There's a lot of talk about AI's potential—its future possibilities and predictions. But let me assure you, the moment for AI is not just coming; it's here, it's now, and it's making waves across all industries. And what’s specifically interesting to me is that it’s changing the world of marketing and digital platforms.

But what about the barriers to entry? It's important to remember that incorporating AI into your operations doesn't mean a full-scale overhaul is necessary. At Media.Monks, we understand that each brand is unique and some may require a more iterative approach. This perspective allows for cost-effectiveness and accessibility while still benefiting from the AI wave. A phased introduction of AI-driven improvements can bring immediate benefits to your customers and your business performance. You might begin with an AI chatbot to enhance customer service, or leverage machine learning to personalize content for each website visitor. Initial steps like this can provide quick wins, delivering enhanced user engagement and improved conversion rates. As these enhancements demonstrate their value, you can gradually expand AI's role within your digital landscape. It's about creating a tailored, strategic path towards AI integration, instead of diving headfirst into the deep end.

So, let’s take a journey into the current and very real applications of AI within the digital platforms landscape, areas where AI is not just delivering promises, but measurable results for marketers.

Here’s where to get started with AI.

Integration of AI with traditional platforms. The integration of AI with conventional platforms is helping businesses refine operations and customer experiences. The merging of CRM systems with AI, for example, allows a brand to learn from its customers’ behaviors in real-time, thus offering better service and products tailored to individual preferences.

Optimizing user experience. AI-driven data analysis is providing actionable insights that directly enhance user experiences. Whether it’s through customized content, personalized interfaces, or the elimination of user flow pain points, AI is driving a new era of user-centric platforms.

Facilitating personalized marketing. Gone are the days of generic, one-size-fits-all marketing. AI is enabling a new level of personalization that makes every interaction feel like it's uniquely crafted for the individual user. From product recommendations to personalized messaging, AI is helping brands forge deeper connections with their customers.

Enhancing analytics. AI-powered predictive analytics are transforming how businesses understand their customers and markets. These tools provide an unprecedented level of insight into future customer behavior, market trends, and potential business risks.

Cross-department collaboration. AI isn’t just for tech teams. It’s providing opportunities for seamless collaboration between departments, helping to create unified, efficient approaches to everything from product development to customer service.

AI solves many of the challenges brands are dealing with right now.

Next, let’s look at some great real-world examples where we have worked on bringing transformational improvements to key KPIs by both iterative and larger form implementation of AI enhancement. Here are some of the challenges we are helping with day to day:

“Help, I’m drowning in a sea of content!” When the volume and complexity of the information is overwhelming for visitors, sometimes standard search just won't cut it. A potential application of AI here is to create an intelligent search functionality that leverages natural language processing and machine learning. It understands user queries better, allows for conversational dialogue and provides more relevant results, continuously improving based on user interaction patterns.

“How do we extend meaningful connections with customers whilst building a community of users?” An AI-enhanced platform could provide personalized content based on customer interests and product usage patterns. By understanding each customer’s interaction with the product, AI can tailor content, extending the brand experience and fostering an engaging online community around shared product experiences.

“How do we cope with the daunting task of managing job applications from a vast pool of diverse applicants and numerous roles?” Here, AI can be employed to develop self-segmentation tools and create individual user journeys based on each user's unique profile and preferences. AI can analyze data at scale, drawing insights that allow a recruitment agency to tailor each experience and guide potential applicants towards roles that suit their skills and aspirations.

“How do we effectively showcase an extensive network of services and provide evidence of campaign effectiveness to potential customers?” By implementing AI-driven analytics, this company could deliver detailed campaign performance reports to customers, even predicting potential future outcomes based on historical data. This approach provides a tangible measure of ROI for clients.

Each of these scenarios illustrates the transformative potential of AI within the digital platform landscape. Broadly speaking, AI complements and enhances our existing strategies, enabling us to craft more engaging, personalized, and efficient experiences for users. AI isn't just a box to be checked; it's a versatile tool that we are using daily to create meaningful and impactful digital experiences.

Prepare yourself for sustained success with AI.

With AI’s potential being realized in real time, the thrill is in watching these developments unfold and harnessing them in transformative ways. Remember, the future is not some distant point on the horizon; it’s happening right now. By embracing AI in a thoughtful and strategic manner, we can achieve immediate wins and lay the groundwork for sustained, long-term success.

On the one hand, according to KPMG research, 40% of consumers are skeptical of companies’ ability to protect their personal data and privacy online. However, BCG and Google and IAB surveys indicate that 75% of respondents only want to see advertisements that are relevant to their preferences. This is why it is critical to have solutions in place that address these two priority needs: privacy and relevant content.



As a senior digital analytics expert and team lead, I have conducted several privacy audits over the years, and through the process have identified some pitfalls that I believe are more common than others and can jeopardize data protection. Fear not, for this article unveils these treacherous traps! Prepare to learn about the top four things to avoid when configuring privacy settings in your digital analytics deployment.

The "Track Everything" Temptation

Ah yes, the allure of “Tracking Everything” that will inevitably cross our minds: “There is no priority or business case, we want to track everything.” But wait, dear data adventurers, let us not abandon the noble principle of “privacy by design.” Casting this principle aside brings great peril to our ethical data practices.



The Track Everything temptation is frequently felt by teams with large budgets that are using multiple analytics tools to track the same data points, spending the majority of their time arguing about which tool is registering the correct numbers. They want to be able to answer any question, when what they should be focusing on is finding the right questions to ask.

This is because the Tracking Everything approach puts privacy at risk: in fact, this statement directly contradicts the principles of privacy by design. It will put the brand in the position of collecting information they don’t need, breaching the privacy of the final user. Essentially, you would be spending a lot of money to risk paying a large fine. 

Regardless of privacy concerns, this behavior is costly in other ways. It reveals some data immaturity, which can lead to difficulties in keeping your data sets tidy and cost-effective. As a result, data consumers may become skeptical of your analytics product.

This prioritization can be accomplished through a well-defined measurement strategy. To define said strategy, consider which signals you would most likely expect to see in your data set when users are engaged (or not).



As a preliminary step, I recommend hosting a workshop with all stakeholders to define priorities and leverages. Then—and only then—start collecting data. This will help you develop a reliable and trusted source of truth that includes all relevant stakeholders.

You can always add data points as you go through your analysis and come up with new intelligent questions, but tracking data simply for the sake of having it is wrong. Your stakeholders will be grateful. People who need to kill ants should not be sold bazookas. Help them grow at a pace they are ready for.

Implicit Consent by Default

I frequently see implicit consent set as the default setting, and other regulations such as GDPR applied on a regional basis. If your CMP's geolocation is blocked for any reason (yes, I've seen this happen), you run the risk of implicit consent being applied to countries where stricter regulations should be applied instead.

If the company operates primarily in a region where implicit consent is permitted, it may make sense for the DPO to accept the risk and leave it alone. However, if you want to be privacy champions, the default setting should be the safest option available—that is, the stricter regulations should be applied by default, while the more lenient regulations should be applied on a country-by-country basis. Consider that most regions that do not have a regulation are evaluating its enforcement, and it is never too early to demonstrate to your users that you care about them.

The Regional Tag Triggers

A common misconception is that marketing tags do not require blocking triggers if they only fire on region-specific sections of websites. 

Because this type of deployment is "tag specific," it will necessitate extensive maintenance and is more prone to human error. Furthermore, even if implicit consent is assumed for a specific region, it is critical to follow privacy regulations such as ePrivacy and GDPR when individuals in other regions (e.g. the EU) access an app or a website.

Instead of relying on tag management system triggers, ensure a scalable and privacy-compliant deployment by centralizing the decision on implicit or explicit consent in your CMP. Ensure that all marketing tags have the same consent set-up, regardless of where they should fire (e.g. fire only if ad_storage is set to granted). If they're marketing tags, they'll always be marketing tags, no matter where they fire!

In Google Tag Manager 360, employing a zone-based approach to group tags with the same purpose can be highly effective. This enables you to configure consent compliance only once for a specific zone (say, "marketing tags") and apply it to all tags that belong to it.

Misunderstanding the Scope of Google Analytics Tags

It is a common misconception that GA tags only set cookies for analytics purposes. However, both GA3 (Universal Analytics) and GA4 make use of features such as Google Signals and Remarketing, which require user consent to use personalization and remarketing identifiers.



Fear not, for Consent Mode arrives on the scene, championing compliance effortlessly. When properly set up, Consent Mode takes care of consent-based features automatically. Yet, if it's not in sight, we must devise an explicit setup.

When Consent Mode is not in place, you can disable Google Signals and/or the Remarketing features programmatically. All advertising personalization features can be disabled by setting the "google_signals" parameter to "false" by default and "true" only when the user consents to being identified for marketing purposes.



In short, consider these common pitfalls and proposed solutions to ensure compliance with privacy regulations when deploying privacy settings for digital data collection. Prioritizing privacy not only protects individuals' personal information, but also contributes to brand trust and, ultimately, improves your customer experience.

Finally, please keep in mind that this is not legal advice, but rather my ethical position on the subject. When making privacy-related decisions, we recommend that you consult with your DPO and legal team.

The reason why we measure more and more is straightforward: so that we can forecast the performance of different strategic scenarios, and thereby help the brands we partner with optimize their media efforts. And just like any other discipline within advertising, the field of media continues to evolve, so let’s put a spotlight on what matters right now and will support your media measurement. 

Welcoming incrementality in the media world. 

First, let’s take a step back and look at what incrementality entails. Simply put, it refers to the lift in conversions or sales that can be attributed to a specific advertising campaign above those that would have occurred regardless—also known as the base. Incrementality has recently been adopted by us media folks, and the term has risen in importance because it’s a media measurement solution that isolates the incremental uplift. This matters because otherwise you can’t tell which media is driving growth and which is just harvesting conversions that you would have gotten anyway. As such, incrementality delivers a far more accurate view of how your media channels are driving conversions.

For example, traditional multi-touch attribution (MTA) often fails to separate the base from the uplift of the advertising campaign. This can lead to overstated results. Instead, in order to accurately measure incrementality, it's important to use MTA in conjunction with incremental techniques like market mix modeling (MMM). This way, you can better understand the true impact of advertising campaigns, move from ROAS to ROI, and as such have a more sensible conversation with your finance teams on the effectiveness of media.

How market mix modeling has got media measurement’s back. 

Market mix modeling—sometimes referred to as media mix modeling, but I prefer the former—is certainly not new to the scene, and this technique has been around in its commercial application to understand media uplifts for several decades now. However, the discipline has significantly improved, especially in the last few years.  

Contemporary MMM has come a long way. In the old days, annual updates would take months to bear results, while today you can get a pilot up and running within six weeks and use automation and machine learning to obtain monthly updates in just a matter of days. Besides, visualizations have also become much better, as today’s reporting dashboards offer analysts a plethora of ways to approach the data sets.

It's important to note that market mix models consider the whole market—including drivers like promotions to pricing, the recent pandemic, seasonality and more—and thus offer a holistic view. If you fail to take these other factors into account, you can’t get an accurate read on media and risk overstating its impact. As such, we’re seeing more and more brands partner with specialist MMM experts to help build the market mix models, or work with them to in-house this capability.

I have to point out that some players out there might say they execute “media mix modeling,” but are actually just building a simple regression with media variables or using multi-touch pathway techniques (which is not an incremental analysis). What’s so concerning about this is that they offer so-called MMM solutions at very cheap rates, which may sound appealing, but the damage of using these cannot be underestimated. Basing your decisions on a cheap but bad model could go wrong and cost you over 40% of your media-driven revenue—compared to an increase of roughly 30% if the technique is applied properly. You can make the call on what’s best for your brand.  

Leveraging AI to accelerate our analysis. 

Another very timely reason why I’m so excited about applying market mix modeling is the recent rise of artificial intelligence and the automation solutions that have stemmed from it—AI has been advancing fast in various areas, and it did not forget about MMM. 

At Media.Monks, we’re bullish about AI. That said, we also know that it’s important to be cautious and do our due diligence, especially as we see many AI providers claiming to build market mix models without having the right experience and tools to do so. When it comes to MMM, we believe that AI and automation solutions can be incredibly useful in speeding up the process, but of course there are also some instances that require manual labor. Let’s take a look.  

Raw data and processing. This can be automated using APIs or templates to stream data in, and then pre-ordained processes automate cleaning, saving lots of time. Beware of providers who take several months to initially onboard data pipes, as you really should be up and running in a matter of weeks.

Initial models. We use evolutionary algorithms to automate the initial model build, running thousands of models instantly in the cloud and scoring them, which enables us to arrive at a base model much faster and save weeks across MMM projects with multiple KPIs.

Final models. Note that this (still) requires manual intervention with a very experienced modeling team. We need to sense-check the models, triple-check the data, and use our extensive experience to spot any anomalies and alternative analysis to interrogate any controversial findings.

Sales effects and ROI calculations. These can be automated without the use of AI—this is just a process that can easily be repeated using code.

Automated reporting. Once all the numbers are calculated, it’s easy to automatically populate dashboards and media optimization tools. One thing that can’t be automated, however, is the answering of bespoke client questions around most effective second length, audience, and more. 

Engagement. Reporting ROIs and optimizations is one thing, but gaining an understanding of and trust in the models is another. Therefore, in the early stages of MMM engagements, it's imperative to have people who can explain the models and results to the wider team—not just marketing, but also finance, sales, the board, to name a few. My advice would be to circle back to this in later stages, once people understand and trust the model, and then you can move to more automated reports.

In short, automation can replace a lot of the heavy lifting of data and results processing and visualization, while AI can be used in the initial modeling stage. But what can’t be replaced is the sense-checking, interpretation, and experience of a good modeler to ensure the results are robust, realistic, understood and therefore usable.

Decreasing time, while increasing results. 

In the context of economically uncertain times, a time-saving—and thus cost-saving—solution like market mix modeling, especially when it’s powered by AI and automation, comes in very handy. Based on these models, media measurement typically enables brands to forecast different sales scenarios. In turn, having a robust forecast of performance is critical in justifying different strategic scenarios to the board, owners and investors of a company.

Incrementality is critical in the quest for accurate ROI, and MMM is a main way to get there. Though this technique has been around for decades, its pace of change and adoption rate is accelerating, which I’m sure will be further driven forward by AI. That said, in order for you to reap the many rewards of this tried and tested technique, it’s critical to work with a media partner who includes the whole mix of sales drivers and can take your models from sheer numbers to clear business actions.

It’s not only important to know which media channels perform best, but also why they do better than others. To help your marketing team figure out what works best for your brand, our media experts have outlined four main factors that help explain the ROI of a particular media channel. 

Engagement. Due to the medium used, some channels are naturally more engaging than others. The more engaging an advert, the more memorable and more likely it is that consumers will react to it. Comparing TV adverts to print, the former makes use of visual and sound effects that allows for more engaging content, whereas the latter is more limited by its medium. When it comes to social media, popular platforms such as TikTok have shown the widespread impact of video content in any form. “From the addition of Reels into Facebook, to the rise of YouTube Shorts and TikTok overtaking Google as the most popular domain, the great shift to short-form video is in full swing,” according to HubSpot. 

Targetability. This factor refers to how well marketers are able to reach their target audience using their selected channels. Online channels tend to have greater targetability than offline due to their ability to choose to show adverts to very specific demographics. Online channels also allow for retargeting customers who have already seen an advert from the same company, which is very useful when it comes to selling a large amount of items like flight tickets. Greater targetability boosts ROI because less spend is wasted on people who see the advert, but aren’t likely to purchase the advertised product. That said, the requirement of targetability depends on what a brand is selling, as some products appeal to broad demographics and thus do not necessitate targeted advertising.

Adstock. The adstock effect—which is also known as the ‘memory’ effect of media—differs between media channels. TV, for example, tends to have a higher adstock because the adverts are often more memorable compared to channels like online display, where the adverts are typically simpler, subtler and therefore less engaging.

Reach. Last but certainly not least, the number of views and impressions that an advert can generate depends on what media channel is used. By working with channels that have large and widespread audiences, you increase the chances that your advert reaches many people. Some channels are more limited and simply have less consumers using them, which can lead to diminishing returns because your ads keep reaching the same, smaller audience.

In all, these pillars help explain the differences in sales uplift per dollar spent when comparing the impact of your ads across different media channels. This knowledge is especially useful when it comes to making a decision about your brand’s optimal media channel mix. However, that said, it is important to note that a channel’s ROI shouldn’t be its only measure of success. Some channels serve a particular purpose such as driving brand awareness, and thus might still deserve a place in your brand’s optimal media mix, despite their potentially lower returns. In short, ROI isn’t the main metric that brands should be chasing—rather, from a business point-of-view, it’s best to consider this in maximizing net profit. Learn more about how we can help now.

Those who engage with AI will be better equipped to deliver personalized, relevant and effective content that engages users and helps them stand out in an increasingly competitive digital landscape, and they may do so by investing in first-party data integration, testing AI-driven bidding and creative tools, experimenting with more visual content, and preparing for the eventuality that AI will change the search engine results page (SERP) ad landscape as we know it.

Late last year, ChatGPT took the world by storm, becoming the fastest product in history to accumulate one million users in just five days. This same technology went on to power the launch of Microsoft’s new Bing search experience. Since then, Bing launched an ads experience that surfaces ads and recommendations based on relevance to the conversation. This seems to be working, as the search engine's audience has grown significantly to over 100 million daily active users.

Unsurprisingly, Google is beginning a limited release of the Search Generative Experience, with ad formats that are highly focused on travel and shopping experiences. Meanwhile, the traditional Google SERP will bring in generative AI responses to further improve how we search for, engage and ingest information we seek. Search ads will continue to show in traditional ad slots, but there will be a totally different experience based on the conversation, rendering relevant links and ads.

Still, artificial intelligence isn’t new to search; in fact, it influences a variety of factors that influence results, from bidding to search query matching to creative optimization. But the introduction of large language models (LLMs) into the equation will significantly impact not only the user experience, but also how content is valued and ranked on the results page and how we buy media. They’ll also significantly change the way we create content. Here are three big ways the future of search will force brands to adapt—and what you need to do now to stay ahead of the curve.

AI-generated content will be a double-edged sword.

 Conversational search opens the possibility of delivering highly relevant, personalized responses to users on the fly. While the benefits to this are obvious, AI’s talent for spinning up content on its own presents a double-edged sword. Some verticals—like healthcare, pharma and finance—will struggle to keep up with the pace of automation given the various rounds of legal and regulatory approval required for their creative before it goes live.  

AI-generated content risks circumventing these hurdles. It’s also vulnerable to spreading misinformation. But brands can mitigate these concerns by ensuring human review before the approval and publication of ads. Through proper tuning and training of AI models, brands can quickly spin up content that incorporates regulatory guidelines that they are beholden to.

Search will be more engaging, visual, and interactive.

The future of search isn’t all text. Search is also skewing toward more visual and experiential content. Sure, image extensions make search more visually engaging to users. But also consider more sophisticated platforms like Google Lens or Snapchat Scan, which use computer vision to make a user’s surroundings searchable. AR is another format that will add a new dimension to search and is already offered by Google, allowing users to engage directly with virtual animals, objects and places in real time.

The idea is to build a more immersive experience versus the infinite scroll. Travel, retail and lifestyle brands may benefit most from this because they already have robust libraries of visual assets to draw from. Others, like B2B brands, healthcare, pharma, and finance, will need to catch up by building libraries of visual and experiential content that engage users to avoid stock images. At the recent Google Marketing Live, new products for asset creation using generative AI were announced, making it easier for those without libraries to build creative in Google’s advertising platform. Generative AI can certainly help brands develop assets at speed and scale, although it’s important to remember that they aren’t yet production ready on their own. There may also be open questions of legal ownership and intellectual property rights.

Data streams will continue making search more predictive and proactive.

Search is already steering in a direction where it can serve more personalized results based on previous activity or what the search engine already knows about you—for example, suggesting local restaurants when searching for food on Google, or recommending related products on an Amazon product page. These experiences generally help users find what they’re searching for faster and keep them coming back for future searches.

It’s not a stretch of the imagination, then, to envision a future in which search engines anticipate user needs before they are typed. They will go beyond keyword query and apply previous behaviors and contextual information—like the intent unlocked by a conversational interface—to generate entirely unique responses for each user. That sounds amazing, but the more conversational search improves, the better it will be at delivering answers that satisfy users’ queries without their having to click through to another website—reducing opportunities for ads in the traditional sense.

The data streams that enable this experience will play an outsized role in how search continues to evolve. Brands who have first-party data will have opportunities to use it to enable even greater predictive and personalized experiences. While we don’t know for sure how this space will evolve—concerns about privacy and transparency, especially globally, may interrupt progress here—it seems likely that search experiences will continue to evolve in this trend. The lesson is clear for brands: the accumulation of data assets and the ability to deploy AI will be differentiators as the SERP ad landscape changes.

Don’t wait to update your search strategy.

Unsurprisingly, a strong data foundation will be crucial to keeping ahead of these changes. Maintain a competitive edge by investing in first-party data integrated across touchpoints in the conversion cycle. Apply conversion modeling to help fuel more relevant ads and higher returns. These insights will prove critical as brands adapt to conversational search, providing them with the insights and tools they need to deliver more personalized, relevant and effective content.

Speaking of content, brands can also future proof by updating their approach to activation and creative. Test AI though bidding, ad creative and playing with broad matches. Experiment with tools like Google’s Performance Max—an AI feature deployed in the GMP suite that allows for cross channel campaign launches and optimizations all from a single campaign configuration—and automated asset generation.

Finally, break away from relying on text by testing more image extensions and invest in performance creative to help stand out. Leveraging AI to optimize and find the best creative combinations will help brands adopt a more asset-based approach and prepare for search’s increasingly experiential, visual and conversational interfaces.

All of these developments are happening right now, and brands will need to adapt through experimentation with emerging AI tools.

By doing so, they will be better equipped to deliver personalized, relevant, and effective content that engages users and helps them stand out in an increasingly competitive digital landscape. And lastly, AI is far from perfect, so check the sources and verify the generative responses.

For one of our clients, their team had spent quite a good amount of time and effort building a series of email marketing campaigns in order to optimize online customer journeys. Some results were great: the CTR, for example, was extremely high considering the industry benchmark. So, how come the team was not able to demonstrate the impact of their investment just yet?

As soon as our team set out to analyze the email campaign data from top to bottom, a common issue sprung into view. It wasn’t really about the email content or the automation settings. The problem was at the top of their email campaign journey: the number of email leads put into the campaign pipelines was significantly low.

Before you start, consider your input.

Data from the 2022 ROI Report by Nielsen shows that increased investment in upper-funnel to mid-funnel marketing campaigns elevated brands’ ROI by 70%. Clearly, it is critical to create and strengthen a marketing system where you can maximize the impact of your input and draw the best outcomes. However, at times, we forget two of the main aspects of implementing a successful marketing strategy: the amount and quality of your input. Both should be satisfied, because without a decent amount of quality email leads at the top of the email marketing funnel, the outcomes will never suffice or provide the expected value for the team’s investment.

The Nielsen report also shows a significant, positive correlation between audience targeting and campaign ROI: high target reach campaigns (those that aim to reach a large number of people within a specific target audience) present a higher ROI than medium and lower target reach campaigns. Considering the power of the upper-mid funnel marketing, audience targeting in the upper funnel can lead to unlock maximized potentials of campaign performance. But in order to secure a healthy amount of customers at the top of the funnel, approaching the right targets in your upper funnel campaigns is pivotal. And here’s the good news: media campaign platforms offer a variety of options for you to fine-tune your targeting strategies by grouping customers into meaningful subsets.

When it comes to navigating the myriad categories and data points used for targeting, I follow a list of strategies and criteria to better target and activate audiences in marketing campaigns, which in turn helps to improve the upper funnel. Note that some strategies may not be viable, depending on the features of the media campaign platforms your team is using or the availability of the online customer data.

• Demographics: gender, age, education, job title, etc.
• Life events: critical lifetime milestones such as change in marital status, having a baby, or buying a house.
• Affinity: specific interests of consumers while browsing online.
• In-market: strong consumer interest in the products or services you are selling.
• Previous interactions with your website or app: visited, viewed a product, clicked “add to cart,” logged in, downloaded brochures, etc.
• Current customers: existing records in CRM matching with the campaign channels.
• Likely-to-be customers: people who are sharing similar characteristics to the existing customers.

By segmenting target customers into the right subsets and serving them relevant content in marketing channels, your team will be more likely to have strong upper pipelines, leading to stronger performance down the funnel. And if your team has robust input and a solid marketing system, ROI improvement is just around the corner!

To overcome that challenge, wise marketers and data scientists resort to customer data platforms: software systems that allow businesses to collect, centralize, and manage customer data from multiple sources in one place. A CDP can help answer an abundance of questions by providing a single source of truth; though before you can get there, it’s important to understand how to handle the complexities and responsibilities that come with it.  

Not long ago, our Associate Director of Customer Data, Elia Niboldi, penned an article on how to leverage CDPs to their full potential. This time, we’re taking a step back with a new whitepaper that explores the key considerations when implementing a CDP. Let’s dissect some of the main takeaways.

With CDPs comes great responsibility.

To put it simply, CDPs aim to provide a comprehensive view of the customer across all channels and touchpoints, which allows businesses to make informed decisions and create better customer experiences. They are incredibly powerful tools, but that also means the data collected by CDPs can be sensitive and needs to be handled in a responsible and ethical manner, even if customers were happy to share it with the brand in the first place.

In other words, CDP data comes with the need for strategy and clear governance around a brand’s interactions with their customers. Having a robust consent management system in place is the bare minimum, an essential process for allowing customers to determine what information they want to share with a business—something that Salesforce Privacy Center handles very well. And this shouldn’t be limited to brands’ first interaction with a client: when changes in regulation occur or customers’ preferences change, they should be provided with an option to manage and update these preferences, and brands can keep track of those from a centralized location through a CDP.

Once the customer has shown interest in creating a value exchange between their data and the brand’s services, it’s important to set frequency capping standards that alleviate brand fatigue and ensure brand communications are effective and positive—rather than annoying and frustrating. The frequency send caps are usually reset daily, weekly or monthly, and can be adjusted based on customer behavior to optimize marketing campaigns and improve the overall customer experience.

Consider the role of CDPs in the CMO’s business.

Because they provide a unified view, CDPs are both a technical and organizational tool that can help break down silos. Traditionally, customer data has been fragmented across systems and siloed within departments, making it difficult for marketers to access that data in meaningful ways. At the same time, it’s naturally hard for technology teams to fully understand marketing needs or their specific use cases for the data they manage. CPDs bridge this gap, serving both the CMO and the CIO.

However, in order for CMOs to access the real value of CDPs, we need to remember they play three key roles: ensuring cooperation between teams, improving optimization use cases and offering better segmentation. A CDP necessitates cooperation between different teams because it’s meant to break down silos and provide a single source of truth that everyone in the organization can draw from. Through that source of truth, marketers can keep track of which channels and strategies are performing particularly well and optimize accordingly. Finally, CDPs unlock superior targeting capabilities that allow businesses to provide personalized experiences that resonate with their customers’ needs and interests. 

Salesforce Data Cloud, for example, combines the data from Google and The Trade Desk to activate audience insights beyond messaging, journeys and onsite personalization into a brand’s search and digital media campaigns. Plus, it funnels a nearly infinite amount of dynamic data to Customer 360 in real-time. This allows for deeper audience engagement, as customer data is continuously updating and feeding audio, OOH, app, web campaigns and everything in between.

Interested in implementing a CDP? Assess your readiness. 

So, you’ve installed clear governance standards around your interactions with consumers and aligned both the CMO and CIO on the importance of having one source of truth. Are you ready to start extracting meaningful customer insights? Not just yet. First, you’ll need to follow a few initial steps to ensure a successful implementation of the CDP:

• Outcome alignment: start by workshopping the priority use cases to deliver the minimum viable product. This needs to be a cross-functional exercise that ideates, quantifies and prioritizes use cases.
• Identity resolution strategy: build the identity graph that allows a customer profile to be stitched together to form a single customer view.
• Data model: design a consistent global measurement framework.
• Team vision: make sure the CDP is coupled with a clear strategic vision and the right team to extract its full potential. This team should include champions from different departments, system integration partners, executive sponsors and operational users. 
• Implementation plan: develop the operational model. For a customer data platform to be implemented seamlessly into a business, pre-built integrations are essential. Establish which integrations are required and use this to choose a CDP solution that suits your operational needs.

All things considered, CDPs shouldn’t be thought of as “set and forget,” but rather “implement and optimize.” A CDP like Salesforce Data Cloud can provide a wealth of benefits for a business, from more efficient data management to improved customer experiences. By setting a clear governance process and taking into account key considerations before implementation, businesses can ensure that they are ready for both the benefits and the responsibilities that come with utilizing a CDP.

In this report, you will learn how to handle key considerations like data governance, efficiency management, virtualization principles, consent management, unification, and activation to build a holistic view of what is happening in your business.

Across markets, brands are met with complex issues like the current economic downturn, inflation and third-party cookie deprecation. Budget cuts and layoffs have led them to reduce their media and content production budgets for 2023 and, as a result, raised their interest in automation solutions for production and advertising operations. With the aim to complete the same workload in the best way possible, many brands are shifting their focus to lower-funnel, performance-driven activities, while seeking ways to use resources more wisely. As they embark on new paths to growth—or dial up investments in innovation to diversify marketing strategies—now is the time for brands to automate whatever formats and assets they can to unlock the time and budget necessary for more outstanding activations. 

Fear of taking big creative risks during tough economic times makes it difficult to deliver groundbreaking creative. Besides, the traditional creative process is often expensive and doesn’t guarantee a campaign will perform well. I believe this is a result of a linear and rigid process which doesn’t allow for iteration, thereby leaving mid-flight performance insights to go unaddressed. Traditional big idea campaigns have their place, but there’s a more cost-effective and efficient solution to develop truly groundbreaking creative: Dynamic Creative Optimization. 

DCO is a silver bullet as brands streamline budgets and teams.

DCO is the process of producing modular creatives that can serve personalized creative variants to users based on different attributes, from their demographics and location to their behavioral and psychographic tendencies. In a time of economic uncertainty and evolving consumer expectations, the ability to quickly adapt creatives to address new challenges, trends or “moments” is a game changer. 

Looking at additional benefits, DCO allows you to streamline creative iteration and corresponding AdOps processes and shorten the activation process. My colleague Mitchell Pok, Director of Creative Services & Technology, says the typical creative process too often ends at delivery, and getting any iterations or changes done can take a long time.

This also means that the lift required to run tests has been reduced, allowing brands to do more proper testing. By methodically iterating on high performing content, brands are able to learn what works best for their target consumers and adjust messaging on the fly.

As you can see, DCO is a means to increase operational efficiency. By templatizing high volume and repetitive creative assets, brands save time and budget, which they can reinvest into other areas: developing innovative formats for new platforms, designing creative for channels that require a more bespoke approach, or fine-tuning low-scale but high-performing tactics to drive personalization at scale—the choice is yours. 

Successful DCO is a true interdepartmental effort. 

To realize the full benefits of DCO, collaboration across multiple teams is required to ensure a seamless process from asset iteration to activation. Unfortunately, different departments like media and creative are often siloed. Historically, the media landscape has always consisted of niche occupations, with specialists rarely moving out of their specific realms. As for creative teams, they’re further downstream and don’t really get to see the results of the assets they deliver, but are left in the dark until the next request comes in. 

However, constant collaboration between these teams helps tie everything together. That’s why we focus on breaking down barriers and making sure everyone involved in a project understands what other departments are doing. As much as there are limitations from a media perspective that creatives need to consider, there are limitations from the creative perspective that media teams need to take into account. The key to bringing these two together is helping them see things from the other’s perspective—for example, by showing designers the granularity at which media can be purchased, thereby empowering them to create more relevant imagery to serve these tactics.  

Cold feet? Run a pilot and see where it takes you. 

To brands that still shy away from DCO, I say: let’s run a pilot. It’s complementary to other measures, and thus can play a big or a smaller role in your strategy. Additionally, if you’re already used to creating and running animated HTML5 creatives, the DCO production process is only marginally more complex, but provides the benefits of quickly updating messaging and testing new variations. Start out by teaming up with a partner who can help you run a pilot and see where it takes you. 

Ultimately, the more you systematically test your creatives and generate insights, the more DCO will help you fine-tune your whole marketing strategy across creative and media. My advice to brands that are ready to become smarter in their creative production is the same as to myself when I browse pottery courses: it’s time to take your chances, because fortune favors the bold.

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.