In times of endless data, it is crucial to collect smarter.

As an analytics expert and practitioner, I know first-hand that collecting data across multiple digital assets and channels can be daunting. This is especially the case when the number of devices connected to the global internet exceeds 21 billion in 2023. Thankfully, our current Iinternet addressing system can handle a lot of these devices, namely up to 3.4×10E38 (that’s 34 followed by 37 zeroes). 

Out of these 21 billion devices, about 66% is made up of Internet of Things (IoT) devices, all of which generate data about their operation, features and settings. Call it connected black boxes or telemetry on steroids, but these devices are sending data home to service providers who use that data for product enhancement.

Such a scale of data collection provides not only the ideal fuel for AI and machine learning, but also the means to establish performance baselines and outliers. Feature usage models, insights and action plans can all be derived from such an unfathomable well of information.

(Re)introducing the Measurement Protocol.

How do these devices measure activity, you ask? This post is a perfect excuse to look at Google Analytics 4's Measurement Protocol as an alternative data collection method that can help you measure all the IoT data you need—and make it compatible with the flat data model you have come to adopt and love. The Measurement Protocol was introduced in the early 2010s with the former version of Google Analytics, the now sunsetted Universal Analytics. Back in the day, the Measurement Protocol was used in very creative ways, so seeing it reborn for GA4 is a great opportunity to (re)discover this lesser-known yet powerful feature in Google Analytics.

In essence, the Measurement Protocol is an API that allows you to send events directly to Google Analytics servers, bypassing the need for bulky software development kits and complex integrations. The minimal software footprint of the Measurement Protocol means it is easily embeddable in every system that can call a URL. As you can imagine, this can be used for all IoT—everything from kiosks to points of sales to IoT devices. Some clear advantages include:

• Standard protocol, so it is compatible with a wide range of devices and platforms
• Easy to use, even for developers with limited experience
• Scalable, so it can be used to collect data from large numbers of users
• Security, through the use of data collection secret keys

Because of its lightweight approach, using the Measurement Protocol means you can collect just the data you need. The lack of an explicit user consent mechanism on most IoT devices will encourage you to adopt a privacy-first approach, so focus on telemetry and not on personal data. 

Uncovering the Measurement Protocol’s inner workings.

How does it all work? Well, when creating a Google Analytics 4 (GA4) property for your IoT project, you first need to create a new web property and then simply click on this newly created data stream to access the Measurement Protocol API secrets panel.

The next step is to create a key, which you will reference in your Measurement Protocol API calls. All you need to do is provide a nickname for your key and you can use the provided ID in your API calls. As you can see from the list below, our Data.Monks use it quite a lot!

Once your keys are set up, make note of your GA4 Measurement ID for your IoT stream and use code to create a URL to the Measurement Protocol service that combines everything we need, including event parameters. In the example below, our connected fridge will send an event when the fridge door is open.

The desired URL should look like this:

https://www.google-analytics.com/mp/collect?measurement_id={your ID}&api_secret={your key}

Now we need to send the above URL as a POST request, with a JSON payload containing the event parameters we want to send along. Keep in mind that, because this is not like a GA4 event sent from a browser or a mobile app, there is no automatic detection and collection of extra elements, as with GA4’s enhanced measurement. In fact, the Measurement Protocol only measures what you send it. From there, post the request in your favorite programming language—Python, in my case.

Sure enough, the event registers in the GA4 real-time interface and subsequent hits will become part of your GA4 reports—and live on to BigQuery if you’ve linked your property to Google Cloud Platform.

And of course, as I’m sure you can already guess, creating dashboards on your devices’ activity is a breeze in Google Looker Studio. That’s all there is to it!

Time to try out the Measurement Protocol yourself.

We have seen that the Measurement Protocol, like other event-level data collection platforms, uses an API-friendly format to send data out to Google Analytics. From a technical standpoint, this is a very easy and efficient implementation, so feel free to get creative for all your IoT projects.

We’ve discussed using the Measurement Protocol primarily for IoT devices (or any device that isn’t a computer, mobile phone or gaming console). Bearing that in mind, you can also use it as a data exchange method in a cloud environment as an API callback after a process completes. This means the Measurement Protocol works great with Cloud Functions or messaging queues like Google Pub/Sub or Kafka.

Finally, circling back to the remark I made about AI, this kind of measurement is indeed an ideal way to collect fuel for an AI/ML model, but AI can also be used to trigger the right event at the right time, and with the right data payload. At this point, AI can improvise and improve on your intended data collection plan, start sending events outside of the scope of its original program, and unlock even more insights. Coupled with Google Cloud Platform’s Cloud ML, the results may surprise you! 

In short, here are your key takeaways about the Measurement Protocol:

• Simple mechanism: any system that can generate a URL can use it
• Encourages concise, compact, privacy-friendly data collection
• Can be used on anything, about anything
• Leverages the power of the Google Analytics 4 flat data model
• Small software footprint: very limited resource consumption
• Complements an AI strategy and unlocks new opportunities

From founding a specialist marketing evaluation and modeling agency to co-founding the Measure.Monks—our data-driven team that builds marketing effectiveness models to help brands deliver more profit—my professional journey can be divided into four milestones: starting, scaling, merging and integrating a business. In this piece, I aim to share my experience (instead of unsolicited advice) with the hope that established and aspiring founders can draw inspiration from it.   

Starting a business 

Looking back, this might have actually been the hardest part of it all. Let’s just say it takes a pretty large leap of faith to go from the comfort of having a salary and holidays to being completely responsible and accountable for your income. People’s common perspective is that you can work when and how you like, but my reality looked quite different. No, I didn’t have a direct boss—but I did have to constantly be on hand for clients to get the business off the ground. In my mind, any day off was a day lost in growth. 

Those early days were testing times, but resilience and hard work got me through them. While resilience is required in taking the bad breaks at the start (which I believe happens to test your mettle), hard work is needed to create more leads to increase your chances of bringing in bigger projects. In hindsight, having a co-founder would’ve helped enormously, but I’ve always been lucky that my wife knows the industry and is incredibly supportive. 

Scaling a business

With more work came more income, and as the team grew, the pressure eased. Upon reflection, I realize how reliant you can be on one client in the early days, which is a very precarious position to be in. If they drop you, your business drops—and this means making tough calls about the team. Fortunately, I could always lean on my advisor and chairman Paul Edwards, who was an invaluable coach, highlighted things I’d overlooked, and helped me manage and expand the team. I’ve found that having an external advisor is not only what kept me sane, but what kept our company’s standards high. In turn, these standards drove our mission, vision and values, which proved key to attracting and retaining talent. 

Merging a business

After years of great growth, we reached the point where we had nurtured an incredible team and built amazing market-leading products. But to truly accelerate our growth, we had to go global—and needed a partner to do so. Avoiding private equity as cash wasn’t the issue, our focus was on access to clients and facilitating global growth. After a lengthy scouting process, we were introduced to Media.Monks. We were immediately blown away by their agility, sheer focus on groundbreaking innovation, and culture of entrepreneurship. Making the merger decision may have been nerve-racking, but we knew we had the support of our people, whom we kept informed along every step of the process.

And what a great decision it was! Our merger was handled superbly by SI Partners, who managed our pitch process and the offers that led to the Letter of Intent, all the way through due diligence, legal and finally signing all the agreements. Having heard horror stories about this process taking up all of the leadership’s time—with a suffering business as the result—I was not looking forward to it, but our M&A partners made it easy to navigate. 

Integrating a business

Wasting no time, Media.Monks quickly initiated integration. Turns out, they are pros at this. As a dedicated Post Merger Integration (PMI) team made everything run smoothly, we immediately felt part of the team. They provided a detailed plan of everything we needed to fall in on, like accounting practices, legal, HR, CRM software, audits and more. However, allowing us to move at our own pace was the real value of the PMI team, which made us feel comfortable in the nearly 12 months it took to fully merge.

On the business side of things, we jumped straight into the global network, sharing our story with any team that would listen, which was met with sincere interest and support. These last few years have led to significant global growth, as we’ve not only gained clients in new markets, but also expanded our Measure.Monks team. At the moment, we have talent located in New York, Toronto, the UK, Buenos Aires, Melbourne and Singapore, and this list will only keep growing. 

While our team folds into the data pillar, we seamlessly work across our media, content and technology pillars. As a result, we regularly venture into new territory, from supporting our agility-focused media teams to running creative measurement and optimization with our content teams to developing new products with our data teams. There’s so much more to be explored, created and delivered—especially given the recent uptake in usage of AI and automation—and that’s why my excitement about this journey never wavers.

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.

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.

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.

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?