Heap vs. Google Analytics 4

If you follow the world of digital analytics, it will come as no surprise that Google has majorly updated their flagship analytics platform to GA4.

We are glad Google joined the party! Mostly, we’re happy to see the analytics world recognize the insight that’s sustained Heap on from the beginning: that freeing analytics from the burden of event tagging is the optimal path to building the best digital experiences.

This page was built to help you compare platforms.

  • What is Heap?

    Heap is a digital insights platform that allows any user, regardless of analytics maturity, to uncover insights into user behavior across the entire digital experience. Heap offers a best in class, automatically-captured data set that captures every user interaction on your site or product, with no coding required.

    To this complete dataset Heap adds data science features that automatically pinpoint the key events that cause user friction, and integrated session replay, which instantly brings Heap users to replays cued up to the exact moments they’re interested in.

    Together, this platform gives teams the ability to rapidly uncover root causes of key user behaviors and take quick action to improve the user experience. With Heap, teams can go beyond high level KPIs and web metrics and ship the features or improvements with the biggest impact.

  • What is Google Analytics 4?

    Google Analytics 4 is an analytics tool that allows users to track and measure traffic and advertising metrics across websites and apps. GA4’s was built primarily to help marketing teams drive better ROI on their website layout, and to optimize the return on advertising spend.

    Secondary to this is integration with tools inside the Google Ecosystem, which satisfy more complex use cases by sending raw data out to BigQuery, and integration with Google Ads that help marketers understand their target audiences and build cohorts to target for advertising.

    Perhaps the biggest change from previous versions of Google Analytics is that GA4 uses an event-based model (which Heap has had from the beginning), which is a significant advance over Google’s earlier sessions-based approach to analytics.

Features

Heap

Google Analytics 4

Heap was built to provide best-in-class data so users can generate deep insights into how people use your website or product. Heap tells you why things happen, shows you what’s working and what isn’t, and directs you to the root causes of key customer behaviors.

Heap’s data science features guide your analytics in real time by highlighting behaviors you may have missed and making suggestions about where to focus your analyses.

With features like funnels, journeys, influence of touchpoints and time-based analytics, Heap can set up automatic queries to answer questions like the following:

ㅤ• Did a feature change after a specific date? 

ㅤ• Which accounts have the best per-user conversion rate for a particular feature?

ㅤ• How does the use of a particular feature influence conversion and drop-off rates?  ⠀

ㅤ• What are the most common paths that users take after landing or before purchasing?  

ㅤ• How does my experiment affect the conversion rate?  

ㅤ• How has my purchase funnel changed since last month? 

ㅤ• Do users returning after a break convert at a higher rate than new users?

Because GA4 is web-based and has a more limited data set, GA4 users get a narrow focus on high-level metrics that tell you what happened on your site or product, but not why those things happened. GA4 offers automated insights, but they are quite limited.

Here are some examples of the types of automated insights and depth of analysis you can take advantage of with GA4:

ㅤ• How many users did I have last week? 

ㅤ• What are my top cities? 

ㅤ• How many users from organic search in the last 30 days? 

ㅤ• What are my top pages and screens by views?

ㅤ• How many new users visited this year?

To investigate deeper, GA4 users need to pull data into BigQuery and have knowledge of SQL.

Heap

Google Analytics 4

Heap’s complete data foundation and analytics features let users paint a complete picture of the customer experience, and guides teams to understand what components of their digital experience are keeping users engaged, improving retention or causing churn.

Heap’s data science layer automatically scans your dataset to show you missed steps, alternate paths to conversion, and areas with significant friction.

Heap offers Session Replay integrated into our analytics suite. For any user event you’re interested in, Heap can pull up endless sessions cued to that exact event. This is a significant advance over stand-alone session replay tools, which require you to search through hundreds of recordings to find what you're looking for.

Heap’s new Journeys feature lets you analyze multi-funnel nonlinear user experiences, with multiple branches and optional steps. Heap also offers traditional funnels, path analysis, advanced retention, time based usage analysis and influence analysis.

Google Analytics provides high-level web marketing analytics that focus on advertising spend and revenue. GA4 is not intended to be a product analytics tool. It lacks functionality that is generally required for analytics and PM teams, like the ability to ask iterative behavioral questions that uncover the root cause of user behaviors.

Though GA4 offers analysis like funnels and retention, because it only offers surface-level metrics, it cannot uncover the drivers of what is causing users to complete a funnel step or be retained. 

For example, in GA4 you can see where there was drop off in a funnel, but you won't have the data to see what interactions between those funnel steps are causing the friction or drop off.

GA4 offers no ability to watch session replays to confirm your hypotheses and see quickly what the right improvement should be.

Heap

Google Analytics 4

Heap was designed with collaboration in mind. Anyone can define an event, add a description to it, perform an analysis and pin it to a dashboard, and add notes on dashboard panels for better visibility into what an analysis means.

Heap offers automated templates and playbooks, and the ability to share any query or dashboard with other users via a unique URL, so other users can edit without changing the original query.

GA4 offers collaboration features like sharing chart links or sharing via email. However, it lacks many key features like notes in dashboards, event descriptions, and customizable playbooks and templates.

GA4 does not give users the ability to create a custom report, save it as a template and import it to a new property. This reduces a team’s ability to easily share information.

Heap

Google Analytics 4

Heap lets any user create behavioral segments based on real actions taken on your site or app. This ensures that teams can segment users according to what they do, not just who they are.

Segments in Heap can be brought into any analysis to understand how different groups navigate your user experience, and how you can personalize communications to them.

Segments defined in Heap can also be pushed out into other tools, where they can drive a multitude of marketing and product related use cases, like personalization and targeted advertising.

GA4 allows users to create user cohorts for advertising segments and define simple behavioral segments. These are predominantly focused on who users are, not necessarily what they do in your product or experience.

To build more specific or advanced cohorts relative to time or specific behaviors, teams need a more technical analyst with SQL knowledge.

Heap

Google Analytics 4

Heap provides a network of over 40 integrations, providing both data-in and data-out connections to products like Snowflake, AWS S3, Redshift, Salesforce, Optimizely, Hubspot, Marketo, Shopify and many more.

Heap’s autocaptured data provides teams the data they need to perfect use cases spanning A/B testing, personalization, ad targeting, NPS surveys, marketing campaigns and more.

Many Heap customers push Heap’s gapless data set into data warehouses like Snowflake, Redshift, and AWS S3 to drive more complex data science use cases and personalization.

Google Analytics 4 allows users to connect and integrate with other tools inside of the Google ecosystem like Google Ads, Google Cloud, Google Ad Manager and more.  However, the only external integration available is Salesforce Marketing Cloud.

Google Analytics 4 allows users to send data out to Google Big Query, which requires users to have a paid account if they want to store more than 60 days of data at a rate of $0.05 per gigabyte of data, which is roughly 600,000 GA4 events. There are also many latency and table timing factors to take into account.

In addition, the event schema only includes a raw event table. Users will have to use SQL in BigQuery to recreate any analyses or cohorts they built in the GA4 UI which can be a limiting factor for non technical users.

Heap

Google Analytics 4

Heap provides the most complete data capture on the market. Our Autocapture snippet and SDK which automatically capture every user view, click, swipe, and form fill with a single snippet of code.

This is achieved without maintaining manual tags and without forcing teams to expend hundreds of hours managing data dictionaries, writing code, and reacting to endless tagging changes.

Forrester found that Autocapture alone produced a 40% labor reduction per product innovation.

To this autocaptured dataset Heap adds  custom tracking and server-side events, which gives customers the most complete and manageable data set available on the market.

Heap collects all this data from the moment teams install the snippet. This gives teams the power to analyze data retroactively, and see all every behavior from every user from the moment of installation forward.

Google Analytics 4 offers the Google Tag, which can automatically detect a few event types, including page views, scrolls, and outbound clicks. Manual tagging is still required when wanting to track basic click events and add additional data at the event level.

Though these features improve upon past iterations of the Google Tag, teams will still need to build an implementation plan, work with technical teams for the initial implementation, and then constantly revise their tags as websites or apps change over time. This requires significant time investment and can easily create a disconnect in ownership across analytics consumers and data engineering teams.

In addition, changes to captured events have to be manually managed through Google Tag Manager. This makes things difficult for less technical users, who aren’t able to easily define anything they want without code.

Read more about the differences in GA4 and Heap data capture.

Heap

Google Analytics 4

Because Heap was built on a more expansive data collection foundation, we had to create a data governance suite capable of managing data at scale.

Users can use point and click, no-code visual labeling to identify elements on a page and immediately define them.

Heap’s governance features include enforced naming conventions to keep events organized and event verification which ensures that less technical users are analyzing the right things.

Users can also monitor their events via a live data feed, where they can watch as events are generated in real time, and immediately take action by defining events that are undefined.

Google Analytics 4 requires users to still manually define, tag, and manage their events. Aside from the associated cost, and known issues associated with manually tagged events (which is why Google is taking a first pass at auto-tagging), GA4 has other known data quality management issues as well.

Data quality management is limited to an out of the box DebugView report, which shows data as it is collected and helps troubleshoot issues. However it has been known to run into issues where it will show “No devices available.” DebugView can also be a limiting factor for untrained users, limiting the level of involvement and control less technical teams can have over their data governance.

There have also been reported data quality and consistency issues with GA4, among a host of other bugs.

How Heap and GA4 can work together

For many teams, Google Analytics integration with Google Ads is a must-have. But that doesn't mean teams have to choose a single solution. Heap's autocaptured data, paired with GA4 advertising data, will enable marketing and product teams to build a complete picture of their customer experience, from personalization to event triggering, digging into granular feature-based customer behaviors, and more.

Read more about how to use Heap and GA4 together here.

Plans and Pricing

Heap

Google Analytics 4

Heap offers 4 different pricing tiers with different offerings for each.  Heaps free tier offers analytics for 1 product/website, unlimited user licenses, automatic data collection and management, 1 year retroactive data, 2 enrichment sources and our data science layer, Illuminate.  For more information check out our pricing page

GA4 has a free tier that offers all of its standard analytics, data collection, and data management features. For more information on audience, conversion, sampling, and retention limits see their feature limits matrix here.

Heap

Google Analytics 4

Heap offers paid tiers that include additional features and data volumes that include what our free tier offers and more. For more information check out our pricing page.

GA4 has a paid version that includes all of the standard analytics, tagging, and data management features included in the free version. Where they differ is in data retention, number of allowed dimensions, audiences, and more.  For more information see their comparison matrix.

See why companies choose Heap over Google Analytics 4

  • "Having everything in one place and with information that’s easy to pick out was crucial. All areas where GA falls short.”

    Mario Tarantino
    Senior eCommerce Manager

  • “As a long-time Product professional, Heap is the most intuitive and helpful product analytics tool that I’ve used to-date. The richness of detail is far and away better than what is available in GA.”

    Michael Brown
    Product Director

  • “Google Analytics was a poor fit for us. We were turning over our codebase once every ~7 months, so instrumentation was always an afterthought. We struggled to add it to our product specs in the early days.”

    Dave Lee
    VP of Engineering