and ready-to-use packages or SDKs that can be used directly within a PHP, NodeJS or other development environment. These clients will interact with the (collection) API of the analytics tool. Also closely related to Tag Management such as Tealium, Google Tag Manager or Segment: providing templates / out-of-the-box integrations for these data collection clients. The data collection client should also support customization in some form (e.g. custom events of properties). |
| Basic reporting | A clear and easy-to-work-with reporting interface where you can get basic insights out-of-the-box. |
| Custom reporting | A custom reporting interface where business specific reports can be build & shared within your organization. |
| User management | Control user access to reporting data / dashboards |
| Integrations | Integrate with other products, think of data enrichment (e.g. auto tagging) and building and sharing audiences |
| API & exports | Exporting data, both aggerated or raw event data. By providing a (reporting) API or real-time / periodical data dumps to (analytical) databases. |
Developments within the market
As mentioned, becoming a data-driven organization is not only about the tool and its features. There are some relevant developments in recent years that have impact on how you should build your data-driven organization:
- Data analysts & technical marketers are scarce.
- The offering and ease of use of analytics related tools improved enormously (e.g. SaaS tools, analytical databases)
- Much easier (and cheaper) to work with large amounts of data (such as web/app clickstream data) within (cloud) analytical databases (Snowplow, BigQuery etc).
- Privacy & regulations (think of GDPR, cookie consent and the current buzz around Google Analytics / EU legislation)
- Move to "server-side" event / tag management
- "Self-service" BI tools on the rise. Related to the adoption of cloud analytical databases.
Types of alternatives
These developments impact decision making but also increases the development and popularity of alternative or complementary tools and frameworks. So I'm not looking into "all-in-one" reporting suites only (as I would categorize Google Analytics). There are other solutions as well:
- All-in-one reporting suites
- Data collectors & data warehouse loaders
- Event hubs / routers
To give you some examples, when talking alternative solutions:
- Switch to an alternative "reporting suite" vendor (duh!)
- Set up a data collector that collects and loads the data into your data warehouse. Use a BI tool to set up and share the reports within your organization. Modern analytical databases and BI tools (Superset, Looker, PowerBI, Tableau etc) are perfectly able to handle this types of data. But you will have to acquire some expertise to set up these flows and reports initially (+ onboarding / training of the end users).
Complementary solutions:
- Set up an event hub for data collection and route the events to Google Analytics or another analytical tool.
- Use the raw data dumps next to the (native) reporting interface to execute more advanced use cases and/or reporting in a BI tool (or to prepare an organization for a life without the application / default reporting interface).
The privacy aspect
Privacy and compliance is becoming much more important (and rightfully so). Think of the location where the data is stored, user consent or what kind of data (e.g. PII, cookies etc) is send to the analytics servers. So make sure you know how a specific tool is dealing with these aspects and what the configuration options are. It could also be a wise idea to self-host a solution for example.
Almost every tool of framework is using a first-party cookie to measure the same user over different visits. But you could also decide to not use any cookies (or only after user consent). You will lose a lot insights, but it's still possible to measure effectiveness of your website for example, however not on user level.
Let's have a look at the different types of solutions.
Note: I haven't tried all the alternatives in depth, so I'm giving a high-level description of the tools, instead of a full comparison. Please let me know if you're missing something!
1. All-in-one reporting suites
In these type of tools the user (reporting) interface plays a central role. Everything is built around the interface. How to create a measurement plan and set up data collection the right way is something else, but when you set up data collection in these tools, the default reports are populated immediately and you're good to go.
| Vendor | Description | Pricing | Hosting |
|---|---|---|---|
| Google Analytics | Analytics offering within the Google Marketing Platform | Free & paid | Cloud |
| Piwik Pro | Analytics suite with a focus on user privacy and data security | Free & paid | Cloud |
| Mixpanel | Focus on product analytics. | Free & paid | Cloud |
| Amplitude | Focus on product analytics. Part or a larger suite / other modules available. | Free & paid | Cloud |
| Adobe Analytics | Analytics product within the Adobe Experience Cloud. Corporate focus. | Paid | Cloud |
| Matomo | Formerly Piwik. Free and open source analytics platform, focused on user privacy and data security. | Free & paid | Cloud / On-premise |
| Heap.io | Focus on auto capturing events and behavioral metrics | Free & paid | Cloud |
2. Data collectors & loaders
Secondly, this category of tools is focusing on the data collection part and storing that data into an (cloud) analytical database (e.g. Snowflake, Google BigQuery or AWS Redshift), without providing an reporting interface.
Snowplow is popular in this category and around for some time. The tool provides an extensive library of data collection clients, such as a Javascript tracker, SDKs, packages for popular frameworks such as NodeJS. Main goal is to get the raw behavioral data to your (cloud) data warehouse in real-time.
Note: Equally important for the other categories as well, a good collection client / API, including a sound data structure / schema (protocol), handling of (first-party) cookies and customizability is not present in every tool. Good example of mature collection protocols: the GA measurement protocol or the Snowplow Tracker Protocol
| Vendor | Description | Pricing | Type |
|---|---|---|---|
| Snowplow | Open source platform for (online) behavioral data collection, streaming data into (cloud analytical) databases. | Free & paid | Cloud / On-premise |
3. Event hubs / routers
This last category has overlap with the previous one, but ads an additional component: routing / distributing the collected events to a range of other systems (on the server-side).
Almost every tool will provide you with an user interface and templates to configure those routes. Sometimes part of a larger suite (for example as a step before aggerating event data to user profiles and audiences).
Rules, filters and transformations can be applied to the (raw) data stream. Next to data warehouses as a destination, other types of data destinations are present as well. Think of analytics tools (category #1), marketing channels (e.g. Facebook) or marketing automation platforms. All without implementing additional Javascript tracking libraries, seen the events are forwarded on the server-side.
| Vendor | Description | Pricing | Hosting |
|---|---|---|---|
| Tealium EventStream | Collect & route events to external applications or the Tealium CDP. | Paid | Cloud |
| Google Tag Manager Server | Self hosted "server-side tag management" solution of Google. Distribute behavioral event streams to Google and non-Google destinations. | Free (only cloud costs) | On-premise |
| Segment | Collect and distribute data to other applications. Also has CDP capabilities. | Free & paid | Cloud |
| Rudderstack | Collect and distribute data to other applications. Focus on developers and has CDP capabilities. | Free & Paid | Cloud |
| MetaRouter | Server-side event router with a focus on compliance and security | Paid | Cloud / On-premise |
| Jentis | Server-side tag management solution hosted in the EU | Paid | Cloud |
| Jitsu | "Open source Segment alternative" | Free & paid | Cloud / On-premise |
Closing thoughts
Selecting an alternative or complementary data collection tool or stack should depend on your type of business and more important, your requirements. The list or requirements is growing, since not only the wishes of the business user are important. The privacy of the user tracked is equally important and it's not given that the tool that shouts the loudest regarding their privacy offering is the best option in your situation.
Start with your business objectives, requirements, the people that need to work with the tool and the expertise and resources available within your organization. Then pick a vendor.
