The Emergence of Vibe Marketing

marketing 6 Apr 2026

Vibe Marketing is a new era of marketing shaped by generative AI creativity, speed, and intelligence enabling marketers and executives to automate marketing campaigns from client brief to concepts, campaigns to optimization.

The concept draws inspiration from the software engineering world, where “vibe coding” describes using natural language and AI-powered tools to accelerate development, especially for users who do not know how to code. In marketing, the idea is similar: generative AI allows teams to quickly create, test, and iterate on campaigns that once took weeks or months to produce. And, vibe marketing enables professionals with no marketing experience to create brilliant campaigns. AI removes the traditional barriers of production budgets, technical language requirements, and long lead times, allowing challenger brands to act with the speed of a startup and the production value and sophisticated analytics of a global leader.

Searches for “vibe marketing ”increased nearly 700% in the past year, and Forrester found that 50% of B2B marketing decision-makers are already experimenting with or currently using generative AI.

To make Vibe Marketing work, it requires human intellect and artificial intelligence working in concert to create an accurate and comprehensive data foundation, sophisticated modeling, generative audiences and creative production, and a scalable method for measuring impact. The result, authentic, relevant experiences that deliver real ROI.

Smarter decisions about audiences, channels, and creative experiences.

Vibe marketing is not about letting AI do everything; it’s about augmented intelligence where AI accelerates insights and recommendations, while humans act as the compass. According to a recent EMARKETER survey, about 91% of marketers called human involvement "critical" or "very important" in evaluating or generating AI-driven creative.

Vibe marketing enables marketers to act on subtle shifts in consumer sentiment that occur before a trend fully breaks. What a marketer needs is the ability to see how their audience’s engagement changes before their conversion patterns change.

By analyzing unstructured data with the right queries and dashboards through tools like BigQuery, AI can surface those contextual signals and build “intent cohorts” automatically, simply prompting humans for approvals. When these insights are paired with Looker Dashboards or Adobe Customer Journey Analytics (CJA), marketers gain the ability to see the "why" behind these shifts in real-time, allowing them to instantly how their AI models have bridged the gap between raw data and a live customer experience faster and frictionlessly.

Clustering users by intent cohorts and understanding the why behind their structure then makes it easier for marketers to allow AI tools to create personalized, dynamic content. A strong data foundation ensures that their "vibe" is backed by the reality of what their audience actually wants not just today, but tomorrow, too.

Test cultural resonance before you spend

Vibe marketing can also help balance the tension between speed and effectiveness. By creating digital twins of intent cohorts, marketers can simulate how specific personas might react to new creative directions in seconds rather than weeks. This allows companies to test and optimize campaigns in a sandbox environment, ensuring that they scale with the strongest, most authentic content.

Measure what actually moves the needle

The final piece of the puzzle is measurement. Because vibe marketing often triggers rapid content shifts across multiple channels, a linear funnel view won't cut it.

For a truly holistic view, practitioners are increasingly turning to marketing mix modeling (MMM) with tools like Google Meridian. Meridian’s near-real-time calibration capability means you can use it to sense channel efficiency shifts as cultural moments occur, not six weeks later. For marketers making fast creative decisions, knowing in near-real-time that connected TV (CTV) is outperforming paid social this week changes where you put the next dollar.

Coordinate the entire stack for speed

The "possible" outcomes of vibe marketing are only achievable if martech and adtech stacks are properly integrated and flexible enough to handle real-time shifts. In 2026, the focus has shifted from simple automation to autonomous, intelligent systems that empower marketers to move from being task-doers to strategic thinkers.

A successful agentic engine requires a seamless loop:

Ingest and aggregate: Centralize your web and app data into a warehouse like Google Cloud to get a clear view of your marketplace coverage.
Analyze and cluster: Use machine learning models to identify high-propensity intent cohorts across BigQuery or Adobe Customer Journey Analytics.
Synthesize and QA: Use generative AI to iterate with multiple creative variations, using humans as the final quality check for brand safety.
Activate and attribute: Serve dynamic content and use closed-loop attribution to feed results back into the model.

Drive smarter decisions and faster execution

Vibe marketing is an incredible opportunity to connect with audiences on a human level while scaling your marketing team’s productivity. But most marketers are stuck somewhere in between understanding the “possible” and not knowing how to achieve it because their adtech and martech tools are configured for the world that existed two years ago. The technical foundation is the key to turning these "vibes" into a true marketing engine.

NRF 2026: Biggest Takeaways for B2B Marketers to Stand Out Amongst a Sea of Tradeshow Vendors

marketing 6 Apr 2026

NRF has always offered a collaborative space for retailers, technology providers, and enterprise decision makers alike, and this year was no different. While new ideas, exciting partnerships, and big purchasing decisions often begin at the annual event in January, just one, high-impact moment isn’t enough to build lasting relationships in today’s experiential environment.

While flashy exhibits turn heads, espresso martinis draw a crowd, and the smell of freshly popped buttery popcorn certainly helps attract booth traffic, the standouts now are those building community, personalizing engagement across the full event lifecycle, and reinforcing their activations with integrated technology that provides actionable data.

NRF creates the blueprint for how marketers and brands can connect one-time engagements directly to long-term, measurable business outcomes and deeper customer relations, backed by technology solutions.

NRF as an Industry Ecosystem

NRF illustrates how a trade show can function as a year-round industry ecosystem, rather than a one-time activation. While the show happens across only three days, its ongoing virtual sessions, in-person meetups, and other available gatherings around the world extend meaningful dialogues beyond the show floor. This sustained engagement creates a common platform where relationships deepen, and ideas evolve over time.

This year’s NRF delivered on its theme of “The Next Now.” The focus was preparing retailers for the future that is unfolding around agentic AI, human-centered experiences, unified experiences, the physical transcending to experiential hubs, and insights.

Because NRF convenes retailers alongside tech providers and other partners, conversations also naturally reflect the interconnected nature of the retail industry today. The activations that resonated most this year were not isolated product showcases, but those that acknowledged the broader landscape and positioned brand narratives and offerings as contributors to an ongoing industry conversation. By embracing this ecosystem mindset, marketers and brands can better signal relevance, credibility, and long-term commitment.

Community as Competitive Differentiator

In a crowded exhibition center stacked with immersive activations and flashy AI tools, attendees this year gravitated toward experiences that offered true substance. The most effective activations weren’t the largest, they were the most intentional.

Brands that hosted smaller group discussions, curated demos, and targeted networking sessions, created space for practical conversations around shared challenges. These interactions inspired more valuable engagement and positioned companies as true partners versus one-time vendors.

For example, brand booths this year tapped cultural touchpoints, with several exhibitors incorporating World Cup fan zones or sports activations such as SAP’s tennis and pickleball themes, and HP/Intel’s sports jersey displays, to draw attention and create immersive physical spaces. These activations worked because they tapped into passions attendees already share like sports, competition, and global events, creating natural gathering points on the show floor. Instead of simply drawing foot traffic, they gave strangers an easier entry into conversation.

The takeaway from NRF 2026 is clear: foot traffic is no longer the primary measure of success. Meaningful dialogue and sustained connection are what differentiate brands, and what can turn event moments into lasting business relationships.

From Ecosystem Engagement to Sustained Momentum with AI

As conferences and trade shows surge back to pre-pandemic scale, the pressure is on to create experiences that are more personalized than ever. But the real question remains: how can marketers implement NRF’s sustained ecosystem and community-based approaches at the same time? The answer lies in technology and data-backed strategies.

By leveraging AI, teams can look beyond isolated actions from one event like booth traffic, engagement metrics, or survey feedback, to understand how attendee experiences impact future pipeline, revenue, and relationships. Traditionally, marketers have relied on surface-level metrics such as number of booth visits, but counting vanity metrics falls short when it comes to understanding how touchpoints over time, or your brand’s ‘ecosystem,’ is influencing business impact.

AI can also boost personalization at the event itself, further inspiring the community-based approach that NRF excels in. While conventional AI relies on human input, AI agents, for example, act independently, functioning as a real-time ‘concierges’ that can tailor suggestions at an event based on user behavior and demographics. This can help to put like-minded attendees in the same group or same session, sparking conversations and insightful discussions. With Gartner projecting that 40% of enterprise applications will include task-specific AI agents by the end of 2026, personalization is becoming embedded and expected infrastructure in events.

Overall, in 2026, high-impact events will be defined by their ability to operate as connected ecosystems powered by integrated data and AI, converting engagement into sustained momentum, rather than isolated activity.

How Deniz Gezgin Is Positioning Eightpoint at the Center of Native’s Evolution

marketing 2 Apr 2026

1. How has the adoption of native direct deals influenced your ability to reach target audiences?

At Eightpoint, native direct deals have fundamentally shifted us from distribution-driven monetization to intent-driven monetization.

Because we control high-frequency user touchpoints—browsers, launchers, and utility apps—we’re not just aggregating traffic, we’re capturing real-time behavioral intent signals at scale, particularly within the U.S. market.

Native direct deals allow us to activate those signals in a way that’s far more precise than traditional ad models. Instead of selling impressions, we’re enabling partners to engage users in context, at the moment of need, which significantly improves performance.

We’re already seeing native direct deals outperform traditional channels because they’re built on first-party behavioral data rather than inferred audience segments.

The result is simple: higher-quality engagement for advertisers, and a better, more relevant experience for users

2. What challenges have you faced integrating native direct deals?

The biggest challenge isn’t technical—it’s maintaining product integrity while scaling monetization.

At Eightpoint, we operate under a strict product-first philosophy. Our platforms are daily-use utilities, so any monetization layer has to feel native not just in format, but in function and timing.

This requires:

- Tight alignment between product, data, and partnerships

- Real-time decisioning infrastructure

- Continuous experimentation to balance revenue vs. user experience

The companies that struggle with native are treating it as an ad unit.

We treat it as a product layer—and that requires a very different level of discipline.

3. How do you evaluate advertising partners?

We look for partners who understand that performance comes from context, not just targeting.

Our evaluation framework is centered around three things:

- Data sophistication – Can they leverage intent signals, not just demographics?

- Optimization capability – Are they actively iterating based on performance?

- Transparency – Do they operate with clear, measurable KPIs?

The best partners don’t just run campaigns—they co-develop monetization strategies with us, adapting to how users actually behave inside our products.

That level of alignment is what drives long-term value on both sides.

4. How do you ensure native ads reach high-intent users?

Our advantage is structural

Unlike traditional publishers, our users are actively doing something—searching, navigating, organizing, or consuming utility-driven content. That creates a continuous stream of high-signal intent data.

We use that to:

- Identify moments of peak intent

- Match relevant commercial experiences to those moments

- Continuously refine placement and timing through data feedback loop

It’s less about serving ads and more about intercepting intent at the right moment.

When you get that right, engagement and conversion follow naturally.

5. How does native advertising fit into your broader strategy?

Native advertising is not a standalone revenue stream for us—it’s part of a broader ecosystem strategy.

Our goal is to build products that users engage with daily, and then layer in monetization in a way that:

- Enhances the experience

- Increases lifetime value per user

- Strengthens partner relationships

Because we control both the user experience and the data layer, native becomes a strategic advantage—not just a monetization tactic.

Over time, this allows us to move from transactional ad relationships to long-term, high-value partnerships

6. What role do native deals play long-term given demand for authenticity?

The industry is moving away from interruption-based advertising toward experience-integrated monetization.

Native direct deals are a key part of that shift—but they will evolve significantly.

The future isn’t just native placement—it’s:

- Personalized, AI-driven experiences

- Dynamic content that adapts to user behavior in real time

- Deeper integrations between brands and product ecosystems

At Eightpoint, we see native deals becoming more like product partnerships than ad buys.

The companies that win in this next phase will be the ones that can combine strong product surfaces, deep user data, and intelligent monetization layers.

That’s where we’re focused.

Eightpoint website: https://eightpoint.io
Deniz’s LinkedIn profile: https://www.linkedin.com/in/denizgezgin/

Scaling Content Smarter: How Floyi Leverages Topical Maps and AI Briefs

marketing 31 Mar 2026

1. What challenges have you encountered in scaling content operations while maintaining alignment with brand voice, audience relevance, and search visibility?

Back in our agency days and while working on our own content sites, we ran into the same issues over and over. Strategy looked good on paper, but the content that went live often missed the mark. We struggled to align voice, audience intent, and SEO in a way that scaled.

That’s when we started building internal processes to manage everything - from persona clarity to keyword targeting to content structure. But the real shift happened when we introduced topical maps into the mix. Looking back, they should have been the first step. Topical maps now sit at the core of everything we do. They give us visibility, direction, and control before a single word gets written.

2. How do you currently leverage SERP analysis and competitor benchmarking to inform your digital content roadmap?

We rely on live SERPs and AIRS (AI ResultS) from platforms like ChatGPT, Google AI Overviews, and Perplexity. We analyze what’s ranking in the traditional search engine, what’s being cited in AI search engines, and how those overlap or diverge. That includes structure, tone, content type, and gaps in coverage. We look beyond who’s ranking and study how they’re ranking.

This dual-layer insight shapes every roadmap, brief, and internal link strategy we use. These workflows began as internal processes, and we’ve since built them into Floyi.

3. How important is reducing time-to-publish in your content strategy, and what tools or processes have you implemented to accelerate ideation and brief creation?

Reducing time-to-publish is a key priority for us. Especially when managing multiple campaigns or clients, the delays between research and execution used to kill momentum. So we started building tools to automate the bottlenecks. We pulled SERP data, analyzed competitors, integrated brand and persona inputs, and generated structured briefs.

That system cut our research time dramatically and eliminated back-and-forth cycles. We later realized other teams needed it too. That’s how Floyi was born. We turned the internal tools we built to speed up our own workflows into something others could use too.

4. What systems do you have in place to ensure that your content briefs are consistent, actionable, and aligned with both SEO goals and user intent?

Every brief we create includes specific data points: search intent, buyer stage, content type, point of view, tone, internal link prompts, and a list of keywords and entities. These are drawn from real-time SERPs and AIRS, so they’re grounded in both what search engines rank and what AI models surface.

What started as a set of Google Sheets, multiple browser tabs, and repeatable checklists is now a structured system we use every day. It keeps strategy and execution tightly aligned, whether we’re working on a single blog post or a large-scale content hub.

5. What mechanisms are in place to balance data-driven content creation with maintaining creative and editorial integrity?

We give writers structure, not constraints. The data guides what to say and who to say it to, but how it gets said is still up to the creative. Our briefs offer clarity on the audience, tone, key talking points, and gaps to address. But they leave space for originality and voice.

We built these processes to remove friction and second-guessing. The best writing still comes from people. Our system just makes it easier for them to hit the mark.

6. How do you see the role of automation evolving in content marketing, and what governance models are you considering to manage quality and accountability?

Automation is expanding, but we see it as a partner to the strategist and writer, not a replacement. We’ve already built governance into our workflow. Briefs are tied to real search queries and buyer personas. Content is mapped to search intent.

Automation handles the tedious parts, but human review ensures every piece meets our standards. That structure has helped us scale without losing control. As automation gets smarter, our quality controls get even sharper.

The Future of Influencer Marketing: David Abbey on AI-Driven Scale”

marketing 31 Mar 2026

1. How is your marketing team managing manual processes in terms of influencer relationships, and how are you addressing scalability challenges?

A lot of brands still rely on spreadsheets, manual outreach, and disconnected tools to manage influencer programs which makes it nearly impossible to scale efficiently. Once a brand grows from 10 to 50+ influencer partnerships, the wheels start to fall off. Teams get bogged down in manual follow-ups, managing approvals, handling gifting logistics, and compiling performance reports. It ends up consuming their entire bandwidth. That’s where automation changes everything. Influencer marketing platforms, like Endlss, that combine outreach, gifting, commission tracking, and communication in one place have become essential to keeping programs scalable. With the right tools, marketing teams can manage 3x the creator volume without needing to grow their headcount, freeing up time for the work that actually drives results.

2. How is your organization evolving its influencer marketing strategy to shift from brand awareness to measurable revenue generation?

Influencer marketing used to be all about reach and impressions, but the most forward-thinking brands today are treating it like a true performance channel. Instead of chasing vanity metrics, they’re focused on driving measurable, attributable growth. To meet that demand, more teams are adopting attribution tools that link creator content to conversions—whether through custom landing pages, affiliate links, or dynamic tracking infrastructure. On our end, we’ve built SmartLinks into the core workflow, so each creator’s impact is measured in real-time, and with partners of ours like Creator Commerce, together we provide co-branded shopping sites to elevate the consumer experience with a trusted shopping experience that increases conversions. Weekly performance reports make it easy to see which partnerships are generating returns and which need to be re-evaluated. That kind of visibility helps transform influencer marketing from a brand play into a predictable revenue stream.

3. How are you approaching influencer selection and outreach to ensure alignment with your brand values and audience segments at scale?

Alignment is everything in influencer marketing and not just in terms of values. The right creator should reflect the brand’s tone, speak to the right audience segment, and have a track record of driving action. Brands are getting more precise with how they vet creators, looking at engagement quality, audience breakdowns, content style, and past performance before making a move. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content.

But finding the right fit at scale is a different challenge. That’s where AI and smart filters are transforming outreach. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content. Combined with branded application forms and full creator analytics, brands are scaling high-quality outreach without losing that human touch. Inviting existing customers to apply is low hanging fruit when you want to scale effectively, and authentically—people who already know and love the brand often make the best partners.

4. What limitations have you encountered with traditional tracking methods (e.g., promo codes, UTM links), and how are you planning to evolve your attribution strategy?

Traditional tracking methods come with real friction. Promo codes can get leaked or shared in unintended ways, making attribution muddy. UTM links often break in-app or get stripped entirely, especially on mobile. This creates a gap between creator activity and the sales data that marketers rely on to optimize spend. To move past these limitations, we’re focusing on more robust attribution tools that work reliably across platforms and devices. SmartLinks, for example, generates unique tracking for each creator and integrates directly into conversion and payout workflows. Clean attribution is foundational to scaling today’s influencer programs responsibly. Whether it's to manage budgets or reward high performers, teams need to trust the data.

5. How are you evaluating new MarTech platforms to determine their potential impact on operational agility and cross-functional collaboration?

When evaluating MarTech tools today, agility is at the top of the list. Marketing teams need tools that are fast to implement, intuitive to use, and flexible enough to support cross-functional workflows. If a platform takes weeks to implement or requires engineering support to operate, it’s already a blocker. The best tools today integrate seamlessly with existing systems, whether that’s ecommerce platforms like Shopify, payment processors like Stripe, or internal communication tools. Endlss replaces four different tools in one, so brand, finance, and CX teams can all work from a single system. At the end of the day, the best platforms don’t just do more; they reduce friction across every team.

6. What competitive advantages do you see in adopting lean, AI-powered influencer marketing platforms compared to legacy tools with heavier infrastructure and higher costs?

Legacy influencer marketing platforms were often built with large enterprises in mind. They’re powerful, but also complex, expensive, and heavy to manage. For fast-moving teams, that’s become a real disadvantage—especially when speed and efficiency are critical. Lean, AI-powered platforms are flipping the script. By automating outreach, tracking, and gifting workflows, brands can move from idea to execution in 20 minutes, not weeks. And because these tools are often modular and self-serve, they’re far more cost-effective. What we’ve seen is most brands using Endlss are cutting their software spend by 50% or more while getting campaigns live that day, not weeks. That kind of agility has become a major competitive edge, especially for brands trying to maximize output with lean teams.

The 4 Voices Strategy: Luke Williams on Elevating Customer Experience”

marketing 31 Mar 2026

1. How does the ‘4 Voices’ strategy you created influence the way you approach enterprise CX and research programs?

The ‘4 Voices’ strategy Customer, Partner, Employee, and Market reflects my belief that there is no single pathway to truth in CX or research. Each voice offers a distinct perspective, and only by listening holistically can we uncover insights that are both grounding and surprising. This multi-perspective approach deepens our understanding, surfaces systemic patterns, and ensures that strategy and action are based on a more complete view of reality. In enterprise environments, this triangulation is essential it aligns teams, clarifies priorities, and converts fragmented feedback to focused, cross-functional execution.

2. How can organizations move beyond simply collecting feedback to activating it across business units?

Collecting high-quality feedback at scale is challenging but without connecting it to business decisions, it becomes a pleasant commodity rather than a catalyst for change. Moving beyond collection means designing feedback programs with business outcomes in mind. Every metric should have a clear owner and a defined action if performance declines. This creates accountability and ensures that signals resonate with both customer needs and operational priorities. Metrics must be meaningful, not abstract translating sentiment into tactical insight. Ultimately, activation happens when data is embedded in workflows, and teams see the clear link between feedback, action, and impact.

3. What methodologies do you recommend for aligning research insights with measurable business outcomes?

To align research insights with measurable outcomes, I advocate for a mixed-method approach grounded in business impact. But outcomes don’t exist in a vacuum customer sentiment, behavior, and intent are always relative: to the market, to competitors, and to past experiences. That’s why I recommend using relative metrics alongside standard KPI; they better reflect the customer’s context and decision-making lens. It’s equally important to model barriers to those outcomes understanding not just what customers want, but what’s preventing them from getting there. When research accounts for both drivers and friction, it becomes a far more powerful tool for driving focused, ROI-positive action.

4. What role does action-first thinking play in closing the gap between customer feedback and business performance?

Action-first thinking fundamentally reshapes how we approach feedback it shifts the mindset from passive analysis to proactive readiness. Instead of waiting to interpret what feedback might mean, we design systems with predefined responses so that signals trigger action, not debate. This posture assumes that teams are ready to respond, and the data simply tells them when. Often, we don’t need four-decimal precision to intervene; where there’s smoke, there’s usually fire. The goal is to empower teams to act autonomously and swiftly to investigate, triage, and improve without waiting for perfect clarity. This is what closes the gap between listening and performance.

5. In your view, what differentiates companies that sustain long-term CX excellence from those that fall behind?

Many companies aspire to be customer-centric but often settle for being merely customer-focused—responding to feedback without truly redefining their strategy around customer value. The key difference is that customer-centric organizations identify what truly creates value for their target personas and actively engineer strategies to deliver on those needs, even when it requires bold pivots. They don’t just improve the current experience—they reimagine it. These companies are also more discerning about whom they serve best and more deliberate in designing for those use cases. Crucially, they build innovation and adaptability into their core developing the muscle memory to evolve as customer expectations shift. The ones who master 10x innovation are often better at 10% improvements, too, sustaining CX excellence over the long term.

6. How will your capabilities evolve to meet emerging demands around real-time CX, personalization, and predictive analytics?

We’re actively investing in capabilities across real-time CX, personalization, and predictive analytics—but just as critically, we’re focusing on preparing customers to embed these capabilities into their everyday routines. Measurement has come a long way—today we can detect a bad experience in real time, even before the customer leaves the parking lot. But that speed is meaningless without companion systems that empower employees to respond with equal agility. Personalization, often misunderstood, isn’t about treating every customer as entirely unique; it’s about recognizing archetypes and delivering mass-personalization that aligns with those distinct cohorts. On the analytics front, we’re extending from predictive to prescriptive using models and knowledge bases to recommend the most probable high-impact actions. While humans will always make the final call, these tools de-risk decisions and help build the muscle for consistent, everyday experience-making at scale.

Get in touch with our MarTech Experts.

Why Creative Commerce Is Powering Marketing’s Unified Growth Engine

marketing 30 Mar 2026

1. Komerz has made two acquisitions in under 60 days with Pathformance in February and Glassbox in March. What's the strategic logic behind moving that fast?

Today’s industry is moving faster than the traditional agency model can adapt, and brands are telling us they can't wait another three years for holding companies to figure out integration. Our acquisition with Pathformance gave us the measurement backbone and the ability to connect advertising investment directly to sales outcomes at the transaction level. Glassbox gives us the upstream brand strategy and creative capability to feed that system with the right content and positioning from the start. Together, they complete a platform we've been deliberately architecting to round out the services we know brands are looking for, giving them the tools they need, all in one place.

2. You're using the term "creative commerce" to describe the category you’re building. For marketing professionals who haven't heard that term before, how would you define it in plain language?

Creative commerce is what happens when brand building and selling are no longer separate activities. For decades, agencies have built brands and retailers sold products and there was never an intersection between the two. Creative commerce means contextual content, data-led activation, and distribution functioning as one accountable growth engine. A brand doesn't just tell a story and hope it converts somewhere downstream. Creative is now built to perform and every distribution decision is informed by brand strategy which can be directly tied to measured outcomes at the transaction level.

3. The marketing industry has been debating brand building vs. performance for years. Is "creative commerce" essentially your answer to that debate, and why hasn't the industry solved it until now?

The industry has always revolved around agencies benefiting from campaigns and retailers benefiting from transactions. There hasn’t been a clear incentive to own the entire chain and connect the pieces together. Now, consumer brands are under immense pressure to demonstrate commercial impact, not just brand metrics. That shift has changed the dynamic and elevated creative commerce from a supporting function to the connective tissue that ties together media, messaging, customer experience, and conversion into a single, accountable system.

4. Pathformance brings measurement and analytics, and Glassbox brings brand strategy and creative. How do those two capabilities actually work together in practice for a client?

I like to think of it as closing the loop that most marketers never get to close. Traditionally, brand strategy informs creative, creative goes into market, and then you wait for quarterly brand tracking data to tell you whether it worked, by which point it's too late to do anything about it. With Pathformance's transaction-level measurement integrated into the platform, we can see in near real time how brand investment is influencing purchase behavior across digital, retail, and marketplace channels. That feeds back into Glassbox's brand strategy work, which refines the creative and messaging. It's a continuous loop rather than a linear campaign process and for brands operating across multiple markets and channels simultaneously, that's a significant operational advantage.

5. Your client roster includes some of the most sophisticated marketers in the world. What are they asking for that the traditional agency model isn't delivering?

We partner with companies that have spent decades building some of the world's most valuable and iconic brands, and they're under pressure to demonstrate that brand investment drives commercial outcomes rather than just brand awareness scores or share of voice. They are looking for a partner that can connect the brand equity work to the revenue line and it’s even better if each of the services they’re looking for can be found under one roof and one seamless team working on their account. In addition, these companies are also asking for speed. The current pace of digital commerce means the old model of annual brand planning cycles simply doesn't work anymore.

6. AI-driven distribution is a core part of the Komerz platform. Can you walk us through what that actually means?

At scale, the volume of decisions involved in distributing content and product across digital, retail, and marketplace channels is beyond what human teams can manually manage. Our AI infrastructure, and what we call the Commercial Growth Operating System, sits across a brand's existing tech stack and connects marketing, distribution, and analytics into a single data layer. That means AI-powered inventory planning, demand forecasting, multi-market operations, and last-mile delivery coordination are all running from the same system, informed by the same brand and performance data. We're not ripping and replacing what brands already have but are rather making those systems talk to each other intelligently for the first time. Plus, because we operate on a performance-aligned model where our incentives are tied to the brand's commercial outcomes rather than upfront fees, the AI is always optimizing for actual growth not just activity.

7. Glassbox was founded in Mumbai and has deep roots in the Indian market. With India's digital commerce market projected to hit $345 billion by 2030, how central is India to Komerz's growth strategy?

India is central to our growth strategy - not just as a market but as a model. The pace of digital commerce adoption in India, the sophistication of the consumer, and the complexity of operating across digital, retail, and traditional trade channels simultaneously makes it one of the most demanding environments for brand growth in the world. If you can build a system that works in India, it works everywhere. Geetanjali and Anil's experience building Glassbox in that environment brings something genuinely valuable to the platform: a ground-level understanding of how brand building and commerce actually intersect in one of the world's fastest-growing economies.

8. How do you build market awareness around this new category of creative commerce and what does winning look like for Komerz in three to five years?

We are building the category by demonstrating results that the existing categories can't explain. When a brand grows equity and revenue simultaneously, in the same system, measured at the transaction level, the outcome tells the story better than any positioning document can. In three to five years, winning looks like creative commerce being the default expectation for how sophisticated brands go to market globally, and Komerz being the company that defined what that looks like in practice. We're not trying to be the biggest agency network in the world. We're trying to make the agency network model irrelevant.

9. For a CMO reading this who is interested in shaking up their current agency relationship, what's one thing you'd want them to take away from what you’re building?

The fragmentation you're likely experiencing between brand and performance, between creative and commerce, between strategy and measurement, is a structural problem, not a talent problem. The model itself needs to change. What we're building is a single system where those things were never separated in the first place. Given our core capabilities and the ecosystem we have created, we can offer full funnel attribution at SKU level; something the world has never seen before. If that sounds like what you've been looking for, I’d love to talk.

The Great SaaS Reset in the Age of AI

marketing 27 Mar 2026

You’ve overseen a growing portfolio of SaaS companies at saas.group. What is fundamentally changing in the SaaS landscape right now?

One of the biggest shifts is that we are seeing software move from being a system of record to becoming a system of action. Historically, most SaaS tools were there to organise information and support decision-making, the actual work still sat with the human. Today software is starting to take on more of that work directly. Instead of just surfacing insights or structuring workflows, AI-enabled products can increasingly execute tasks end-to-end. That’s a very different value proposition.

It changes everything from product design to pricing to market size. When software starts doing work instead of just supporting it, the ceiling for growth increases massively. We are no longer just competing for software budgets but also competing for labour budgets. That expands the total addressable market dramatically, but it also raises the bar for what “good software” looks like.

SaaS valuations have recently dropped significantly. Why do you think the market is struggling to price these businesses right now?

Investors are still applying old frameworks to a new reality. Classic SaaS multiples were based on predictable recurring revenue, high gross margins, and relatively stable cost structures. AI breaks some of those assumptions, especially around cost, where inference and compute introduce variability.

At the same time, AI-native companies may grow faster but have less defensibility early on. So you get this mismatch: traditional SaaS looks slower, AI looks riskier, and neither fits neatly into existing valuation models.

There’s a lot of talk about “the death of SaaS.” Do you buy into that narrative?

No, but I do think “lazy SaaS” is dead. If your product is just a thin layer of features, you should be worried. But if you’ve spent years building workflows, integrations, trust, compliance, and reliability, that remains incredibly valuable. In fact, I think we will see a wave of customers returning to mature, well-built products after experimenting with quick AI tools that don’t hold up in production environments.

The sector is certainly evolving and we shouldn’t expect SaaS companies of the future to look like the past. I believe that the companies that adapt effectively amidst the AI revolution will be bigger, more impactful, and more valuable than anything we’ve seen before. But the transition period will be messy, and not everyone will make it.

Can ‘legacy’ SaaS companies compete against AI-Native companies?

Mid-market SaaS companies are often dismissed as legacy tools, but I would argue that this is one of the biggest misconceptions in the market right now, and they are actually best positioned for AI transformation?

These SaaS companies already have three crucial things AI-native startups are still trying to build: customers, revenue, and domain expertise. AI transformation isn’t just about building new products, it’s about embedding intelligence into real workflows that already exist at scale. In many cases, these companies also sit on years of structured and unstructured customer data. That becomes a natural foundation for building AI features that are actually useful and differentiated.

So while startups get a lot of attention, the real AI transformation is often happening inside these “boring” SaaS businesses, because they have something to transform. These aren’t small feature updates. They’re strategic shifts in how the product delivers value, and we’re seeing this play out across multiple companies. Take AddSearch – they have pivoted from a traditional website search product to an AI-powered answer engine. Instead of just returning links, they now generate direct answers, which fundamentally changes the user experience and has driven meaningful growth. Keyword.com recognised early that search itself is changing. They launched a product to track brand visibility across AI platforms like ChatGPT, Perplexity, and Gemini. That repositioning opened up an entirely new growth vector.

With Prerender.io, the shift is even more structural. Originally, they helped JavaScript-heavy sites get indexed by traditional search engines. Now, as AI crawlers increasingly shape how content is discovered, their infrastructure is becoming critical for AI visibility as well. They’re evolving into a platform that ensures discoverability across both search engines and AI systems, while already operating at massive scale, serving billions of pages.

How are cost structures changing for SaaS businesses with AI?

This is one of the most underestimated shifts. Traditional SaaS had very predictable cost structures - mostly fixed costs, and high margins, but with the boom of AI, we are seeing variable costs tied to usage. That means margins can compress if founders are not careful, and also requires pricing to evolve, as you can’t charge a flat fee if your costs scale with usage.

Companies that figure out how to balance performance, cost, and pricing will have a big advantage.

At saas.group, you focus on acquiring SaaS companies with strong product-market fit. Has AI changed your acquisition strategy?

It has reinforced our core thesis more than anything. We look for businesses that have already done the hard work building robust products, strong customer relationships, and defensible positions. AI can amplify those strengths, but it can’t replace them, which is why we are looking to acquire a product that already has depth and then working with founders to layer AI on top to unlock new growth. AI native startups move faster and can rethink everything from first principles, but incumbents have distribution, data, and customer trust.

How should valuations evolve in this new environment?

For a long time, SaaS benefited from assumptions such as high multiples on ARR, heavy adjustments for stock-based compensation, and a willingness to prioritise growth over almost everything else.

That is now being replaced by a much more grounded framework. Investors are increasingly looking at real profitability, on a GAAP basis, and asking harder questions about cost structure, for example around sales and marketing efficiency, and now AI-related compute costs as well.

At the same time, AI is introducing new variables. Revenue may be less predictable if it’s usage-based, and margins can be more dynamic because of inference costs, meaning you can’t rely on simple rules of thumb anymore.

Valuation is becoming less about applying a multiple and more about understanding the underlying business, with a focus on true earnings quality, defensibility and efficiency.

What advice would you give founders building SaaS companies today?

This is a tougher environment for founders, but also a more exciting one. The bar is higher, and so is the upside. The old playbook of scaling sales and marketing spend ahead of revenue is under pressure. Capital is more expensive, and investors are looking closely at efficiency.

It’s no longer enough to show growth, you need to show that your business actually works as a business. That means real margins, disciplined spending, and a clear path to GAAP profitability.

At the same time, AI is raising expectations on the product side. So the challenge is doing both: building something meaningfully better while also running a tighter, more efficient company.

The founders who win will be the ones who combine product ambition with financial discipline.