Sure, I’ve seen my fair share of over-hyped duds along the way—we all remember hyped-up “innovations” like 3D TVs that promised to change the way we create and consume video. But once in a blue moon, something comes along that will undeniably change the world. Recently, that’s generative artificial intelligence, yet I still see some brands shun the technology, worried about its risks.

As a creative, I’m not worried about AI taking my job away the way others might. I’m more concerned that by not embracing AI, I risk being left behind. The same risk is true for brands who are reluctant to fold AI into their workflows. Why? AI is making creativity more accessible than ever before; cinematic, high-quality content is no longer exclusive to the skilled few.

The democratization of AI will make some things easier, but not without challenges.

Technology has always transformed the creative process—in some ways making it simpler, and in other ways requiring creatives to adopt new skill sets. When the Lord of the Rings trilogy pushed boundaries, it led to the creation of new technology, like motion capture and its evolution into performance capture, and new talent hotbeds designed around making the most of those innovations. Today, AI is likewise challenging all of us to adapt.

First, there is the need to scale up production. The speed of creating content with AI is raising the expectation to make more. In this respect, AI doesn’t necessarily make content production easier; it makes it more sophisticated and ups creative potential. Making a mark remains a challenge.

We’ve already seen this before with CGI. Today, you can render a scene in three minutes in Maya that once took six hours. But fire up the program and it looks more like engineering software than something creative. Cobbling a scene together requires as much of an understanding of mathematics as it does of design. Using the technology to its fullest potential required the confidence to embrace it and tinker with it.

The biggest risk is in doing nothing at all.

It’s easy for brands to default to what’s familiar. I can relate; remember what I said about being stuck in my ways? But those who rest on their laurels risk losing market share to challengers who are quicker to the uptake and embrace experimentation. Smaller brands and influencers are already leveraging the availability of advanced video tools to make their mark. Closing that gap is key to reduce the risk of being forgotten.

Throughout my career, I have witnessed the transformative power of integrating technology and experimentation into one’s own creative DNA, and I am confident that this approach will continue to drive success for creative teams who dare to embrace it. On my team, we’re elevating our already best-in-class talent by augmenting their creative process with AI. As a team, we understand that it may require getting our hands a little dirty, and sometimes going back and forth with a chatbot more than expected, but the rewards are immense. By incorporating AI tools into every stage of the creative process, from ideation to concept art and beyond, we enable ourselves—and our clients—to surpass standard limitations, supercharge our output and create captivating content that leaves a lasting impact. And we can’t wait to see how it develops even further.

Start small, but think big.

The good news for risk-averse brands is that you don’t have to choose between being too conservative or too experimental, throwing caution to the wind. There’s no need for a binary approach to whether you’re in or out with AI adoption; there’s plenty of room to experiment within guardrails. You just need to start playing with the simpler ways to enhance your output (like generating numerous backdrops with AI, or digitally replacing products to make content more dynamic and personalized) and iterate from there as your team becomes more skilled.

If a creative with 20 years in the business can confidently embrace AI without reservations, so can you! While the AI boom may feel like untrod territory, it’s not the first time we’ve needed to creatively adapt—and with new customer expectations and increased competition through the democratization of content creation, there’s no better time than now to start. Otherwise, you might just be left behind.

While I believe search will continue to thrive and drive results for advertisers, how we buy search is undoubtedly changing as is the search engine results page (SERP) itself. One of the largest shifts in how we buy across the Google space has been Performance Max (PMax). It’s a keyword-less, AI-powered campaign type that uses machine learning models to optimize bids and placements across Google (including search) to hit a core objective. Advertisers don’t bid on specific keywords; instead, they rather rely on AI to handle bidding and targeting through audience signals, the advertiser's own website/URLs, and creative assets. This serves ads across the Google network to match search queries and browsing behavior to those most likely to convert to the desired action. So, while the search experience is similar to the user, how we as digital marketers buy search is changing rapidly.

Early adoption of Performance Max was mixed and vertical-specific—now the tide is shifting.

Advertisers have embraced PMax with mixed readiness, many fighting the loss of control they have come to expect from Google ads over the years. In the earlier days of PMax, I felt that hesitance as well, especially for non-retail verticals and for very complex advertisers. I also knew from years of navigating similar evolutions in Google Ads that there would inevitably be new features and modifications based on feedback from agencies and advertisers. Sure enough, our testing has shown that while we may not be ready to say goodbye to keywords, PMax does get us one step closer to the option of scalable keyword-less targeting.

Google is now transitioning to a more visual SERP with a reliance on visual formats and a generative search experience. Google has bet big on Performance Max, Broad Match, and AI-driven products in general. Google has also released more insights, creative tools, targeting, and testing levers in the platform to improve the product and allow for more insightful ways to leverage data across marketing efforts, including informing audience selection and creative. Additionally, Google is testing and launching automated and generative asset creation to help advertisers with the hurdle of costly and time-consuming creative iterations.

Let’s recap how PMax has evolved in the last year.

There continue to be new features launched to improve this product and improve performance for campaigns. I’ve detailed out a few below that are more significant in terms of our utilization.

• Uplift experiments and PMax vs. rSC (standard shopping) testing: These tests allow advertisers to compare the performance of PMax campaigns against other types of campaigns, such as standard shopping campaigns, and against other PMax campaigns with different settings or strategies.
• Brand exclusions and account level negatives: These features allow advertisers to prevent their PMax campaigns from showing ads for certain brands or keywords. This can be helpful for preventing ads from showing for competitors or for keywords that are not relevant to the advertiser's business.
• Video builder upgrades and improved asset-level group reporting: These features make it easier for advertisers to create and manage their PMax campaigns. The video builder allows advertisers to create videos for their PMax campaigns without the need for any video editing experience.
• The improved asset-level group reporting provides advertisers with more insights into the performance of their PMax campaigns.
• Final URL expansion helps you optimize your Pmax campaigns' performance. This will help find a more relevant landing page based on the user's search query and intent and allow for a customized dynamic ad headline that matches the landing page content.
• SA360 floodlight bidding support which allows advertisers to use their own conversion data to bid on PMax campaigns. 
• The ability to not input assets and run shopping/feed ads through the PMax ad type. While it’s not 100% guaranteed that auto-generated assets won’t run, our testing proves that the majority of spend goes to shopping placements.
• Allowing for scripts to be leveraged to showcase spend by tactic (video views, etc.).

We were an early adopter and rolled out a very systematic approach to testing.

Our team has been highly committed to testing PMax since the initial product launch announcement, working to incorporate all its new functionalities and experiment options. We’ve tested PMax against standard shopping and Dynamic Search Ads (DSA). We’ve also tested new customer bidding, and even multiple asset groups with distinctly different creative versus just a single asset group and more. While we tend to see higher performance across our retail clients utilizing the feed-based and local solutions, we are now seeing growth in other verticals, even with sensitive lead-gen advertisers such as healthcare, as a result of new features.

Here’s an example of our testing. A healthcare client recently did a head-to-head test with DSA and was able to scale 10x in investment and lead volume by tapping into the power of PMax. In a few short months, the shift to PMax drove a 13% increase in leads with an 8% reduction in CPA. Setting thoughtful, flexible targets; breaking out campaigns by business need; having objective targeted creative; and giving machine learning the space to find the right people at the right time with first-party data allowed the brand to scale.

Here is what we anticipate will come next:

• Visual/image site links will be favored over text site links.
• There is a possibility that PMax is the end goal for Google Ads, with limited to no ability to bid on selected keywords (i.e., a keyword-less future).
• 2024 could be the year we lose other match types. I’m hoping we keep exact match, but we know broad match will stay for some time given the investment in the new broad match.

A look ahead at the road ahead for keyword bidding.

Over the last year, Google has been an excellent partner, listening to feedback from experts in the field and investing in more data insights, testing ability, training, and targeting controls. In addition, the new generative creative levers in the platform can arm us as search marketers to harness the power of AI while utilizing our knowledge of the Google network and specific brand needs to drive client wins.

Are we ready for a fully keyword-less search buying experience today? No, but given the progress of Performance Max over the last year, it’s becoming more likely that pairing broad match and Performance Max will be in every best practice deck from Google partners for the foreseeable future.

Performance marketers have been an underutilized resource to date, but their years of experience using AI for marketing success make them well suited to play a large role in broader AI adoption. Blind disciples of every generative AI shortcut will get burned and those resistant to change will become irrelevant. Nobody knows this more than performance marketers. 

As it relates to the digital marketing AI arms race, Google, and to a lesser extent Meta, weren’t nearly as proactive at highlighting their work relative to Microsoft (the largest investor in OpenAI, the company responsible for GPT-4). The irony is that Google and Meta had been at the forefront of incorporating their long-standing investments in AI, which was already deployed in almost every corner of Google and Meta Ads platforms and products.

Google and Meta represent nearly half of all digital ad spending in the US and represent an even larger share of the typical performance media budget. AI integration in Google and Meta has most prominently centered around machine learning algorithms for bidding and ad serving. That said, there are examples of generative AI as well (suggesting ad copy and creating distinct ad copy from permutations of existing headlines and body copy), and AI’s tentacles can be felt everywhere in the Google and Meta ad ecosystem. Prominent examples include:

• Performance Max (Google) and Advantage+ (Meta) are effectively end-to-end automated campaigns that use AI to target, generate ads and optimize toward set goals.
• Automated bidding sets dynamic bids in real time using machine learning to more efficiently optimize toward the highest ROI.
• Responsive Search Ads (Google) uses AI to mix and match different portions of copy to deliver the best permutation for the individual searcher (right ad to the right audience at the right time).
• Recent Google Marketing Live (GML) and Meta Connect 2023 conferences announced products around AI-powered assets, AI-generated images, generative AI to create ad copy and auto enhancements to text placement, brightness, etc.

In that same vein, performance marketers, most of whom earned their stripes running or overseeing Google and/or Meta Ads, are particularly well suited to guide advertisers through this next major stage in digital transformation. The nearly half decade of experience most performance marketers have both harnessing and reining in AI tools justify them playing a central role guiding marketing teams in developing and deploying generative AI adoption.

What about this experience gives performance marketers an advantage? 

• Threading the needle between uncritical adoption and complete resistance to change
• Understanding of the importance of high-quality data inputs 
• Understanding the importance of setting guardrails and tweaking those over time 

Bringing healthy skepticism to the table.

Seasoned performance marketers have had to adapt and learn new types of automation many times over, and can share their war stories. From broad match keywords, Meta auto-placements and iteration after iteration of automated bidding on Google gone awry, we’ve seemingly seen it all. Google and Meta were trailblazers in incorporating AI into ad products, and reps would very earnestly push adoption of products that could be buggy and at worst underperform manual alternatives. However, Google and Meta were also diligent about refining those products over time and performance marketers who were not willing to continue testing at all over the last few years were quickly left behind. Broad match keywords, automated bidding, Advantage+ shopping campaigns and many more products delivered more scale at comparable efficiency to non-AI driven products. 

As AI plays a more permanent role across creative, customer journey, audience identification and more, this balance will be crucial. Blind disciples of every generative AI shortcut will get burned and those resistant to change will become irrelevant. 

Garbage in = garbage out.

One of the biggest distinctions in a strong performance marketer versus a mediocre one is her understanding that the inputs to automation can have a profound effect on outcomes. Performance marketers who press the easy button and switch from hundreds of manual bids per week to auto-pilot don’t get strong results. Worse yet, they’re quick to declare, “It doesn’t work!” Data volume and quality are the foundation of an effective AI deployment strategy. Knowing which data sources to use and exclude, and which campaigns to match with each specific type of automated bidding, is a crucial skill. Performance marketers know to incorporate lead quality data to B2B auto-bidding, initiate testing on campaigns with higher conversion volumes, and not to launch immediately after a strong holiday or back to school period.

In this sense, performance marketers have years of “prompt engineering” reps without even realizing there was a name for it. Marketing organizations stand to get AI into market faster, and benefit sooner from the positive results, by tapping into that experience. 

Performance marketers are masters at fine tuning.

The last level of mastery that performance marketers have achieved has to do with learning the intricacies of the algos. We have applied max CPCs, cost caps and negative keywords to rein in the occasionally deleterious effects of AI unchecked. At a high level, AI can be fickle and human intelligence is crucial to avoid these blips. We have seen a top performing ad set stop delivering seemingly out of nowhere, only to have a minor 5% increase in ROAS target return it to normalcy. We’ve learned to mine for insights around how, why and where AI is working:

• Is stronger performance because we’re seeing increased CTR or conversion rate?
• Are we getting in front of the same audience more cost effectively or reaching a better audience?
• Did we create better ads, or did the platforms get better at matching them to the right people?

We ask these questions daily. That curiosity bordering on paranoia allows performance marketers to squeeze the most out of AI, as well as limit downside risk. 

Performance marketers have a feel for AI’s rhythms, like a mechanic knowing just which bolt to tighten to get the rattling sound in the car to stop. This mileage, or put anachronistically “human intelligence,” is tough to replicate. 

This AI mileage and its broad applications are why performance marketers should have a seat at the table. As an agency leader I’m better equipped to weigh in on how we utilize AI to address tasks, reporting, data integration, scripts and implement processes around AI because of that performance DNA.

59% of Gen Z watch longer videos they discovered on short-form video apps, demonstrating the need for broadcast rights-holders to embrace ecosystem-level thinking. We’ve worked alongside brands like Meta, Hasbro, TikTok and Verizon to evolve their broadcasting approach and meet the habits of today’s viewers through experiences that are immersive, interactive, and reach audiences where they are. Now, we’re developing an AI solution that will further revolutionize this next-generation broadcast workflow to create more engaging, personalized content for consumers with Fan-Focused AI Highlights.

Fan-Focused AI Highlights clips hyper-relevant content at speed and scale.

Fan-Focused AI Highlights, currently in development, uses AI and machine learning to instantly clip highlights in live broadcasts. The AI model is capable of segmenting individual people and objects in live broadcasts and effectively eliminates the need for manual selection and editing, a typically time-intensive process.

The speed and volume of content unlocked by Fan-Focused AI Highlights is crucial to delivering the snackable content today’s sports viewers crave. Gen Z now consumes more highlights (50%) than live content (35%), validating the appetite for a moment-based approach to content delivery that is also more personalized.

EVP, Global Head of Experience at Media.Monks and former NCAA player Jordan Cuddy offers one example of how this trend is impacting the world of sports. “With Lionel Messi now signed onto Inter Miami, many of his fans may not care to watch American soccer,” she says. “Rather than sit through a 90-minute game, they just want to see the eight minutes where he’s touching the ball.” Her point is backed up by the fact that 80% of Gen Z fans not only follow a professional athlete online but seek to watch the events those athletes participate in, as well as follow the brands they engage with. With Fan-Focused AI Highlights, you could automatically clip together a reel of the game focused on Messi’s—or any athlete’s—best plays with ease.

Deliver on the hunger for affinity-based content.

The same approach above could apply to even more niche content and viewer interests. Imagine a basketball game that AI automatically slices into social media content focused on footwear worn by the athletes, then pushed out to an audience of sneakerheads by an athletic apparel brand. This is easily achieved with Fan-Focused AI Highlights—helping brands and broadcast rights holders alike reach audiences in more relevant ways, while also expanding the quantity and value of your broadcast rights.

We’re in a new era where people are no longer defined by demographics broken up by where they live; now it’s about identity groups. Rather than carve up territories on a map, broadcasters can creatively package up content for numerous subcultures simultaneously, leveraging the power of AI and machine learning to distribute custom highlight content to tailored interest-based audiences more accurately and effectively. This is a massive opportunity for rights holders, as 73% of sports viewers perceive rights owners’ use of fan data as “disappointing” (23.4%) or “below expectations, but catching up” (49.7%).

Adapt broadcast content to fit today’s viewing habits.

Fan-Focused AI Highlights is the latest solution within our software-defined production offering, which effectively eliminates the need for a large physical plant—like large control rooms or OB trucks that cost tens of thousands to rent per day or the dozens of crew members to maintain them—in favor of versatile, nimble broadcast workstreams. Single-use appliances designed for one task alone make way for NVIDIA GPUs in the cloud (or a server rack), adding additional efficiency, flexibility and reduced cost, while remote teams allow rights holders to hire the best talent for the job regardless of their proximity to the event.

Software-defined production has even enabled us to do what was never done before. Working with UNC Blue Sky Innovations, we streamed the first sporting event in stereoscopic 3D at 60 frames per second and an 8K resolution, directly to VR headsets. The custom-designed pipeline features a RED Digital Cinema camera; RED CPUs that decode, color correct and de-warp footage directly from that camera; a Blackmagic controller for live switching and encoding (from NVIDIA GPUs for a high-quality bitrate); and a 1GB network to deliver the feed to an AWS instance on its way to VR headsets.

All this equipment took up the modest space of a standard foldout table—a small footprint for an innovative pipeline and history-making broadcast. Still, broadcast professionals are a traditionally superstitious bunch, and it’s easy to see why moving much of the equipment and processes to software could leave them wary: what if you run into connectivity issues or a data center goes down? The same data centers that AWS uses also host banks and other extremely sensitive operations, meaning there are multiple safeguards in place to ensure service isn’t interrupted. And if one does go down, we can spin it up on another one. With multiple redundancies in place, any technical difficulty with software is faster and easier to fix than if your truck generator went down.

A sustainable approach to innovation.

In addition to reduced risk and additional flexibility, software-defined production offers another important benefit: sustainability. Media.Monks won a Sustainability in Leadership award at NAB Show by greatly reducing the carbon footprint of broadcasts with AWS. In addition to avoiding travel-related emissions, the software-defined production workstream is powered by 95%+ renewable energy, further reducing environmental impact.

With Fan-Focused AI Highlights added to the mix, brands can continue to deliver even more personalized, relevant content designed for today’s audiences with less emissions, risk, cost and people on the ground.  As viewers crave a more moment-based approach to the media and entertainment they consume, this revolutionary broadcast model helps brands expand the value of their broadcast rights in innovative new ways.

First off, Salesforce announced its new Einstein 1 platform. Built on Salesforce’s improved metadata framework, the platform allows companies to connect any of their data to build low-code, AI-powered apps. As Salesforce prepares the launch of its generative AI interface, Einstein Copilot, this fall, Einstein 1 will give marketers a taste of how generative AI can fuel new CRM experiences.

But that’s not all: unleashing the power of AI relies on robust enterprise data, and Salesforce is teeing up Data Cloud to become the central data hub for Einstein Copilot. To help brands find their footing in this brave new territory, Salesforce is offering no-cost access to Data Cloud for certain existing customers, meaning there’s no better time than now to build a solid data foundation in preparation for AI’s implementation in your business. Let’s dive into deeper detail of what was announced, what it means, and how you can get started.

So, what was announced at Dreamforce?

Salesforce Sales Cloud and Service Cloud customers with Enterprise or Enterprise Unlimited editions will be granted access to Salesforce Data Cloud, in addition to two Tableau creator licenses. As part of this access, Salesforce will provide 250,000 credits, which enable customers to onboard onto Data Cloud and develop Sales and Service Cloud use cases at no additional cost.

Data Cloud is the foundation that unlocks the generative AI features within the Salesforce platform, which in turn help clients drive efficiencies for their business. With this new move, Salesforce is making Data Cloud more accessible to customers and allowing them to begin leveraging the power of the platform more quickly—which is key, because the best data pipeline wins when it comes to realizing the potential of AI. 

What are the best beginner use cases for Data Cloud?

Access at no additional cost, coupled with 250,000 credits, grants customers the ability to start using the Data Cloud platform and build foundational use cases that then lead to more advanced use cases and further utilization. So, what kinds of use cases should you start with? Salesforce has identified the following two:

1. Unify Prospects for Targeted Selling: Consolidate data across multiple Sales Cloud organizations to identify opportunities with priority customers and increase revenue.
2. Unify Customers for Personalized Service: Consolidate data across multiple organizations to empower services agents with a unified, 360 view of the customer.

These use cases are geared towards customers looking to consolidate data across multiple organizations—be it due to being a portfolio company with multiple companies or brands, or having separate Salesforce instances intentionally stood up.

How can I get started?

Prior to activating any Data Cloud use case, customers should first evaluate their data management standards to ensure a proper data foundation. While zero cost access to Data Cloud presents a relatively low-risk opportunity, unwinding a poorly thought-through integration can lead to complex and time-consuming work later down the line. Therefore, it’s imperative for customers to take the time to develop a clear plan, considering what data to ingest and also how to ingest and organize that data.

At Media.Monks, we aim to help customers feel confident in their ability to leverage Data Cloud to drive value for their businesses. Our team of Data Consultants and Salesforce Architects assess customers’ first-party data strategy and technical architecture in order to build a detailed implementation plan, ensuring customers are set up for success and are receiving long-term value from their Data Cloud implementation.

Gain more insights from Dreamforce.

Join us for our Dreamforce to You events happening across the globe. There, we’ll dive deeper into how to design and build an effective data strategy and pipeline and how to unlock the power of Data Cloud for your business. Learn more about the events, hosted in London and Chicago. Stay tuned for more Dreamforce to You events around the world!

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.