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



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



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

Leveraging textual data to determine brand sentiment.

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



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

Put your data to work to improve your business. 

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



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



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

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

Never let your customer data go to waste. 

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

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.

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.

At Media.Monks, we’re always on the lookout for ways to integrate the best AI technology into our business. We do this not just because we know AI is (and will continue to be) highly disruptive, but also because we know our tech-savvy and ceaselessly curious people are bound to experiment with exciting new tools—and we want to make sure this happens in the most secure way possible. We all remember pivotal blunders of these past months, like private code being leaked out into the public domain, and thus it comes as no surprise that our Legal and InfoSec teams have been pushing the brakes a bit on what tech we can adopt, taking the safety of our brand and those of our partners into consideration. 

So, when OpenAI—the force behind ChatGPT—updated their terms of service, allowing people who leverage the API to utilize the service without any of their data being used to train the model as a default setting, we were presented with a huge opportunity. Naturally, we seized it with both hands and decided to build our own internal version of the popular tool by leveraging OpenAI’s API: MonkGPT, which allows our teams to harness the power of this platform while layering in our own security and privacy checks. Why? So that our talent can use a tool that’s both business-specific and much safer, with the aim to mitigate risks like data leaks.

You can’t risk putting brand protection in danger.  

Ever since generative AI sprung onto the scene, we’ve been experimenting with these tools while exploring how endless their possibilities are. As it turns out, AI tools are incredible, but they don’t necessarily come without limitations. Besides not being tailored to specific business needs, public AI platforms may use proprietary algorithms or models, which could raise concerns about intellectual property rights and ownership. In line with this, these public tools typically collect data, the use of which may not be transparent and may fail to meet an organization’s privacy policies and security measures. 

Brand risk is what we’re most worried about, as our top priority is to protect both our intellectual property and our employee and customer data. Interestingly, a key solution is to build the tools yourself. Besides, there’s no better way to truly understand the capabilities of a technology than by rolling up your sleeves and getting your hands dirty.

Breaking deployment records, despite hurdles.  

In creating MonkGPT, there was no need to reinvent the wheel. Sure, we can—and do—train our own LLMs, but with the rapid success of ChatGPT, we decided to leverage OpenAI’s API and popular open source libraries vetted by our engineers to bring this generative AI functionality into our business quickly and safely.

In fact, the main hurdle we had to overcome was internal. Our Legal and InfoSec teams are critical of AI tooling terms of service (ToS), especially when it comes to how data is managed, owned and stored. So, we needed to get alignment with them on data risk and updates to OpenAI’s ToS—which had been modified for API users specifically so that it disabled data passed through OpenAI’s service to be used to train their models by default.

Though OpenAI stores the data that's passed through the API for a period of 30 days for audit purposes (after which it’s immediately deleted), their ToS states that it does not use this data to train its models. Coupling this with our internal best practices documentation, which all our people have access to and are urged to review before using MonkGPT, we make sure that we minimize any potential for sensitive data to persist in OpenAI’s model.

As I’ve seen time and time again, ain’t no hurdle high enough to keep us from turning our ideas into reality—and useful tools for our talent. Within just 35 days we were able to deploy MonkGPT, scale it out across the company, and launch it at our global All Hands meeting. Talking about faster, better and cheaper, this project is our motto manifested. Of course, we didn’t stop there. 

Baking in benefits for our workforce.   

Right now, we have our own interface and application stack, which means we can start to build our own tooling and functionality leveraging all sorts of generative AI tech. The intention behind this is to enhance the user experience, while catering to the needs of our use cases. For example, we’re currently adding features like Data Loss Prevention to further increase security and privacy. This involves implementing ways to effectively remove any potential for sensitive information to be sent into OpenAI’s ecosystem, so as to increase our control over the data, which we wouldn’t have been able to do had we gone straight through ChatGPT’s service. 

Another exciting feature we’re developing revolves around prompt discovery and prompt sharing. One of the main challenges in leveraging a prompt-based LLM’s software is figuring out what the best ways are to ask something. That’s why we’re working on a feature—which ChatGPT doesn’t have yet—that allows users to explore the most useful prompts across business units. Say you’re a copywriter, the tool could show you the most effective prompts that other copywriters use or like. By integrating this discoverability into the use of the tool, our people won’t have to spin their wheels as much to get to the same destination.

In the same vein, we’re also training LLMs towards specific purposes. For instance, we can train a model for our legal counsels that uncovers all the red flags in a contract based on both the language for legal entities and what they have seen in similar contacts. Imagine the time and effort you can save by heading over to MonkGPT and, depending on your business unit, selecting the model that you want to interact with—because that model has been specifically trained for your use cases.

It’s only a matter of time before we’re all powered by AI. 

All these efforts feed into our overall AI offering. In developing new features, we’re not just advancing our understanding of LLMs and generative AI, but also expanding our experience in taking these tools to the next level. It’s all about asking ourselves, “What challenges do our business units face and how can AI help?” with the goal to provide our talent with the right superpowers. 

It goes without saying that my team and I apply this same kind of thinking to the work we do for all our clients. Our AI mission moves well beyond our own organization as we want to make sure the brands we partner with reap the benefits of our trial and error, too. This is because we know with absolute certainty that sooner or later every brand is going to have their very own models that know their business from the inside out, just like MonkGPT. If you’re not already embracing this inevitability now, then I’m sure you will soon. Whether getting there takes just a bit of consultation or full end-to-end support, my team and I have the tools and experience to customize the perfect cape for you.

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.

We’ve all seen and heard how generative AI can supercharge creative content production by creating large volumes of images, video and copy in just seconds. This is only one sliver of AI’s potential, because conversational interfaces that learn from us will profoundly transform the way we search for and discover products and information. And we’re already seeing it happen before our eyes: Instacart offers contextual advice for grocery shopping, Zalando created a virtual fashion consultant, and Intercom launched a GPT4-powered business messaging solution that can solve 50% of customer questions instantly. AI is changing the way people interact with your brand, and this is igniting a paradigm shift in brand interfaces and product design.

For many brands, this creates a challenge. How do we connect people with the right answer, content or product they are looking for? The examples above hint at an answer: AI and LLMs go a long way in making consumer experiences more intuitive. Here’s how we’re thinking about it in our Platforms practice.

New search behaviors will elevate the role of the dotcom.

One area that will drastically influence consumer behavior is search. Search is already the default starting point for consumers, but Google’s new AI-powered results page will soon be the only place a user needs to visit, bringing comparison and conversion onto one screen.

This brings some urgency to how brands approach their own platforms, because to bring their products to the top of search, they’ll need to think less about keywords and more about context and intent. What context would users search for around your products? What would they intend to do with it? What values do your offerings deliver to people?

No one yet can say how to solve SEO in the future. But we can help brands begin to integrate this layer of information into their catalogs and user experiences now to prepare for that kind of change—because in this new world, I see an elevated role for the brand dotcom. Think of Google as the department store that carries all brands, and your platform as the expressive branded spaces users will choose to go to connect and build a relationship. Delivering on this expectation will be the key factor to success in the age of AI.

AI is elevating the brand experience.

I’ll extend the department store analogy a little bit further to illustrate the role of AI on modern digital platforms. A good store employee only asks if they can help at the right moment, and AI will likewise be to gently and organically nudge users through conversation. The difference is that AI will be fully trained on your brand, products and services and can represent those perfectly. Think personal product advice, answers, cross- and up-sales, all in the context of a user’s intent.

A restaurant chain, for example, might use natural language to transform its ordering platform, especially for catering and large orders. Rather than scroll through a menu, users could describe an occasion, like “I’m throwing a birthday party for my 5-year-old son. We’ll have 15 people, mostly children.” The system can then take that information and recommend a customized party package. Any allergies among or dietary restrictions in the group? Not a problem—the AI can edit the order for the customer to review. Think of AI as a butler for your brand and its customers.

More personalized experiences give more opportunities for relationship building.

These little details—why you’re ordering, when you need it by, plus any additional personal requests—go a long way in getting to know your customers. The results are both better customer experiences and the ability to forge hyper-personal relationships, ultimately fulfilling the original promise of digital.

We are finally moving beyond segments and personas. A properly programmed AI understands every user’s personal sentiments, curiosities and needs, because it’s able to pull from and connect different pieces of data from across the consumer ecosystem. It can remember those facts and become more personal with every interaction, like offering personalized promotions and loyalty incentives honed to every user’s context. This new type of personalization shows great promise for conversion.

It's also great for building customer loyalty, because AI unlocks interactions that are designed specifically for building longer lasting relationships with them. As customers engage over time, their interactions across the platform produce greater and more detailed insights that can be used to further optimize the experience and deliver upon their unique needs.

Start with a sprint, then optimize and personalize.

AI will continue to shape consumer expectations and behaviors, underscoring the need for platforms that can pivot with speed and agility. It’s more important now than ever to be able to listen, learn and adapt to how your customers are engaging.

On the flip side, that means your implementation of AI is also always a work in progress. If any of the above sounds interesting to you, rest assured that you don’t have to make a full overhaul of your website. It starts with looking at what you already have and seeing if your tech stack can support these hyper-personalized experiences. Innovation sprints or experimenting with building better experiences—on the main dotcom or maybe in a separate domain—are great places to start, as are smarter search functions that are fairly easy to implement. Then optimize continuously to perfect your toolkit and extend your ability to personalize.

It's too early to say with utmost specificity how AI will shape customer experiences years down the line. But by realizing how recent AI developments are serving pre-existing marketing goals—more personalized user flows, greater customer loyalty, and an elevated brand experience—it’s clear that now is the time to lay the foundations for AI-powered customer journeys.

Want to learn more about how our platforms team can support you in building more personalized experiences?

If you missed it (or could use a refresher), no worries—we’ve collected insights from across the week that set the agenda for what brands and their partners are focusing now and into the next year. Want to see the key themes at a glance? Find our deck at the bottom of the page.

Surprise: everyone was talking about AI.

It’s no surprise that among all the themes covered at Cannes this year, generative AI was the toast of the town. Our programming at Les.Monks Café centered on how marketers are using the tech now—or how they can lay the foundation for the revolutionary effects of AI in the very near future. “AI: Powering Transformative Customer Experiences” was one panel touching on these topics.

At the top of the conversation, Jay Pattisall, VP & Principal Analyst at Forrester, shared insights from his recent forecast report co-authored by Michael O’Grady. “In Q1, 19% of marketers in the US have used generative AI in their marketing execution. By Q2, that grew to 56%. There’s a really substantial growth,” he said, noting that early use cases include content development as well as media strategy and buying.

But what does this look like? Carlos Ricardo, Sr. VP Marketing Services & Creative Production at HP, laid out the brand’s strategic balance in identifying opportunities now versus building toward future goals. “We established what would be the potential business impact in terms of prioritization,” he said. “So, we determined 14 different work streams that we are currently working on which we call ‘Day Zero’—experiments that have already started.” In addition, the team has mapped out plans for 30, 60 and 90 days into the future to keep its AI transformation on-track.

Solange Bernard, Sr. Director/Head of Marketing Communications at Tim Hortons, also offered a peek behind the counter at how they’re using AI: “The way we’ve been approaching it is twofold. One, you see it as an opportunity to be more efficient. And then there’s also creative content development—there’s a lot of excitement in what we could be doing.” Bernard noted AI tools have enabled the team to take their first steps into virtual production to scale up creative.

AI plays a key role as an integrator that unlocks growth for brands.

As Pattisall shared, AI is more than unlocking creative content at scale—there’s also great potential in media. Later in the panel, Media.Monks Co-Founder Wesley ter Haar explained that when you bring both disciplines together through dynamic creative optimization, you truly unlock AI’s revolutionary impact. “For me, it brings to the front the original intent and promise of digital advertising: this idea that we can be real-time responsive, have highly personalized goals, and highly targeted feedback loops.”

This sentiment was echoed at our “TuesdAI Breakfast Session” with our EMEA CEO Victor Knaap and EMEA Chief Growth Officer Maria Nordstrom. With the discussion focused primarily on the basics of generative AI, Knaap explained the importance of integration across the business to “make an enterprise-ready pipeline where we can go all the way from insights to the assets that run on media,” and that he expects to see brands implement structural changes from the top down to accommodate.

One example: the work we’ve done with BMW and Mini, in which “atomic assets”—bits and pieces of creative, like the car model or environment featured—come together based on user profiles and data. “So, you get an infinite amount of assets that can be served into media,” says Knaap, noting that this infinitude can even resonate with audiences you haven’t formally targeted—leading to newer insights along the way.

The secret to cultural relevance? Leaning into communities that align.

AI wasn’t the only topic for discussion at Cannes this year. There was also a lot of talk on building cultural relevance and authenticity, especially when it comes to serving a movement or community. This is already top of mind for many brands during Pride Month—but the 50th anniversary of hip-hop during August this year offers a case study of this concept in real time as brands lean into the culture.

This was a key topic in our panel “Hip Hop 50. Then. Now. Forever,” hosted in collaboration with Billboard, ADCOLOR and Sony Music Group. “Any time a brand wants to utilize or activate a culture, it’s got to be really thoughtful, and it’s got to have intention,” said Eric Johnson, Executive Creative Director, North America at MassiveMusic. “It’s really important for brands to honor the culture and understand the culture.”

With the group diving into legendary brand collaborations in the early days of hip-hop—like Run DMC’s historic signing with Adidas in the 80s after endorsing the shoes in their songs—Cashmere President and Chief Creative Officer Ryan Ford expressed the importance of seeking these natural alignments. “We’re trying to help brands understand where the authentic alignment is already. It’s not just about slapping a ‘Hip-Hop 50th Anniversary’ logo on your product, right?” Instead, he says, you need to think hard about how to show up for the cultural moment.

Mike Van, President at Billboard, offered one approach. “[Hip-hop culture] is inherently entrepreneurial, it’s bootstrap culture all the way. It’s all about financial independence and empowerment, and you have a whole generation now of consumers and fans of hip-hop who are thirsting for that kind of content.” The opportunity: brands can become arbiters to build knowledge within the culture.

Real purposeful marketing focuses on real solutions. 

One area where cultural relevance is key is purpose-driven marketing, which shows no sign of slowing down on the award circuit. That said, the nature of what passes for truly transformative, purposeful work has evolved from previous years. “We’ve moved beyond just raising awareness for good causes. We need tangible solutions,” Sara Cosgrove, our Global Director of Awards & Creativity, on our “Women Connect” panel.

Cosgrove was joined by Jo Wallace, Global Executive Creative Director, and Ashley Knight, Strategy Director, in the panel, which was moderated by Luciana Haguiara, Executive Creative Director, Latam. With Wallace and Knight having served on juries this year, the group pushed back against work that tacks on a cause. “It has to have absolute relevance,” Wallace said. “We’re noticing a real disparity between brands that have a genuine purpose and a reason to function in that space and to bring good, and brands where there’s some laziness—you’re trying to bolt on this purpose and it shows.”

A favorite piece of work among the team is our Havaianas Pride Research project, where we teamed up with Havaianas, Datafolja and All Out to create Brazil’s biggest LGBTQ+ survey. Questions related to community had been excluded from Brazil’s official census, so the survey was designed to make the community and its needs more visible. Its findings were released on a beautifully designed web platform developed by the Media.Monks team. 

There’s no single definition of “good” creative.

Speaking of impactful work, the leadup to Cannes this year didn’t have the single frontrunner that was expected to sweep all the awards—but that’s not a bad thing, because it’s indicative of more diversification of what “good” creative is. An increasingly diverse talent base, plus more diverse and representative juries at the festival, will continue to affect what work is awarded and further challenge industry norms and expectations.

“It all comes down to empathy. There’s never been as many diverse crises we’ve been facing as a group of people, as an industry, and I think the expectation is creativity needs to do more to create empathy among these groups,” Knight shared. “Having more diverse perspectives that can speak to other people’s circumstances and needs can only be a good thing and that’s where I see a lot of change in the work this year.”

So, what makes outstanding work? The Women Connect panel made a rubric: have a clever insight that links to the brand, don’t overcomplicate things, play to emotion, and give newcomers the chance to challenge their more established peers. This helps bring new perspectives to the fore that can uniquely relate to audiences.

Supporting green talent is the idea behind our NextUp.Monks competition, in collaboration with Cannes Lions, which aims to elevate up-and-coming creative talent. This year, teams competed by answering a creative brief from Meta, "VR for Good," which challenged participants to transform how Gen Z thinks about VR and show how a headset can change minds and transform lives. We finished the week with a toast to the six competition winners—Vasyl Ilba, Mykyta Zolotoverkhyi, Ashwin Paul, Jorene Chew, Anna Zhang and Yazad Dastur, Jr.—who touched on their processes and experiences bringing the brief to reality.

One interesting tidbit came from Dastur, on zeroing in on an idea that has an impact. “While we wanted to do something different, we didn’t want to do something big. We aligned really quickly that we wanted to focus on a very small problem that would be able to help everyday life.”

Looking ahead, brands are planning their transformation strategies.

Throughout Cannes, we got a glimpse into what brands are thinking about right now. But what should they be doing now to prepare for next year—and beyond? S4 Capital Executive Chairman sat down with Salesforce President Sarah Franklin and Diageo Chief Digital Officer Susan Jones to speak on “Gathering the Transformation Trio” and aligning C-suite leadership across agency, technology and brand for continued success.

Franklin kicked things off by touching on the evolving role of the CMO. “You’re seeing more CMOs as more strategic in the business, the pathway to these more strategic roles, even the pathway to the CEO which, I think, speaks volumes for how much is on the shoulders of the marketeers and how strategic they are,” she said. Sir Martin Sorrell’s advice to building more strategic brands: be agile, take back control, and invest in first-party data.

On agility, Jones spoke on the need of continually evaluating and re-evaluating your activities. “Test new things as they come long to understand how they work, and then take a step back and go, ‘Is this sustainable? What’s a better way?’” This agility helps empower teams to reorganize around changed or emerging needs.

When it comes to in-housing, Sorrell notes that the important thing is bringing teams together to work far more efficiently—something that AI can help unlock. “Being able to disseminate knowledge across the organization on the assumption that you’ve ingested the right data, and that you’ve opened it for access to all, is the really powerful thing—and it means you’re going to become a much more singular force for agencies to deal with,” by breaking down the silos and politics that typically slow things down.

Finally, “You have to have a strong foundation of your data. Your data has to be in order,” particularly when it comes to setting up artificial intelligence. But as the lifeblood of your brand, a solid data foundation can accomplish even more. Franklin mentioned Formula One, whose “Drive to Survive” Netflix series brought in a new fanbase, many of whom are women. “So you see how something which is very orthogonal to their actual business, which is a TV series, created this whole new community of fans for them. And what they’ve done from the data side to be able to really drive that personalization has been really impressive.”

The festival captured an industry at a pivotal moment.

While AI dominated the conversation at Cannes this week, it’s important to consider some of the pre-requisites touched on elsewhere throughout the festival: getting your data in order, integrating the business to achieve new outcomes and ensuring you lead with authenticity with everything you do. The beautiful part? Once you’ve made a solid foundation on each of these, you’ll be prepared to fully unlock the potential of technologies like generative AI. 

That’s it for Cannes this year—and we can’t wait to see how AI, more intentional creative and greater diversity on teams will continue to influence creativity next year.

For all these reasons, the praise for AI among marketing experts is ceaseless; but while much ink has been spilled about it, we’ve merely begun to scratch the surface. This month, our team was part of Salesforce Connections, where the topic of AI and its impact on customer relationships dominated conversations. The two jam-packed days of sessions were all about creating strong, lasting customer connections with meaningful interactions—using AI combined with the power of first-party data to ideate, create and deliver them.

As the event kicked off, Salesforce unveiled their new AI-driven tools for personalized customer experiences, and we were honored to be featured in their announcement as one of the partners contributing to their generative AI ecosystem. If you want to learn more about AI and its impact on customer relationships, here are some of the main takeaways from our people’s keynotes and panel discussions.

AI empowers CMOs as their roles evolve. 

On the first day of the event, our Chief Innovation Officer, Henry Cowling, and S4 Capital Group Executive Chairman, Sir Martin Sorrell, shared the main stage. Together with industry experts such as Salesforce President Sarah Franklin, they unpacked the role of AI in consumer engagement at the CMO+ experience, which convenes an intimate group of the world’s most influential CMOs for hyper-relevant networking.

As we know, the role of the Chief Marketing Officer has undergone a significant transformation. The CMO is no longer solely responsible for overseeing marketing campaigns; instead, they have become a crucial strategic partner to the CEO and the C-suite. Today, the CMO’s role is to take business insights and bring them to market so that value can be realized. But in an always-on environment, that value can only be realized through personalization at scale, data-driven decision-making and the addressing of regulatory pressures and privacy concerns.

To unlock all of that and more, the aid of AI is key. For years, data experts have banked on predictive analytics and machine learning algorithms to help identify patterns, trends and customer segments, leading to more targeted and effective marketing strategies. Now, we’re finally approaching the promised land of hyper-personalization, where real-time insights inform each customer’s experience on a granular level and personalized content can be generated at scale.

Enhance customer experiences with cross-functional data systems.

Cowling puts it simply, “In an AI-enabled marketing landscape, the smartest data set wins.” First-party data is at the center of any journey of digital transformation, and through their marketing leaders, brands have an opportunity to capitalize on their unique data and intellectual property. By doing so, they can strengthen customer relationships and offer groundbreaking experiences that are key for success.

However, leveraging your data to its full potential also demands that CMOs partner with peers within and outside their organization. It’s important for teams to look beyond their specific perspectives and collaborate with their counterparts in IT, data science, product development, customer service, sales and even external partners who can provide valuable solutions. 

On top of that, the transformative potential of AI is fundamentally changing how we see, understand and use data. Therefore, it raises new ethical, social and legal questions, requiring a reevaluation of our current systems and frameworks. The sooner brands address those with the input from their different teams, the sooner they’ll be able to leverage AI as a customer engagement tool.

Activate loyalty with a strong data strategy.

Hot on the heels of Cowling and Sorrell’s insightful chats, our customer relationships experts and Directors of Go-to-Market Ashley Musumeci and Nich Seo shed some light on the future of loyalty. Their presentation emphasized an important fact: in today’s ever-elusive landscape of consumer attention and loyalty, brands have a golden opportunity to set themselves apart by delivering exceptional customer experiences.

Contrary to outdated notions, true customer loyalty goes beyond a rewards program and requires ongoing, meaningful interactions with your customers. It hinges upon the emotional connection customers develop through their collective experiences with a brand—and there are three approaches to consider as you strive to create those bonds:

• Forge deeper connections by making sure consumers feel your brand values and understand them. Achieving this entails aligning personal values, creating impactful content, implementing personalization strategies and fostering a sense of community. It also requires the ability to anticipate customer needs and desires, exceeding their expectations at every turn.
• Offer meaningful experiences that provide genuine and relevant value for people’s interaction with your brand. This can be achieved by creating unique branded moments that leave a lasting impression, tapping into exclusivity, enhancing gamification elements to make interactions more engaging and embracing a purpose-driven approach.
• Building integrated ecosystems that expand the customer-brand relationship in innovative ways. For example, by connecting all physical and virtual touchpoints and forming an accessible universe that consumers can seamlessly navigate. 

Now, you may be thinking: this sounds great, but where do I even start? As we discussed earlier, data plays a crucial role in the marketing lifecycle, enabling the delivery of enhanced value. Therefore, the initial step toward creating seamless, personalized and meaningful experiences is to establish a robust data strategy. Customer data resides on various platforms, and collection points are scattered throughout the entire customer journey. To overcome this challenge, identify integration points between existing technologies and unify the ecosystem.

The importance of cross-functional collaboration and leveraging first-party data to enhance customer experiences cannot be overstated. With AI enabling hyper-personalization, real-time insights and effective decision-making, it’s crucial to embrace AI-driven tools and establish a robust data strategy as a foundation for success. Now is the time to harness the power of AI and unlock its full potential in driving customer engagement and empowering CMOs.

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.

To stay ahead of the curve in customer engagement, brands must move beyond delivering AI-generated content (AIGC) to delivering AI-powered consumer experiences (AICX). While AIGC levels up content by creating content at scale, AICX levels up the customer experience by creating personalized interactions. Applying AI to CX is not just about chatbots for customer service, it's about adopting a customer-centric approach across all functions, from product development to marketing, and across all touchpoints, from digital to retail, all enabled by AI. AI has the ability to create intimate and hyper-personalized one-to-one interactions across all touchpoints of the consumer decision journey, and will be a key factor in how brands are perceived, valued and ultimately loved by consumers. Here’s how, in three ways.

AICX levels up storytelling.

Applying AI to consumer experience means moving from advertising a brand world, to participating and engaging in a brand world. The most discerning audiences in the world are demanding nothing less than a seamless and immersive brand experience: 73% point to brand experience as an important factor in their purchasing decisions, right behind price and product quality. Here are the ways brands are already leveling up engagement via immersive brand experiences powered by AI, and developed by Media.Monks:

• Building smart and immersive flagship stores. Chinese EV brand JIDU launched the world's smartest showroom in Beijing, powered by Baidu AI technology. This immersive space offers a unique brand experience with life-size avatars and captures valuable user data.  
• Embedding consumers directly into any story. Intel’s AI-powered technology connects young people in China to their remarkable heritage by scanning and mapping their faces in real time onto historical figures in a series of animated films that were projected on the city walls of Xi’an.

AICX levels up personalization.

The benefits of personalized customer experiences—one-on-one interaction between a customer and a brand—helps to massively uplift sales and loyalty. Research suggests that personalized and tailored CX drives over 66% of customer loyalty—more than price and brand combined. Meanwhile, 78% of consumers are more likely to make repeat purchases and recommend companies that personalize their interactions. 

• Creating hyper-personalized celebrity content at scale. In collaboration with Spotify, we created an interactive listening experience with a deep fake version of The Weeknd giving users a personalized greeting using data from their Spotify accounts. We created an AI to synthesize the artist’s voice allowing for an interactive listening experience between the fan and the artist himself. Over 600K fans visited in the first few hours—and The Weeknd knew every one of them.
• Personifying an AI personal shopper. What if your customers could have a conversation with an AI personal shopper that could recommend style tips and products, creating cross-selling and repeat purchase opportunities for higher customer lifetime value? For a long time, brands have tried to do this with chatbots to handle questions or complaints in a customer service capacity further down the funnel, but now conversational UI creates a smoother experience and will be used earlier in the funnel for ecommerce exploration. 

AICX levels up co-creation.

Involving consumers in product development very early on in R&D is proven to drive product success, advocacy and conversion. Companies that involve customers in the product development process see numerous benefits, including a higher customer advocacy, than those that don't. The proof is there, but brands find it very difficult to execute on immersive co-creation because it typically involves different departments and different organizational structures. That’s where AI comes in. Here are some AI-powered co-creation applications that we’re helping brands experiment with today.

• Generating consumer insights. At the Estee Lauder R&D Experience Center in Shanghai, the beauty brand engages consumers and key opinion leaders in product testing, validation and co-creation sessions for new products. Touch tables and magic mirrors powered with AI technology enhance workshops and ideation sessions and support the generation of consumer insights from participants in the room.
• Co-creating new products with customers in real time. What if a fashion lifestyle brand could run live design sessions with their community, their best creative directors and celebrities in a livestream that could generate the must-have sneaker, handbag or you-name-it based on their input…in real time? Not only would it speed products to market, but community co-creation and input would virtually guarantee success.

Get ahead by embracing AICX now.

The evolution of AI technology—from analyzing and processing data, to creating more content faster and at scale, to facilitating personalized storytelling through experiences that deepen the collaboration and intimacy between brands and its customers—is extremely exciting…and disruptive. There are new developments in the technology and its application for brand engagement literally every day, and brands that are able to harness AI’s power will be able to build stronger, more meaningful relationships with their customers and drive business growth like never before. Again, this will mean looking beyond AI’s power to merely build efficiency; in an environment of content overload, delivering AICX is how brands can truly deliver value.