In this article, we are going through the steps to enable Parallel Tracking to export over 1 million events to BigQuery without upgrading to GA360.

How to check your Firebase Analytics event count

If you have GA4 to BigQuery data export already enabled for your Firebase Analytics account, you can see the daily event count in the GA4 admin panel.

Go to Admin panel –> Product links –> BigQuery links and look for the “Total estimated daily event volume to be exported” in the Event data section.

Alternatively, you can navigate to Reports –> Engagement –> Events and see the event count there.

Select the last 30 days and examine the total number of events. If it’s close to or over 30 million, consider implementing Parallel Tracking to avoid data loss.

How to enable Parallel Tracking for Firebase Analytics GA4

Enabling Parallel Tracking for Firebase Analytics GA4 to export over 1 million events to BigQuery (or another data warehouse) is rather straightforward. Below are the steps to get the pipeline up and running.

Enable Server-Side Google Tag Manager

For Android apps, you need to update your AndroidManifest.xml file by adding the following line.

<meta-data android:name="google_analytics_sgtm_upload_enabled" android:value="true" />

For iOS apps, you need to update your Info.plist file by adding the following line inside of the first <dict> element.

<key>GOOGLE_ANALYTICS_SGTM_UPLOAD_ENABLED</key>
<true/>

Sign up with Parallel Tracking

Next, you need to get an account for Parallel Tracking. Please schedule your free consultation using the link below.

Link for Parallel Tracking.

Your account manager will then assist you by connecting the Parallel Tracking data processor with your BigQuery dataset.

Once BigQuery is connected, all Firebase Analytics GA4 events will flow into your BigQuery dataset like normal, and there will be no limit to the number of hits you can export per day.

Enable Server-Side routing in GA4

In the GA4 admin panel, navigate to Data streams and enable server-side tagging for each data stream. To do so, you need to select your stream, click “Configure SDK settings” and then “Configure server-side Tag Manager”.

You need to insert the “Server container URL” provided by your account manager or use your own if you are using Server-Side Tag Manager already.

While validating the setup, you can keep the percentage at 10%. After validation, this should be changed to 100%.

How Parallel Tracking works

Parallel Tracking for Firebase Analytics GA4 duplicates all events that your app is sending to GA4 to another data processing endpoint, where data gets processed 100% the way GA4 itself does it.

In order to enable event duplication, all events must be routed through Server-Side Google Tag Manager. This is a standard process described in the official documentation from Google.

The data you are receiving from Parallel Tracking is 100% the same as you would get with the native BigQuery export. The schema is exactly the same. The only difference is that there is no limit on the number of events you can export per day.

Conclusion

If you need to export over 1 million events per day from Firebase Analytics GA4 into your BigQuery dataset and don’t want to upgrade to GA360, then Parallel Tracking is your best option.

Getting started with Parallel Tracking for Firebase Analytics GA4 is very easy; your account manager will walk you through all the steps and provide all the technical support you need.

Learn more about Parallel Tracking and schedule a free consultation session.

The post How To Export Over 1 Million Events From Firebase Analytics GA4 To BigQuery Without GA360 appeared first on Reflective Data.

In this article, we’re going to explain some of the approaches you should be taking to make sure you get the most out of your valuable data without losing any of the valuable insights. Oh, and without having to upgrade to GA360.

Common problems with GA4 for high-traffic websites and apps

1. Quota Limits and Sampling

• Issue: GA4 has quotas for API calls, data collection, and BigQuery exports. For high-traffic sites, these limits can be exceeded, leading to incomplete or delayed data.
• Symptoms:
  • Delayed or partial data in reports.
  • Data sampling in the interface or when querying large datasets.
  • Only partial data is exported to BigQuery every day

2. Latency in Data Processing

• Issue: High traffic volumes can lead to delays in GA4 processing, resulting in longer wait times for data to appear in reports.
• Symptoms:
  • Real-time reports lagging or missing data.
  • Delays in standard report updates.

3. Data Thresholding

• Issue: GA4 applies data thresholding for reports that include user identifiers or demographic data, especially when dealing with large volumes of traffic.
• Symptoms:
  • Reports showing incomplete or aggregated data.
  • Warning messages about thresholding.

4. Event Limitations

• Issue: GA4 has a limit of 500 distinct event names per property. High-traffic sites often generate a large variety of events, which can exceed this limit.
• Symptoms:
  • Events not being logged or missing in reports.

5. Data Retention Settings

• Issue: GA4 defaults to a data retention period of 2 or 14 months for detailed user data, which can be insufficient for long-term analysis.
• Symptoms:
  • Historical data is no longer accessible in GA4 after the retention period.

6. Overwhelming Volume of Custom Dimensions/Parameters

• Issue: GA4 allows up to 50 custom dimensions per property. High-traffic apps often push this limit, causing issues with tracking extra custom data.
• Symptoms:
  • Missing or dropped dimensions in reports.

7. Cross-Platform and Cross-Domain Tracking

• Issue: High-traffic businesses with complex setups (e.g., mobile app + website) may face difficulties implementing seamless cross-platform tracking.
• Symptoms:
  • Duplicate or fragmented user sessions across platforms.

8. Debugging and Testing Challenges

• Issue: High traffic can make it difficult to test changes without impacting production data.
• Symptoms:
  • Errors in tracking configurations affecting a large user base.

9. Increased Cost for BigQuery Analysis

• Issue: Exporting large volumes of data to BigQuery can result in significant costs, especially when running frequent or complex queries.
• Symptoms:
  • Unexpectedly high cloud bills for BigQuery.

10. User Privacy and Compliance Challenges

• Issue: High-traffic businesses are more likely to face scrutiny regarding GDPR, CCPA, and other privacy laws.
• Symptoms:
  • Compliance risks due to insufficient data anonymization or consent management.

If any of the issues mentioned above sounds familiar, continue reading as there are solutions to all of them.

How to tackle common issues with GA4 for high-traffic websites and apps

As companies start hitting the limits in the free version of GA4, oftentimes they consider upgrading to GA360. True, it will solve some of the issues like offering a higher number of daily exported events to BigQuery, more custom event parameters, dimensions etc. but eventually, you may end up hitting those, too. Besides, upgrading to 360 doesn’t solve some of the issues around GDPR, and data sampling can still be an issue. Not to mention the cost…

The solution we are discussing in this article is known as Parallel Tracking.

In short, here’s how Parallel Tracking for GA4 works.

1. Tracking Code Adjustment

A minor update to the Google Analytics tracking code is necessary to enable streaming all hits to Reflective Data’s endpoint. This approach, called Parallel Tracking, is compatible with all types of GA4 implementations, including GTM, server-side setups, gtag.js, and third-party applications.

2. Event Processing

The Data Processing Engine captures and processes all events in the same manner as Google Analytics. Designed for nearly unlimited scalability, it avoids the data processing restrictions found in GA4. All operations are hosted on Google Cloud, with the flexibility to choose your preferred region.

3. Data Storage in Your Warehouse

By default, data is stored in Google BigQuery, though we support other data warehouses such as AWS, Azure, and Snowflake. All data is fully processed and ready for reporting within seconds.

Reflective Data does not store any events or other data on its servers at any time.

How does Parallel Tracking solve common limitations in GA4

1. Quota Limits and Sampling

• Issue: GA4 has quotas for API calls, data collection, and BigQuery exports.
• Solution:
  • Parallel Tracking for GA4 has no limits on the BigQuery exports. We know sites that are exporting well over 10M events per day without losing any events.

2. Latency in Data Processing

• Issue: High traffic volumes can lead to delays in GA4 processing, resulting in longer wait times for data to appear in reports.
• Solution:
  • With Parallel Tracking, you can have GA4 events processed and stored in your data warehouse within seconds.

3. Data Thresholding

• Issue: GA4 applies data thresholding for reports that include user identifiers or demographic data, especially when dealing with large volumes of traffic.
• Solution:
  • Parallel Tracking for GA4 allows you to work with 100% of the data. No limits on data cardinality or event counts.

4. Event Limitations

• Issue: GA4 has a limit of 500 distinct event names per property. High-traffic sites often generate a large variety of events, which can exceed this limit.
• Solution:
  • With Parallel Tracking, there are no limits to distinct event names. Create, trigger and send as many unique events as necessary for the use case.

5. Data Retention Settings

• Issue: GA4 defaults to a data retention period of 2 or 14 months for detailed user data, which can be insufficient for long-term analysis.
• Solution:
  • Using Parallel Tracking to send GA4 data into a data warehouse of your choice (BigQuery, S3, Snowflake, Redshift etc.) ensures you have full control and ownership of your data.

6. Overwhelming Volume of Custom Dimensions/Parameters

• Issue: GA4 allows up to 50 custom dimensions per property. High-traffic apps often push this limit, causing issues with tracking extra custom data.
• Solution:
  • Parallel Tracking doesn’t set any limits on the number of custom dimensions, metrics or event parameters that you are allowed to collect.

7. Cross-Platform and Cross-Domain Tracking

• Issue: High-traffic businesses with complex setups (e.g., mobile app + website) may face difficulties implementing seamless cross-platform tracking.
• Solution:
  • With Parallel Tracking for GA4 comes the ultimate flexibility around distinguishing and joining data from various sources. This includes web domains (including subdomains), mobile apps, web apps, server applications and more.

8. Debugging and Testing Challenges

• Issue: High traffic can make it difficult to test changes without impacting production data.
• Solution:
  • Parallel Tracking comes with a robust environment for staging and testing all changes before pushing them into production. Never lose valuable data because of a broken tracking system again.

9. Increased Cost for BigQuery Analysis

• Issue: Exporting large volumes of data to BigQuery can result in significant costs, especially when running frequent or complex queries.
• Solution:
  • Parallel Tracking gives you full control over your data pipeline. This means you can adjust the settings to collect only what you need to avoid overpaying for the data warehouse vendor.

10. User Privacy and Compliance Challenges

• Issue: High-traffic businesses are more likely to face scrutiny regarding GDPR, CCPA, and other privacy laws.
• Solution:
  • With Parallel Tracking you can choose in which region your data is processed and stored. For GDPR compliance, for example, several sites keep all their data within the EU (this includes collection, processing and long-term storage).

As you can see, Parallel Tracking is the best companion to your GA4 implementation on a high-traffic website. Not only is it multiple times more affordable than its alternatives (including GA360) but it’s the only platform that provides a solution to all of the common issues.

If you want to learn more about GA4 Parallel Tracking, please schedule a free consultation session with one of Reflective Data’s account managers or data engineers (depending on your needs).

The post Optimal GA4 Setup For High-Traffic Websites and Apps in 2025 appeared first on Reflective Data.

GA360 is expensive and we felt like, for our use case, it would be too much. I was delighted to learn that Reflective Data can help us collect over 1M events per day in our BigQuery dataset for a fraction of the cost.

Jurgita, Digital Data Analyst, Vilnius

GA360 is a great tool, and for the enterprises that can afford and justify the cost, probably the best analytics tool they can invest in. At the same time, many companies don’t have the budget to pay upwards of $150k for an analytics tool.

The challenge

One of the best features of GA4 over the previous version is that even the free version has the BigQuery export pipeline available.

The free version of GA4, though, has a limit on how many events can be exported each day. More specifically, this limit is set at 1M events per day. Barbora, like many companies with large traffic numbers, was reaching this limit in GA4 but at the same time wasn’t able to justify the cost of GA360.

The solution

At Reflective Data, we built a solution called Parallel Tracking for GA4.

Here’s how it works.

Parallel Tracking duplicates all HTTP requests going from your site or app to GA4 and sends them to Reflective Data’s processing endpoint.

Data is then processed just like GA4 does it and inserted into your BigQuery dataset. There is no limit on the daily event count with Parallel Tracking.

Getting started with Parallel Tracking is easy as 1, 2, 3.

Step 1: Tracking code modification

After signing up for Parallel Tracking, you’ll have to enable it by modifying your GA4 implementation. We have plug-and-play solutions for Google Tag Manager, gtag.js, Measurement Protocol and most other implementation methods that GA4 has.

Step 2: Pipeline activation

In order to activate the pipeline, all you need to do is provide Reflective Data with a Google Cloud Platform Service Account key file. After providing the key file, GA4 data will start flowing into your BigQuery dataset.

Step 3: Querying the data

Once the pipeline is active, you can start querying your GA4 data just like with the default GA4 to BigQuery data export.

Conclusion

If your daily event volume in GA4 is close to or exceeds 1 million then you have two options. Upgrade to GA360 or implement Parallel Tracking. If the event volume exceeding 1 million is the only reason you’re looking to upgrade, Parallel Tracking is likely a better option as it is several times more affordable compared to GA360.

Ending with another quote from the customer here.

I guess you could say we were your average enterprise with LOTS of legacy data infrastructure that had been built over many years. This system was extremely complex and very expensive to maintain. When Reflective Data came in, they acted as true professionals, worked very closely with our IT and came up with the plan that pleased everyone.

Today, being on the new cloud-based data infrastructure for almost a year now, I can say with all certainty that this project was a success. Not only are we saving tens of thousands of dollars every month on the infrastructure alone, the amount of hours it takes to maintain the system has gone from hundreds down to a ten or so. This has huge impact on our business. Implementing anything new would’ve taken at least 6 months with the old system, now it’s a matter of week or two to get everything up and running.

Last but not the least, Reflective Data saved us another $150k year as we no longer needed to use GA360 and instead implemented their Parallel Tracking solution for GA4.

Daria, VP of Marketing, Warsaw

It’s feedback like this that makes us love the work we do even more! Get in touch and learn how we can help you, too.

For more case studies, see here.

The post Case Study: Overcoming The 1 Million Event Limit in GA4 Without Upgrading to GA360 appeared first on Reflective Data.

Google Analytics 4 (GA4) is not yet fully GDPR compliant. Google is working to make GA4 compliant with the GDPR, but it is a complex process that is still ongoing.

Here are some of the challenges that Google is facing in making GA4 GDPR compliant:

• The GDPR requires companies to obtain explicit consent from users before collecting or processing their personal data. GA4 collects data about users, including their IP addresses, which can be used to identify them.
• The GDPR also requires companies to provide users with access to their personal data and to allow them to delete their data. GA4 does not currently provide users with these options.
• The GDPR requires companies to take steps to protect the privacy of user data. GA4 does not currently take all of the necessary steps to protect user privacy.

Google is working to address these challenges and to make GA4 GDPR compliant. However, it is not clear when GA4 will be fully compliant with the GDPR.

In the meantime, companies that use GA4 should take steps to comply with the GDPR on their own. This includes obtaining explicit consent from users before collecting or processing their personal data, providing users with access to their personal data, and allowing users to delete their data. Companies should also take steps to protect the privacy of user data.

If you are using GA4, you should consult with a privacy lawyer to ensure that you are complying with the GDPR.

Here’s how we keep GA4 GDPR compliant

In short, for traffic coming from the EU, we skip the the step where GA4 tracker sends data to GA4 servers. Instead, we process this data within the EU (just like GA4 does on their servers in the US) and send it straight into a BigQuery dataset located within the EU.

Reflective Data Parallel Tracking Solution

Reflective Data offers a solution that can help you make your GA4 implementation GDPR compliant. The Parallel Tracking solution allows you to collect data from users in the EU without sending the data to the US. The data is instead processed and stored in the EU. This ensures that your GA4 implementation is fully GDPR compliant.

Schedule a Free Consultation

If you are interested in learning more about how to make your GA4 implementation GDPR compliant, you can schedule a free consultation with Reflective Data. During the consultation, one of Reflective Data’s data experts will discuss your specific needs and help you determine if the Parallel Tracking solution is right for you.

Questions?

If you have any questions about how to make your GA4 implementation GDPR compliant, please feel free to ask in the comments below.

The post GA4 Is Not GDPR Compliant – Here’s How to Use GA4 in the EU Safely appeared first on Reflective Data.

This came as an unwelcome surprise for anyone working in the experimentation industry. Even if you didn’t use the tool itself, it was the first tool most newcomers used to get themselves into experimenting on their websites.

For a while, Google Optimize going away was everything people talked about on Twitter and LinkedIn.

Exporting Experiment Data From Google Optimize

Since several of our existing clients asked for it, we built Google Optimize Data Exporter to store your experiment data for as long as you need it. It supports almost any data destination, including popular ones like Google BigQuery, Amazon S3 and Snowflake.

Google Optimize Data Exporter runs on the same robust and scalable Reflective Data Infrastructure that you hopefully already know and love.

We’ve made the process of exporting your Google Optimize data as simple as possible. Here’s a quick overview.

1. Planning and scoping

Get in touch with one of our data analysts to plan your Google Optimize Data export. The main questions to answer are the list of experiments, dimensions, metrics, time frames and the data destination you wish to use for your Optimize data export.

2. Data export and storage

Executing the plan. Our data analyst will configure Reflective Data Export System to pull the requested data from your Google Optimize instance and store at your chosen data storage destination.

Most exports use Google BigQuery as a data destination.

3. Reporting and consultation

The post Export Experiment Data From Google Optimize – While You Still Can appeared first on Reflective Data.

Possible use cases are virtually limitless and depend on what kind of business you run. In this article, I’m providing some of the more common ways together with examples of how to benefit from having a data warehouse.

In no specific order, here we go.

Better attribution leads to more optimized marketing and ad spend

It’s no secret that some analytics tools and most ad platforms tend to default to attribution models that work best for them. I mean, they use models that show as if they’re bringing you the most traffic and conversions. Some tools let you modify the model or attribution windows to some extent but generally, it’s quite limited.

Let’s say someone scrolled past your ad on Facebook without clicking on it. Later that day they receive an email from you with a nice offer – they end up visiting your suite and making a purchase. Guess what Facebook will tell where this purchase came from? You guessed it, they will attribute it to seeing the ad on Facebook.

Now imagine having all this attribution data available in your data warehouse in a raw format. This will give you full flexibility over which attribution model and window to use. No longer “apples to oranges” comparisons when it comes to your traffic sources.

Combine this with data from other sources like your CRM or backend and you’ll have the ultimate attribution machine in your hands. Use attribution models like Markov Chain or even AI/ML-based models and use metrics like LTV or churn for long-term analysis.

Proper attribution with an “apples to apples” comparison is the key to optimizing your marketing and ad spend.

Perhaps the best way to start collecting this kind of data in your data warehouse would be Parallel Tracking.

More accurate audiences lead to better targeting and savings in ad spend

What if I told you there was a way to evaluate visitors or groups of visitors based on their likelihood of becoming a customer or even a high-value long-term loyal customer? All before they even land on your website or make their first purchase. Imagine building your advertising audiences based on those predictions while constantly improving the model. Well, the big players have been doing this for years but today the barriers have lowered and most businesses could leverage this kind of technology. Trust me, it’s easier than you might think.

There are three main components to getting started with AI/ML-based audience future value predictions.

1. Set up a data warehouse – build yourself or hire Reflective Data to consult and help with the technical setup
2. Configure data pipelines (the systems that transfer data from various sources into your data warehouse) – We’ve got you covered if you need help
3. Leverage a system similar to BigQuery ML for the predictive models – Yes, we can help with the ML part, too

Instead of spending your precious marketing budget on people that’ll never buy from you, focus on those that have the highest potential of becoming high LTV customers.

$$$

Data-driven product recommendations lead to higher AOV

Product recommendations, if done right, can provide enormous value for both the customers and merchants. Now that we have tools like Recommendations AI from Google, getting started with your own recommendations engine has never been easier. Or cheaper.

All you need is data, preferably in a data warehouse like BigQuery. For this use case, I’d recommend using Parallel Tracking to get your data into your data warehouse.

Case study: How IKEA managed to increase their Click Through Rates by 30% and AOV by 2% by leveraging the Recommendations AI. LINK

Data-driven marketing automation drives more sales

Still sending generic emails to all customers or manually building audiences? Welcome to the modern days where we can leverage the rich customer data available in our data warehouses. Use it to automate many critical parts of our marketing. Including emails.

A few scenarios to consider.

• Predictive model described above detects people with the highest future customer lifetime value (LTV). Nudge those people a bit by sending them an offer that’s a bit more generous than what you’d send to everyone. Believe me, it’s worth it.
• Include personal product recommendations in your marketing emails.

To get high-quality data into your data warehouse, you need a data pipeline that connects with your existing tools.

Data-driven personalization drives more sales and higher AOV

There are many ways how you can introduce personalization to your customers but one aspect is true for all of them – they all need good quality data as input.

Below are a few common personalization solutions our clients have implemented.

• Customize the front page of your e-commerce (or any other) website based on each customer’s previous browsing and shopping behavior. This can include banners, offers, discounts, product recommendations and more.
• Customize the website experience based on the type of customer that has landed on your site. Depends on your business type and target audience but generally individuals, people representing small businesses and those shopping for a large enterprise all expect a somewhat different experience.
• Personalize overall messaging based on visitors’ behavior. Let’s say you’re an insurance company and someone landed on your blog article comparing different car insurance options, then they move on and read a few more related articles. Now, when they land on your main website, it’d be good to welcome them with relevant car insurance options as opposed to generic content.

To get high-quality data into your data warehouse, you need a data pipeline that connects with your existing tools.

Analyzing user behavior can lead to savings in customer support costs

Maintaining a proper support team can be costly. Many companies that we’ve worked with could’ve (and eventually have) saved anywhere from 5% to 60% in support-related costs by having relevant data available in their data warehouse, analyzing it and, of course, making the changes based on insights found.

Data points to collect here include.

• Live chat messages
• Call transcripts
• Site search behavior
• Help center behavior
• Social media, Reddit and other platforms

To collect this kind of data into your data warehouse, you need a data pipeline that connects with the tools necessary.

Potential ways to execute those insights include.

• A/B testing
• Chat bot
• Re-arranging help center
• Train support personnel
• Improve your product UI/UX
• Better onboarding guides

Running an experimentation program on top of a data warehouse will save $ on tool vendor costs

Most conventional experimentation (A/B testing) tools like Optimizely, Google Optimize and others operate in silos and keep their own copy of all the data they need to operate. In reality, though, this dataset is very similar to many other tools that you may already use, including Google Analytics. Having another tool to operate in a silo isn’t (cost) effective and can often lead to data discrepancies.

Modern experimentation tools like Eppo, Growthbook and others don’t create another siloed copy of your data. Instead, they will sit on top of your data warehouse and use data you already have – nicely integrated with various sources. Not only is this much more cost-effective but it also helps you avoid creating yet another source for data discrepancies. Not to mention that you don’t have to define all the goals beforehand as you can simply use SQL to define those after the fact. Or while the test is already running.

A simple data pipeline and an open-source tool like Growthbook is all you need to get started. Save tons compared to tools like Optimizely and get a more robust solution that enables you to test across all parts of your tech stack. Front end, back end, APIs, databases and more.

Using a data warehouse in favor of a conventional analytics tool will save $ on tool vendor costs

There are free analytics tools out there but they all come with quite serious limitations which leads most bigger companies to look for a paid solution. What most tools really do is provide you with a tracker, data processing endpoint and a few shiny reports – oh, and ask a fortune for doing so.

In reality, all you really need is a tool like Parallel Tracking that connects with your existing Google Analytics (both UA and GA4) trackers while duplicating hits to another processing endpoint and then sending them straight into your data warehouse. Free from data collection, cardinality or other limitations which are often seen even in the paid analytics platforms. From there, connect your BI tool and build all the reports you might ever need. All while saving like 10x compared to something like GA360.

As a bonus, you’ll likely have data from other sources in your data warehouse, too. This unlocks the potential to do even more advanced analysis that’s not possible with any one analytics tool alone.

Conclusion

It’s not a question of whether you need a data warehouse or even whether the ROI is there. It’s more about the specific tools and tech you’re going to implement from the get-go – switching a vendor later is a lot of effort and costly, too.

Our recommendation is to avoid CDPs and other fancy buzzword tools and instead get something more robust – BigQuery as your data warehouse, a set of data pipelines, a BI tool for reporting and then go from there.

Data engineers at Reflective Data would be more than happy to answer your questions in the comments below or hop on a quick free consultation session.

The post Increase Your Company’s Profits by Getting and Leveraging a Data Warehouse appeared first on Reflective Data.

While Google Analytics 4 (GA4) comes with the native BigQuery export feature available in the free version and some of the other quotas aren’t as tight anymore (compared to UA), GA4 still has a fair share of limitations we need to account for.

The limit I’m covering in this post is set on the BigQuery export. More specifically, the GA4 to BigQuery native export feature has a limit of 1 million hits per day.

From the docs.

Standard properties have a daily BigQuery Export limit of 1 million events. If your property consistently exceeds the export limit, the daily BigQuery export will be paused and previous days’ exports will not be reprocessed.

Considering the BigQuery export will be a core element for most power users, this can become a serious problem for any business with a good amount of traffic. Again, this limit is not on users or sessions but on hits. A single visitor can generate hundreds or even thousands of hits, depending on your business type and GA4 configuration.

Ways to overcome the 1 million hits limit in GA4

In short, you’ll have three paths to choose from.

• Collect less data
• Upgrade to GA360
• Use Parallel Tracking

Below is a more detailed overview of each of these solutions to help you find the best one for your use case.

Reduce the number of hits collected with GA4

There are two parts to this one. First one being that you simply take a second look at the list of events you’re currently collecting and consider skipping some of them. Could be scroll events or other automatically triggered events like product impressions.

The second part is that you can actually filter in/out the hits that are sent from GA4 to BigQuery. So, perhaps you could send only the most critical data to BigQuery.

Now, neither of those solutions is really a good one because no one should be forced to give up on some of the useful data and insights because of some arbitrary hit limit. So, if you’re serious about your data game, check out the other two options.

Upgrade to the GA360 version of GA4 and get more quota

Understanding that GA360 doesn’t fit every budget and a simple hit limit shouldn’t be enough to convince you into spending this kind of money but if you’re considering GA360, then know that it also comes with a much more generous BigQuery export quota.

The official documentation doesn’t say the exact number but they claim the daily limit to be “billions of events”. That’s probably enough for most use cases.

Use Parallel Tracking for a limitless BigQuery export

If a compromise of collecting less data isn’t want you want to make and GA360 is too expensive then Parallel Tracking is your best option.

Depending on your GA4 setup, the implementation is very simple. Either a small update to your tracking code or GTM tag and unlimited data pipeline is up and running.

As a bonus, you can toggle on an option that processes your data into sessions (just like in Universal Analytics – together with all the metrics and dimensions from UA) in a separate BigQuery table. How awesome is that?

Learn more about Parallel Tracking for GA4.

Wrapping up

If you’re serious about analytics, BigQuery export is a must-have. Unfortunately (but expected) the free version of GA4 comes with an export limit of 1 million hits.

In case you’re already exceeding this or are getting close, now is the time to look for ways to overcome this limit. My recommendation is that you take a closer look at the Parallel Tracking, get in touch with the team and see if that’s the solution you should implement.

As always, feel free to post your thoughts and questions in the comments below.

The post How to Avoid Google Analytics 4 [GA4] BigQuery Export Quota Limit of 1 Million Hits appeared first on Reflective Data.

While Google Analytics is such a great tool and I’ve been using it for years myself, some of our clients have run into problems and limitations that made them search for other options. In this article, I’m going to discuss some of the reasons why you might need an alternative (or addition) to Google Analytics and what most of our clients have found to be the best option for them (and why).

  This article will prepare you for July 1, 2023 when Google will stop processing Universal Analytics data.

Before I get any deeper into the topic, this solution may not be ideal for every business but if the reason you may be considering another analytics tool is in the list below, it’s likely the one you should consider.

• Forced transfer to GA4
• Sampling
• Data collection limits (10M hits/mo,  20 dimensions & 20 goals)
• Sending data to the US
• Forced data aggregation
• No access to raw hit-level data
• API limitations

So, if you found the reason you’re looking for an alternative to Google Analytics in the l, the solution we’re going to discuss now is probably a good fit for you.

Parallel Tracking as an alternative to Google Analytics

Before I give you the reasoning why Parallel Tracking is a very good alternative to Google Analytics, let me give you a quick overview of how it works.

Parallel Tracking is a data pipeline that’s built based on Google Analytics Universal Analytics (UA) and follows the exact same data structure and processing logic. It can do so while letting you choose whether to send any data to Google Analytics itself or not. Parallel Tracking will send both raw and processed data straight into your data warehouse. BigQuery being the default storage solution.

What makes it stand out from most other analytics tools is that it uses literally 100% the same tracking setup as Google Analytics UA. This means you can use analytics.js, GTM, Segment, Measurement Protocol and almost all other methods for implementing it while using the syntax you’re already familiar with.

In the following sections, we’re going to take a closer look at the core areas of the Parallel Tracking solution. In each one, I’m going to compare it to Google Analytics and explain why it’s probably the best alternative out there.

Implementation

If you’re using Google Analytics today, getting Parallel Tracking implemented will normally take 2-8 hours. All plans come with full implementation support included. Parallel Tracking works out of the box with most Google Analytics implementations, including the following.

• analytics.js (Google Analytics snippet in your source code)
• Google Tag Manager
• Measurement Protocol (Google Analytics server-side)
• Segment.com
• Shopify
• Littledata
• WooCommerce and most other apps and plugins

  PS! All of those integrations will work after July 1, 2023 when Google will stop processing Universal Analytics data.

In case it’s a fresh setup, the integration time will depend a lot on the complexity of your site. As it’s using the exact same tracking syntax as Google Analytics, the setup time will be the same as it would take to install Google Analytics from scratch.

Considering this, Parallel Tracking is by far the quickest, least painful and most effective tool to replace Google Analytics with.

Reporting

While Google Analytics Universal Analytics has a familiar and rather intuitive user interface for quick reporting, most analytics platforms don’t. This includes the new Google Analytics 4 which will be the only version after July 1, 2023 when Universal Analytics reaches its end of life.

As Parallel Tracking will send both raw and processed data into your own data warehouse (i.e. BigQuery), you can choose the reporting layer of your choice. For most users, Google Data Studio (free) should be enough. More advanced users are successfully connecting it with tools like Looker, Tableau and Power BI.

By the time Universal Analytics sunsets, there will be a set of getting-started reports, templates and potentially even a full user interface to ease your reporting with Parallel Tracking.

This means that Parallel Tracking is on par with most other analytics tools when it comes to reporting and offers you virtually limitless flexibility by connecting natively with most BI and dataviz platforms. Plus, Parallel Tracking is probably the only one that gives you sessions and related metrics like bounce rate out-of-the-box!

Integrations

Google Analytics is known for its wide range of integrations. Starting from Google Ads and ending with most of the CMSes and A/B testing platforms on the market.

Since Parallel Tracking uses the same tracking system as Google Analytics, it’ll natively support most of those integrations! This includes Google Ads (even cost data).

The fact that you’ll have full control over your data in BigQuery means you can integrate with almost any data source out there. Ads platforms, CRMs, A/B testing, e-commerce, custom backends – you name it. As a bonus, since Parallel Tracking is just one of the hundreds of data connectors Reflective Data offers, you can get it all from one place.

Considering the above, Parallel Tracking is on par or even exceeds Google Analytics in terms of integrations and easily beats almost any other analytics tool on the market.

Cost

Google Analytics is free for most users but the free plan comes with a set of serious limitations (discussed further in the next section). The premium, GA360 would cost you at least $150k a year which makes it one of the most expensive offerings in that segment.

Parallel Tracking’s pricing is based on usage – the number of hits collected per month to be more specific. The number of integrations and a method of implementation can be pricing factors, too. So, I recommend you get in touch for the exact quote customized for your business and use case. Based on my experience, most clients will be happy with plans in the range between $375 to $950 per month.

Comparing the pricing with other analytics platforms, Parallel Tracking should be quite affordable for most businesses and it’s a lot cheaper than GA360. Even if you’re collecting hundreds of millions of hits each month.

Limitations

Google Analytics, especially the free version, is known for its wide range of limitations.

Here’s a table comparing Google Analytics against Parallel Tracking in terms of quotas and other limitations.

Reliability

Google Analytics tends to be very reliable. I can recall some bugs and losses in data but it is very rare.

Parallel Tracking is built based on the same tracking system as Google Analytics and runs on Google Cloud – making it one of the closest competitors in terms of reliability and scalability. Fortune 500 companies have put their trust in Parallel Tracking to collect and process hundreds of millions of hits per day.

Should something go wrong in data processing (as has happened with Google Analytics a few times), Parallel Tracking can promptly re-process the raw data, minimizing the risk of losing any data for good.

Comparing this with other tools, some of which are self-hosted and most of them using custom-built tracking syntax, makes Parallel Tracking stand out for sure.

Support

While Google Analytics doesn’t offer support themselves, it does have a huge community making it easy to find answers to almost any question you might have.

Since Parallel Tracking is based on the same tracking syntax, data is processed and stored just like in Google Analytics, you can still benefit from that massive community – most answers will be applicable for both.

Besides community, Parallel Tracking comes with technical support available with all plans. You don’t have to worry about the implementation, custom configuration and maintenance. Just contact support and you’re almost guaranteed to get a solution to whatever you were struggling with.

While there are other tools with technical support available, many of them don’t offer it with their lower-end plans. Certainly, no other tool benefits from Google Analytics’ massive community like Parallel Tracking, besides Google Analytics itself.

Privacy and EU laws

Since GDPR and other data protection and privacy laws were introduced, Google Analytics and several other analytics platforms have struggled to meet the requirements. Some EU countries have gone as far as saying using Google Analytics to track their citizens online is illegal.

With Parallel Tracking, you are in control of almost everything that could be related to privacy and data protection. This includes saying where data is processed and stored (within EU, in the US, on-premise).

This flexibility goes both ways, meaning that if you’ve asked your visitors’ permission, you can use Parallel Tracking to collect PII data like an email address or phone number, too. Something that’s not an option with Google Analytics.

Unlike almost any other analytics platform (besides open-source and self-hosted ones), Parallel Tracking does not store any data on its servers. After processing data, it is stored in your data warehouse. This means data is never shared with or sold to third parties.

Is it really the best replacement for Google Analytics?

After reading this article, I hope you have a pretty good understanding of how Parallel Tracking works and why it’s likely to be one of the best alternatives to Google Analytics. Both UA and GA4.

One of the only downsides this solution has is that, compared to the free version of Google Analytics, Parallel Tracking will still cost some money. If you’re using this kind of data for making business decisions, that shouldn’t be an issue as it’s still more affordable than most of its competitors. Including GA360.

Click here for a quote and consultation on Parallel Tracking.

Further reading

We’ve written on similar topics before. Have a look at these articles to learn more about Parallel Tracking and why it’s a good alternative to Google Analytics.

• Product overview
• Hit-Level Unsampled Google Analytics to BigQuery Without 360
• Query Google Analytics Data Using SQL
• How to Avoid Google Analytics Sampling and Data Limits?
• How to Query and Analyze Google Analytics Data with BigQuery
• Case Study: Storing Google Analytics Data Within The European Union or Locally
• Measure Long-Term Metrics Like Customer Lifetime Value (LTV) Using Google Analytics
• Case Study: Sending Data From Shopify and Google Analytics to BigQuery for More Advanced Analysis

I’d be happy to answer any questions you might have about Parallel Tracking in the comments below.

The post Best Google Analytics Alternative for 2023 appeared first on Reflective Data.

The amount of load that Reflective Data was able to lift from the shoulders of my team was unbeliavable. Without their help, we’d still be building out the pipelines and never gotten to the level of advanced analysis and ML that we’re able to do now. They let us focus on what brings the most value to our business.

Melanie, Director of Data Analytics, Frankfurt

At Reflective Data, we’ve worked with companies big and small. This means we have seen all levels of maturity when it comes to the infrastructure and knowledge around data pipelines and data warehouses.

Some of the most challenging projects have been enterprises with quite some infrastructure, legacy pipelines, and of course, opinions. Smaller businesses are just starting to adopt the concept of having all of their data stored in a data warehouse but many enterprises have been doing this for a decade!

The challenge

When many of the enterprises that we’ve worked with started building their data pipelines, they didn’t have tools like Airflow, BigQuery etc. that we use and love today. This means the bulk of it was built in-house. Even the concept of cloud computing was in its early days and most operations were kept on-premise.

The challenge with this kind of setup starts by understanding the existing setup. In some cases, the documentation is close to none and the people that built it are no longer with the company. This alone can take a month or so – mapping everything out, understanding the structure, creating the plan for moving forward.

Another challenge is getting everyone on the team on board. More often than not there are people who value the work that has been put into the old system over the years so much that it blinds them from seeing the obvious benefits of moving to a much more modern infrastructure.

The solution

When working with the enterprise and legacy infrastructure, nothing happens overnight. Below are the phases of a typical project of getting an enterprise client onto a modern cloud-based data infrastructure.

Phase 1: understanding and mapping the existing situation

With most enterprises, it’s not just one team or system that depends on the data infrastructure. More often than not, this is the backbone of the entire business. This means we need to make sure we understand every aspect of the current system, where it gets the data, how it’s being processed and what processes depend on this data.

Phase 2: planning the infrastructure

We do our best to work closely with all teams involved to make sure their needs are taken into account. This means a series of on-hands meetings where we learn about their use cases and problems they’re having with the existing setup. The output of this phase is a clear plan for moving forward, including the tool stack, reporting mechanisms and several feedback rounds to make sure everyones’ needs are taken into account.

Phase 3: implementation

Depending on the in-house knowledge, resources and other aspects, a company can decide to implement the plan themselves and continue using Reflective Data as a consultant or hire us to handle the technical execution as well. By far, the most effective arrangement in our experience has been where we do the bulk of the work while including a few technical people from the client’s side in every step of the process. In some cases, those people is hired specifically for this purpose.

Phase 4: monitoring, reporting and integrations

The whole point of having high-quality data is to make it actionable. Of course, we handle core integrations within the implementation phase but in a sense, data infrastructure is a growing organism that needs constant attention. Reflective Data is here to build long-term relationships with its clients, ready to help whenever there’s a new data source to be added, a report to be built or if a new team member needs training.

Conclusion

Moving away from a legacy data infrastructure is one of the best actions an enterprise can take towards being more data-driven, more effective in managing the infrastructure and, in all reality, keeping up with the competition.

Ending with another quote from the customer here.

I guess you could say we were your average enterprise with LOTS of legacy data infrastructure that had been built over many years. This system was extremely complex and very expensive to maintain. When Reflective Data came in, they acted as true professionals, worked very closely with our IT and came up with the plan that pleased everyone.

Today, being on the new cloud-based data infrastructure for almost a year now, I can say with all certainty that this project was a success. Not only are we saving tens of thousands of dollars every month on the infrastructure alone, the amount of hours it takes to maintain the system has gone from hundreds down to a ten or so. This has huge impact on our business. Implementing anything new would’ve taken at least 6 months with the old system, now it’s a matter of week or two to get everything up and running.

Julien, VP of Marketing, Austin

It’s feedback like this that makes us love the work we do even more! Get in touch and learn how we can help you, too.

For more case studies, see here.

The post Case Study: Building and maintaining a Data Pipeline and Data Warehouse for the Enterprise appeared first on Reflective Data.

In this article, we are going to explore some of the ways we can leverage Google Analytics to track churn, LTV and other really useful metrics.

Depending on the software you’re using, there may be some off-the-shelf solutions that you can install. For example, if you’re on Shopify then you can use something like Littledata to send a more accurate LTV value into a custom dimension in Google Analytics. More often than not, though, there is no good solution available or you just need more control over the setup.

One common misconception is that such long-term retention metrics are relevant for a few specific business types only. Yes, metrics like churn are vital for SaaS and subscription products but any company that gets return business should have their long-term KPIs in place. And I don’t mean simply tracking them but actually analyzing them and optimizing the business with those metrics in mind.

Acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing one. It makes sense: you don’t have to spend time and resources going out and finding a new client — you just have to keep the one you have happy.

Harvard Business Review

So, if you’ve been focusing on getting new customers and metrics like revenue or transactions, this article is just for you!

How to measure retention metrics like LTV and churn

The long-term retention metrics most relevant to you depend on the type of business you’re working with but the most common ones are customer lifetime value (LTV) and churn. Below is a list of other popular retention KPIs. Think about the ones that would be relevant for your business.

Common Customer Retention Metrics

1. Customer Churn
2. Revenue Churn
3. Existing Customer Growth Rate
4. Repeat Purchase Ratio
5. Product Return Rate
6. Days Sales Outstanding
7. Net Promoter Score
8. Time Between Purchases
9. Loyal Customer Rate
10. Customer Lifetime Value

Source

Almost all retention metrics require a proper User ID implementation. This means you’d have to identify the user over time and even if they’re using multiple devices or browsers. Luckily, in most cases, actions like completing a purchase or signing up for a subscription do involve some kind of authentication.

While it’s possible to track retention metrics with Google Analytics alone, in most cases you’d get much better (more accurate) results when combining it with some other technology. Let’s explore two of the more popular options.

Sending retention data into Google Analytics

This solution involves sending retention data into a custom dimension or a custom metric in Google Analytics.

The exact workflow depends on the software (CRM, CMS, database etc.) your site uses but the general process would look something like this.

1. Create a custom dimension in Google Analytics (should be user-scoped)
   
2. For logged-in/identified users, pull/calculate the values for the relevant retention metrics from a database or other system (CRM, CMS etc.)
   Something like this if your order data is stored in BigQuery.
   
3. Make the retention metrics available in the data layer
   
4. Use Google Tag Manager to send your retention metrics to Google Analytics, using the custom dimension or metrics slots/indices according to how you configured them in step #1

Now, having this data available in Google Analytics, you do whatever you want with it. Here are a few examples.

Using LTV in a Google Analytics custom report

LTV in the Google Analytics user explorer report

Notice the difference between LTV that Google Analytics is reporting by default ($439) and the value we see in our custom dimension ($2,016). This is because Google Analytics can’t keep a track of the user as accurately as your backend system or an e-commerce platform you’re using. The same goes with other retention metrics, getting accurate metrics requires some custom work.

The list of possible use cases for this kind of data is unlimited. For example, think about creating custom segments in Google Analytics for customers that are in the top 10% in terms of LTV and see what differentiates them from the rest of the visitors. Besides making more/larger purchases, of course. Things like their traffic source, what pages they landed on, what A/B test variants they saw etc. can be quite insightful.

Talking about ways you can analyze your data and the insights it will give you. Let’s take measuring retention metrics to a whole new level by sending data into a data warehouse.

Storing data in a data warehouse

If you’re just getting started with retention metrics and you still mostly optimize for generic metrics like leads, total transactions and total revenue, then you’ll still be better off with having them in Google Analytics. Compared to not having them at all, that is. If you’re serious about analyzing and optimizing for retention and customer lifetime value then you need a data warehouse.

Here’s a quick step-by-step guide that will lead you in the right direction.

1. Send all Google Analytics data into a data warehouse (i.e. BigQuery). Tools using the Reporting API (most of them) can get you started but for true unsampled hit-level data you need something like Parallel Tracking.
2. Send, pull, push data from other relevant sources into your data warehouse. This should include your database, CRM, CMS, marketing tools, ads platforms, customer support, live chat and every other tool that has data about your customers and their interactions with your brand. Self-service tools like Stitch will get you started but we’d recommend more flexible managed solutions.
3. Solution for accessing data stored in your data warehouse. You’d need something (could be separate tools) that can handle ad-hoc queries, dashboarding, automated reports, and building data models. Tools like Google Data Studio will get you started. Something like Looker or Tableau would be better. Our recommendation is to go with a managed service that will put together the best set of tools for you and configure the rest as well.

If having retention metrics in Google Analytics enabled you to do all sorts of useful new reports and analysis then your options with the setup above are truly limitless.

Having a proper data warehouse will be your competitive advantage. Not only does it give you an option to get a very good overview of the current status of your business and your customers, but it will also allow for truly optimizing the user experience and user journey. Leading to a better user experience and improved retention metrics. Remember, acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing one!

One way to describe the usefulness of a data warehouse is by giving you a few sample questions. Questions that would be very difficult to answer without a data warehouse.

• Purchases from which traffic channels are most likely to be refunded at some point in the future? Might lead to revising your marketing budget.
• Which traffic sources have the highest retention/LTV?
• What is the correlation between subscription value ($) and churn rate?
• What is the long-term impact of your campaigns or A/B experiments? Do quick wins lead to higher churn or lower LTV?
• Does data from different sources add up? Maybe Google Analytics is missing some transactions that are in Shopify or perhaps some of them are duplicates?

Here’s an example of the last one on the list above.

As you can see, Google Analytics is missing a good amount of transactions and this requires further investigation. Definitely something you should include in your Google Analytics dashbaord.

This was just a short list of ideas to get you thinking about what is possible with a proper data warehouse. You can trust me when I say that I’ve seen companies become truly data-driven after they’ve implemented a tailor-made data warehouse and started digging for insights they couldn’t before.

Working with automatically recurring events

It is important to keep in mind that some retention metrics can change without the user themselves taking any action. You need to make sure that those cases are being tracked and taken into account. Here are a few examples.

• Recurring orders/payments
• Subscription expirations
• Payment method expirations
• Orders being changed/cancelled (i.e. due to missing item)

If your data warehouse was configured properly, you should have this data already available. Just make sure to include it in your analysis and reports.

In case you don’t have a data warehouse and you’re trying to solve this with Google Analytics alone then you need to use the Measurement Protocol. Some of the more common subscription platforms like ReCharge for Shopify have this built-in or solvable with some 3-rd party solutions but quite often custom development is required. In which case, you should think about implementing a data warehouse instead.

Conclusion

If you’re in a business where customers are expected to generate value more than once (repeat purchase, subscription etc.) then you need to start focusing on your retention metrics.

Google Analytics can get you started with the basic metrics and limited accuracy. A much better setup would be Google Analytics combined with Parallel Tracking and if you’re serious about optimizing for those metrics then you need a custom-built data warehouse where all marketing data is pulled together.

Feel free to post your ideas and questions in the comments below. If you’d like to get some consultation and discuss your ideas further, get in touch with us.

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