Navigating the Amazon Ecosystem with AI.

Many of us know Amazon for its ecommerce prowess and popular media platforms like Prime Video and Twitch. But Amazon boasts over 40 different owned properties, and this extensive network creates a rich tapestry of first-party data, offering a significant understanding of consumer behavior. However, truly harnessing this data and understanding how shoppers move across properties—from watching a show on Fire TV to shopping on the Amazon website—can be complex.

Beyond Amazon's owned properties, Amazon DSP also offers access to a massive network of third-party inventory through Amazon Publisher Direct and various ad exchanges. This opens up even more avenues to reach your audience wherever they are online. The challenge, then, becomes identifying the most efficient path to deliver your impressions. This is where Amazon's human-centered AI truly shines.

Introducing Brand+ and Performance+

Amazon's AI models, Brand+ and Performance+, are built on the foundation of Ad Relevance, an AI machine learning tool developed to increase addressability in cookieless environments. These models are not about replacing the human media buyer; instead, they are designed to collaborate, offering transparency and control. You can see exactly what the AI is doing, why it's doing it, and crucially, you can intervene, nudge, and redirect to ensure it aligns with your strategic goals.

While both models utilize the same underlying logic, their primary differentiator lies in the outcomes they are designed to generate:

• Brand+: Focuses on the top of the funnel, prioritizing awareness, reach and frequency, with a secondary goal of future conversions within a 12-month conversion horizon. This model is ideal for building long-term brand salience and memory structures. We've seen Brand+ campaigns perform exceptionally well with video creative and streaming inventory.
• Performance+: Centers on consideration and conversion within a shorter 30-day conversion horizon, aiming for immediate conversions and measurable ROI. This model is highly effective for driving actions like purchases. While it can utilize streaming and online video, we often recommend focusing on online video (OLV) and display inventory for optimal results.

How do these models work?

The intelligence behind Brand+ and Performance+ can be broken down into four key components:

1. Advertiser Event (Human-Defined): The process begins with you! You define the specific conversion events you're tracking. This could be anything from website purchases tracked via the Amazon ad tag, offline conversions integrated through a Conversion API, purchases of specific products on Amazon via ASINs, or even mobile app installs and actions reported by an MMP. This human input is crucial, ensuring the AI aligns with your unique business objectives.
2. Addressability Graph: Based on your defined advertiser events, the AI creates a comprehensive "addressability graph." This map provides a multi-property overview of how your converting customers interact across the vast Amazon ecosystem and beyond. It helps understand their journeys, their paths and their engagement patterns.
3. AI Prediction (Lookalike Audiences and Predictive Scores): From the addressability graph, the AI intelligently creates audiences similar to your existing converters—essentially, a lookalike audience. It then generates predictive scores for individuals within this lookalike audience, identifying those most likely to convert based on the creative being served and the desired outcome within a specific timeframe. This ensures your ads are delivered to the most receptive audience.
4. Campaign Delivery and Continuous Optimization: The AI then dynamically delivers impressions through the most efficient channels, whether it's Amazon Prime Video, Magnite or Amazon Publisher Direct. What's truly powerful is the continuous learning loop. The AI constantly tracks post-impression conversions, updates its model, and refines its targeting as more data becomes available, ensuring your campaigns are always optimizing for better outcomes. This process requires a consistent feed of advertiser events, as static data alone isn't enough.

Easily set up your campaigns.

Setting up campaigns with these AI models is incredibly straightforward, often taking less than two minutes for Performance+ and even quicker for Brand+.

For Brand+, once you've defined your conversion event and set your budget, you're ready to launch.

For Performance+, you have the flexibility to choose from three sub-models:

• Customer Acquisition: Targets net new shoppers who haven't interacted with your brand, excluding those who have converted in the past 90 days.
• Remarketing: Focuses on users who have engaged with your brand or product but haven't converted in the last 90 days.
• Retention: Aims to re-engage customers who have interacted with your brand and have not converted in the last seven days, fostering loyalty.

We often recommend starting with all three sub-models for Performance+ and then reviewing their performance to optimize your strategy.

Empowering marketers beyond automation.

Even with AI doing the heavy lifting, you retain full access to all the familiar optimization tools and reporting capabilities you'd expect from Amazon DSP. This includes granular inventory reports, creative performance analysis and insights into geographic and audience metrics. You can still leverage pacing controls, day-parting, frequency settings and the powerful insights from Amazon Marketing Cloud (AMC).

For first-time users, we always suggest running Business as Usual (BAU) campaigns alongside Brand+ and Performance+. This allows you to truly appreciate the incremental value these AI models deliver. We also recommend a minimum flight of two months for both models to allow the AI sufficient time to learn and optimize.

Demonstrating real-world impact.

We've seen firsthand the significant impact these AI models can have. In a recent link-out campaign (tracking conversions outside Amazon's ecosystem) for an advertiser, our manual interventions—specifically refining sub-models and optimizing frequency caps based on AMC insights—led to an incredible 400% increase in ROAS for the Performance+ campaign. Ultimately, this campaign delivered 1.6 times more ROAS compared to the BAU approach.

Similarly, for a link-in campaign (tracking purchases on the Amazon website), the Performance+ campaign achieved a 50% higher ROAS than BAU. While the CPM for Performance+ was 13% higher, the CPA was 66% lower, underscoring the AI's ability to drive more efficient outcomes by focusing on valuable conversions rather than just cheap impressions. This highlights the ongoing industry conversation about prioritizing effective inventory over merely the cheapest.

A partner for success.

At Monks, we're proud to be an AWS Partner, with a 13-year partnership with Amazon. Our expertise in AI has been recognized with awards like the AI Pioneer by The One Show and AI Agency of the Year from AdWeek. We are also a certified AdTech Reseller and Activation Partner for Amazon DSP.

If you're looking to simplify your media buying, unlock the full potential of Amazon's ecosystem, or just want to explore how these human-centered AI models can transform your advertising strategy, we're here to help. Reach out to us for a tailored session or a casual conversation about achieving your goals with Amazon. We'd love to partner with you on your journey to growth and innovation!

Working with brands like Chime who have sophisticated multi-channel advertising programs, my colleagues have unlocked benefits ranging from enhanced automation to scalable personalization, all while driving cost efficiency. This topic took center stage during a panel discussion at Campaign’s AI Deciphered 2024, featuring Brayden Varr, ACD at Chime; Ashwini Karandikar, EVP of Media, Technology, and Data at The 4A’s; Jesse Waldele, and SVP of Digital Operations and Client Success at Dow Jones; along with myself.

Steve Barrett, our moderator and VP and editorial director at PRWeek & Campaign US, opened the panel discussion with a pivotal question: What do the new AI-powered tools mean for the role of a media planner? Below are some key insights from our session.

Creative and media are becoming closer together.

Historically, creative and media roles operated in silos, each adhering to their own set of responsibilities. This setup is far from ideal, because the intersection of creativity and performance analysis is crucial for brands to succeed. As Karandikar put it, "The creative arm needs to speak the language of performance while still capturing the brand message.” However, reaching a point where team members could put aside their production tasks to collaborate with others was, at the very least, challenging.

Now, with AI managing repetitive tasks and various aspects of content generation, creatives can concentrate on higher-level strategic thinking and innovation. As a result, they can collaborate more effectively with media planners—and vice versa—gaining deeper insights into target demographics and enhancing campaign optimization. Plus, it opens up new possibilities for media planners who may not have the support of creatives on their teams.

Varr, approaching the topic from the creative and design side, highlighted how performance marketers are leveraging AI tools to enhance creativity on limited budgets. “If you work in the performance space, you probably have zero creative production budget,” he said. “But we have to get noticed. With tools like Adobe and Midjourney, we can create content that helps us stand out in these feeds more than ever, and it’s positively impacting our key metrics that we evaluate every day.”

What’s more, in highly regulated markets, brands often face restrictions that prevent them from using demographic data, such as age, gender, or location, to target consumers. Instead, they can rely instead on creative content as a key input. That means exploring various attributes—such as interests, behaviors, or emotional triggers—within the creative itself to connect with consumers. Media buyers are increasingly collaborating with creative teams, brand strategists and media strategists to craft campaigns that resonate with target audiences. 

It’s not just about automation; it’s about intelligence.

As mentioned earlier, automation allows creatives and media planners to focus on high-value tasks rather than routine analytics. But it doesn’t end there. Gen AI excels at processing vast datasets to reveal patterns and trends that human analysts may miss. By generating actionable insights, generative AI helps inform media strategies, allowing planners to optimize their approaches based on real-time data rather than relying solely on historical experiences.

Waldele said, “Where we really see a huge opportunity is not just in media plan automation, but in media plan intelligence. We can create media plans that are performing and infused with that intelligence.” To provide an example, working with enterprise clients with complex media programs, we can automatically tag thousands of potential attributes within our creatives. This used to be a cumbersome task that required building complex taxonomies to capture various elements of an ad. Marketers had to create detailed tags for simple attributes like “red background” or “blue background,” while also specifying the product’s context. This method imposed limits on the number of characters allowed in an ad name and often left teams struggling to log every attribute of the creative that could impact performance.

Now, the automatic tagging capability streamlines the process and enhances the depth of analysis. AI can pick up subtle details that were previously imperceptible to the human eye—such as whether a card is positioned at the top of a phone or next to it—and understand how these elements resonate with audiences. Moreover, many ad platforms integrate generative AI to dynamically alter creatives in response to immediate audience feedback. It’s a significant leap forward—not just in terms of efficiency, but in the depth of intelligence that drives media planning today.

There’s value in taking risks.

From highly personalized creative content to real-time insights, brands have much to gain by incorporating generative AI into their media strategies. Still, many find themselves caught up in the challenges, risks and considerations. As Varr said, it’s easy to say no to these tools, but doing so can be costly. Once your competitors start effectively harnessing the untapped potential of generative AI, catching up becomes a formidable challenge.

While smaller brands tend to be more agile and willing to take risks, established enterprises have just as much—if not more—to gain from embracing innovation. To secure your team’s buy-in, Varr suggests, “Find someone who believes in it, and then demonstrate the impact. If you can do that, that’s when you’ll succeed.”

We are entering a new era of collaboration, agility and intelligence. By breaking down traditional silos and fostering collaboration among creative, media, and strategic teams, organizations can leverage the full potential of their insights and automate routine tasks. This empowerment not only enhances creative quality and campaign effectiveness but also positions brands to respond swiftly to market dynamics. In a time when agility and informed decision-making are crucial, those embracing generative AI will not only stay ahead of the competition but also redefine what's possible in media planning and execution.

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

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

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

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

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

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

Virtualization defines the transformation of digital.

As the world opened back up, an era of digital transformation gave way to the transformation of digital. By this, we mean virtualization: a set of new audience behaviors, cultural norms and technology paradigms resulting from 30 years of digital transformation, hyper-accelerated over the past five years. Virtualization, covered in our report earlier this year, marks a revolution in consumer behavior as people demand more from the digital platforms they engage with, which implicates the ways they look at digital experience, community, ownership and identity. For example, the design of ComplexLand, a virtualization of the hype-fueled annual event ComplexCon, was built around the insight that today’s fashion trendsetters are becoming just as invested in their digital identities as their corporeal ones.

The Social Innovation Lab, who explores up-and-coming trends in social, delved deeper into what motivates some of these behavioral changes in The Search for Meaning. By exploring how technology shapes the ways consumers find and make meaning in their lives, the Social Innovation Lab uncovers how brands can adapt to the new era in digital.

Experiments in the metaverse drive real success.

One of the biggest manifestations of the virtualization trend has been the rise of the metaverse, which many brands have experimented with this year to find their footing. Duolingo celebrated the birthday of its lovable (and persistent) mascot by hosting a game jam in Roblox and building larger-than-life public artwork in Decentraland. Logitech for Creators reinvented the awards show format by building the first music awards show in the metaverse, the Song Breaker Awards.

The fashion industry in particular has found a lot of success in the space. Liam Osbourne, Global Client Partner at the FLUX.Monks, our dedicated fashion and luxury team, shared with Vogue some insight on how the metaverse is an opportunity to become more inclusive. For brands exploring that question and more, the FLUX.Monks have authored a quick bulletin on why the metaverse matters.

As the metaverse continues to take shape, now is an excellent time for brands to continue experimenting in the space, which was a large part of a discussion shared between SVP Web3, Metaverse & Innovation Strategy Catherine D. Henry; Chief Innovation Officer Henry Cowling; and Mike Proulx, VP and Research Director at Forrester as part of our Meet Me in the Metaverse series. Eager to get started experimenting in the metaverse yourself? Our map of the metaverse can help you find the right home for your brand within this quickly evolving space.

Web3 and other emerging tech begin to mature.

In addition to the metaverse, other emerging technologies have fueled transformative digital experiences—most notably Web3. We showed up at NFT.NYC, the biggest Web3 conference on this side of the screen, with an immersive installation for Cool Cats that blurred the boundary between virtual and the real. Meanwhile, Gucci opened the virtual door to an immersive gallery space used to host an auction of NFT artwork.   

More than just a new tech infrastructure, Web3 marks a foundational shift in brand-consumer relationships, a topic covered in a bulletin we released in collaboration with Salesforce this year titled Web 3: The Future of Customer Engagement. For those wondering how to begin making moves in Web3, check out insights from our In a Monk’s Opinion series, which lays out everything you need to know about NFTs and the blockchain. One tip from the Labs.Monks: be sure to make your NFT projects sustainable.

Speaking of the Labs.Monks, our R&D team has continually released missives on the bleeding edge of tech throughout a year of innovation. Their most recent report on generative AI explores the potential of AI tools like Dall-E and Mid Journey that have captured creatives’ imagination (and people’s social feeds). One example of what the tech can achieve: unlocking efficiencies in animation and other production needs.

Creativity and media go hand in hand.

Throughout the digital era, it’s been tempting to focus attention on vanity metrics. But as CMOs invest more dollars into media (and face increasing budget scrutiny with a possible recession), they will benefit from transforming their approach to a more holistic strategy that blends media and creative to optimize their spend. Speaking to Digiday, Media.Monks Global Head of Media Melissa Wisehart unveiled how our integrated media pillar is designed to help brands make this leap.

Uni's relaunch campaign demonstrates this more holistic approach through the development of both the creative and media placement by one partner. With three creative variations and four measurement initiatives, we ensured the creative rolled out across today’s most relevant channels according to their purpose within the brand ecosystem.

When it comes to creative optimization, wellness brand Hatch found great success—and shared some of their secrets in an episode of In a Monk’s Opinion featuring Hatch’s VP Growth Marketing Holly Elliott. Many brands that rely on digital platforms for their marketing face a series of challenges: rising acquisition costs; a limited ability to manage their performance, attribution and audience targeting; and the risk of losing brand authenticity. Hatch assuaged these concerns by striking a balance between creative and performance. In the episode, Holly and our creative performance experts offer insight into how historical performance data can fuel further creative iterations.

Brands prepare for the oncoming privacy era.

While media optimization and performance may be top of mind now, budget conscious CMOs are also eyeing another obstacle on the horizon: overcoming their reliance on third-party cookies as attitudes in privacy shift and as Google aims to sunset third-party cookies with the Chrome browser. And speaking of cookies, VP of Data Services and Technology Doug Hall recently shared ways marketers can rethink cookie consent and management using the Privacy Sandbox platform.

Add to the mix that Google is sunsetting GA360 to make way for its new GA4 platform, meaning brands have plenty of adjustments to manage in the near future. Thankfully, GA360’s sunset was postponed to July 2024, meaning they have more time to perfect their migration strategy. Our data experts put their heads together to create a short guide on how to maximize your move to GA4 before the deadline.

We’ve also got some inspiration based on how other brands have future-proofed their data strategies with great success. We began our partnership with Molson Coors in 2021 with the goal to bring more of its digital media in-house. By taking an ambitious, holistic approach focused on modernization, we’ve since helped the brand future-proof with a robust, in-house digital media team: a data transformation that ranges from data acquisition, data activation and enrichment, and optimization.

And in the commerce space, leadership from Canadian retailer Reitmans shared the role cloud computing played in building a single source of truth throughout its entire organization, joining online consumer behavior with data from over 400 brick-and-mortar locations. Check out the episode of IMO to learn how a strong data foundation helped the brand adapt at speed.

Speaking of data foundations, emerging technologies like Web3 offer new ways of connecting with consumers and strengthening relationships. In an episode of Meet Me in the Metaverse, Ashley Muscumeci, our Director, Go-to-Market, sat down with Jordan Cuddy, Chief Client Officer at Jam3, and Avanthika Ramesh, Senior Product Manager, NFT Cloud at Salesforce, to explore how building a resilient data foundation will help brands get a head-start into the Web3 future. One key insight: despite being a new space, the same rules apply when it comes to user consent. “Even if you are bridging Web3 and Web2 data to bring all these identifiers about a consumer together, it’s really important that the user opts in and provides consent to merge these identities,” says Ramesh.

Commerce goes social and creators go virtual.

In recent years, creators have expanded their digital footprint into new spaces—like gaming and social audio—and have even adopted new content ventures to translate audience engagement into revenue for brands. So, what does the intersection of content, commerce and entertainment look like today? The Social Innovation Lab launched a report earlier this year, The Year of Digital Creators, to explore the state of the creator economy in depth, available in English, Spanish and Portuguese.

One example of how creators have transformed the consumer journey is through the rise of live commerce, the subject of a recently released Social Bite from the Social Innovation Lab. Live commerce blends communities and real-time connection to offer entertaining, interactive and personalized experiences for audiences, and the short deck offers a glimpse into the live commerce journey and how brands can activate audiences every step of the way.

What’s next in the realm of digital creators? Expect more and more virtual influencers in the form of CGI-rendered fictional characters or avatar alter-egos of real people. With the rise of the metaverse and more accessible motion capture technology, virtual influencers are primed to become a more common presence in brands’ influencer marketing strategies—and if you’re curious about the role one could play in your own marketing, check out another Social Bite about how virtual influencers are coming alive.

Here's to a new year of innovating!

With so much innovation in the last year spanning experiences, content creation and optimization through data, there’s a lot to celebrate as we cap off 2022. Looking ahead into the new year, these trends will continue to shape brands’ strategies as they seek to engage with hyper connected audiences in the new digital era.

Where will you begin? Reach out to start your 2023 journey with confidence.

Bidding to value isn’t new. In fact, a lot of advertisers have been doing it for many years. Where an advertiser is supplying revenue data directly to the platform, such as revenue from a tag or linked ecommerce data from Google Analytics, value bidding is already taking place. However, for businesses with more complex or longer sales cycles, or driving multiple channels of interaction with customers, understanding value can be an arduous and complex task.

Use value-based bidding to maximize ROI.

In a nutshell, when you use bid strategies in your media buying platforms, the main difference between a Target CPA (cost per acquisition) and a Target ROAS (return on ad spend) bidding strategy is that while Target CPA adjusts your campaign bids to help you meet a predefined cost per conversion goal, Target ROAS adjusts bids to help you maximize the value of conversions you’re receiving as a result of your advertising, and thus focuses on ROI. 

For Google Ads and the new Search Ads 360 in particular, Google has been clear about the fact that CPA bidding or bidding for conversions is limiting the ability of bidding algorithms to eke out performance, as you are assuming that all customers that interact with ads are bringing in the same business value. 

However, we all know that this is not the case. Customers come in all shapes and sizes; some will take longer to make decisions to purchase or interact with your business, some are going to be customers interested in smaller purchases, while others still will be looking at larger purchases or longer sales cycles. This can also become even more complex when customer touchpoints move from online to offline, such as an outbound call center. 

It wouldn’t make much sense to bid for all of these customers with the same value logic. By focusing on segments of customers based on the value they would bring to us, we can maximize our return on our ad spend. This is especially true for B2B or subscription businesses, where not all prospective clients are equal. 

The complexity of value-based bidding only needs to be as complex as you need it to be for your business, but the level and complexity of the data you are sending to your performance platform will provide you with much more robust reporting metrics, and more data for bidding algorithms to get things done.

As we can see, as we move up the complexity of our bidding goal, moving away from clicks/conversions to value and then profit, we need to supply the platform with less proxy metrics, and more revenue and value data. At the most mature stage, the ultimate goal for businesses is to send customer lifetime value data to the platforms to enable automated bidding and to predict future customer buying behavior based on their previous purchasing patterns.

Test and set up value-based bidding using proxy metrics.

For direct sales and subscription businesses, value-based bidding would of course involve simply passing back the value of the sale or rolling subscription back to the platform as an offline conversion, for example in Campaign Manager or Google Ads. However, if your marketing is targeted towards lead generation and longer sales cycles, bidding for value becomes slightly more complex, requiring the use of proxy value metrics. 

For example, let’s say that you have four stages within a typical sales journey, all trackable via conversion tags or Google Analytics, or perhaps via integration with CRM as an offline conversion. It could look like this:

Lead Submitted (25%) → Marketing Qualified Lead (20%) → Sales Qualified Lead (15%) → Closed Deal 

We need to work backwards from the Closed Deal value, to assign a value to a Lead submission:

Closed Deal $1000 → SQL $150 → MQL $30→  Lead Submitted $7.50

Given that a Closed Deal is worth $1000 in this example, we divide each subsequent stage by the prior stage conversion rate.

We can now understand the value of the first conversion point in the customer sales cycle and assign a value to the lead submission, then perhaps do the same for other conversion points on your site (for example, phone calls or “contact us” forms). These values can then be assigned to our bid strategies to assign the real value of customers to your business. Remember, machine learning is only as useful as the information that is being supplied to it!

Once you have values assigned to conversion points, you can use features such as Custom Columns in Search Ads 360 or Google Ads to add these values for your automated ROAS bid strategies, then let the platform algorithm do all the hard work with this new information. 

Look ahead to predicted lifetime value.

Of course, the ultimate goal we should seek with bidding in performance media is to add more of a predictive value to our target, so that the bid strategy is able to bid on keywords that are likely to drive longer lifetime value, rather than one-off purchases, short-term subscribers or low value B2B customers. This can be done by adding predictive intelligence to our bidding platform, and involves integration of CRM with a data platform and machine learning tool, such as Google BigQuery and BQML. 

You can then export these predicted values to your platform of choice as offline conversion data, and point the bid strategy at this particular goal to maximize, which in this case predicts lifetime value. This is where we think all marketers should aspire to be and plan towards, and it’s something we bring up often with clients as an important horizon goal to have with the future of their first-party data. 

Customer value-based bidding, combined with media platforms bidding algorithms, will help you monitor the real impact of advertising on your business and make the right decisions to develop growth strategies, ultimately allowing you to capture the customers that generate the most value, and those that matter most. Again, the data you share with platform algorithms is a crucial factor in competitive success, and unlocking insights related to value will prove crucial to brands looking to improve performance within an intensely competitive digital landscape.

Media.Monks will take on media strategy, planning and buying, and measurement for Unity globally. With subject matter expertise in gaming, VR, Web3 and the metaverse, Media.Monks’ integrated team will scale up media to engage Unity’s core gaming business and its B2B audience.

“Media.Monks is the right fit for our business given our shared expertise and belief in how RT3D, the metaverse and the next phase of the internet are changing not only gaming but many other industries,” said Carol Carpenter, CMO, Unity. “We are excited to partner with them to unify our media efforts globally, and work together to deliver unique solutions for customers.”

In addition to the media AOR assignment, the Media.Monks creative development teams use Unity software to deliver real-time 3D solutions for clients across a wide range of industries. Recently, the Unity technology powered Media.Monks’ development of an award-winning AR experience, ‘Anne Frank House: The Bookcase for Tolerance,’ honored at the Cannes Lions Festival of Creativity in the Digital Craft category, and many more including The Webby Awards, The One Show, ADC Global and D&AD.

Learn more about the work Unity and Media.Monks are doing to build successful B2C brands in the metaverse by tuning in to an on demand discussion between Unity’s VP of Accelerate Solutions, Ryan Peterson, and Media.Monks' SVP, Tim Dillon. Tim will discuss insider lessons and insights gained from working with major consumer brands––from getting started in the metaverse, ways to leverage a real-time 3D game engine to making a genuine impact, and more. Listen now.

This review was led by Tenx4, an agency search consultancy who specializes in helping Global B2B Brands identify the right agency partner. “We’re on a mission to fix the broken agency RFP process to be about ‘the fit’ rather than ‘the win’ and it is clear that the partnership between Unity and Media.Monks is the perfect fit,” said Ashley Cohen Chandler, Partner, Tenx4.