So What’s the Difference?

MMM (Marketing Mix Modeling) is your big-picture view. It shows how different marketing channels (and non-marketing factors) contribute to outcomes over time. Think long-term trends, full-funnel impact, and strategic planning.

Incrementality testing, on the other hand, is all about proving causality in the short term. You’re running experiments — usually geo-based or audience holdouts — to isolate whether a particular campaign, tactic, or channel actually drove lift.

Here’s how I like to explain the difference:

MMM:
– Shows how channels perform over time
– Timeframe: Months and years
– Data: Aggregated (e.g. weekly sales + spend)
– Best for: Budgeting, planning, channel mix
– Speed: Slower (but broader)

Incrementality Testing:
– Shows what actually caused lift in the moment
– Timeframe: Days or weeks
– Data: User or geo-level
– Best for: Testing, optimizing, creative/audience strategy
– Speed: Faster (but more focused)

Why You Shouldn’t Pick One Over the Other

Here’s where it gets interesting: MMM and incrementality make each other stronger.

– If you’re only running MMM without calibrating it with real-world test results, you’re building a model based on assumptions. Good ones, maybe — but still assumptions.
– And if you’re only running tests without zooming out to the bigger picture, you might be optimizing in a vacuum — missing how channels work together or how your market’s evolving over time.

MMM gives you historical context. Incrementality gives you causal proof. You need both.

When It Makes Sense to Use Them Separately

There are definitely times when one method makes more sense than the other:

Use MMM on its own when:

• You’re planning budgets across all channels for the next 6–12 months.

• You need to justify brand spend that doesn’t show immediate lift.

• You’re working with limited tracking — MMM is great in cookie-constrained environments.

Use incrementality testing on its own when:

• You want to test a new tactic (like YouTube Shorts or a new audience segment).

• You’re looking to validate a media partner’s claims.

• You need fast feedback to make decisions on in-flight campaigns.

From Analysis to Action: Where Real Intelligence Begins

At Brainlabs, we don’t believe in guessing what works—we prove it before you spend a single dollar. That’s the power of our Real Intelligence approach: combining MMM with incrementality testing before strategy creation, not after.

Most agencies build campaigns first, then scramble to measure what worked. We flip that model completely. Our process starts with analysis—not assumptions—so strategy is built on truth, not retroactive validation.

MMM uncovers strategic patterns. Incrementality testing verifies what truly drives results. Your first-party data tells us what happened, but together, these tools tell us why. When used upfront, they shape strategy, not just measure it. That’s the difference.

This means when we learn that video completion rate is more predictive of purchases than CTR, or that reallocating budget from direct mail to paid search improves ROI—these insights aren’t post-campaign reflections. They’re foundational inputs before a single creative is developed or media plan is built.The Real Intelligence approach fuses machine learning, statistical modeling, and human decision-making to drive smarter action, faster. It’s how we transform your data into competitive advantage from day one.

The post MMM vs. Incrementality Testing? appeared first on Brainlabs.

The Strategic Dilemma

Picture this scenario: You’re planning a major campaign launch. Your media team presents two paths forward:

Path A (Segment & Target): Select a few carefully curated third-party audiences and target them consistently throughout your campaign.

Path B (Experiment): Test many platform audiences and optimize to the best performing ones during the campaign.

Platform audiences are cheaper and likely more accurate, but they don’t always fit brand needs, like targeting category buyers with Facebook interest audiences. Third-party audiences are more tailored to brand requirements but typically cost more due to CPM premiums.

Most marketers would instinctively choose the precision of Path A. But what if this intuition is wrong?

The Simulation That Settled the Debate

Rather than rely on expensive trial-and-error campaigns, I used Monte Carlo simulation to run 100,000 virtual campaigns comparing these approaches. Here’s exactly how I set it up:

The Experimental Approach

Audiences: Platform-specific (Meta, Google, Amazon)
Cost: $5 CPM
Reach: 0.5M – 5M audience sizes
Performance: 1-3% conversion rates
Strategy: Start with 20 audiences for 2 weeks, optimize to top 10 for next 4 weeks, then top 5 for final 6 weeks

The Segment & Target Approach

Audiences: Third-party data audiences
Cost: $7 CPM ($2 premium)
Reach: 0.65M – 6.5M audience sizes
Performance: 1.2-3.6% conversion rates (20% performance premium)
Strategy: 5 audiences for the full 12 weeks

The Shocking Results

After 100,000 campaign simulations, the first result was almost anticlimactic:

Average Performance: Nearly Identical

Experimental Approach: $1.5M spend, 302M impressions, 0.023638% response rate

Segment & Target: $1.5M spend, 214M impressions, 0.023970% response rate

The difference was so small it’s essentially meaningless.

But when I examined the distribution of results, a stunning pattern emerged that changes everything we think we know about audience strategy.

The Hidden Truth About Risk

While average performance was nearly identical, the risk profiles were dramatically different:

The Segment & Target approach created a dangerous feast-or-famine scenario. The distribution showed it could deliver exceptional results on the high end, but also catastrophic failures on the low end. You’re making a high-stakes bet on just 5 audiences carrying your entire campaign.

The Experimental approach delivered something more valuable: consistent, predictable performance. By optimizing from a broader pool of 20 starting audiences, it virtually eliminated the risk of terrible results while maintaining strong averages.

The Counterintuitive Discovery

Here’s what challenges everything we believe about audience targeting: The approach that seems more controlled and scientific (segment & target) is actually the higher-risk strategy.

The experimental approach reduces risk by selecting from a broad pool of audiences throughout the campaign. You’re unlikely to get exceptional performance, but you’re also unlikely to fail catastrophically.

The segment & target approach offers higher upside potential, but with significantly higher downside risk. All 5 audiences could perform exceptionally well, or they could all underperform drastically.

What This Means for Your Strategy

The simulation reveals a fundamental truth: Both approaches perform similarly on average, but an experimental approach reduces risk while a segment & target approach offers higher upside and downside potential.

This insight forces a critical question: How much do you want to gamble with your audience strategy?

For the experimental approach, you need to be confident that your optimization process can effectively identify and scale the best-performing audiences from your initial test pool.

For segment & target, you need to be confident that your third-party data will deliver at least 20% better performance than platform audiences to justify the cost premium, and you need to be prepared for the possibility of significant campaign underperformance.

The Bottom Line

After 100,000 simulated campaigns, the data tells a clear story: The experimental approach isn’t just a viable alternative, it’s often the superior choice for risk management.

The question isn’t which approach delivers better performance (they’re essentially equal on average). The question is whether you’re optimizing for predictable results or willing to accept higher variance for the chance of exceptional outcomes.

The approach that feels safer might actually be the riskier bet.

Real Intelligence in Action

This simulation study exemplifies what we call Real Intelligence, the approach that sits at the core of our media operating system. We use it to develop a real understanding of real customers, influence real action, and deliver measurable, real business outcomes.

We start from a place of first principles, which means separating signals from noise to draw conclusions from real data, not assumptions. Rather than accepting conventional wisdom about audience targeting, we continuously test hypotheses to inform bigger strategic decisions, in this case, our entire audience strategy.

By running 100,000 simulations, we discovered that our industry’s assumptions about risk and precision were backwards. This is Real Intelligence at work: using data to challenge what we think we know, then building strategies based on what we actually know to be proven.

The post The Great Audience Targeting Paradox: What 100,000 Simulations Revealed About Risk vs. Performance appeared first on Brainlabs.

Customers are now demonstrating search intent in more diverse places than they used too, as discovery-driven commerce grows, creating new competitive advantages for brands that can seamlessly blend entertainment, validation, and transaction in social ecosystems.

The trend is only heading in one direction, with the 2025 GWI Social Media Report revealing that nearly half of 16-34 year olds cited searching for products as one of the main reasons they use social media. They’re not just changing platforms they use for search, they’re rejecting the premise that discovery and purchase should be separate experiences. 

The traditional funnel stages haven’t vanished; they’ve collapsed into micro-moments of social discovery. Brands must act fast to adapt to this fundamental shift.

So, what is Social Search?

Prior to this evolution, if a consumer was looking for answers such as ‘best restaurant in London’ or ‘healthiest dog food,’ there was only one place they turned, Google (sorry Bing). But users are now turning to their social media platforms to answer these questions, using the platform search functionalities to find answers.

But why the shift?

Anyone that has worked in traditional Paid Search knows that the greatest signal a user can give you is their intent. The unique power of this channel is an immediate, in moment response to direct requests from consumers. This is not the place for educating users about a brand’s product or generating demand, but social platforms can unlock this opportunity. Here’s why:  

The Authenticity Economy – when on TikTok or Reddit, consumers know their queries are being answered by a real person on the other side of the screen, not a business with a big SEM budget or a carefully crafted article full of keywords. There’s a level of trust that comes from this. These public forums invite criticism and discussion so the recommendations and answers are perceived as genuine – whether they are or not…

A Visual Medium – Most platforms are natively built around images and videos, so when looking for answers on ‘best gochujang recipes’ or ‘where to stay on the Amalfi coast,’ why wouldn’t you prefer to see what the final recipe looks like or what the view is like on a beach in Italy? 

Speed and Seamlessness – a couple of years ago a study was conducted that found Brits scrolled more than 3x of the length of the Eiffel Tower per day. Platforms like TikTok, Reddit and Instagram are easy to navigate, and serve multiple functions such as entertainment, inspiration, networking, commerce or shopping, and search capabilities. Because of this, users are less likely than ever to leave the app. 

Forward-thinking brands are already reorganizing around integrated teams that think in terms of social ecosystems rather than channel silos. These teams don’t just optimise campaigns; they cultivate communities that blend entertainment, education, and commerce. Tech platforms are supporting this by adapting their Search functionality to be simple and ingrained in the user experience.

How are big tech platforms adapting to this change ?

TikTok is on the front foot here, developing their Search Ads campaigns, with keyword search term targeting expected to roll out widely in H2 2025. Reddit and Pinterest likewise are gaining momentum – as their platforms have always had search principles integrated into their audience and placement offering. 

Interestingly the dominant forces in the industry are slower to move in actionable ways, Meta has not identified this as a priority within their ad buying tools. Instead they are focusing on automation of existing functionalities and monetising Threads/Whatsapp over investment in keyword bidding functionalities. Similarly, it will be important to keep an eye on how Google reacts over the coming months… will the traditional engine be adapted to meet changing consumer demands? We will certainly be watching this space. 

Social users behave so differently on each platform that they may also evolve to capture category-specific search behaviors. Pinterest owns lifestyle discovery. TikTok dominates product performance. Reddit controls opinion validation. In addition to adding search features—they’re becoming the definitive authority for different types of purchase decisions. The winners won’t just be platforms with the best search algorithms, but those that create the most seamless path from discovery to transaction within their ecosystem. Amazon understood this early with one-click purchasing. Social platforms are now building the same frictionless commerce experience around content consumption

So, what can advertisers do to capitalise on this?

Ecosystem Thinking Over Channel Optimization – This has never been more important, with the principles of Search starting to be integrated into ad buying functionality across Social platforms. Ensuring a streamlined and efficient workflow between Search, Social and Content teams is going to be essential to maintain flexibility as the platforms develop and landscape changes.

Authority Through Community 

1. Your competitive edge increasingly comes from the communities that advocate for your brand across platforms, so a creator strategy is essential. Authentic content is key, and brands will need to be strategic with who they partner with, as well as deliberate in the type of content they develop. Ensuring they are present to answer the questions their customers are asking, without jeopardising trust.

2. Branded content also needs to maintain the creative “red thread” that exists across other marketing channels – driving authenticity not only in messaging but in brand identity. 

Test, test, test! – With functionality across the different Social Ads Platforms varying and ever-changing, having a structured test & learn strategy (and budget) is a must to make sense of the new landscape. Working with media partners on understanding platform nuances, planning tool availability, and shifting best practices will help brands start to shape their strategy for 2026 and beyond.

It’s an exciting time for the industry and this ongoing behaviour shift represents a great opportunity for marketing teams focused on future-proofing their strategies. As the roles of channels are beginning to blur together, it is essential to have these conversations now, to build for an integrated future. 

The post Social Search Is Changing Consumer Behavior appeared first on Brainlabs.

The prevailing narrative suggests a seismic shift in consumer search behavior, where the dominance of Google and Amazon is being eroded by a new ecosystem of Social and AI-driven platforms. To move beyond speculation, we built a proprietary dataset, analyzing the detailed purchase journeys of 3,000 UK and US consumers. This data allows us to map the real-world behaviors that define the modern search research journey. Our analysis reveals that while the landscape is diversifying, the story is not one of simple replacement. Instead, the market is fragmenting into three distinct behavioral personas, each with a unique research DNA:

The Traditionalist: A significantly older demographic that sticks exclusively to the foundational giants of Google and Amazon, representing the most direct path to purchase.

The Augmenter: Our data reveals this is the largest segment, representing the mainstream consumer (25-44), who begins with Google or Amazon but then adds multiple other platforms like YouTube and AI chatbots.

The Dissenter: Our analysis identified a younger demographic that bypasses the duopoly altogether, discovering products organically on social and video platforms like TikTok and Instagram.

Understanding these three distinct journeys is critical for marketers, as each requires a unique channel strategy to ensure a brand’s message is coherent across the consumer’s bespoke path to purchase.

The Evolving World of Search: Beyond a Two-Platform Model

The prevailing wisdom is that we’re at a major inflection point for the world’s search habits. Search—the act of looking for information using text, images, or voice—has transformed . What was once a market dominated by the default choices of Google and Amazon has being fractured into a diverse ecosystem of platforms.

We’re told we’re living in a new era of ‘Social Search’ and ‘AI Search,’ where users are supplementing the traditional duopoly for a world of searching on platforms like TikTok, Reddit, and ChatGPT. As marketers, we’ve dutifully coined the new terms and accepted the premise that the old habits are dead, cynically perhaps because the new thing is always what sells to clients or internal stakeholders.

This diversification of platforms has been accompanied by a fundamental change in user behaviour. We’ve moved from a model where users relied on one or two primary platforms into a far messier world of touchpoints, with people using multiple channels in their search journeys. 

And so the story goes: Google’s role is evolving, its habitual dominance being complemented by the rise of social and AI search, creating a more fragmented world for users and advertisers. The narrative is set. All that needs to be considered now is the critical question: is it actually true?

Answering this question is remarkably hard. It’s hard for two main reasons:

1. Traditional measures of share of search focus on traditional search engines, they don’t include how often a user is using social or AI search. For example, see stat counter’s view of share of search.
2. More cynically, none of these companies want to share data on their search volumes. Google and Amazon don’t want to suggest that their volumes are shrinking and the social and ai search platforms are benefiting from the prevailing narrative that they’re growing, why disavow the market of the belief that they’re claiming some of Google’s $175 billion¹ in search revenues.

However, there is some evidence that Google might not be shrinking, implying that talk of their demise may at least be a little premature. Google searches are growing. Spend on Google isn’t dropping. Nor is their share price. And the “winners” seem a little quiet on the topic…

Google searches are growing.

Google searches are growing. Spend on Google isn’t dropping.

Google searches are growing. Spend on Google isn’t dropping. Nor is their share price.

Google searches are growing. Spend on Google isn’t dropping. Nor is their share price. And the “winners” seem a little quiet on the topic…

We set out to get a real representation of the search landscape today

To cut through the noise of unsubstantiated claims, we commissioned an in-depth survey of 3,000 representative consumers across the UK and the US. We focused on a key battleground in the search landscape: researching a recent product purchase.

Our survey was designed to capture the full complexity of a modern research journey by asking users about three key areas:

1. The Purchase Context: We first grounded each response in a real transaction by having users select from a comprehensive list of categories, from ‘Automotive’ to ‘Fashion & Accessories’.

2. The Platform Toolkit: To understand the full ecosystem, we then asked: ‘To do your research, which platforms did you use to search for information?’ This multi-select question included everything from Search Engines and Amazon to YouTube, TikTok, and AI Chatbots, revealing the complete ‘platform toolkit’ for each user.

3. Additional details – we asked users to identify where their journey started (‘Which platform did you use to start your search?’) and where it ended (‘On which platform did you make the final purchase?’). We also asked them to estimate how long it took to go from research to purchase.

Modern share of search platforms

The data below illustrates the modern distribution of search platforms. The chart shows the “share of search platforms”— the proportion of all the platform touchpoints identified which were each of the platforms. E.g. if we counted up all platform touchpoints identified by users and had 10 in total, Google being 2 of these would give Google a share of 20%.

It’s important to note that our survey focused on which platforms were used, not the frequency of use. Our goal is to understand how the habit of turning to incumbents like Google and Amazon is evolving in this new, diversified landscape.

Insight #1 – The incumbents are bruised, not broken

Our survey results confirm that consumers are using a wide array of platforms for search. This in itself is noteworthy – AI platform search share is 6.3% across the dataset – impressive growth by any measure. We also see social search with high adoption as well. However, our data shows that the ingrained habits of using Google and Amazon remain formidable, as they command a significant share of search platform touchpoints (27%).

Another interesting finding is that the adoption of AI platform search share is more than four times higher in the US than in the UK (7.9% vs. 1.8%), with Social Search also showing higher adoption rates. This aligns with the common observation that the UK often lags behind the US in the adoption of new technology.

Insight #2 – Brand websites show a stronger foothold in the UK.

In our cross-country analysis, a key difference emerges in the role of brand-owned websites. In the UK, they represent a significant 17% of search touchpoints, more than double the 8% share in the US. This may be because lower adoption of social and AI search platforms like TikTok and ChatGPT in the UK means the traditional journey—which directs users out to third-party sites—remains more prevalent, directly benefiting brands. 

Insight #3: The story is one of curation, not chaos

Contrary to the narrative of a messy, endless journey, the average research path uses surprisingly few platforms,  involving just 1.9 platforms in the UK and 2.5 in the US. 

The real story is one of curation, not chaos.

Users are developing new, category-specific habits. For example, our data highlights the heavy use of AI when users consider financial services. This implies that instead of simply adding more steps, users are building efficient, personal “platform toolkits”—instinctively choosing the best platform for the specific task and their own search preferences.

Do note importantly that the combinations of platforms used in journeys was very variable – the implication being that you can’t really predict the journey, you have to plan for them all.

Patterns in the chaos – the traditionalist, the augmenter and the dissenter

The data provided by our survey paints a clear picture of a changing search landscape. We can assume 5-10 years ago it was just Google and Amazon, but now we have a much more diverse platform set in use. Understanding how this transition is happening, by examining not the aggregate but by looking at individual customer journeys, is essential for building robust communication and channel plans – ensuring we meet our customers where they are with a message which is coherent alongside those they’ve encountered on other search platforms.

Analyzing the nearly 3,000 individual journeys in our dataset, we have identified, we have identified three clear groups of users by looking at each research journey in our survey. 

Deeper insight #1: Google and Amazon are still a part of 81% of journeys in total
Actionable takeaway: Audit you current media spends in search, if you’re spending >80% on Google and Amazon, think about trialing a recalibration to other platforms.

So whilst Google and Amazon combined make up only 45% of touchpoints across the dataset as we saw in Modern search’s platform share, they occur in 81% of user journeys, meaning that they are probably your best bet to manage to reach as many of your customers as possible on a very reduced platform set.

Deeper insight #2: Men are nearly twice as likely to use AI to augment their searches as women
Actionable takeaway: Pilot an “Answer Engine Optimization” (AEO) strategy, especially if your brand targets men and involves a complex, information-heavy purchase (like finance, auto, or tech)

Deeper insight #3: Social search adoption is high across genders, with 69% of men and 66% of women using a social platform.
Actionable takeaway: Treat your social channels as mid-funnel research platforms, not just top-of-funnel brand channels. Audit your content calendar to ensure your posts directly support product research.

Final Reflections

Our analysis of 3,000 consumer journeys reveals a search landscape that can be simplified into three core personas: the Traditionalist, who relies on the efficiency of the Google/Amazon duopoly; the Augmenter, who validates choices across a wide ecosystem; and the Dissenter, who bypasses the giants altogether. While these profiles provide a clear framework, they are generalizations. The Traditionalist journey is highly predictable, but our data shows the Augmenter and Dissenter groups contain hundreds of unique, individual platform combinations, revealing a vast “long tail” of personal research behaviors.

This complexity is best illustrated by the Dissenter. Our data shows this is not one group but two, with Dissenters in the US favoring a new ecosystem of social and AI discovery, while those in the UK navigate directly to brand and retail sites. This critical divergence proves that any effective marketing strategy must be local, modular, and built for the specific, nuanced ways different consumers are now finding their answers.

A Note from the Author

The method of dissent is local, which is key if this is the future of search

The “Dissenter” segment—those who bypass Google and Amazon—is not a single global persona. Our data reveals that Dissenters in the US and UK follow fundamentally different research paths, requiring distinct marketing strategies.

• When a US consumer bypasses the duopoly, they migrate to a new ecosystem of discovery platforms. The data shows:
  – 46% use YouTube
  – 28% use an AI Chatbot

• In contrast, when a UK consumer dissents, they follow a more traditional, direct path, favoring brand and retail websites they already know. The data shows:
  – 50% visit a Brand’s Website
  – 31% go directly to Other Retailers

This presents a fascinating paradox: the UK has a proportionally larger population of Dissenters, yet they use the “next-generation” social and AI platforms less than their US counterparts. This raises a critical question about the nature of the transition we are witnessing.

Why is this the case? The data suggests two competing hypotheses.

Hypothesis A: The US is a Time Machine

It’s possible that the transition to social and AI search is happening universally, drawing users away from all traditional touchpoints—not just Google and Amazon, but also from brand and retail sites. The difference we see today would simply be a consequence of this transition happening more quickly in the US.

Hypothesis B: The Cultural Divide is Real

An alternative hypothesis is that UK customers are on a different trajectory entirely. Our analysis consistently shows a higher UK preference for brand and specialist sites across all traditionalists, augmenters and dissenters. This may be a fundamental consumer trait, possibly driven by a greater mistrust of “big tech” aggregators or a cultural desire for more direct, controlled journeys.

Only future data, tracked over time, can definitively tell us whether the UK will follow the US path or if these two forms of “dissent” will continue to evolve in parallel. For now, marketers must acknowledge that the future of post-duopoly consumerism is not one-size-fits-all; it’s local.

The post The Three Faces of Modern Search: The Traditionalist, the Augmenter, and the Dissenter appeared first on Brainlabs.

TL;DR

Brainlabs tested integrating Broadcaster YouTube content—specifically Sky Sports’ Women’s Euros coverage—into a Unified CTV campaign alongside Netflix and YouTube. The aim was to capitalise on high-attention broadcast moments beyond the live event, tapping into highlights, interviews, and analysis that keep audiences engaged. By using cross-publisher frequency capping and Programmatic Guaranteed buys, the campaign:

• Delivered +1.4M added unique reach without extra budget
• Achieved 142% of planned reach for the same cost
• Drove 77% product search lift and 61% brand search lift
• Captured demand-led spikes in engagement after key matches

Broadcaster YouTube offers a brand-safe, high-attention channel to extend reach, build effective frequency, and align with cultural moments—without undermining broadcaster or SVOD revenue—making it a powerful lever in Unified CTV strategy.

Insights provided by:

Alex Glover
Managing Director,
Programmatic

Paddy Peel-Barnard
Associate Director,
Programmatic

Nathan Ridout
Senior Account Manager,
Programmatic

Open PDF

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Today, the job isn’t to “plan” media. It’s to design systems that listen, adapt, and influence in real-time. This requires rethinking everything– from briefs to budgets.

Campaigns Lies We Tell Ourselves

1. Campaigns Are a Lie We Tell Ourselves
We still talk about campaigns like they matter. We build launch dates, media bursts, wrap-up reports. But the reality is that most consumers don’t see the start or end of a campaign. They see fragments — an ad in the middle of a podcast, a TikTok remix, a review on Reddit. They interact with brands not in bursts, but in drips.

Campaigns are a fiction of control while the current reality is chaos. The better model is always-on influence ecosystems — where brands don’t speak at people, but exist in the fabric of culture, tech, and daily decision-making.

2. Planning Assumes Stability — But Consumers Are Shape-Shifters
Some still build plans based on audience segments: “urban millennials,” “affluent parents,” “eco-conscious Gen Z.” These segments are comforting but useless. The same person can act like four different consumers in one day — frugal in the morning, indulgent at night, eco-warrior over the weekend, impulse buyer during lunch.

We simply cannot pretend static personas are the reality. We should embrace behavioral fluidity, taking into account digital and behavioral signals. We need systems that respond to mindsets — not static segments — guided by signals like time of day, mood, weather, interests and search intent.

3. Buying Attention Is Cheap. Earning Memory Is Hard.
The advertising industry has become addicted to what’s measurable: CPMs, ROAS, click-throughs. But the real job isn’t exposure. It’s encoding memory to build association with Category Entry Points.

Most media plans optimise for the moment. Few optimise for the moment that matters later — when someone is in the aisle, in the app, or in the mood. Great advertising creates mental associations, not just short-term clicks.

This means we need to stop obsessing over performance dashboards and start planning for long-term and mid-term mental availability.

Instead of constantly asking “Did it convert?” we need to ask- “Will it be remembered when it counts?”

Media Creative siblings waiting

4. Media and Creative are siblings waiting to be reunited!
The wall between media and creative has become dangerous. Today’s most effective work doesn’t separate “where it appears” from “what it says.” The message is the media.

Media environments shape perception: A brand on LinkedIn says something different than that same brand on Discord. A pre-roll on YouTube is not the same as an Instagram story. The future of advertising is bringing back the contextual creativity we once had in the offline channels (ie. OOH vs. Print vs. TV) but apply it to all the platforms we are now on.

Media must be the bridge between data, platforms and creatives.

5. Forget Channels. Build Influence Loops.
Consumers don’t go through a funnel, they are constantly in a messy loop of discovery, distraction, delay, and decision. A tweet might trigger a YouTube search. A friend’s Tiktok might lead to a purchase on Amazon three weeks later.

Brands need to think like influence engineers: mapping the micro-moments, conversations, and nudges that shape choice. This requires orchestration, not sequencing. Distribution, not interruption.

In Conclusion
People don’t owe brands attention. The best strategy going forward? Be useful. Be entertaining. Be unforgettable.

In a world where media is infinite and attention is finite, our job is no longer to plan media. It’s to design adaptive systems of influence — systems that work even when we’re not watching, that compound over time, and that leave a trace in the consumer’s mind,

The future of media isn’t about reach or frequency. It’s about influence. We shouldn’t try to fill in a media plan, we need to build a system of influence.

The post The End of Media “Planning” appeared first on Brainlabs.

More Power, More Possibilities

The TV landscape has been changing for a while now. This ignites excitement in some, and scepticism in others. But the truth is, it IS changing, and it is changing fast. The decline of traditional linear TV and the rise of Connected TV (CTV) has created a wealth of new possibilities for advertisers, alongside some complex hurdles. CTV’s advanced targeting and measurement offer a step-change in capability, but the sheer number of platforms and services has resulted in fragmented campaigns and operational headaches, limiting overall impact.

This whitepaper explores whether the key to future CTV success really lies in bringing these disparate parts together, if it can be done, and if it works. We believe that advanced, cross-publisher frequency capping is a critical tool in achieving this unified approach. Drawing on the results of a real-world test campaign, we demonstrate how consolidating CTV buys across YouTube and Netflix within DV360 can deliver greater incremental reach, optimize advertising spend, and enable the kind of fast, performance-led campaign adjustments that linear TV simply can’t offer. We are not saying it’s one or the other, but we are saying that CTV is now a huge part of TV, and if you aren’t winning there, you are missing a key piece of the puzzle.

TV Just Got an Upgrade

The shift to streaming

We all know that TV is one of the strongest comms channels at our disposal, and for good reason. The high attention, storytelling power at scale has made it the cornerstone of advertising.

But linear TV consumption is on the decline while CTV consumption is on an upwards trajectory and is set to continue. This shift has been fuelled by smart TV adoption (80% of UK households), pandemic-driven content demand, and significant content investment by major streaming services like Netflix, Disney+, and Amazon Prime Video

Linear TV consumption is on the decline

With fewer people watching, and spending less time when doing so, our opportunity to reach our target audiences diminishes in Linear TV alone.

CTV is on the rise

Not only are more users watching CTV, but they are spending more time doing it – showing that CTV is an environment that our audiences are finding increasingly engaging, and giving advertisers more opportunity to engage with them.

Source: OfCom Media Nations Report 2024

Source: Wurl, 2024 CTV Trends Report

This means that not only are more people watching CTV, but they are spending more time doing it – representing a larger proportion of their overall viewing time than ever before, and importantly a larger opportunity for advertisers to reach their consumers. And by being on the same physical screen as Linear TV, CTV offers the high-impact, high-attention, storytelling power that advertisers are familiar with from a TV campaign. And of course, viewers don’t know the difference!

Ultimately, these viewing trends mean that advertisers have more opportunity than ever to engage with their consumers on CTV.

Combine this with the targeting capabilities and measurement solutions that CTV has to offer, and it means we can be more effective at reaching our desired target audience, gain a better understanding of how well our campaigns are working, and ultimately use this to deliver more effective campaigns and maximise return on investment.

The challenge & opportunity of CTV advertising

And where the eyeballs go, the ad dollars follow! BVOD, AVOD and FAST have been an integral part of connecting advertisers to their customers on CTV, and now that the SVOD players have introduced their ad-funded tiers, the opportunity has grown even bigger, but become more complex. (And with fragmentation comes complexity)

So What’s the Down Side?

The fragmentation of the TV landscape across numerous linear and digital content providers presents a significant challenge. While Total TV consumption is up, audiences are dispersed across more services, creating silos. CTV requires a holistic approach to break through these silos and capitalise on the advanced targeting & measurement capabilities. But obviously there is no one complete solution or all encompassing ecosystem to this that exists in market – that would be too easy.

The future of effective CTV campaigns lies in consolidating these fragmented ecosystems. While some publishers (like YouTube, Amazon Prime Video & ITV) remain within their proprietary platforms, others are increasingly offering programmatic access – and this is where the opportunity to consolidate lies.

To do this, we need to understand which services and DSPs offer the best access & capabilities, which publishers are integrated, and how to best leverage these combinations for optimal results.

Household penetration of SVOD providers is increasing, and more and more partners are entering the market. The more recent launch of their ad-funded tiers has been a game changer, and as user volumes behind them scale, this provides a huge opportunity for advertisers to engage with them in high-attention environments.

The CTV landscape is fragmented

Source: OfCom Media NAtions Report 2024

But users are increasingly splitting their attention across multiple providers & services, as they prioritise content and choice. This means that although the opportunity is growing rapidly, so is the fragmentation of where our audiences spend their time.

And people dont have just one streaming service

Despite the fragmentation, the combined reach of significant CTV publishers can surpass traditional TV – The key challenge lies in unifying these publishers

This paper explores how advertisers can break through CTV silos for a unified approach. For the past two years, we’ve rigorously tested various CTV publishers, gaining valuable insights into CTV performance & delivering strong results, even within the fragmented ecosystem!

Consolidating the silos: Our path to Unified CTV

XMR Case Study

#1 Agency for dedicated CTV spend 2023*

From our Cross Media Reporting, we have proven that we can reach targeted audiences more effectively with CTV partners

Lift Study Results

YouTube CTV
3.4M
33% Total Spend

Linear TV
1.9M
67% Total Spend

Our CTV campaigns have consistently delivered strong lift results – positively influencing audiences through the funnel, even within a fragmented ecosystem

And our testing has seen great results:

• 4%
• Ad- Recall
• Consideration
• Search Lift
• Awareness
• Purchase Intent

But now we’re ready to connect these pieces, starting with DV360. Why DV360? The scale and sophistication of YouTube on CTV, integrated into the full funnel, provides a strong base for further CTV exploration. Adding to that, DV360’s recent advancements in publisher integration including cross-publisher frequency capping, and its roadmap for consistent audience definitions & holistic lift studies, make it the ideal focus for this whitepaper series.

*Proportional spend, amongst peer set

Selection of core testing partners across various buying platforms

Now, to break through these silos, the power lies in applying a frequency cap to the audience and not the platform. This allows us to balance the relative cost & the reach of publishers within the CTV environment so that we can maximise/optimise reach and frequency for our desired audience – Something, that despite the best efforts of the broadcasters, can still not be done today.

Our vision is a unified ecosystem: one campaign, one platform, one audience definition, seamlessly running across AVOD, BVOD, FAST & SVOD.

But we know we need to test our way there….

And the first thing we want to test: Does frequency capping even work?

Fresh off the back of the UK Netflix integration into DV360, and some successful initial proof of concept testing of this with Netflix, we were provided with an opportunity to run an awareness campaign for an advertiser whose brand ambassador aligned to an impending Netflix original content release.

So how did we test this?

This gave us the chance to deliver a high-attention brand moment in line with the content launch on Netflix, but to use YouTube to extend that reach and build frequency against our core target audience. Optimising between the two, across one consolidated buy, and all frequency capped!

Exciting stuff, hey?

Previous Netflix Testing:

• + 17 % Brand Awareness
• + 15 % Brand Favorability
• + 16 % Consideration

Well it’s simple. Now that we have our vision of a unified ecosystem, and a holistic approach across this, our campaign provided us with the opportunity to test Netflix as the first integrated SVOD partner in this. So what we wanted to learn was:

But what did we want to learn?

• Does Frequency Capping actually work across Netflix & YouTube?

Not only does it work, as in, “does the technical integration work”, but does it work in its ability to balance the relative cost and reach opportunities between YouTube and Netflix.

And why is that important? Well, if frequency capping works between these two publishers, in one environment, we know we can consistently identify our audience across both, and crucially, optimise between the two to maximise our reach effectiveness efficiently.

You can do this with modelled identifiers and geo level data to provide a decent view, but it doesn’t give the level of granularity that we wanted to prove our hypotheses and deliver our vision.

In a nutshell – YES! The campaign was a roaring success.

And did it work?

• 342 % Product Search Lift
• 113,000 Lifted Users

Due to our frequency capping, we comfortably beat our total reach target. We were able to harness the added reach offered by multiple publishers, but prevent the usual wastage you would get by running in silo (whether this be on Linear or CTV). The frequency cap allowed us to manage that overlap, and identify the most cost effective routes to delivering our target reach & frequency across the campaign!

And the really interesting point is, as a result of this, and by reinvesting the budget we would have spent on that overlap, we were able to deliver an incremental 386k+ reach against the desired audience in this campaign – which you could not have achieved in silo!

But it doesn’t end there…

The campaign completely outperformed all of our targets and benchmarks when it comes to Brand Recall & Search Lift (which were the two key Brand indicators we were aiming for in this campaign)

And the Frequency Capping worked too:

• 386,687 Added reach from frequency capping
• + 51 % Total Reach vs. Target

Because, at Brainlabs, we relentlessly look for the highest performance in everything we do, mid-campaign we did some digging. We identified from our live Brand lift study, that there was a particular demographic that was responding best to our campaign.

We decided to test how we could focus our campaign on this audience, and use our frequency capping to maximise our incremental reach against that specific audience. The theory being, if this audience are responding best to our advert, the more of them we reach, the more our brand metrics will increase.

So we narrowed our targeting (to isolate this audience), we tightened our frequency (to the average level we were seeing deliver these brand results), we pulled some other in-platform levers, and we crossed our fingers. The theory was solid, but we have never been able to optimise a TV campaign like this mid-flight before. And the campaign was already performing so well, so we didn’t want to negatively impact our first consolidated test with Netflix.

Whether it was due to excitement, or nerves, we spent the following days monitoring this closer than ever, with regular early morning or late evening slack messages being exchanged like “Frequency is holding strong at 1.8”, or “Pacing picking back up!!!”.

And to our delight, our optimisation worked, and it was made possible by the frequency cap.

We used Frequency Capping to optimise our campaign mid-flight

Incremental Added Reach

Our optimisation forced the platform to prioritise finding new members of our target audience, that we had not yet engaged, and leverage the opportunities across YouTube & Netflix to do this as effectively as possible. This led to a huge spike in the volume of incremental added reach we were able to deliver.

Added Reach Trend

Date
Added Reach
Frequency Optimization

Ad Recall

Not only were we able to find and engage with more of our high-performing audience – but this audience & the new frequency cap were chosen to maximise this performance. And it did exactly that! Our ad recall spiked, and continued to rise throughout the remainder of the campaign.

This unified CTV test in DV360 allowed us to not only prove that frequency capping works, but that the frequency capping has allowed us to tap into the power of the advanced targeting and measurement available to CTV that Linear TV doesn’t have.

We utilised live Brand lift results (measurement), responded to this by isolating a high performing audience (targeting), and maximized our incremental reach of them through a frequency cap – you can’t do that on Linear TV!

Lifted Users Lift (%)

Date
Lifted Users
Lift %
Frequency Optimization

Frequency capping also allows us to respond to the live performance insights we receive from holistic CTV campaigns, and focus our advertising on the audience to the degree that will maximise campaign success.

Frequency Capping = Unlocks the power of digital targeting & measurement

We learned that Holistic Unified CTV buys work.

So what did we learn about Unified CTV?

The ability to frequency cap between multiple publishers reduces the uncontrolled overlaps and allows us to focus budget to maximise reach of our target audience. Basically, it means we can reach more or the right people with the same budget!

Frequency Capping = Incremental reach & effective buys

And what’s Coming Next ?

As more and more publishers are integrated into buying platforms, and the platforms expand their functionality with these partners, our opportunity to deliver a Unified CTV campaign becomes more and more exciting. DV360 in particular is introducing consistent audience definitions across publishers (through google audiences), and consistent Brand Lift studies through Google BLS – so our testing roadmap includes:

• Publisher expansion: What is the optimal mix of AVOD, SVOD, BVOD & FAST partners for reaching our target audiences efficiently and effectively?
• Singular audience definition: Can a consistent audience definition in Google Audiences lead to more predictable and scalable results across partners?
• Holistic, in-platform Brand Lift Studies: How does brand lift vary across different CTV services & content types, and how do they contribute to the overall campaign?

Each of these will help us get closer to our vision, and our ability to run multi-publisher, Connected TV campaigns. We truly believe that programmatically bought TV campaigns are the present & the future. Not only as an easier entry point for advertisers into the TV space, but as a modernised, targeted, accountable & effective channel to drive Big Brand Objectives.

Insights provided by:

Alex Glover
Managing Director,
Programmatic

Nathan Ridout
Senior Account Manager,
Programmatic

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The post Unified CTV: All the Power of TV, but Better than Ever appeared first on Brainlabs.

Impact by Tariffs and Recessionary fears

The Consumer and Brands

Consumers don’t react to actual market conditions but rather a perception of market conditions And their personal financial position. (See Consumer Sentiment above) Today’s consumer faces adversity from all sides; higher prices from tariffs, higher interest rates, and non-existent savings. Prepare for a more stretched consumer than in 2008 or 2020. During a recessionary period it is beneficial to consider the psychological state of your audience, take into consideration the consumers’ emotional reactions to the economic environment. Those consumers with a positive outlook will continue to spend through a downturn.

Personal Savings are almost gone

The personal savings through Covid have not be sustained, the consumers have spent through this additional cash over the last 2 years.

Consumer Sentiment remains low but rising

Geopolitical instability can drive reduced sentiment even as markets are outperforming the ‘08 recovery. Main Street vs Wall Street.

Credit Delinquency Rates – 1.5% to 3%

Delinquency Rates are back above pre-Covid levels but nowhere near the ‘08 crisis.

Credit Card Balances continue to rise

+50% from Covid lows and +30% vs pre-Covid. Buy Now Pay Later and Credit Cards are being used more frequently as consumers run out of savings.

Consumer Staples, Utilities and Real Estate remain strong due to a flight to safety, defensive positioning, and expectations of future rate cuts. Consumer Discretionary and Information Technology have been the worst performers. IT contains the semiconductor subset facing headwinds from geopolitical uncertainty, plus bloated valuations prior to tariff uncertainty. Automobiles and Textiles have seen ~30% declines from global supply disruption and fear of a consumer slow down.

Your marketing efforts should not be dependent on broad public equities performance but we understand the need to react to a changing business environment. These are the times when marketing dollars go furthest. Think through the second and third order effects and consider how you can support the business around you.

Overview Recession fears have grown in 2025, uncertainty and volatility continue to grow, and the consumer appears to be in a tenuous financial position. Consumer brands, Industrials, eCommerce/SMBs, certain B2B services are all impacted. Direct effects, add a 125% tariff(averaged out as as the cost is diversified across the supply chain, think Vietnam, India, Indonesia) on new Nikes, iPhones, or most of what you buy. There are second and thirds order effects, ie your clients can no longer afford to swap service providers or upgrade their usage. How will your brand be impacted, what verticals and industries? How do consumers react? What have brands done in prior recessions?

The Market for Marketers

Here to support your brand in any market

For more information on your sector or brand please reach out to: luke.f@brainlabsdigital.com

React by becoming more aggressive with the right audiences and with the right message

DotCom (2001), the GFC (2008), and Covid (2020), saw 10-15% declines in ad spend. In 2020, Audio and OOH spends declined 20-30% (excluding political advertising) and Digital grew by ~8%.. Don’t destroy your funnel, think about trading down not out, reduce your frequency to save the incremental customer, focus on existing vs new, move to value and quality messaging. Be bold and increase your share of voice amidst declining CPMs and noise.

Brands that increased or maintained advertising spend during recession saw 256% higher sales growth post-recovery than those who cut spending (only 19% growth) – McGraw-Hill.

What we are seeing

CPMs by Platform

LinkedIn CPMs flat/declining (B2B uncertainty) vs. overall digital CPMs up ~100% since 2019.

Digital advertising remains resilient, with Alphabet, Amazon, and Meta projected to control >50% of the market by 2029

Recession opportunity: CPMs drop as competitors pull back (e.g., 2008 saw -27% newspaper, -22% radio, but only -2% digital)

Competitive Advantages in Downturns

• Cost efficiency: Lower media prices (+256% sales growth post-recession for brands that maintained spend).
• Reduced noise: 8% higher purchase intent when ads align with content.
• Market share capture: Amazon’s Kindle launch during the 2008 recession succeeded due to less competition.

Reducing Digital Wastage

• Contextual targeting: Ads placed alongside aligned content boost ROI by 8%.
• Platform shifts: Prioritize YouTube/CTV (70% video consumption via streaming by 2029) with strict brand-suitability tools.
• Audience refinement: Use exclusion lists to avoid misaligned content and diversify beyond English-language campaigns.

Rework Messaging

Existing customers:

• Highlight value/quality over discounts.
• Leverage retention campaigns (cheaper than acquisition).

New customers:

• Align with social causes (69% prefer brands supporting conscious causes).
• Use geo-targeting for local audiences amid declining tourism

Winning in the Downturn

Overview As discussed a slowing economy and tariffs impacts the consumer, with knock on effects for brands. It also impacts the way we buy media, whether that’s the cost of buying, or the channels we buy on.

Global ad spend growth downgraded to 6.7% in 2025 ($1.15 trillion), a $20 billion cut from prior forecasts due to tariffs, EU regulations, and stagflation risks. Let’s take a quick look at how Digital CPMs have been impacted and think through how we can approach a changing digital landscape.

For more information on your sector or brand please reach out to: luke.f@brainlabsdigital.com

Digital CPMs

CPMs by Platform

Advertisers intend to decrease budgets due to tariffs. The size of these cuts is not known. 41% of advertisers expect Social budget cuts, this is likely one of their largest line items. 43% of respondents expect “Other Traditional Media” cuts, likely OOH, Experiential, and Print. Only 13% expect CTV cuts, consumers tend to stay home during economic stress, meaning their usage of streaming services and connected TV will increase. As advertisers pull back on these channels it will create opportunities for brands to find efficient niches and new audiences that have less exposure from competition.

Here to support your brand in any market

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The post Slashing your ad budget in a downturn? That might be your most expensive decision yet. appeared first on Brainlabs.

And yet, according to a report from Gartner Inc., 63% of marketers still struggle with personalization, and 23% struggle with selecting the right technology to support these efforts. Based on an earlier survey, these issues may stem from the fact that only 40% of marketers have a clear personalization strategy and roadmap, even though personalization takes up 14% of their marketing budget. 

What really makes it work

Before giving up on personalization, let’s get back to what really makes it work; 

Personalization is not simply the availability of new channels for marketers to learn their habits and the use of predictive analytics to send them the best offers. It’s not simply one tactic among many.

Personalization should be a way of collecting data respectfully and using it strategically to nurture a relationship that improves the consumer experience and deepens their trust with brands. 

The outcome marketers should focus on is not simply what they’ll be able to drive in terms of engagement, conversions, or sales. brands need to aim for an experience that might best be described as “delightful relevance.” This is a goal within reach. 

It’s also possible that marketers have struggled with their personalization efforts because they’ve missed an important step. 

There’s an assumption, perhaps, that by integrating consumer data from various sources—such as CRM, brand sites, cookies, and I.D. data — consumers will be happy to get more targeted advertising based on predictive analytics as a matter of course.

In many cases, however, marketers need to be aware that taking a more personalized approach requires careful upfront planning to ensure you’re getting the most out of your data sources. 

Getting the most out of your data sources. 

1. Start With The Right Segment: Trying to adopt personalization across the board is hard. Segmenting based on your most valuable customers, or those most likely to offer information, means you’re working with data where opt-in happened at the outset. This forms the basis for a far more granular and effective remarketing campaign. This means ensuring your line items and naming conventions are consistent across platforms, and your sources are tagged correctly.  For your SaaS campaigns, you may want to segment your audience by various industries to ensure your message is personalized to the right verticals, and having your audience groupings set up correctly in DV360 will make all the difference.

2. Where Consent Isn’t Explicit, Provide Context: Imagine if we hadn’t needed GDPR or the CCPA to force brands to create banners informing consumers about cookies and asking them to click “I accept.” Now imagine the same thing in a data-driven remarketing campaign, where something as simple as “based on your activity with us” could help explain why consumers see a particularly creative or offer. Providing your audiences with a valuable experience with helpful information such as featuring accurate pricing in your eCommerce ads, helps to ensure a meaningful ad experience.

3. Collaborate, Rather Than Coerce: Along with transparency, the best data-driven marketing will increasingly be predicated on the extent to which brands offer consumers self-service capabilities. This will go beyond simply being able to unsubscribe from e-mail messages, but sophisticated preference centers that let them feel they are an active participant in the relationship with a brand. High-impact or eye-catching creative that provides a personalized experience can make or break performance.  For travel brands, for instance, you can showcase enticing trip ideas with beautiful photography, and call out pricing deals and discounts customized to your audience location.

4. Choose Omnichannel Over Isolation: The model’s brands can develop using machine learning and related technologies to predict consumer needs will be wasted if they’re only used for online ads. Consumers will come to expect tailored experiences across every touchpoint, from digital out of home, location-based ads, e-mail, and even social media like Instagram Stories. 

5. ABA: Always Be Asking

Marketers are accustomed to measuring the results of their work, but the metrics for personalization should include how often they are actively engaging with consumers to gather feedback on the experiences they’re providing. This can go beyond checkboxes on websites and emails but proactive surveys, focus groups, and even in-store or in-person events. Marketers should also continuously assess what data is actively being captured, be it on a website and CRM through to ad interactions, to ensure they’re making the most of every touchpoint and ensuring those measurements are being fed back into their DMP, ad server, and feeding insights into how digital campaigns are being executed.

Remarketing has so much potential beyond following a customer’s every movement. By leveraging a thoughtful approach to customer data and using those valuable inputs back into GMP, marketers will be able to advance their personalization tactics to drive meaningful connections.  Personalization should be the product of an above-board dialogue because delightful relevancy is in both parties’ best interests. 

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Every year, there are more and more developments in marketing and technology. Now more than ever, we are facing changes to our industry that are exciting at best, and harrowing at worst. As always, Brainlabs is here to help. We have identified and broken down a few trends that are coming on strong in the next year so you don’t have to.

Marketing AI Has Come of Age

For some time, AI has been deemed “the marketing of the future”. To some, this has become a buzzword for something that never materializes. However, 2022 seems to be the year when we will finally get to experience for ourselves what marketing AI can do for us. We can say this confidently because we are seeing evidence of it already. Right now, many AI functions are baked into the main marketing platforms we use. Especially notable are auction-time bidding functionalities, which take all of the additional signals of intent across known data, audience data, and data that businesses provide, and allow us to activate them at the individual level and at scale.

Recommendations for 2022

• Feed the machine. Give these platforms the data that allows them to optimize their results.

• Test Google’s Performance Max Campaign (or at least keep it on your radar!).

• Evaluate these AI tools based on how they perform today, not on past results. Performance of automated solutions has improved greatly.

• Keep an eye out for AI in Creative. Google is going interesting places with this.

Privacy Changes Will Push Companies to Add Value to Customers’ Lives

Although it seems that this topic has been hashed and re-hashed, its importance has not diminished, given that everyday we get closer to a world with less data available for companies to use. Businesses today face two major challenges. The first is educating their consumers about why companies are collecting their data, and what value it brings to share one’s data with the brand. The second is the result of the fact that the browser cookie was not built to be used as an advertising solution. Thus, it has been an imperfect solution, and we need to move away from it. To this end, we need to think about the value we are adding to consumers’ lives, and how we can show them this value in innovative ways. Think Spotify Wrapped and DuoLingo.

Recommendations for 2022

• Think through creative ways of adding value to your customers’ lives and educate them on the benefits for them in sharing their data with you.

Brands Will Have to Continue on a Hybrid Model

We can’t ignore the fact that people are getting the opportunity to return to physical experiences. This will be a key component of 2022, although we will not necessarily be returning to old behaviors. What we will most likely end up with is a hybrid form of shopping. For example, a world where users go into the store to browse, but not to make the purchase, which they do online. Similarly, people are now used to features like curbside pickup. In short, companies are now expected to give customers the flexibility to shop however they prefer.

Recommendations for 2022

• Take a hard look at your in-store workforce and ensure you are setting people up for success both in and out of the store to avoid your employees being overwhelmed and quitting on the spot.
• Map your customer journeys to tailor to the different needs people may have. Think about how that journey may shift at different times during the year or during the week.

While we have covered three significant trends that are coming in 2022, we have yet to discuss two others: social commerce and the Metaverse. We could try to give you a quick overview of these trends, but frankly, there’s a lot of ground to cover. If you’d like to get the full run-through, you can access the recording of our webinar here. You won’t want to miss it!

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