Google Analytics AlternativesData AnalyticsDecision MakingAnalytics Implementation
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 Additionally, those comparison lists become outdated very quickly, with features and pricing changing even from week to week.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.