What’s the best alternative to Google Analytics?
It’s a commonly asked question with no one “universal” answer for all cases. There are literally hundreds of potential alternatives to Google Analytics, but at the same time there is no single alternative that matches exactly what GA can do. While we might think of GA as being the simple default of web analytics tools — it is anything but basic. It is the ubiquitous default tool, yet it has also has a wide range of abilities and can be used for many different tasks. With so many different use cases, giving one single answer for everyone is simply not possible.
To answer this question for our own unique use case, we first need to figure out which tracking and reporting functions are actually requirements. Maybe all we need is something super simple that counts visitors and pageviews and where they came from. Or perhaps we’re looking for a full product analytics suite to unify reporting across mobile app and web.
Next we’ll need to identify which products fit those requirements, and determine what the essential questions are to narrow that list of options. This can be a challenging task, and has lead many people to simply stick with GA rather than do this investigation.
My book on Google Analytics alternatives is designed to help you create a decision framework and help you answer these questions.
Alternatives to Google Analytics
The following are the 15 products evaluated in my book. All of these alternatives are widely deployed, with 1,000 or more installations among the top 1M websites. Each of these 15 is potentially the best for someone depending upon what they need. I also consider the possibility that GA4 may in fact be your best option going forward.
Simplified Web Analytics (pageview-based like traditional web analytics, but with less data collected by design) including:
Cloudflare Web Analytics, Statcounter, Chartbeat, Fathom, Plausible Analytics, Visitor Analytics.
I selected these 15 products by choosing the most widely deployed tools that could potentially be used to replace GA. Tools had to have a self-service version available so I could test all 15 with live website data. This restriction excluded a couple widely used Google Analytics alternatives, including Adobe Analytics.
About the Book
Who is it for?
The book is aimed at professional analysts with experience in GA3. It’s not highly technical, but it does assume the reader has knowledge of things like: third-party vs. first-party cookies, conceptually how tag managers work, basic analytics instrumentation, etc. It’s accessible to readers with a wide level of technical expertise.
What’s in it?
Part one is general background about web analytics, with a focus on establishing a decision making framework. For example, it defines what implicit vs. explicit event tracking is and when you might want one vs. the other. Part two is product evaluations of 15 different tools, including GA4.
These tools run the gamut from simple to complex. Cloudflare doesn’t even have events, versus Snowplow which is a robust data creation and pipelining platform. Both Cloudflare and Snowplow show up on lists of “Google Analytics Alternatives”, but they are incredibly different.
If we were writing about alternatives to Microsoft Word, this would be like comparing Windows Notepad to the LaTeX platform Overleaf. Both are word processors in the broadest sense of the term, but they are used in very different circumstances. The former is trivial to use and can’t do very much, whereas only specialists have likely even heard of the latter – but it’s incredibly powerful and is the tech powering more content than you might expect. GA is like Microsoft Word in this situation: ubiquitous, and used by both professional writers editing a 400 page book as well as your uncle to send you his chili recipe. Both Notepad and Overleaf might be alternatives to Word, but they aren’t competitors to each other.
To know what tool makes the most sense, first we need to understand scale and scope. Then we have to understand what tool fits in with our use case. This is the process my book helps you with!
What are the most popular alternatives to Google Analytics in 2023? Measured by installations among the top one million websites and limited to options that have at least .1% of the market, the most widely used tools are:
|Number of installs
(of top 1M)
|Evaluated in my book? If no, why not?
|1. Google Analytics
|2. Yandex Metrica
|No (privacy concerns)
|3. Baidu Analytics
|No (privacy and language)
|4. Cloudflare Web Analytics
|7. Adobe Analytics
|No (lacks self-service version)
|14. Fathom Analytics
|15. Plausible Analytics
|17. AT Internet / Piano
|No (lacks self-service version)
|18. Visitor Analytics
Data provided by BuiltWith.
This list does not include options that are focused on session capture (e.g. Fullstory, Clarity, HotJar, etc.), or tools in the product analytics arena that have limited marketing analytics functionality (e.g. Pendo).
Curious as to what’s in the book? Here’s the first chapter!
Google Analytics Alternatives – A Guide to Navigating the World of Options Beyond Google
Chapter 1 – Introduction
It’s an interesting time to work in analytics. As web and digital analytics have matured and attracted more attention over the last decade, there’s also been significant technological and regulatory change. I think of web analytics as an accidental industry, one that began as a generally ignored offshoot of IT that’s grown to be an important part of marketing, product, and development.
Web analytics remains new and ever-changing, evidenced by the lack of a consistently used name or definition within the field. What kind of analytics is it? Web? Digital? Product? Mobile App?
Google Analytics (GA) has been the predominant analytics tool since its launch in 2005 and is so entrenched, it’s become synonymous with the term “web analytics.” Google has also helped move the field from its roots in IT towards marketing — integrating GA into its larger advertising technology stack and focusing functionality on marketing analytics.
However, on July 1, 2023, this era will come to an end, as Google Analytics Universal Analytics (UA) will no longer process new data. Six months after that, access to historical data within UA will also disappear. This abrupt end to 18 years of contiguous data has upset many people and brought into question Google’s long-standing dominance in the field.
1st : 1995-2004
Webtrends, Analog, AWStats, Omniture
Logfile Analysis, Hit Counters
2nd : 2005-2022
3rd : 2023-
Google Analytics 4 and ?
Hybrid Client+Server, First Party Data, Multi-platform Data Collection
In this guide, we’ll look at some of the most popular alternatives to Google Analytics and create a decision-making framework to select a new solution. Instead of declaring a single best solution, it’ll inform you of the available options and how to choose between them.
Even though this is a guide to GA alternatives, we will approach GA both as a baseline for comparison and as a potential solution itself. You may find GA4 is the right solution for you, but my goal is to help you make that choice in an informed way.
I come at analytics from a technical perspective, so this guide will be oriented towards technical details more than business concerns.
You also won’t find any big lists of feature comparisons. Instead, I look at the point of view that each tool comes from and its most appropriate use cases.
Giant feature comparison matrices are helpful, but they can also be misleading and cause choice paralysis. Just because a feature is available doesn’t guarantee it’ll be able to solve the particular issue you have. The vendor’s idea of what checks that feature box may not align with how you envision that feature. Additionally, those comparison lists become outdated very quickly, with features and pricing changing even from week to week.
We’ll go through a series of Universal Analytics alternatives, review their pros and cons, and compare them to each other. I’ll do my best to highlight features that I think are most representative, but we will not go deep into implementation details. I also treat GA4 as one of these alternatives, endeavoring to look at it with fresh eyes rather than in the shadow of UA.
The choice at hand is between GA4 and its competitors, not UA. UA can provide us with context for our comparisons, but that’s all. Many people in the industry (myself included) hoped Google would somehow find a way to continue the functionality of UA, but they’ve given no indication of this, so we have to proceed assuming UA’s days are numbered.
Tool choice is only part of the equation of a successful analytics implementation. No solution will meet your needs perfectly — it’s about finding the best fit and then putting in the time to learn, customize, and implement the tool. All too commonly, companies abandon Tool A for Tool B because they think the former isn’t right for them, but in reality, they simply failed to use it well.
The background context we will go over in Part One of this guide is not only to help with tool selection, but to provide a better understanding of the paradigm the tool works within. It’s my belief that this deeper understanding will also help to better utilize whichever tool you pick.
Google Analytics Alternatives: Table of Contents
Part One: Background
Chapter 1 – Introduction
Chapter 2 – The Product Space
Chapter 3 – The Google Ecosystem
Chapter 4 – It’s Time to Consider Alternatives
Chapter 5 – Types of Tools
Chapter 6 – Pricing
Chapter 7 – Event Tracking Methodology
Chapter 8 – Support and Community
Chapter 9 – Should I Self-Host?
Chapter 10 – Open Source
Chapter 11 – How Important Is Tracker Speed?
Chapter 12 – Advanced Features
Chapter 13 – Cookies & “Future-Proofing”
Chapter 14 – Privacy & Compliance
Part Two: Product Evaluations
Traditional Web Analytics
Chapter 15 – Matomo Cloud
Chapter 16 – Piwik PRO
Chapter 17 – Clicky
Simplified Web Analytics
Chapter 18 – Cloudflare Web Analytics
Chapter 19 – Statcounter
Chapter 20 – Chartbeat
Chapter 21 – Fathom
Chapter 22 – Plausible Analytics
Chapter 23 – Visitor Analytics
Chapter 24 – Google Analytics 4
Chapter 25 – Mixpanel
Chapter 26 – Snowplow
Chapter 27 – Amplitude Analytics
Chapter 28 – Heap
Chapter 29 – PostHog
Chapter 30 – Conclusion