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.

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.

For one of our clients, their team had spent quite a good amount of time and effort building a series of email marketing campaigns in order to optimize online customer journeys. Some results were great: the CTR, for example, was extremely high considering the industry benchmark. So, how come the team was not able to demonstrate the impact of their investment just yet?

As soon as our team set out to analyze the email campaign data from top to bottom, a common issue sprung into view. It wasn’t really about the email content or the automation settings. The problem was at the top of their email campaign journey: the number of email leads put into the campaign pipelines was significantly low.

Before you start, consider your input.

Data from the 2022 ROI Report by Nielsen shows that increased investment in upper-funnel to mid-funnel marketing campaigns elevated brands’ ROI by 70%. Clearly, it is critical to create and strengthen a marketing system where you can maximize the impact of your input and draw the best outcomes. However, at times, we forget two of the main aspects of implementing a successful marketing strategy: the amount and quality of your input. Both should be satisfied, because without a decent amount of quality email leads at the top of the email marketing funnel, the outcomes will never suffice or provide the expected value for the team’s investment.

The Nielsen report also shows a significant, positive correlation between audience targeting and campaign ROI: high target reach campaigns (those that aim to reach a large number of people within a specific target audience) present a higher ROI than medium and lower target reach campaigns. Considering the power of the upper-mid funnel marketing, audience targeting in the upper funnel can lead to unlock maximized potentials of campaign performance. But in order to secure a healthy amount of customers at the top of the funnel, approaching the right targets in your upper funnel campaigns is pivotal. And here’s the good news: media campaign platforms offer a variety of options for you to fine-tune your targeting strategies by grouping customers into meaningful subsets.

When it comes to navigating the myriad categories and data points used for targeting, I follow a list of strategies and criteria to better target and activate audiences in marketing campaigns, which in turn helps to improve the upper funnel. Note that some strategies may not be viable, depending on the features of the media campaign platforms your team is using or the availability of the online customer data.

• Demographics: gender, age, education, job title, etc.
• Life events: critical lifetime milestones such as change in marital status, having a baby, or buying a house.
• Affinity: specific interests of consumers while browsing online.
• In-market: strong consumer interest in the products or services you are selling.
• Previous interactions with your website or app: visited, viewed a product, clicked “add to cart,” logged in, downloaded brochures, etc.
• Current customers: existing records in CRM matching with the campaign channels.
• Likely-to-be customers: people who are sharing similar characteristics to the existing customers.

By segmenting target customers into the right subsets and serving them relevant content in marketing channels, your team will be more likely to have strong upper pipelines, leading to stronger performance down the funnel. And if your team has robust input and a solid marketing system, ROI improvement is just around the corner!

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

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

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

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

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

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

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

Teamwork to make the RMN dream work. 

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

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

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.