At a glance:

Incorporating your own first‑party data with Marketing Mix Modelling (MMM) can make both sets of data far more useful and practical than when they are used in isolation. The combined data set enables marketers to measure customer lifetime value, tailor insights to different audiences, separate short‑term activation from long‑term brand impact and validate results with experiments. When integrated well, MMM enhanced by first-party data delivers more precise ROI measurement, better segmentation and LTV insights, improved long‑term impact assessment, and more direct activation.

Enable cohort and lifetime value measurement. 

Linking MMM results to customer cohorts turns marketing measurement from a short-term revenue uplift to a forward‑looking view of customer lifetime value. Rather than treating every conversion the same, cohort analysis groups customers by useful traits—for example, how they were acquired (paid search, social, referral), which campaign or creative they saw, the week they first engaged, or the product they bought first. 

These cohorts are then monitored for purchase history, retention patterns and other lifecycle behaviors. It is by following these groups that you convert short‑term sales lifts into projected lifetime value (LTV) and clearly see which marketing efforts are actually building lasting customer relationships.

Support audience‑level modelling and segmentation. 

Audience‑level modelling and segmentation transform MMM from a one‑size‑fits all budget allocation tool into a more nuanced decision system. By leveraging first-party attributes like demographics and churn risk, you can build a segmented MMM to measure how various groups respond to your media and messaging.

This matters, as aggregated findings can hide variation. An overall channel ROI can look attractive, while most of the incremental profit actually comes from a narrow, high‑value segment. 

Conversely, a channel that drives many low‑margin, one‑time buyers may inflate acquisition counts but reduce overall profitability. By modelling at the audience level, you quantify not just volume of incremental conversions but the quality (profitability, retention potential) of those conversions.

Improve long‑term measurement.

Owned data—like email open, loyalty program activity, app usage or brand tracker scores—adds a layer of behavioral context that raw sales and media‑spend data can’t provide. These signals reflect shifts in awareness, consideration and ongoing engagement that often precede sales by weeks or months. 

When you feed them into an MMM, it can become possible to detect customer intent that would otherwise be lumped in with short‑term promotional effects. For example, a sustained rise in loyalty program activity, or improved brand tracker sentiment following a brand campaign, is a strong indicator that future purchase probability has increased, even if immediate conversions remain muted.

Bringing owned metrics into the model therefore helps separate activation from brand building and gives you a clearer view of long‑tail impacts. Instead of attributing delayed sales solely to the most recent tactical spend, the MMM can assign appropriate credit to earlier brand investments that moved customers along the funnel. 

The result is more accurate measurement, better forecasts of future returns, and a stronger business case for investing in brand and retention activities alongside short‑term activation. 

Enable better experimental design and validation. 

Use first‑party data to run and measure experiments (holdouts, geo tests, A/B tests) and feed the results into the MMM as truth or priors. This strengthens causal inference and calibrates model estimates against observed incrementality.

Using first‑party data to design and measure experiments dramatically strengthens your ability to prove what really is moving your KPIs.  With customer and behavioral data, you can undertake holdouts, geo tests and randomized A/B tests on well‑defined cohorts to measure true incremental lift, and then feed those experimental results back into the MMM.

That data can be used as validation points or as Bayesian priors to nudge the model results toward observed causality, reducing reliance on purely observational correlations.

However, the loop goes both ways. The MMM analysis can help prioritize which experiments to run (which channels, segments, or messages look most promising or uncertain). Together they create a virtuous cycle—cleaner causal inference, more trustworthy ROI estimates, faster learning, and better allocation decisions—all while leveraging the identity and engagement signals you already own.

Drive operationalization and activation.

When your MMM uses first‑party signals, its recommendations become more tailored to your business and more actions focused. Instead of saying “spend more on channel X,” the model can suggest which exact customer groups to target or pause, what messages to send, and where to reallocate budget for the biggest incremental impact. 

Those audience‑level suggestions can be pushed straight into your owned channels—email campaigns, app pushes, CRM journeys or loyalty offers—so the right people get the right message at the right time.

That also lets you close the loop. Measure how those actions change behavior, feed the results back into the model, and keep refining both the measurement and the activation rules. This will enable you to make quicker decisions, waste less spend, and build marketing that actually follows through on what the data tells you. 

Get the most out of your first party MMM integrations. 

To maximize the value of your analysis, follow these key steps to ensure your first-party data is MMM-ready.

• Invest in data plumbing and governance. Clean, consistent data is the foundation. Standardize taxonomies (channels, campaigns, creatives), enforce naming rules and put quality checks in place so everyone uses the same definitions.
• Map the customer journey. Link CRM records and purchase histories back to media exposures wherever possible. Knowing which touchpoints led to a sale makes cohort and LTV analysis much more accurate.
• Combine MMM with cohort LTV and survival analysis. Use MMM to estimate short‑term lift, then apply cohort retention and repeat‑purchase models to project lifetime returns and true acquisition value.
• Use hybrid measurement. Complement MMM with experiments and uplift tests on first‑party cohorts to validate and refine model outputs. Experiments provide validation and calibration points for your models, building trust and confidence in its findings.

Build modular models that support audience‑level or channel‑level sub-models so recommendations can be operationalized quickly into owned channels.

In summary, integrating first-party and owned data significantly enhances your MMM. By incorporating these datasets thoughtfully, you can achieve more precise ROI measurement, deeper LTV insights, and more direct activation—all while maintaining a privacy-safe framework. 

These techniques are exciting, as Google has published data demonstrating that double-digit percentage uplift in conversion value is possible. Results clearly depend on having the very best data, the best modeling capabilities and the best activation strategy, which is where Media.Monks teams play an essential role.

Who is this for? Everyone!

Are you in digital marketing as an “analytics person”? Primarily data focused? Technical? You’ll know about enhanced conversions (EC) and value-based bidding (VBB), but beyond the tagging, do you know what’s going on in the media systems and what it’s actually for?

Or are you a “non-technical” marketer? Your talents for campaign setup and management don’t overlap with tagging. Again, you’re across EC and VBB but where does the data come from? Why’s it so tricky to get right? What’s the hold up with the tags?

Regardless of our role specifics, we all need to have as full understanding of the solutions as possible. We need to get a handle on what happens “on the other side” so we can deliver the very best solutions for clients, and for users. Here’s the scoop you need. This is relevant to people on the Search Ads/Display & Video/Campaign Manager side as well as those on the Google Analytics/Google Tag Manager side. Here’s an opportunity to share knowledge… LFG.

Set the scene.

Cookie atrophy is a poorly kept secret. Browser tech continues to erode cookie usage. Third-party cookies are being deprecated from Chrome in 2024, which holds a dominant market share that’s significant for marketers. That doesn’t mean we are on safe ground when it comes to first party cookies though; just check through the details on Cookie Status to see the reality.

As data volume diminishes with sufficient signal quality, we can still use modeling techniques to mitigate for gaps in data, but that’s not a robust solution in isolation. We continue to make every effort necessary to maintain data volume, whilst evolving our tactics to improve efficiency.

This is where EC becomes a playbook entry to maximize observable conversions, while VBB drives greater efficiency by enabling optimization for value rather than volume.

Maximize observable data.

If we have less data, we must have better data quality. By that, we mean clean and clear data where we can clearly see conversions and channels. This means that the data still has utility even if it’s not complete. Where we may have holes due to browser tech and cookie loss, for example, we can still use first-party data to get better conversion accuracy. Enhanced conversions help us see more conversion data, but in a privacy-safe manner.

What it does.

Basically, on the conversion/sale/thank you page, a tag will fire—let’s say a floodlight tag for simplicity. The user’s email address is hashed (encoded using the SHA-256 algorithm), and then added to the tag data which is then sent to Google. This hashed value is then used to match the user with Google’s data to recover conversions that are absent from your data set.

You can use a range of values in addition to, or instead of, the email address. The email address is normally fine. It’s hashed, so no third party (not even Google) sees the data and it’s deleted after use. Google has published in-depth details on how the data is used, and this is essential reading for your teams.

Use best practices for tagging.

Ideally, you’d expose pre-hashed personal identifiable information (PII) on the dataLayer variable which can be picked up easily by Google Tag Manager (GTM) and added to the floodlight.

You can scrape the Document Object Model (DOM) to extract the data, but this is not a robust, long-term solution. You can use Google tag instead of GTM if a tag management system is not available. For offline conversions (leads), you can also upload conversion data via an API.

Collaboration is key.

Tech, data media and legal teams should work closely in order to correctly implement and then validate changes in data volumes.

This is not legal advice, so you need to get buy-in early from your legal team. Advise your teams to make sure EC usage is covered in your privacy policy and cookie policy and that consent is fully informed with a clear opt-out option.

Make sure you know the conversion page path, and that the PII variable is available. Scraping the DOM might be okay for a proof of concept, but don’t rely on it as a permanent solution.

Media teams need to make simple configuration changes and then report accurately on conversion volume changes. Use your data science teams to establish causality and validate EC is working. Liaise with your media teams regularly after rolling out EC to maintain scrutiny on the data volumes and changes. Be impatient for action (get it done!), but patient for results—manage expectations regarding timing, change may take weeks.

Using value-based bid optimization.

As we progress along the path of digital maturity, our tactics adapt and evolve. Where it’s normal and fine to optimize for click volume in the early days, the optimization KPI changes as our business grows. We aim to reduce cost, grow revenue, build ROI and ultimately optimize for long-term profit.

Optimizing a campaign for click volume was a brute-force budget tactic. Optimizing for value (profit stems from value) is a more precise allocation of budget. How the budget is allocated is the clever part.

Optimize for value.

Consider an ecommerce site where the obvious valuable outcome is a sale. There are other outcomes that serve as signals to indicate a user may be a valuable customer: viewing a product detail page, adding to cart, starting a checkout. All actions lead to the conversion, all with varying degrees of value. As each outcome is completed, fire a floodlight to inform GMP that the user has performed a “high-value action” worth €x. These actions and values are then used to automatically optimize the bid for the user.

Previously, defining the values associated with an action was a matter of experimentation. Now you can use an online calculator to refine these numbers.

This approach to value-based bidding needs a level of data volume and quality that is delivered by using EC with VBB—and is extremely powerful. It has few moving parts, but the values are static, commercial values that don’t always reflect the user’s likely behavior. To address this, let’s look back at an older solution to see how we can level up this approach.

Using coarse-grained optimization.

Previously, we’ve used machine learning to build a predictive model that will output an answer to “how likely is it for user X to convert”? At scale, the data is imported into GMP as an audience, and we use this to guide where the budget is spent. A simple approach here is to build a set of audiences from the model output to drive bid optimizations:

• “No hopers” with the lowest propensity to convert: €0.
• “Dead certs” with the highest propensity to convert: low or €0
• “Floating voter” with medium propensity; needs convincing: €maximum

This technique has delivered great results in the past. There are shortcomings, however. With three audiences, the segmentation by propensity is quite coarse. As the number of audiences ramps up, there is more to compute and more to maintain in terms of infrastructure. The user needs to revisit the site to “get cookied” and be included in a remarketing audience.

There is a more modern approach that addresses the shortcomings from these techniques.

Modeled VBB optimization goes even further.

We’ll now blend these two solutions with server-side data collection (sGTM). Server-side data collection has a number of key features that make it very appropriate for use here:

• First, it allows data enrichment in private—we can introduce profit as a value for optimization without exposing margin data to third parties.
• Additionally, first-party cookie tenure is enhanced by server-side data collection. Your first-party cookies are set in a way that prevents third-party exposure—browsers like this and take a less harsh view of them. This is better for your first-party data quality.
• There is no need to revisit the site to establish audience membership; all cookie-ing is done in the pixel execution.

So now, we can fire floodlights for our sales conversions, attach per-item profit data at the server level and optimize bids based on user profitability. Awesome, but what about the predictive model output?

At the server-side data collection point, sGTM can integrate with other Google Cloud Platform (GCP) components. As well as extracting profit data, we can interrogate a propensity model, and for each high-value action per user, ask what the propensity is for the user to convert. The predictive score is then attached to the floodlight to drive VBB.

This has fewer moving parts than the older solution. It solves for the coarse-grained audience feature by delivering per user scoring as the data is collected. Again, we team this up with EC to maximize conversion visibility and drive powerful marketing optimizations.

Optimize your marketing with EC and VBB.

These techniques have existed in isolation for some time. With a broader understanding of the data requirements, and the activation of the data, we’re all in a better position to use privacy-first marketing optimizations to deliver efficiencies for clients, and ultimately, a better, more useful online experience for consumers.

Modeled Value-Based Bidding addresses these challenges for marketers:

• Third-party cookie deprecation and tightening privacy regulations pose significant headwinds for brands looking to connect with consumers.
• With first-party data sources and data volumes growing at breakneck speeds, many marketers are overwhelmed by managing data manually.
• Companies that have large data sets can’t manage manual bid strategies with one person or even a team.
• More and more advertisers are looking to understand how AI and machine learning can fit into their digital advertising and data strategies to help drive efficiencies.

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.

CRM is crucial to any first-party data strategy because it sits at the center of every customer interaction: through behavioral and environmental triggers, your customer is feeding inputs that influence future experiences, like product recommendations and personalized messaging. Essentially, data makes personalized experiences possible—and when done right, those experiences in turn generate more data that brands can act on. With the death of the cookie on the horizon, these insights will become even more critical to your marketing strategy.

There’s no better time than now to unify data within a CRM ecosystem to improve the efficiency of teams, inform future business strategies and, of course, enhance customer experiences overall. These efforts involve building data pipelines that help them better learn about their customers and engage with the right message at the right time. With the help of automation, a powerful collaborator that helps teams pull off outcomes that only eluded them before, the sales team can focus on only the most qualified leads. 

Not sure where to get started? No worries; I’ve gathered a couple brands who have successfully transformed their CRM ecosystems to fuel personalized experiences at scale.

Translate behavioral cues to key business insights.

Beyond driving conversion, one of the most impactful results of a strong CRM strategy is being able to leverage behavior data to guide better consumer experiences—of which Australian Community Media (ACM) makes a prime example. ACM is a large media organization that operates over 140 local news mastheads across Australia, serving both free visitors and paying subscribers. That’s a lot of relationships to manage and readers to serve. To those ends, the brand relies on email marketing and onsite personalization via Salesforce Marketing Cloud to reach and continually engage with readers.

ACM wanted to better understand subscriber behavior to create more personalized, relevant experiences in the form of automated content recommendations. Previously, this content was manually selected by editors or determined by publish date. Using Marketing Cloud Personalization, we were able to pull from subscribers’ engagement and platform behavioral data (like affinities toward news categories) to build personalized recommendations—boosting not only relevance but also employee efficiency.

This data did more than simply help serve personalized content to email subscribers. Armed with insights into which topics readers enjoy the most, editors can now easily plan out future content and focus on the kinds of stories their readers care about the most. More broadly, these same insights allow ACM editors to better predict engagement across the user journey—showing how CRM data can extend beyond marketing to unlock critical business insights that ultimately serve audiences. The best part: automated content recommendations free the editors to focus more on these strategic concerns of how to build better impact.

Elicit engagement to personalize at scale.

If you struggle to glean insights from audience behavior, here’s a tip: make it as easy as possible for customers and prospects to tell you more. This simple step was the cornerstone of Woodlea’s CRM refresh. Woodlea is a master-planned community of 7,000 lots located 30km west of Melbourne. With a need to focus on buyers at the right time, their sales representatives wanted to be able to give special attention to novice buyers. But this posed a challenge: how could they personalize communication and experiences at scale?

We began by helping the brand insert forms into email sent to buyers, a move that increased engagement while generating significant user data in the process. The newly interactive emails included simple questions and a prompt for recipients to build out their profile in Woodlea’s customer portal (powered by Salesforce Experience Cloud). The fact that these forms were embedded into the actual email content made it a seamless user experience and increased the percentage of leads who engaged. This first-party data then fed back into Woodlea’s Salesforce CRM, allowing for automated lead nurturing and qualification. These efficiencies freed the sales team to focus on two key buyer personas: those ready to make a purchase and first-time buyers who needed more attention throughout the buyer’s journey.

Enhance CRM to start building your first-party data foundation now.

The best time to transform your customer experience was yesterday, but there’s still time before cookie deprecation to experiment with new ways of generating first-party data—and CRM is at the heart of the process. From eliciting user engagement to gain key insights, to building efficiencies through automation and automation, linking data and inputs across a connected CRM ecosystem goes a long way in serving stronger, more personalized customer experiences and key business goals—so don’t wait.

Need help or don’t know where to start? Reach out to learn more.

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.

In case you need a little refresher, retail media networks are advertising platforms established by retailers on their own digital platforms—it’s sort of like in-store advertising, but in digital format. This creates a new revenue stream for retailers, as RMNs enable them to monetize their first-party data through the launch of ad products. Essentially, it’s all about the data, as ad monetization with RMN allows retailers to gather new information on the behavior and interests of their customers, enhance their first-party data strategy, and engage with new audiences by meeting their specific needs. Ever since third-party cookies have started crumbling, RMNs have emerged as the sweet treat that both retailers and advertisers need—and the demand for this solution is rising at an incredibly fast pace. 

The tried and tested RMN trend will continue to accelerate in the new year. Here are four developments that retailers, advertisers and digital marketing services partners alike need to prepare for.

RMNs go global. In short, every retailer around the globe is going to have a retail media network, if they don’t already—it’s simply becoming pure table stakes. What started in North America, with Amazon leading the charge, has been rubbing off on businesses in every other part of the world. I can guarantee that only a year or two from now, even small-scale regional retailers will have an RMN, whether that’s in Italy, Thailand or Argentina. Why? Because once the flame of a business trend has been ignited and fueled by a new, but proven economic paradigm, the fire simply has to spread. That said, it is important to note that all of this may seem like a scenario with no downsides, but there is a potential one: if RMNs are not executed well, retailers and advertisers run the risk of diminished customer experience. For this reason, many brands choose to team up with a partner that’s specialized in this technology. 

Put creative differentiation at the core. Up until very recently, the game of retail media completely revolved around data and mathematics, with people and technology coming in to better execute what is essentially a quantitative effort. As such, RMNs allowed very little space for creativity. Now, however, we are seeing retailers such as Amazon move up the funnel and into the world where creativity truly matters: branding. When it comes to building brand awareness and bringing in new customers, data definitely counts—but it’s the creative that can make a real difference. So, while you still need the smartest people and the savviest technology to handle quantitative details, retail media is more and more a game of bringing in the right creative. Considering there’s so much more opportunity for creative differentiation, the brands that are best able to bridge data with creativity are the ones that will succeed. 

Tailor unified real-time strategies. So far, most brands (and even some of their partners) have been deploying retail media networks per channel, which means that an advertiser’s budget and approach for Amazon may differ from its budget and approach for Walmart. In other words, they have been working in silos and optimizing within the lengths of each different platform. However, this is all about to change. In the near future, I believe brands will view retail media networks as interconnected advertising channels instead of a handful of unrelated platforms. With that, it will become more and more feasible for brands to build a single retail media strategy, which allows them to be more flexible and seamlessly move between different channels. In the same vein, they will start to use unified real-time optimization tactics to capitalize on arbitrage opportunities between various retail media networks. This essentially means that brands will take more of an active daytrading type of approach. While some parts of this process can be automated, many others will still require manual efforts and human intervention in the form of more centralized retail media teams—both at the side of advertisers and their partners. 

Deliver dynamic in-real-life placements. As retail media networks—which are currently completely digital—expand, retailers will start to move ads from their online platforms to their offline spaces. For example, Amazon has announced that it will install more digital signage in its Whole Foods stores across the US and connect their DSP to their in-store screens. This will enable the retailer to use first-party data to dynamically serve ads in a previously analog framework and programmatically target consumers, thereby transforming the century-old concept of paid POS into an extension of digital advertising. 

In short, dynamic IRL placements can help retailers and advertisers enhance their targeting. However, one issue with this approach is that it’s still based on backward-looking data. Simply put, ad placements are currently based on average demographic numbers. This means that a retailer doesn’t actually know who is in its store in real time. However, with Amazon’s Just Walk Out technology, where cameras and sensors follow customers throughout their entire in-store journey, retailers and advertisers will be able to gather aggregated data of everyone who’s in a particular store in real time. This technology may already be in use in Amazon Go stores, but I believe it will take a few more years before it can scale, especially considering its significant privacy concerns—so I’ll save this prediction for another time.

Teamwork to make the RMN dream work. 

Over the last year, retail media networks have blown up and blown our minds. The impact of this technology is far-reaching and will only continue to expand in the coming years. Now, it’s up to retailers and advertisers to dive in and start monetizing their first-party data. As for my final piece of advice, make sure to team up with an experienced partner that can help you along the way—this will benefit every party involved. 

Curious to learn more about retail media networks? Get in touch with our team via sales-ecommerce@mediamonks.com.

What are the core capabilities of this technology? First up, CDPs support data aggregation, giving you a better and more unified view of your (prospective) customers. Second, they help you unify multiple data sources through a single ID manager, thereby facilitating ID resolution and management. Third, CDPs help you understand how customers act on different channels and thus enable you to predict consumer behavior. Finally, CDPs support customer activation. They’re first-party data tools that focus on making sense of different data sources, while executing effortless activation. 

Essentially, CDPs can help you diversify your brand’s targeting strategies and reach audiences at scale, all by leveraging your first-party data. If you ask our Associate Director of Customer Data Elia Niboldi, first-party data is your most valuable asset, not only because it’s durable and exclusive to your company, but also because it will be central to any future targeting strategy—and Customer Data Platforms are here to help you leverage this data. Niboldi sat down with Ian Curd, Global Consumer Data Director at Diageo, Martin Kihn, SVP Strategy, Marketing Cloud at Salesforce, Jackie Rousseau-Anderson, Chief Customer Officer at BlueConic, and Chris Thomson, Account Director, Strategic Finance Accounts at Treasure Data, to talk all things CDPs and why now is the time to dive into this complex technology.

These are not off-the-cuff suggestions, as the impact of ignoring or misinterpreting these recommendations is plainly visible. With privacy currently being the fastest moving field in our industry, we’re reaching the point where most—if not all—professional discussions have a privacy angle. While that’s great in terms of profile, it’s not really good in terms of quality.  

If you ask me, most cookie banners are subprime usability blockers that annoy users and turn them away. At worst, they’re dark patterns obscuring malice. When the most common denominator is so prevalent—that being lousy banners—we get what is called banner blindness, a phenomenon where web users (un)consciously ignore any banner-like information. When that symptom kicks in, it’s a downhill race to the bottom.

A likely sequence of events then plays out: marketers settle on a nice and easy bottom feeding tactic, the whack-a-mole game of privacy merry-go-round spins through another orbit as either tech, public opinion or regulators (or all of the above) make a new move to counter it. Recently, for example, it was Brave’s turn in the game of ignoring the privacy elephant in the room. The company announced it was going to block cookies by default and roll out a cookie pop-up blocking feature to Android and desktop users, which is arguably a step backwards. Rather than adding any clarity around what data is collected and why, the browser actually acts on behalf of consumers and removes choice from the user. It’s important to highlight that regulation is not anti-business, but it’s pro-consumer. Privacy-enhancing technology needs to respect this narrative. 

My former colleague (and still just as wise) Myles Younger powers his crystal ball with some nostalgia to suggest consent pop-ups are dead. “Someday soon we’ll look back on cookie consent pop-ups the same way we look back on “300 hours of free AOL” CD-ROMs littering our sidewalks. The farcical dying gasp of a dying way of transacting a digital thing,” Myles argues—and he is not wrong. It’s been seen before, as observed by the analytics supremo Simo Ahava, who argues that Do Not Track was a failure from the start. Diving into the implications of Safari’s Intelligent Tracking Prevention, better known as ITP 2.1, on web analytics, Ahava says that “Funnily enough, ITP 2.1 removes support for the Do Not Track signal in Safari, denoting the end to this miserable experiment in WebKit. Had more sites respected DNT when determining should visitors be tracked or not, perhaps we wouldn’t have seen ITP 2.1 in its current shape.” 

Consent management is anti-user.

Why are these well-meaning initiatives failing? Google surveyed over 7000 people across Europe in 2021 and found that users want to have control of their data. Recent follow-up research quantified the degree to which the feeling of control influenced customer confidence in brands. The conclusion? A positive privacy experience on a site has a measurable positive impact on a brand.  

So, how can you create such a positive privacy experience and avoid the pitfalls that we’ve seen with Do Not Track (DNT) and the current crop of Consent Management Platforms (CMP)? If it’s up to Google, brands should make the experience:

• Meaningful by showing people what they get in return for sharing their data
• Memorable by reminding people what data they shared and when
• Manageable by providing tools for people to manage their privacy

The demise of third-party cookies means the future of first-party cookies. 

For many, applying this mnemonic to first-party cookies is a work in progress. Cookie consent banners are still relatively new, even though third-party cookies have been under threat for many years. We know which browsers restrict their use and we expect these restrictions to extend to Chromium browsers in 2023.

If digital marketing can’t function without third-party cookies, this has the potential to hit big tech in the coffers, and we cannot allow this to happen. There’s a clear motivation to solve existing use cases by utilizing privacy-enhancing technology—this is where The Privacy Sandbox comes in. According to a Google statement, “Privacy Sandbox for the Web will phase out third-party cookies and limit covert tracking. By creating new web standards, it will provide publishers with safer alternatives to existing technology, so they can continue building digital businesses while your data stays private.”

We’ll see the next phase of testing kick off in 2023 when the Privacy Sandbox API is publicly available for testing on Android. Right now, this is API testing, which means that they’re testing for developers rather than users. The user testing phase is where it gets real for real people. This is the opportunity to succeed, think of Google’s mnemonic, instead of failing like DNT and CMPs.  

Cookie management sitrep.

Right now, you can open the settings in your browser on each device and scroll through the list of cookies for each site, and decide to delete them. You can then visit the site and repeat the exercise in the “manage cookies” section of the CMP.  However, this current process doesn’t fit in terms of being manageable. In fact, the term laborious doesn’t even begin to describe it.

When it comes to qualifying as meaningful, cookie management has a low score because it’s so opaque—how can you tell who else is getting access to the cookies and for what purposes? As for memorability, most users only remember the frustration and tedium, but little else regarding their choices.

So, considering the future of cookie management, how might the Privacy Sandbox address the choices users have to make with regards to “tracking” and their privacy? While this section is entirely speculation and therefore not an official roadmap, it’s aspirational with the aim to be realistic and pragmatic. My thoughts are as follows. 

• Users get to decide what topics they are interested in and willing to share with third parties.  
• Users allow the browser to build a list of topics, but the user reviews and controls the list periodically asking to be reminded on a set schedule.
• Users can choose to set their topics to apply across all sites they visit. Any advertising they see on any site they visit will use and respect these settings.
• Users can choose to review their topics preferences on a per site basis. Users get to curate (and review) their own whitelist/blacklist for sites or types of sites.
• Users ask to be reminded to check their preferences every so many days, weeks or months.
• Users can choose to reset all data in the browser automatically every so many days, weeks or months.

Now, let’s apply similar controls to first-party cookies:

• Users will be able to tell the browser what type of cookies they will accept, and whether they want to be measured—anonymously or otherwise.
• Users can specify this applies to all sites, some sites (whitelist) or types of sites.
• These settings are reviewed on a scheduled basis.

What are the right default values to apply on first use? The good news is, there are no default values. On first use, and on a frequent basis, the user must explicitly set their own first use values. In other words, no values are suggested or automatically preselected.

How is this different from a CMP banner? Set it once, and make a conscious set of decisions with no intrusive user experience on every site or app you use. This could actually be set at a “profile” level across all devices and all browsers. This requires less mark-up and coding to be done by site owners. In short, there’s less to maintain, less to go wrong, less to slow down and less to cause friction.

How is this different from DNT or Brave? A more granular approach and a genuine user-controlled choice are the fundamental differences that make this approach manageable and meaningful. The range of choice is meaningful and the act of making a choice is manageable as it is made as friction-free as possible. Moreover, having to make a choice is memorable, as well as the ability to set reminders to review these choices at your convenience.

Now is the time to apply these lessons for the future.

The challenge for The Privacy Sandbox is to reduce friction, increase transparency and enhance authority. The privacy improvements will cater for existing use cases as well as provide a manageable, meaningful and memorable privacy experience for users.

That said, what’s the takeaway for digital marketers? Google said that “The Privacy Sandbox on Android will be a multi-year effort,” so what to do right now? Circling back to the start of this article, it’s important to:

• Take control of your data
• Embrace the regulatory spirit
• Go beyond the bare minimum
• Make it meaningful, memorable and manageable

Though we accept the looming end of the third-party cookie, this doesn’t mean we have to stop digital marketing. New privacy-enhancing tech changes the methodology, as the same use cases are catered for with new tech to enable better ad serving. Having learned from the success of these technologies applied to the end of third-party cookies, we can confidently focus the lessons on our first-party data collection. What works across sites and apps must also have the same utility on individual sites and apps. Keeping that in mind, the end goals remain:

• Build a relationship with your customers
• Be transparent
• Be useful
• Be responsible with data

All in all, achieving these goals and aiming to provide a better experience has immense value for your customers and your business.

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.