As a digital treasure hunter, I know better than to leave this gold in the ground—and as a Google partner, I also know how to mine it. Through Google Cloud Platform’s (GCP) Natural Language Processing (NLP) AI, digital marketing partners can help brands conduct sentiment analysis, among other methods, to gather insights into customer behavioral patterns, expectations, complaints and moods, and therefore determine the level of brand loyalty. 



The quantitative data that you obtain through this research method allows you to build dashboards and visualize brand sentiment across regions. The aim here is to discover any areas for improvement, as these data points can be used to optimize a brand’s mobile and web applications or products and services—thus informing their next steps in the experimentation process and helping them get closer to meeting their audience’s needs. 



Over the last few months, I’ve focused on integrating sentiment analysis into our experimentation offering, and it’s quickly changing the game. In the spirit of sharing learnings and making sure no brand leaves their valuable data untouched, let’s talk about why this method is as good as gold. 

Leveraging textual data to determine brand sentiment.

Imagine you’re a top-tier global brand in the food and beverage industry. You’ve recently added new features to your app, and so you’re eager to find out if customers are enjoying this enhanced experience. Right now, there are over 500 thousand reviews on the Google Play Store. Scouring through them would most certainly go a long way, but who’s got that kind of time? It’s a classic case that we see all the time: brands tracking everything, but not doing anything with the info they keep track of. However, this trove of data from active customer interactions is only a treasure if it’s activated and applied effectively. 



This is where sentiment analysis comes in. Made possible by GCP’s suite of tools, this research technique analyzes digital text to determine the emotional tone of a message, such as a review. As part of experimentation, which is all about creating impactful changes to meet the needs of your customers, sentiment analysis allows you to translate qualitative textual data into quantitative numerical data. The aim is to surface key insights about brand loyalty—in the case of said brand, how customers feel about the app’s new features. And then? That’s right, much-needed data activation.  

Put your data to work to improve your business. 

Diving into the nitty-gritty of conducting sentiment analysis, you’ll see it’s very easy to adopt this method. With this AI solution, there’s no need for marketers to manually go through one review after another to get a sense of people’s opinions.



Here's the rundown. Once you have access to a Google Cloud account, you can organize your qualitative, transactional and behavioral data in Google Sheets and Google Cloud Storage. Then, use Apps Script (or another cloud client library) to create a custom menu and leverage GCP’s natural language API. Once you've enabled the natural language API and created an API key, you can start processing your data in a request to the NLP API and then automatically perform sentiment analysis. Ultimately, this opens the door for you to act on those insights through A/B testing campaigns, web and app optimization, brand marketing, and product marketing.



GCP’s Natural Language Processing API is so powerful because it combines sentiment analysis with named-entity recognition, which is a sub-task of information extraction that seeks to locate and classify named entities mentioned in unstructured text into predefined categories. For example, in the sentence “I get a cappuccino every day and I love that I can now earn points on the app and get a discount on my favorite product” we can already identify two types of entities: the product and the platform. So, the tool not only provides information about people’s sentiment, but it also connects this sentiment to the entities in the text.

Of course, this isn’t all new—it’s just become mainstream now that Universal Analytics has officially sunsetted, and we’re all moving on with GA4 (if you haven’t yet, this is your sign to do so).

Never let your customer data go to waste. 

Understanding user behavior, expectations and struggles should always be at the core of your efforts. Such critical information fuels all your experiments and supports you in fine-tuning your products and services. So, next time you’re thinking of leaving reviews unread and letting gold wither away, think again—because this easy, AI-powered solution and the partners that know how to apply it are here to help you extract meaning from your valuable first-party and zero-party data. And to add some fresh cherries to the pie, Google has new AI services that would allow you to automatically reply to those reviews and comments, using a Large Language Model (LLM)—but more on that next time.

Working as a Senior Data Specialist, I’ve come across a series of common mistakes that prevent enterprises from leveraging this tool to its full potential—and consequently, accessing the true value of their data. During a series of panels at Melbourne MeasureCamp, I was lucky enough to host a session on these observations and some recommendations so that brands can bank on actionable insights into user behavior and application performance. If you missed it, continue reading for the main takeaways.

Learning #1: Only one Firebase project can be linked to one GA4 property.

An important thing to consider when it comes to integrating Firebase with GA4 is that only one Firebase project can be linked to one GA4 property. This means that if there are multiple Firebase projects, it’s necessary to transfer all applications—regardless of operating systems or development cycles—into one project and link it to the main GA4 property. 

This requires careful planning and a deep understanding of how Firebase projects are set up.  Keep in mind the potential technical challenges and limitations in migrating apps from one project to another. For example, certain app developers may have their own preferences in terms of project setups, so you need to talk to your development team and understand what that looks like. 

Also, be aware of dependencies such as Crashlytics or BigQuery exports setup when moving apps from one project to another. Each Firebase project can have multiple stack integrations, and we should be ready to reconfigure all of them. Make sure you have historical data and map out timelines for these app migrations.

Learning #2: Standard naming unlocks customer insights. 

The main reason why you’d want to integrate Firebase with GA4 is that it provides valuable insights about user journeys across web and app platforms. However, the only way to unlock those insights is by ensuring standard naming conventions for web and app events. 

First, you’ll need to create a Google Sheet or an Excel spreadsheet to standardize the naming of events and parameters. Here’s an example:

As you can see, we recommend having standardized event names and parameters across web and app platforms in GA4. It may seem simple, but it's not uncommon for organizations to use different conventions on different platforms, making it harder to cross-reference the data.  

Other tips to make the process easier include:

• If you have a website, but no app implementation yet, rely on your web and GA4 Recommended Events to name the event and implement these for the app.
• If you already have an app implemented with Firebase, use the mapping sheet to understand which events from the app can be mapped to web. It is easier to rename web events with GTM than doing so for the app.
• Align with both web and app development teams for naming conventions. For example, using camelcase (e.g. SignUp) vs snake case (sign_up)

Learning #3: Be Aware of Data Collection Limits.

When you use Firebase to collect data from your apps, it’s important to be mindful of the data collection and configuration limits. Firebase Analytics does not log events, event parameters, and user properties that exceed certain limits—which means that the platform will drop the events and stop tracking valuable data even if you exceed the limit by a few characters. 

In my experience, this mistake is especially common among developers who implement the Firebase SDK without really knowing about the limits. These are some of the main caveats and my respective recommendations for them:

• Event parameters limits: 25 parameters per event may seem a lot, but it may add up if you’re sending ecommerce events. GA4 and Firebase will drop the events and event parameters if you exceed this limit.
• Be careful not to go over the maximum length of the event parameter value, which currently stands at 100 characters. Be aware of user-generated values (e.g. listing name in marketplaces)
• Remember that Firebase does not accept array type parameters.
• When setting up BigQuery export for GA4 (with both app and web streams), check the usage in advance so that you don’t get shocked with the cost for the storage and querying the data. Pro tip: Set up daily aggregated tables for important metrics instead of querying directly from raw export tables.

In conclusion, it is essential to be aware of limitations around linking Firebase projects with GA4 property and plan ahead for your migration. Create a mapping sheet to map the events across the website and apps and standardize app and web events naming. Take note of Firebase data collection limits and make sure you are not going over the limits and risk losing your data. Finally, learn how to debug apps using Firebase Debug Mode, a bonus tip that can save you time and headaches.

“It’s so much more powerful being a part of a team that’s full of mixed backgrounds and experiences,” says Doug Hall, VP of Data Services and Technology. “Tech isn’t just for computer science graduates. If we didn’t have a rich tapestry of skills and experiences woven into the team fabric, we’d have a homogeneous glom of great skills, but we’d be more likely to do the same things this week as we did last, and in the same way.”

My move from non-tech to tech-first taught me that many things surrounding data are isolated from business needs and outcomes, even though you don’t want this to happen. When teams operate in silos and data works in isolation, so does experimentation. This inevitably leads to random acts of marketing and chaotic reporting. Rather than siloing teams, data should unify them—even if they have totally different KPIs. For instance, marketing teams look at lead generation, engagement and visibility, while product teams focus on retention and acquisition. In short, if everyone has a separate way of tracking and collecting data, this also means that everyone is looking at different things. 

Viewing the full picture is pivotal to success. 

The bottom line is that all of this information is data, and everyone should be aligned on what type of data will actually help the company move forward. Companies may invest in tools that serve one or more departments—GA4, for one, can support marketing and product teams—but the way each team or department collects data should be a company-wide decision. In turn, this means that a company’s data collection mechanism needs to be strong and reliable to be able to support every team and department in a business and help spur progress. The goal is to unite, not separate. That’s why it’s critical to align what matters in terms of data collection and measurement with the company’s business needs. 

The operative word is context—whatever we do, we must keep this in mind. Getting your company or clients to believe in the data at hand starts with analysts and measurement marketers understanding where the business is right now and where it can go. By actively participating in the inner workings of a business—with a focus on resource allocation and the processes that generate money—and analyzing relevant and purposefully collected data, you can help steer your company or clients towards profit. 

As such, I recommend every digital analyst to get familiar with a business’ internal processes. You can use this knowledge to implement tracking and analytics systems that align with the company’s procedures. A good example of this is how we helped the multinational alcoholic beverage company Diageo deploy GA4 across its 150 brand websites. As Hall explains, “Due to alcohol regulations around the world, most countries require an age verification gateway, which is a major conversion blocker that goes above and beyond consent management. This means that measurement and optimization are crucial for Diageo—and that’s how we knew that deploying consistent measurement across all brand sites was the best solution.”

In short, every digital analyst should come to understand the business context and goals to make sure the tag management and analytics tools are both implemented effectively and in line with the needs of the business. The secret sauce here is to closely collaborate with business-focused team members like marketers, consultants and account managers, who can provide guidance on what data is needed and how it will be used. Sure, we can rely on our experience and heuristics, but that doesn't mean any of our assumptions can be valued as truth. Once you’ve actually combed through a specific business context, you can start to define the right strategy for your business—and even then, it’s a matter of seeing how things play out before you can confirm or reject your hypothesis. Experiment, experiment, experiment!  

Become data mature to make your cash flow. 

Ultimately, this all feeds into a company’s data maturity, which Forbes defines as “a measure of an organization's ability to use data, along with how well the organization leverages those capabilities.” It’s not just about making data-driven decisions, but also about making sure data resources are accessible across an organization. The more data mature you become, the more you can scale—a topic that Coquet will discuss in more detail during the upcoming SUPERWEEK conference.  

With scale comes growth, which, in turn, can lead to new opportunities—and let’s be honest, this is an outcome that every business is after in their search for better tools, better consultants, and better digital marketing partners. When it comes to collecting data and tracking user behaviors (with consent, but this goes without saying), businesses do not want to miss out on any opportunity to get new customers, while staying relevant to their existing ones so that they continue to trust and purchase from them. More happy customers equals more cash flow. In the end, profit is the ultimate validation of growth (and that you’re doing a good job), both from a product and a customer experience perspective. 

Three takeaways to make your data take off. 

While it may take some time to find the most advanced tech stack or the best digital marketing partner—one that truly understands your business and all its needs—there are some changes you can make today. Trust me when I say that these actions will pay off in the end and help your cash flow grow. 

First of all, start by defining the problems you are aiming to solve and the questions you are seeking to answer with your data before you implement anything. This will help fine-tune your efforts and ensure that you are using the right tools and approaches to address the specific challenges you face. 

Second, consider (and research) the possibility of teaming up with a data consultant or specialist, who is able to provide expert advice and guidance on what tools and approaches are best for your specific problems and questions. This is particularly helpful if you are working on a complex or unique challenge that requires specialized knowledge and skills.

Third, teamwork always makes the data dream work. It’s crucial to collaborate with your team members and exchange your knowledge and experience—as Doug said, the more mixed the expertise, the better. By closely working together and sharing what you know, you can pool your collective knowledge and experience in setting up your measurement strategy. Keep in mind that within a business context, every team has its own problems and questions. As a leader, it's important to begin by having them define these, which, in turn, will reveal how aligned your team is around the company’s needs.

The main lesson that you should learn from this article is that context is key. At the end of the day, understanding human behavior, the business landscape, and how a company and its clients bring in money is what makes a successful digital analyst. I didn’t know this ten years ago, but I do now and I’m very happy to share these insights with you—find Julien, Doug, me and many other Data.Monks at SUPERWEEK 2023 and learn more about what really matters in managing your data.

To help brands in their transition, our data experts have written a guide that explores how brands should approach personalization going forward—including how to assess new technology providers, frameworks and methodologies to structure your planning—and long-term goals to strive for. Access your copy by filling out the form immediately below.

Need answers at a quick glance? Continue reading on for a quick FAQ that will help you get started.

Will I still be able to use Google Optimize after September 2023?

No, Google plans to sunset the product entirely. This can be taken to mean that the product will no longer be accessible past this date, and experiments running at this date will turn off.

Does this apply to Optimize 360 as well as the free product?

Yes. Google intends to sunset the product entirely, across both the free and 360 tiers. Note that by the sunset date, all organizations should have migrated their GA360 contracts to GA4, meaning that Optimize 360 is provided free of charge.

Should I use Optimize with UA or GA4 up until the sunset date?

This is entirely dependent on your existing UA and GA4 setups. UA 360 will continue to be available until Optimize’s sunset date, so you can continue to use it if you are more comfortable with that dataset. Otherwise, you can use GA4 data to power reporting and audiences. Linking Optimize with UA is available even after renewing GA360 contracts with GA4.

What should I do if I want to continue testing and personalizing my website after Optimize is sunset?

You will need to procure an alternative testing and personalization platform. Our report details the factors that should go into making that decision, and you should note that alternative platforms will have different commercial models than Optimize.

Will I still be able to use Google Analytics with a new third-party Experience Optimization tool?

Google has announced that they are investing in integrations between GA4 and third-party tools, with the intent being that GA4 will act as a centralized measurement hub that can be used to analyze and report on experiments that are delivered via a different platform. Media.Monks can provide more details on these integrations as they are made available by Google.

What will happen to my historical data?

Optimize uses Google Analytics data for reporting, meaning the raw data from past experiments will still be available in GA (and BigQuery if using GA360). Regardless, we recommend our clients collate test results in a central register to build an insights and learnings repository to fuel future decision-making.  Media.Monks can support the creation of a learning repository before the sunset if required.

Key watch-outs:

• An Optimize container can only be linked to UA or GA4 one at a time, not to both. Media.Monks do not recommend running experiments out of dual containers, so you should choose whichever dataset has the most actionable data.
• There are many factors that go into selecting an alternate vendor, and a proper assessment takes time. Organizations should bring this process well ahead of September 30 to ensure the continuity of capabilities.
• Deploying, validating and ramping up a new testing/personalization platform could take a number of months, meaning organizations should start the selection process now.

To get detailed steps on how to prepare for the Optimize sunset and plan your post-Optimize goals, simply fill out the form above to download our report.

The marketing and advertising industry is united by the common goal to leverage technology in new, creative ways—not only to translate data into personalized campaigns, but also to enhance internal operations. This is where automation could come in, if it wasn’t for the fact that marketing is traditionally quite limited in using such technologies. Simply put, many marketing teams haven’t been exposed to automation beyond connecting CRM to email marketing, even though there’s so much more out there.

From business to advertising, development and production operations, digital process automation can help unlock efficiencies across the entire enterprise. There are ample automation solutions for digital operations that can provide support across a company’s content, data, media and tech efforts. While the positive impact of enterprise automation reaches far and wide—from enhancing digital ad operations to improving internal people services—the biggest benefit is the ability to free employees from repetitive tasks or unnecessary complexities in the workflow, so as to enable them to direct their attention and energy towards more relevant work. Especially considering the current economic headwinds, it’s crucial to make sure that your talent can maximize their impact. 

My team of Automation.Monks is specialized in enabling the workforce to embrace automation as a new way of working and realize its full potential. We encourage people to automate as much as possible, no matter how big or small individual tasks are—if a machine can do the work, we should probably let it. This requires us to rethink how we operate within a business and how we collaborate with both tech and each other. The team not only focuses on automating our internal people, operations and processes, but also on supporting brands in automating their enterprise. So, let’s take a look at how we aim to achieve maximum efficiency, visibility and connectivity with automation solutions built by our analytics practice.

Helping brands future-proof their business with GA4.  

Our bread and butter is helping brands automate their people, platforms and processes. The objective is to ensure that these work in ways that businesses can accelerate their digital transformation and prepare themselves for the modern era. One of our go-to tools for measurement is Google Analytics. As Google’s GA360 is scheduled to sunset in the summer of 2024, many brands are busy migrating to the new GA4 product to take advantage of the measurement platform of the future. Among many other useful updates, this tool helps brands collect website and app data to get a better sense of the customer journey by utilizing event-based rather than session-based data. It also includes privacy controls and behavioral and conversion modeling that help fill the gaps in your data caused by the cookieless future.  

GA4 adoption requires collaboration between departments across an organization, and is therefore a change management process as much as it is a technology solution. The flatter, richer insights from GA4 data can help brands deliver more value and achieve faster competitive advantages—particularly when the adoption process is planned, communicated and managed to promote knowledge-sharing and digital maturity growth. Many brands have benefited a great deal from automating their business processes. Let’s take a look at the positive impact experienced by some of our key partners.

We’re supporting a global CPG brand that strives to increase their business revenue YOY by 3%, with a media objective of 3% ROI growth—ambitious, but certainly possible. As its partner for global content production, we advised the brand to focus on the efficient use of first-party data, while also establishing the use of connected data collected from across the customer journey. We pitched GA4 as the obvious solution, keeping in mind our pillars of quality, speed and value. Using custom built in-house automation, we helped the brand rapidly deploy 169 GA4 properties in minutes—all the while managing the shift to GA4 and maintaining top quality first-party data during the adoption. The results were impressive: through our collaboration, this brand has been able to unify its marketing efforts and metrics across 37 brands in 150 countries, ultimately realizing a growth of more than 70% in global ROI since 2017.

Another major brand that we were able to help automate at scale is Diageo, the multinational alcoholic beverage company. Diageo has made the ambitious commitment to increase their market share from 3% to 6% in FY 23. In order to realize this, the brand needs accurate tools with actionable insights. Once again, we presented GA4 as the straightforward solution. Considering the planned sunsetting of GA360, we immediately started planning the GA4 adoption process. Diageo’s scope covered as many as 150 brand websites, including 39 D2C sites (and counting) where we collect transactional data, which is a large-scale task. We leveraged custom Google Tag Manager templates for the GA4 tagging as well as our in-house automation tool to automate the GA360 rollout for the GA4 deployment in minutes versus what would normally take a team weeks to accomplish.

Deploying a common data layer taxonomy, harmonized across all brand sites, allowed for true apples-to-apples data comparison. On top of that, it was pivotal to delivering high-quality, privacy-first, consented first-party data. As a result, the team was able to both save over 200 hours of work and assure quality and reliable repeatability. Moreover, any future updates to measurement are consistently applied across all brand sites using the same solution—and align with Diageo’s overall strategic goals.

Designing the future of Media.Monks with clients in mind. 

Automation is critical to driving your enterprise into its next phase of digital transformation. Though solutions are largely technology focused, it’s important to be aware of the fact that automation can only truly succeed at scale and have a significant impact on your operations if it’s spread across and woven into every facet of your company culture. By incorporating many new, exciting and innovative tools into the tech stack and empowering your people, you can lift your business to the next level of operational excellence. For example, my team is currently working on productizing cloud-based “starter kits” that can easily deploy within a company’s tech stack and seamlessly integrate with existing or modified operational processes. CMOs can leverage these tools to connect their media efforts to the rest of the organization, which allows for better visibility, data interoperability, and measurement in aligning media efforts to business objectives. This is just one of the many ways in which automation can help organizations become more efficient in reaching their marketing and advertising goals.  

No matter the size of your business or the industry that you’re in, enterprise automation enables you to both streamline your people, processes and operations and enhance the output of different teams. In other words, it allows you to do more with less. Our advice? Start building an automation-first company culture now. Recognize where the biggest pain points are in your workflows and thus where your workforce needs to level up with new skills. When it’s deployed in line with your business goals and objectives, automation will maximize your existing talent and set up your business for future success. However, since there’s no one-size-fits-all solution for enterprise automation, the key is to start now.

Bidding to value isn’t new. In fact, a lot of advertisers have been doing it for many years. Where an advertiser is supplying revenue data directly to the platform, such as revenue from a tag or linked ecommerce data from Google Analytics, value bidding is already taking place. However, for businesses with more complex or longer sales cycles, or driving multiple channels of interaction with customers, understanding value can be an arduous and complex task.

Use value-based bidding to maximize ROI.

In a nutshell, when you use bid strategies in your media buying platforms, the main difference between a Target CPA (cost per acquisition) and a Target ROAS (return on ad spend) bidding strategy is that while Target CPA adjusts your campaign bids to help you meet a predefined cost per conversion goal, Target ROAS adjusts bids to help you maximize the value of conversions you’re receiving as a result of your advertising, and thus focuses on ROI. 

For Google Ads and the new Search Ads 360 in particular, Google has been clear about the fact that CPA bidding or bidding for conversions is limiting the ability of bidding algorithms to eke out performance, as you are assuming that all customers that interact with ads are bringing in the same business value. 

However, we all know that this is not the case. Customers come in all shapes and sizes; some will take longer to make decisions to purchase or interact with your business, some are going to be customers interested in smaller purchases, while others still will be looking at larger purchases or longer sales cycles. This can also become even more complex when customer touchpoints move from online to offline, such as an outbound call center. 

It wouldn’t make much sense to bid for all of these customers with the same value logic. By focusing on segments of customers based on the value they would bring to us, we can maximize our return on our ad spend. This is especially true for B2B or subscription businesses, where not all prospective clients are equal. 

The complexity of value-based bidding only needs to be as complex as you need it to be for your business, but the level and complexity of the data you are sending to your performance platform will provide you with much more robust reporting metrics, and more data for bidding algorithms to get things done.

As we can see, as we move up the complexity of our bidding goal, moving away from clicks/conversions to value and then profit, we need to supply the platform with less proxy metrics, and more revenue and value data. At the most mature stage, the ultimate goal for businesses is to send customer lifetime value data to the platforms to enable automated bidding and to predict future customer buying behavior based on their previous purchasing patterns.

Test and set up value-based bidding using proxy metrics.

For direct sales and subscription businesses, value-based bidding would of course involve simply passing back the value of the sale or rolling subscription back to the platform as an offline conversion, for example in Campaign Manager or Google Ads. However, if your marketing is targeted towards lead generation and longer sales cycles, bidding for value becomes slightly more complex, requiring the use of proxy value metrics. 

For example, let’s say that you have four stages within a typical sales journey, all trackable via conversion tags or Google Analytics, or perhaps via integration with CRM as an offline conversion. It could look like this:

Lead Submitted (25%) → Marketing Qualified Lead (20%) → Sales Qualified Lead (15%) → Closed Deal 

We need to work backwards from the Closed Deal value, to assign a value to a Lead submission:

Closed Deal $1000 → SQL $150 → MQL $30→  Lead Submitted $7.50

Given that a Closed Deal is worth $1000 in this example, we divide each subsequent stage by the prior stage conversion rate.

We can now understand the value of the first conversion point in the customer sales cycle and assign a value to the lead submission, then perhaps do the same for other conversion points on your site (for example, phone calls or “contact us” forms). These values can then be assigned to our bid strategies to assign the real value of customers to your business. Remember, machine learning is only as useful as the information that is being supplied to it!

Once you have values assigned to conversion points, you can use features such as Custom Columns in Search Ads 360 or Google Ads to add these values for your automated ROAS bid strategies, then let the platform algorithm do all the hard work with this new information. 

Look ahead to predicted lifetime value.

Of course, the ultimate goal we should seek with bidding in performance media is to add more of a predictive value to our target, so that the bid strategy is able to bid on keywords that are likely to drive longer lifetime value, rather than one-off purchases, short-term subscribers or low value B2B customers. This can be done by adding predictive intelligence to our bidding platform, and involves integration of CRM with a data platform and machine learning tool, such as Google BigQuery and BQML. 

You can then export these predicted values to your platform of choice as offline conversion data, and point the bid strategy at this particular goal to maximize, which in this case predicts lifetime value. This is where we think all marketers should aspire to be and plan towards, and it’s something we bring up often with clients as an important horizon goal to have with the future of their first-party data. 

Customer value-based bidding, combined with media platforms bidding algorithms, will help you monitor the real impact of advertising on your business and make the right decisions to develop growth strategies, ultimately allowing you to capture the customers that generate the most value, and those that matter most. Again, the data you share with platform algorithms is a crucial factor in competitive success, and unlocking insights related to value will prove crucial to brands looking to improve performance within an intensely competitive digital landscape.

When it comes to the privacy arena, what trends are you seeing agnostically?

Privacy is one of the fastest changing and most complex realms in the digital space, even more so than Web3. On top of that, privacy is an ever-present undercurrent—ongoing in everything that we do. With a plethora of global and regional players involved—the tech sector, regulatory bodies, public opinion—we can safely say there’s a complex interaction at play, which makes coming up with any long-term prediction or silver bullet solution practically impossible. As a consequence, our waterways can quickly go from clear to muddy. What follows is a sense of fear, uncertainty and doubt among many companies. 

In working with companies across the board, we still see a lot of confusion around technical terminology, with partners raising questions such as, “What is personal data compared to Personally Identifiable Information (PII)?” To be frank, we believe this is in part driven by clickbait. Headlines propagating that “GA is illegal” cause unnecessary confusion and concern, when the fact of the matter is that Google’s GA4 as a product has gone through a massive rebuild from the ground up to address and tackle the issues in question. As a baseline trend, we’re receiving more and more questions about privacy matters with regards to all products in the digital marketing ecosystem—and we welcome them with open arms, because we’re here to help solve the riddle. 

How are you helping clients navigate this new, data-focused advertising landscape?

Our objective is to help our partners take proper control of and ownership over their data collection and activation. Therefore, we first focus on basic data hygiene, conducting health checks and audits. It’s very important to know what your company has in store, so we ask questions like what data is collected, which cookies are set, how is the collected data used, and who else is getting the data of your users? Creating a graph of 3P consumers and beyond is complex and thus requires high levels of scrutiny. 

Though the third-party cookie deprecation has been pushed back until at least late 2025, we don’t like to wait around and carry on in the same way we have always done. Rather, we’re embracing a first-class, first-party and privacy-first strategy, for instance by helping companies migrate to GA4—because we see no reasons for taking a reactive approach. We make sure our partners get on the front foot as fast and efficiently as possible, with a strong emphasis on automation. When you’re working with large data volumes, you can’t rely on human-centric processes to manage compliance. For instance, we have implemented automated machine learning as part of the data pipelines in order to prevent PII ingestion. There’s no way that a company can afford to manage a breach retrospectively or be proactive without automation—simply put, this is the most efficient way to scale. 

What are the main lessons that you have learned on this journey?

First of all, we clearly communicate to every brand we partner with to always aim for transparency, make a plan, and move beyond the minimum. Let’s be honest, the economic headwinds that we are all currently facing mean that every dollar, euro and pound spent needs to deliver a return more than ever before. As such, preserving data quality is our top priority. To give you the full scoop: everything we do to be more transparent, protect users’ privacy, and apply rigor and governance to data collection and activation is, in fact, enhancing the data quality, too. So long as you go about your data the right way, you can’t go wrong. 

Speaking of data quality, another key lesson that we have learned is to use time to the fullest. Yes, industry leaders like Google may unexpectedly push back plans, but rather than seeing this as an issue, we believe it can work to our advantage. Setting up the privacy tech for this tool is quick and easy—the hard part is changing the people and processes, which we know can take a while to get completely right. Though we expect that many brands will interpret this extension of GA360 as extra time to look around and perhaps jump ship to another technology, we believe that this is a risky strategy. Instead, we recommend our partners to take this change of plans as an opportunity to perfect, rather than a chance to pivot. There are no excuses to delay GA4 migration. It's imperative to use this time to manage change, translate data workloads, dashboards and data pipelines, and ensure all those GA360 assets become high quality GA4 assets.

Do you see gaps in performance between brands that invest in privacy and those who don’t?

There's a clear and definite advantage to taking a strong privacy-first approach to data—and companies are catching on. People are realizing (or, at least, starting to realize) that we’re not playing a zero-sum game and the exchange of data in return for personalization and better ad targeting is the data privacy transaction we all engage in—with reciprocity being the key word. Those who are best able to complete this transaction at scale will be rewarded with the best results, whereas those who continue to walk the third-party cookie path will fall behind. For some time now, we’ve been helping many of our partners run on a healthy diet of deterministic and probabilistic data and not trip over the mix of consented first-party and modeled data, and we can tell you: they are in good shape. 

Want to discuss next steps? Get in touch. 

Everything you do for privacy feeds back into your data quality, hence the opportunity to sharpen and perfect your process of migration to GA4 is one to take with both hands. It’s essential for brands to look beyond the obstacles of GA4 and work to get the best first-party data off the back of the migration.

We’re here to help make sense of changes in the privacy landscape, how to make use of GA4 data, and how such technologies can support all your marketing needs. If you have any questions with regards to GA4 migration, please reach out to growth@mediamonks.com. We’ll schedule some time to discuss any questions you may have and see how we can support your analytics needs.

Insights for this piece were contributed by Doug Hall, VP Data Services and Technology, EMEA; Julien Coquet, Director of Analytics, EMEA; Suzanne Jansen, Head of Data Strategy, EMEA; Véronique Franzen, Senior Director Business Consulting, EMEA; Jakub Otrząsek, VP Data, APAC; Sayf Sharif, VP Data, NAMER; Michael Neveu, Director of Data, NAMER; and Wenting Wang, Senior Director of Data & Analytics, UK.

Now, marketers can relive the excitement and ideas of the conference (or encounter them for the first time) in a new documentary. THE GAME features insights from speakers—including Vice President of Data Services and Technology (EMEA) Doug Hall and Senior Director of Analytics, EMEA Julien Coquet—to discuss how recent developments in data collection, activation and regulation are reshaping the strategies of brands and their partners.

For a clear understanding of where the industry is headed, find the documentary in full below. Look forward to more Superweek next year, running from January 30 to February 3 in Egerszalok, Hungary!

In our work, we’ve found that Google Data Studio does an excellent job with both. A free, online tool that transforms public and private data into interactive knowledge, the platform powerfully visualizes data at teams’ fingertips, without betraying security.

Augmenting Data Visualization with Google Data Studio

Google Data Studio helps people comprehend and manage data. Using the platform, you can:

• Identify business trends: Interact with the data in the form of charts, maps, graphs and tables, using popular features like filters and segments.
• Turn budget data into customized reports: A fully functional Business Intelligence (BI) platform with customizable interactive dashboards and reports.
• Assess the performance of your websites and/or business: By connecting website analytics with business data, you can find outliers in performance. 
• Analyze customized data and find useful data points for varied success criteria: Users may manipulate the data to suit their needs, having varying levels of user access.

Furthermore, access permissions can be managed across the whole organization with help from Google Workspace. Dashboard owners can also prevent other users from further sharing a dashboard and limit their options when it comes to downloads or exports to mitigate the risk of unauthorized data sharing. 

However, even though the different access levels and sharing controls are intended to prohibit the wrong people accessing sensitive/personal data, the process of sharing a dashboard or restricting access to one can be difficult to manage at scale. Google’s documented solution to personalize data results proposes using more data sources and using the blending functionality to create many-to-many filters. There are scenarios where this can be feasible (especially when BigQuery is not involved), but it could involve making changes to the data source that would then need updating if permissions changed. 

Managing Complex and Interconnected Data Streams

It’s easy for data to become overwhelming, and when a dashboard is connected to a data source used by multiple people, the dashboard owner wants to ensure that each user only sees the data that is relevant to them. This becomes an especially important requirement if the data in question is considered sensitive. So, how does one factor in this kind of a requirement in a dashboard using pre-existing Data Studio capabilities?

Using an email filter is one way to restrict access to irrelevant or sensitive data. When this feature is in use, Data Studio searches for the user's email address in the column that carries all user email addresses in the data source, and if located, Data Studio will filter and present only the relevant part of the data for that user. This is designed to provide a higher level of restriction around data that directly relates to an individual, known as “row-level data security.” The feature is available for any data source. If the data is in BigQuery, you can filter by the user's email address using the email parameter in a custom query. 

This feature isn’t without caveats, though. By default, the row-level data security function falls short in scenarios when more than one person has access to the same row of data. For example, if we are looking at internal business metrics about an employee in relation to a multilevel organizational structure, there might be an ongoing need for a manager to be able to have access to the data of the employees they manage.

Streamlining Internal Data Sharing

Rather than creating a dashboard using a personalized data source for each employee, then sharing access to this dashboard with others one-by-one, Media.Monks set out to create one dashboard with a data source that contains all required data and shares it in a way that gives access to a group of specified people. The proposed solution is based on BigQuery’s capability to use nested fields, enhanced further by using Google Sheets for simplified access management.

The example below provides maps between owners of business units and managers of varying levels. Thanks to this mapping, a direct manager can access the data of any employees sitting beneath them in the report, as well as a person who sits two layers up in the organizational structure. 

First, it is important to correctly map out who should have access to which part of the data set using a Google Sheet. This might vary depending on the data in question, and should be carefully considered before sharing the final dashboard.

Looking back at the example, ‘Business Unit Name’ was identified as a mapping field between the data source being used and the access management sheet. The ‘BU Owner’ field is used to list all email addresses of people who shall have access to the data. ‘BU Owner’ will become the email field when setting up an email filter. What is particularly interesting about this solution is that any updates to permissions made via the access management sheet will work in real time. 

A few technical steps remain to put the solution together. The access management/permission sheet needs to be linked to BigQuery. Data transformation is also required in order to create a nested field (array) out of the provided email addresses, which is achieved with an SQL function “split”: SELECT bu_name, split(trim(bu_owner), “ , “ ) bu_owner FROM *Insert Data Source*

The result shows each team/Business Unit name, along with the corresponding emails of the users that have access to that team’s data, like this:

The last step here is to join the original data source with the table representing the Google Sheet, then save it either as a view or a table. For more complex queries and situations it is advised to save the results as a table, which improves performance. Some further tweaks may be required to increase performance and leverage BigQuery’s BI engine. 

The logical principle here is that only a single user may access the dashboard at any point in time. The advantage, however, is that when the Business Unit Owner field gets ignored, the data maintains its consistency, as there are no row multiplications. 

The dashboard is then connected to BigQuery’s destination entity. In the Data Studio UI, the owner of the data source must enable the email address filter feature and appoint ‘BU Owner’ as the filtering field. If you need to make changes to who can have access to another person's data, you only need to do so in the access management sheet, as they will be reflected in the connected data source instantly. 

Toward Efficient Practices for Handling Data Internally

Sharing sensitive data with the right people, particularly in a multi-level organization, is a process that should be undertaken with utmost precaution. How people at different levels apply data to the business depends on their attitude to data—ranging from a core driver of the business to a point of annoyance and confusion—underscoring the importance of making data accessible and comprehensible to all who use it. By augmenting data visualization and securely sharing the most relevant data to members of the team, it’s only a matter of time when data becomes the priority. I eagerly wait for that day.