Interview

What scaling AI reveals about governing personalisation

artificial intelligence 12 Feb 2026

By Mark Drasutis, Head of Value, APJ, Amplitude

As brands increasingly seek to understand and act on customer behavior, they need to continuously analyse user journeys, identify patterns and friction, and recommend or execute next steps in real time to deliver true personalisation. AI is accelerating this shift, redefining personalisation by moving brands beyond static journeys to experiences that adapt dynamically to customer behaviour.

Australia’s National AI Plan sends a clear message to marketing and product teams; AI can only scale if it is safe, transparent and responsibly governed. Yet, while AI capabilities are advancing rapidly toward greater autonomy, most organisational governance remains manual and fragmented.

With conversational AI and agentic AI becoming the primary interfaces for digital experiences, governance needs to operate at the same speed and complexity as the systems it oversees. Brands need capability uplift and accountability in equal measure or they risk falling behind.

The trust gap limiting AI-driven personalisation

AI-driven personalisation is being held back not by technology but by trust and transparency – a gap driven by weak governance, unclear accountability and a lack of workflows to manage AI safely. This matters because trust in AI remains fragile in Australia. A University of Melbourne-led study found that while half of Australians already use AI regularly, only one in three feel confident trusting it.

That trust gap is widening as personalisation evolves. Traditional rules based marketing, built on fixed segments, pre-defined journeys and manual triggers, is being replaced by real-time, generative personalisation where decisions are made continuously by AI. This shift demands new operating models, stronger governance frameworks and far greater visibility into how AI systems make decisions.

As agentic AI becomes more embedded in personalisation, teams are moving beyond static segmentation toward systems that can learn continuously from behaviour, test autonomously and adapt experiences in the moment. But even the most advanced systems will fail if customers don’t trust the intelligence behind them.

Australia’s National AI Plan reinforces that trust and transparency are not optional – they are the foundation for safe, scalable AI-driven personalisation. Done well, brands can deliver meaningful, adaptive experiences without compromising privacy, fairness or customer confidence.

AI governance needs to be built in, not bolted on

As AI takes on a bigger role in shaping personalised customer experiences, the governance behind those systems becomes just as important as the technology itself. The rise of employees using AI tools independently outside formal approval channels creates security and compliance risks. Organisations cannot rely on ad hoc controls anymore – they need transparent systems that formalise how AI is accessed, monitored and governed so teams can innovate without losing control. Boards and executives are accountable for AI strategy, governance and ethical application, emphasising that oversight must be enterprise grade, not experimental.

Effective guardrails start with visibility. As AI drives personalised decisions, brands need full clarity on how those decisions are being made. Brands need to trace which data an AI model uses to make a decision, understand the prompts, models and parameters behind an output and maintain clear logs that show how AI shapes the paths customers take and the outcomes they experience. Without transparency, it becomes impossible to spot bias, drift or unintended behaviour.

What matters in practice is real time visibility. When teams can see how AI driven decisions influence user behaviour, conversion and retention, they can assess whether those decisions are delivering value or creating unintended consequences. This kind of visibility is what allows personalisation to move from experimentation to something dependable.

Some early adopters are already putting this into practice. ZIP, an Australian fintech company, is already using AI agents on Amplitude’s MCP server to embed their domain knowledge directly into their LLM workflows, improving how personalised journeys are monitored and optimised. The result of this was a 60% increase in customers starting an additional repayment flow and the removal of more than 4,000 days of navigation friction.

This visibility makes it possible to intervene early, course correct when required and prevent minor issues from scaling into larger problems. For marketing and product teams, this means AI driven personalisation becomes safer, more predictable and more aligned with actual customer behaviour. AI governance cannot be patched on later. It must be embedded into the core of decisioning systems so AI operates safely, predictably and in line with both regulation and customer expectations.

Invest in continuous oversight for continuous experimentation

Echoed in the National AI Plan, real time personalisation means AI is constantly adapting, which requires continuous oversight rather than periodic manual checks.

When AI underpins the customer experience, these risks compound quickly. Automation without continuous oversight risks locking incorrect decisions at scale. Continuous oversight is what ensures experimentation remains safe, explainable and aligned with customer expectations on personalisation.

AI Agents are most effective when they work alongside humans, not in place of them. They can monitor customer behaviour, surface opportunities and support controlled experimentation at speed, while humans can remain responsible for setting strategy, defining guardrails and approving customer facing changes. A leading Australian bank currently using Amplitude’s AI Agents has advanced their data-driven experimentation, allowing them to uncover key customer behavioural patterns and traffic shifts with central human oversight. Autonomy can be adjusted over time as confidence grows, but accountability remains firmly with people.

This in-loop model ensures personalised experiences adapt based on real customer behaviour, while still reflecting brand intent, fairness standards and evolving privacy expectations. Products can optimise continuously, but only within approved parameters, keeping customer experience safety and performance aligned.

AI has the potential to fundamentally reshape personalisation, but only when trust, transparency, and governance scale alongside the technology. Without them, AI accelerates risk and limits growth. With them, it becomes a powerful and defensible competitive advantage.

The brands that succeed won’t be those that deploy the most AI, but those that govern it with intent and discipline. Now is the time to move beyond experimentation - strengthening oversight, embedding clear governance and building transparent data foundations that allow AI to scale safely and deliver personalised experiences customers genuinely trust.

How Mundial Media Uses AI to Decode Cultural Context

artificial intelligence 11 Feb 2026

Tony, there's a lot of talk about multicultural audiences being "important." Can you explain?

Multicultural audiences are no longer a segment; they’re the primary drivers of U.S. economic growth. Multicultural consumers are fueling most of the country's buying power. But reaching this deeply nuanced, diverse audience in a privacy-first ad technology environment has never been more difficult. Mainstream ad platforms weren’t built for this.

You've been vocal about mainstream ad platforms becoming "too automated." What's the problem with automation?

Automation is powerful for handling large volumes, but it falls short when it ignores cultural layers. Culture shapes everything from how people interpret signals to what motivates them. For instance, a Puerto Rican millennial in New York and a Mexican American Gen Z in Texas could show similar online patterns, yet their cultural influences create distinct needs. That's why tools like Mundial Media’s proprietary Cadmus AI technology are designed to decode those deeper contexts.

How does Mundial Media achieve that precision at scale?

It starts with processing hundreds of millions of signals daily and pinpointing where audience interests and cultural shifts overlap. This enables us to effectively reach over 50 million users, balancing broad scale with targeted accuracy.

Privacy regulations are tightening, and third-party cookies are disappearing. How is Mundial Media navigating this shift?

We've long prioritized first-party data and contextual cues over invasive tracking. Cadmus AI, trained on over three years of compounded AI learnings, delivers precise, real-time cultural understanding, privacy-safe scale, and high-performing contextual targeting, the “right ad at the right moment” without outdated cookies, IDs, or legacy identity signals.

Mundial Media emphasizes that your team embodies the diversity of the audiences you serve. Why does "lived experience" matter in the technical world of ad tech?

Technical tools alone can't capture bias and subtleties; that's where personal insights come in. Our diverse team brings an innate grasp of what makes messaging authentic, spotting resonant visuals, and avoiding stereotypes. This human element sharpens AI's effectiveness beyond raw data analysis.

You mentioned Cadmus AI has been trained on "over three years of compounded learnings." What does that continuous training look like?

It's an ongoing cycle where each campaign refines the system. Over time, this builds a smarter model for predicting what engages various segments and when to deliver messages for maximum relevance.

What does delivering "the right ad at the right moment" mean in a culturally nuanced context?

Delivering 'the right ad at the right moment' in a culturally nuanced context is about relevance rooted in understanding. It means knowing why a moment matters, who it matters to, and how a brand can show up in a way that feels natural and aligned with the audience's mindset.

With Cadmus AI, we know when brands want to target NFL football versus global football or soccer, and when Beyoncé has a major moment, it's a moment your brand should be part of. It's using cultural insight to match a message with the emotional and social context people are in at that exact moment, so the brand feels relevant to what they actually care about right then.

Can you give an example of how Mundial Media can help brands capitalize on major cultural moments?

The 2026 FIFA World Cup is the perfect example. We're talking about 6 billion viewers worldwide, over 29 million multicultural fans in the U.S. alone. This is arguably the decade's biggest multicultural marketing moment. The opportunity here goes beyond traditional sponsorship. What actually works is showing up authentically. Cadmus AI helps brands understand when and how to participate in ways that honor what these moments actually mean to different countries. Those are real emotional connections – hometown pride. Brands that respect that earn trust, and trust drives everything else.

What's the biggest misconception brands have about reaching multicultural audiences?

Many view it as a simple add-on, such as translating content or ticking diversity boxes, while seeing these groups as peripheral. In truth, they're central to modern culture, large consumer spending, innovating trends and adopting early, making them essential for any brand eyeing long-term growth.

Looking ahead, how do you see AI and cultural understanding evolving in advertising?

With AI democratizing data processing, the edge will come from embedding cultural depth to handle nuance and authenticity. We're advancing both tech and expertise to merge these, creating systems that target precisely while respecting human contexts in an increasingly complex landscape.

How Marketing Agencies Can Protect Client Data in an Era of AI-Powered Threats

artificial intelligence 11 Feb 2026

Marketing agencies are uniquely positioned as custodians of client data across dozens of platforms. How has this role evolved in terms of security responsibility, and why is 2026 a critical year for agencies to address this?

Marketing agencies have fundamentally transformed from service providers into data custodians, often holding the keys to their clients' most valuable digital assets. A typical agency today manages credentials for 50+ client accounts across advertising platforms, analytics tools, social media, CRMs, and content management systems. Each login represents a potential entry point not just to the agency's infrastructure, but directly into client operations.

2026 marks a critical inflection point for three reasons. First, AI-powered attacks have made credential harvesting exponentially more sophisticated; attackers can now analyze user behavior patterns and craft targeted phishing campaigns that are nearly indistinguishable from legitimate communications. Second, regulatory frameworks around data protection are tightening globally, with agencies increasingly held liable for breaches originating from their access points. Third, clients are becoming more security-conscious in their vendor selection process. We're seeing RFPs that explicitly require agencies to demonstrate robust security protocols, including how they manage shared credentials. Agencies that can't articulate their security posture are losing contracts to competitors who can.

How can agencies transform their security practices from a checkbox requirement into an actual competitive advantage during pitches and contract renewals?

The agencies that win in 2026 are those positioning security as a core competency, not an afterthought. During pitches, leading agencies now include dedicated sections on their security infrastructure, demonstrating their zero-knowledge password management system, showing how they can onboard and offboard team members to client accounts in minutes rather than days, and explaining their audit trail capabilities.

The competitive advantage comes from trust. When an agency can tell a prospective client, "We use enterprise-grade password management with military-grade AES-256 encryption, and no one, not even our leadership, can access your credentials without proper authorization," that's powerful differentiation. We're working with agencies that have made their security protocol a key selling point in proposals. It demonstrates professionalism and shows they take their custodian role seriously. In an industry where one breach can destroy years of client relationships, that message resonates.

AI-powered phishing attacks are becoming increasingly sophisticated. Can you describe what modern social engineering attacks targeting marketing agencies actually look like in 2026, and what makes agencies particularly vulnerable to these AI-driven threats compared to other industries?

Today's AI-powered attacks targeting agencies are remarkably sophisticated. We're seeing threat actors create fake emails that perfectly mimic client communication styles, analyzing previous email threads to replicate tone, terminology, and timing patterns. An account manager might receive what appears to be an urgent request from their client's CMO asking for immediate access to campaign data or credentials, using language and formatting that's virtually identical to legitimate requests.

Agencies are particularly vulnerable for several reasons. First, they operate in a high-velocity environment where urgent client requests are routine, and attackers exploit this culture of responsiveness. Second, agencies typically have multiple team members accessing the same client accounts, creating more potential entry points. Third, the creative nature of agency work means employees regularly click on links to review creative assets, making them more susceptible to malicious links disguised as client deliverables or campaign previews.

The most dangerous attacks we're seeing involve AI tools that harvest credentials while appearing to provide legitimate services. An employee might install what seems like a helpful SEO analysis tool or content optimization app, not realizing it's designed to capture login credentials and monitor user behavior.

Beyond technical solutions, what role does human awareness and training play in defending against these evolving threats?

Technology provides the foundation, but human awareness is your critical last line of defense. The most sophisticated password management system in the world can be undermined by an employee who falls for a convincing phishing email or shares credentials via an unsecured channel.

Effective training goes beyond annual compliance modules. Agencies need ongoing security awareness that addresses real-world scenarios; what does a credential harvesting attempt actually look like? How do you verify an urgent request is legitimate? What are the red flags in AI-generated phishing attempts? The key is making security awareness part of the agency culture, not just an IT department concern.

We also emphasize the importance of establishing clear protocols for credential sharing and verification. When someone requests access to a client account, what's the verification process? Training employees to pause and verify, even when requests seem urgent, can prevent the majority of social engineering attacks. It's about creating a security-conscious culture where asking "Can you verify this request through a secondary channel?" is encouraged, not viewed as slowing down work.

How should agencies think about credential management differently when they're not just protecting their own data, but serving as the gateway to client accounts across platforms?

Agencies need to shift from thinking about passwords as individual assets to viewing credential management as an enterprise-wide access control system. When you're managing keys to client kingdoms across dozens of platforms, you need infrastructure that provides visibility, control, and accountability.

This means implementing a zero-knowledge architecture where credentials are encrypted at the source and can only be decrypted by authorized users. It means having granular access controls so team members only access the specific client accounts relevant to their projects. It means maintaining detailed audit trails so you can track exactly who accessed which credentials and when, which is essential for both security and client trust.

The critical shift is moving from reactive to proactive management. Rather than manually hunting for passwords when someone needs access or scrambling to change credentials when someone leaves, you need systems that allow instant onboarding and one-click offboarding. When a client relationship ends or a team member transitions, you should be able to revoke access immediately without requiring manual password changes across multiple platforms. This isn't just about security; it's about operational efficiency and demonstrating to clients that their data is managed with enterprise-level rigor.

If you could recommend three immediate actions that agencies should take this quarter to strengthen their security posture, what would they be?

First, implement a business-grade password management solution immediately. This is your foundation; everything else builds from here. For less than $400 annually for a 20-person team, you eliminate the single biggest vulnerability in your security stack. Every day you continue managing client credentials through spreadsheets or browser-saved passwords is a day you're exposed to preventable breaches.

Second, conduct a Shadow IT audit. Require every team member to log every software tool and platform they're using into your password manager, sanctioned or otherwise. You cannot protect what you cannot see. This gives you a complete inventory of your software ecosystem and often reveals surprising security gaps where sensitive data is being stored in unapproved tools.

Third, establish and document your credential management protocols. Create clear written policies for how credentials are shared, how access is granted and revoked, and how urgent requests are verified. Make sure every team member understands these protocols and knows that following them isn't bureaucracy, it's protecting both the agency and your clients. Share these protocols with clients during onboarding and in annual reviews. It demonstrates professionalism and gives them confidence in your security practices.

For agencies that have historically viewed cybersecurity investments as cost centers, how should they reframe this thinking given the current threat landscape?

The calculation has fundamentally changed. A single credential breach can cost an agency a major client relationship, trigger regulatory penalties, and destroy years of reputation building. We've seen agencies lose six-figure accounts because they couldn't demonstrate adequate security controls. Conversely, agencies that position security as a strength are winning competitive pitches specifically because of their security infrastructure.

Consider the math: implementing enterprise-grade password management costs roughly $54 per user annually. Compare that to the cost of a single client breach: legal fees, notification requirements, lost business, reputation damage. Or consider the competitive advantage: if robust security protocols help you win just one additional mid-sized client per year, the ROI is exponential.

But beyond risk mitigation and competitive advantage, there's operational efficiency. How many hours does your team waste hunting for passwords, resetting forgotten credentials, or manually managing access when team members join or leave projects? Proper credential management eliminates this friction, making your team more productive and your operations more professional. This isn't a cost center, it's a revenue enabler and an efficiency multiplier.

Looking ahead through 2026, what emerging threats should agencies be preparing for now, even if they haven't fully materialized yet?

The intersection of AI and social engineering will become increasingly dangerous. We're already seeing early versions, but expect to see AI-powered attacks that can conduct real-time conversations, adapting their approach based on responses. Deepfake audio and video will make verification of urgent requests significantly more challenging. Imagine receiving a video call from a "client" requesting immediate credential access.

Watch for increased targeting of mobile devices. As remote work remains standard and team members access client accounts from personal devices, mobile endpoints become attractive targets. Agencies need to ensure their security infrastructure works seamlessly across devices without compromising security.

Finally, regulatory compliance will expand. More jurisdictions will implement data protection regulations that specifically address third-party access to client data. Agencies that can demonstrate compliance, showing encrypted credential management, detailed access logs, and clear data handling protocols, will have significant advantages in enterprise client relationships.

The agencies that thrive in 2026 won't be those that react to threats after they emerge, but those that build security into their operational DNA now. Password management as the first line of defense isn't just about protecting credentials, it's about demonstrating to clients that when they trust you with their digital assets, that trust is respected with enterprise-grade security at every level.

Turbo-Speed AI Saves Auto Sales: Silent Partner’s Contactter.ai Drives Buyer Engagement

sales 10 Feb 2026

Why has traditional sales automation failed to deliver true conversational intelligence in real customer interactions in the automotive retail industry, and what distinguishes conversational AI from rule-based automation in high-stakes sales environments like automotive retail?

Traditional sales automation in automotive was never designed to handle real conversations. It was built to trigger actions — send an email, fire a text, drop a voicemail — based on simple rules and timelines. That works fine for task management, but it breaks down in real customer interactions where intent shifts quickly, questions come out of sequence, and emotion plays a role in decision-making.

Conversational AI is different because it is built to interpret context, intent, and timing in real time. In automotive retail, where the stakes are high and buyers expect immediate, relevant responses, static automation simply can’t keep up. Conversational AI adapts to how people actually communicate instead of forcing customers into predefined workflows.

How can conversational AI tools act like a top-performing salesperson without replacing the human sales team?

Conversational AI can behave like a top-performing salesperson because it mirrors the habits that make great salespeople successful: speed, consistency, and the ability to ask the right questions at the right moment. What it does not do is replace the human element that closes deals.

At Contactter.ai, the AI handles the initial engagement, qualification, and follow-up at a speed no human team can match across every channel. That ensures no opportunity is lost due to delay. When the conversation reaches a point where judgment, negotiation, or relationship-building matters most, the human sales team steps in. The result is not replacement, but leverage. Salespeople spend more time selling and less time chasing leads that have already gone cold.

What makes sales-focused conversational AI fundamentally different from customer service chatbots, and what enables Contactter.ai to maintain context across text, email, and voice as a single continuous conversation for automotive buyers?

Sales-focused conversational AI is fundamentally different from customer service chatbots because the goal is entirely different. Customer service bots are designed to reduce workload and deflect inquiries. Sales-focused AI is designed to build momentum and move conversations forward.

Contactter.ai was built as a single conversation engine across text, email, and voice, rather than separate tools stitched together. That shared context allows the system to understand that a text reply, an unanswered call, and a follow-up email are part of one ongoing conversation. From the buyer’s perspective, the experience feels continuous and human rather than fragmented and repetitive.

What signals does Contactter.ai use to determine when a conversation should transition to a human salesperson?

The decision to transition a conversation to a human salesperson is based on intent signals rather than arbitrary rules. These signals include buying language, questions about pricing or availability, readiness to schedule an appointment, trade-in discussions, financing-related questions, or a clear request to speak with someone.

When those signals appear, the AI escalates the conversation with full context so the salesperson doesn’t have to start from scratch. That handoff is critical because it preserves momentum and ensures the human enters the conversation informed and prepared.

How does Contactter.ai’s direct integration with CRM and DMS systems enhance its real-time decision-making during sales conversations for auto dealerships?

Direct integration with CRM and DMS systems allows Contactter.ai to operate with real dealership data rather than assumptions. The AI can reference inventory availability, customer history, prior interactions, and dealership workflows while the conversation is happening.

This real-time access improves decision-making, prioritization, and handoffs. Instead of acting as a standalone chatbot, the AI becomes part of the dealership’s operating system, aligned with how the store actually sells and services customers.

Navigating the New Era of Mobile User Acquisition: Inside Zoomd’s 20-Year Journey

marketing 3 Feb 2026

Tell me about Zoomd’s business.

Zoomd is a mobile-first marketing solution company enabling global advertisers to generate new mobile app users cost-effectively as they grow their business profitably. By working with our proprietary UA platform, a mobile DSP, content creators, and Albert.ai technologies, Zoomd is able to deliver a full-funnel, holistic solution, running advertising campaigns, empowering our clients to generate new users across gaming, entertainment, commerce, and fintech verticals.

Zoomd has been working in User Acquisition for a while.

Zoomd (originally founded in 2007 as a search startup and later merged with Moblin in 2012), has nearly two decades of mobile user acquisition experience. We’ve managed user acquisition campaigns through all of the changes in the industry, from the pivot to social media and then video, to the implementation of privacy legislation and operating system changes, which restructured user acquisition best practices.

Through all of the industry changes, we’ve focused on uncovering opportunities for our clients and partners to cost-effectively manage their user acquisition campaigns across channels and regions. This experience has made Zoomd proactive and more nimble, enabling us to anticipate the changes being made by big tech, marketing, and end-users, resulting in profitable user acquisition campaigns for our clients.

Today, as a publicly traded company, Zoomd Technologies Ltd. (TSXV: ZOMD) (OTC: ZMDTF), offers comprehensive and privacy-friendly user acquisition across the leading platforms as well as programmatic and direct channels through the open mobile web.

How is User Acquisition different today?

User acquisition on mobile devices has changed a lot since we started running user acquisition campaigns in 2007. The initial campaigns were across a non-programmatic open mobile web or in-app ads in other apps. Back then, Facebook was a desktop website.

Apple’s App Store launched in July 2008 with 500 apps. Google Play, which unified the Android Market, Google Music, Google Movies, and Google Books into one app store, only launched in March 2012.

Today, user acquisition is more competitive and complicated, with campaigns running across social media networks, in-app, and programmatically over the open web via Demand and Supply-Side Platforms and exchanges.

Privacy, which wasn’t an issue in 2007, became important in 2018 when the General Data Protection Regulation (GDPR) took effect in Europe, followed in 2020 by the California Consumer Privacy Act (CCPA), the first of many US-based state-based privacy laws. In 2021, Apple limited Identifier for Advertisers (IDFA) when the company rolled out App Tracking Transparency (ATT) framework with the release of iOS 14.5. This required users to explicitly opt-in before they could be tracked for advertising purposes, significantly limiting the targeting data available for advertising. Google also rolled out enhanced privacy protocols, though they were less aggressive than Apple’s.

Having worked in user acquisition since there were just a few hundred apps, managing campaigns through all of the aforementioned changes in the industry has made Zoomd a stronger and more effective mobile marketing partner. We’ve literally seen and done it all, and are therefore ready to help companies manage the new changes and challenges that will happen in 2026 and beyond. For example, the EU’s Digital Markets Act (DMA) requires gatekeeper platforms like Apple’s App Store to allow European users to download apps from alternative app marketplaces and to use alternative payment systems outside of the app.

Today… is it all Google and Meta?

Though Google and Meta are important, there are a lot of other channels used in successful and profitable user acquisition campaigns. Depending on the target audience and geographies, Zoomd runs campaigns across many social platforms, including Snapchat, Reddit, Pinterest, Twitter, as well as on regional platforms, like Kwai and Bigo Live. For example, for one European campaign, Snapchat was the platform that delivered the most money-making users, which we anticipated based on our team’s experience.

Beyond the platforms, we’re big believers in the open mobile web. Through programmatic channels, we’re able to successfully convert lots of users cost-effectively across app categories and geographies through display and video ads, including user generated content videos.

What Key Performance Indicators (KPIs) are important for User Acquisition?

The KPIs that advertisers monitor can vary widely based on the app’s maturity and function, but ultimately, they all share the same objective: driving growth and profitability. That’s why so many of our campaigns prioritize revenue-focused KPIs, such as first deposit amount or average order value. By centering user acquisition strategies around these profit-driven metrics, marketers can accurately measure campaign success and ensure they are acquiring valuable, high-quality users who contribute to the bottom line.

Vanity metrics, such as clicks and engagement rates, are also important for crunching data and gaining a better understanding of creative effectiveness, segmentation, etc. User acquisition creative must do more than generate clicks – it must drive prospective users into the funnel to download and install the app and take an action, like depositing money or placing an order.

What should a marketer new to User Acquisition understand before launching his or her first campaign?

A new marketer should first understand his or her company’s business model and the actions that deliver profitable users. That’s the marketing funnel and the north star that will lead marketing and user acquisition activity. Once the marketer understands the business, we’ll work together to set up the campaign based on the company’s business and our experience across similar geographies, target audiences, and product categories to ensure that the user acquisition campaign is on target and within the budget.

Closing thoughts on the future of User Acquisition?

As I said, there are changes happening in the app stores, like the opening up of alternative app stores and payment systems in Europe. App marketing is a dynamic market, which is why for user acquisition, it’s important to work with an agile partner having extensive experience with app marketing across verticals, geographies, and platforms. Artificial Intelligence (AI) is now in every aspect of our work and lives, and it’s also necessary for effective user acquisition. Advertisers need a partner that moves fast, with the trends, to ensure that their brand doesn’t fall behind. After nearly 20 years of actively working in user acquisition, Zoomd is a trusted partner that understands the market and can profitably manage user acquisition campaigns.

Beyond the Click: Why Transforming B2B Attribution Starts with AI

marketing 3 Feb 2026

Over the past three decades, technology has transformed attribution from a rough art into an exact science, allowing businesses to peer through the darkness and attribute actual spend to campaigns. However, as B2B sales cycles continue to lengthen, are our current attribution strategies keeping up?

To find out, I recently caught up with Chris Golec, a martech industry veteran who pioneered the ABM category and founded Demandbase. Chris is now the Founder and CEO of Channel99, an online platform that enhances attribution and marketing decision-making using AI.

To begin, why do you feel the current attribution standard (Click-Through Attribution, or CTA) is failing B2B marketers?

It all comes down to the increasing complexity of the B2B purchase process. Historically, to ensure proper attribution, we've expected a prospect to click an ad before purchasing. This "click-through" method is tidy and easy to track via UTM parameters.

The problem with this approach is that it captures only a tiny slice of the B2B sales funnel. Unlike selling a pair of sneakers to a consumer, the B2B process is long and involves multiple decision-makers. With an average of 266 touchpoints to close a B2B deal, relying solely on CTA means you are losing an entire forest of latent interest just to find a few one-off clicks.

If CTA is only a "tiny slice," what does the rest of the B2B sales funnel look like? What is the alternative?

The alternative is View-Through Attribution (VTA for short). This approach assigns credit when a buyer sees an ad, video, or piece of content and later converts, even if they never clicked the ad directly.

The results speak for themselves. Marketers who screen for VTA see a nearly 79% jump in conversion compared to CTA alone. It uncovers a wealth of insights, often revealing a hidden "first touch"—like organic social or industry review sites—that CTA completely misses. In today’s world, data-backed results are a matter of survival for marketers. VTA allows us to identify these audiences early and often.

If the data is so much stronger, why are nearly 25% of marketers still relying solely on click-through?

Ease is often the greatest threat to progress. CTA is simple to measure and works with standard analytics dashboards. More than any other factors, these two traits have made it the industry standard.

But this convenience comes at a cost. While CTA measures click-based campaigns well, it falls woefully short when asked to evaluate any other types of campaigns. Impressions, brand exposure, and social interactions often have just as much influence on deal closing as a digital ad does, yet CTA leaves these data points untouched.

That’s not even the worst part, though. Without view-through data, marketers naturally craft campaigns to attract high click volumes, but those often don’t speak to the intricacies of B2B sales, or connect with users in a position to buy. That’s why my #1 piece of advice to marketers is this: Craft your campaigns and measure success with sales at the center, nothing else. When it comes to attribution, that means leveraging VTA and CTA.

Let’s talk about implementation. For companies ready to modernize, what are the "traps" they need to avoid?

The biggest trap right now is privacy. Regulations like GDPR and CCPA have fundamentally reshaped what marketers can track. The third-party cookie is under threat, and consent requirements have already reduced attribution data volume by 30-40%.

To avoid a measurement crisis, businesses must invest in cookie-free view-through technology. These systems capture engagement signals at an account or company level without violating privacy standards.

In addition to privacy, are there any other issues that modernizing companies should look out for?

Absolutely. Once you solve the privacy piece, you still have to contend with company culture. The insights that VTA can provide are so substantial compared to CTA that a lot of old assumptions get called into question as soon as the new data comes in. Having a company-wide growth mindset and being willing to abandon time-tested strategies for a new, data-backed approach isn’t always easy, but it’s essential for success.

For our readers who are ready to make the switch, how do they begin? Is there a roadmap?

Absolutely. For any organization that’s ready to go all-in on VTA, I would recommend these five steps:

Build a new foundation: Adopt privacy-forward account identification. Leave third-party cookies behind and focus on identifying accounts, not just anonymous users.
Unify your datasets: B2B buyers don't live in a single channel. You need to integrate tracking across every touchpoint—display, ABM, organic social, email, etc.—to connect exposures to revenue.
Screen for data validity: New data streams are useless if they are full of noise. Incorporate rigorous ad verification to ensure you are measuring actual target audience impressions, not bot traffic.
Recalibrate performance benchmarks: Be prepared to throw out the old click-based playbook. Reallocate budget toward tactics that are truly driving influence from your target accounts and addressable market, the rest is noise..
Loop in the sales team: Don't keep these insights in the marketing silo. Share them promptly so both teams can understand which accounts are generating deals.

Any final thoughts?

For the past two decades, our most widely used attribution metric started and ended with the cursor. But with the rise of AI, that reliance will be tested like never before.

By shifting to AI-powered, view-through attribution, a far more robust and complete strategy picture comes into focus for marketers. It’s past time to move beyond the click.

Redesigning Marketing Operations for the AI Era: Key Insights from Incubeta.

artificial intelligence 30 Jan 2026

How is AI changing the way marketing teams structure their creative and media workflows today?

AI is shifting workflows from linear handoffs to more connected, parallel processes. Instead of creative, media, and analytics operating in silos, teams increasingly work from a shared intelligence layer where insights, audience signals, and performance feedback flow continuously. At Incubeta, we see the biggest impact when AI accelerates iteration and personalization at scale, while strategic and creative decision-making remains firmly human-led.

What principles does Incubeta prioritize when helping brands redesign workflows around AI?

The first principle is that AI should augment human expertise, not replace it. We redesign workflows so AI handles speed, scale, and pattern recognition, especially in production and optimization, while people focus on strategy, creativity, and brand stewardship. The second principle is integration. AI delivers the most value when creative, media, and data systems operate as one connected workflow rather than separate layers.

Why is a human-centered approach still essential when applying AI across marketing operations?

AI is only as effective as the behaviors it’s designed to influence. A human-centered approach ensures AI-driven outputs reflect how people actually think, feel, and make decisions, rather than optimizing solely for short-term performance signals. At Incubeta, we use AI to support better human judgment and customer understanding, not to override them.

How do behavioral science frameworks like StoryVesting or the Bow Tie Funnel guide AI-driven marketing decisions?

Frameworks like StoryVesting and the Bow Tie Funnel give AI direction and purpose. They help ensure automation and personalization reinforce trust, relevance, and long-term value rather than simply increasing volume or efficiency. These frameworks also align internal teams around a shared customer logic, making AI-driven execution more consistent and easier to operationalize.

What does an AI-ready data workflow look like from Incubeta’s perspective?

An AI-ready data workflow is unified, accessible, and decision-oriented. It connects media, customer, and performance data into a single environment that supports real-time analysis and activation. At Incubeta, we approach this through a Data-as-a-Service mindset, where data is treated as a continuously available, governed layer that fuels planning, activation, attribution, and prediction. This allows teams to move from reporting what happened to anticipating what will happen next and acting with confidence.

How does AI improve attribution and predictive modeling in modern marketing organizations?

AI is fundamentally changing how attribution and predictive modeling support decision-making. Instead of forcing fragmented customer journeys into last-click or channel-based reports, AI-driven models account for multiple touchpoints, creative variables, and rapidly shifting behaviors to show what’s actually driving incremental impact.

Predictive modeling then builds on those signals to forecast outcomes, scenario-test media and creative investments, and evaluate trade-offs before decisions are made. As measurement systems become more advanced, marketers are moving away from trying to perfectly reconstruct a journey that no longer exists and instead using AI-driven modeling to plan what comes next with greater confidence, even as privacy constraints and signal loss accelerate.

The result is a move from reactive optimization to proactive, forward-looking planning, where reporting becomes a decision engine rather than a justification exercise.

What role do platforms like Google Marketing Platform and Google Cloud play in enabling AI-powered decision-making?

Google Marketing Platform and Google Cloud provide the infrastructure needed to connect data, activate insights, and scale AI responsibly. Together, they enable advanced analytics, modeling, and automation while maintaining governance and transparency. Incubeta works closely within these ecosystems to help brands operationalize AI in ways that support both performance and accountability.

How is AI reshaping collaboration between creative, media, and analytics teams?

AI creates a shared language between teams by grounding decisions in common data and insights. Creative teams gain faster feedback, media teams gain clearer signals, and analytics teams can focus on higher-value modeling instead of manual reporting. The result is more cohesive collaboration and fewer disconnects between strategy, execution, and measurement.

What practical steps can marketing leaders take to govern AI usage across their organizations?

Effective AI governance is less about restriction and more about clarity. Marketing leaders need to define where AI is appropriate in the workflow, where human judgment is required, and how outputs are reviewed before activation. At Incubeta, we see the most progress when governance is built directly into everyday processes, so AI use feels intentional and repeatable rather than experimental or risky.

What signals or outcomes help demonstrate AI’s impact to executive leadership?

Executives respond best to outcomes tied to efficiency, effectiveness, and decision quality. This includes faster time to market, improved personalization at scale, and clearer links between marketing activity and business results. Framing AI as an operational and strategic advantage, rather than a standalone tool, helps make its value tangible to the C-suite.

Are there any exciting developments on the horizon at Incubeta in 2026?

As we kick off 2026, the excitement at Incubeta is palpable. One of the standout moments I’m particularly looking forward to is the launch of our new podcast, Digital Edge in Q1. This podcast will bring together a dynamic range of voices, offering diverse perspectives from across industries on key topics like the future of AI, marketing effectiveness, and much more.

I’m honored to be a guest on an upcoming episode, where I’ll dive into AI architecture and share how organizations can set themselves up for success with AI. If you’re eager to gain actionable insights and hear from industry leaders on how they’re driving innovation in marketing and advertising, make sure to tune in!

Beyond Static Floors: What Publishers Need to Know About Dynamic Flooring in Programmatic Advertising

marketing 23 Jan 2026

1) When publishers talk about their “programmatic floor strategy,” what does that typically look like in practice today? How are most teams approaching this?

In practice, most publishers’ “floor strategy” is still largely manual and backward-looking.

Typically, teams set static CPM floors in GAM (via UPRs) or in their wrapper/Prebid configuration, segmented by broad buckets like geo, device, or placement. These floors are usually based on historical averages, past performance, or rough heuristics rather than real-time demand signals.

They revisit and tweak them periodically — weekly, monthly, or around seasonal moments — but the core approach rarely changes: fixed prices applied across highly variable auctions.

So most “floor strategies” today are really just static rule sets designed for a slower market. They are not built for the speed, variability, or complexity of modern programmatic auctions.

2) Modern programmatic auctions happen in milliseconds with DSPs constantly repricing based on real-time signals. How should that reality influence the way publishers think about setting price floors?

It should completely reshape how publishers think about pricing.

If buyers are making decisions in real time, then floors cannot be static. They need to reflect what is actually happening in that specific auction — not what happened last week or last month.

Instead of asking, “What should my average floor be?” publishers should be asking, “What is this impression worth right now, given who is in the auction and how they are behaving?”

That means treating floors as dynamic, responsive signals — not fixed thresholds — that react to live demand, bidder behavior, and competition in real time.

3) Many publishers measure price floor success by looking at CPM changes, often comparing this week to last week. Is this the most effective way to measure this?

No — it is one of the weakest ways to evaluate floors.

CPM alone is an incomplete metric. It only tells you the price of impressions that are sold. It says nothing about suppressed demand, lost bidders, or auctions that never cleared.

On top of that, before-and-after comparisons (this week vs last week) are fundamentally flawed because the market is constantly changing. You end up measuring market volatility more than pricing impact.

This is why floor changes often look “good” or “bad” depending on timing — not because of the floor itself, but because of shifting demand conditions.

4) What other metrics should publishers actually be tracking to understand whether their floor strategy is working?

The single most important metric is holistic RPM — revenue per thousand ad opportunities (requests) across all programmatic channels (Prebid, Amazon, AdX, Open Bidding).

This metric captures:

● Price impact (CPM)

● Volume impact (fill)

● Buyer participation and routing effects

Crucially, this must be measured per ad unit first, then aggregated to site level. Site-wide averages hide too much.

Beyond holistic RPM, publishers should also track:

● Bid density (bids per auction) — a proxy for competition

● Win rates by bidder — to see which partners are reacting to floors

● Timeouts / drop-offs — signs of demand suppression

● Clearing price distributions — where auctions are actually settling

Together, these give a much clearer picture of whether floors are helping or hurting.

5) What happens at the auction level when a publisher sets a floor that doesn’t align with what DSPs are willing to bid in that moment?

Two things typically happen, depending on the direction of misalignment:

If the floor is too high, DSPs don’t negotiate — they exit. Bidders reduce participation, route budgets elsewhere, or stop bidding altogether. Fill drops, and auctions become less competitive.

If the floor is too low, auctions clear too easily. Bidders don’t need to bid aggressively, competition thins out, and you end up leaving meaningful value on the table — selling inventory below what buyers were actually willing to pay and losing yield in the process.

In both cases, revenue is lost — it just appears differently: either as lower fill or lower effective prices.

6) When publishers analyze floors using site-wide or monthly aggregated data, what critical dynamics are hidden from view?

A lot.

Aggregated data hides:

● How different ad units respond to floors

● How specific bidders behave in specific geos or devices

● Time-of-day demand patterns

● Differences between mobile vs desktop, app vs web, or browser types

You might see “healthy” site-wide CPMs, but underneath that some placements could be massively underpriced while others are choking demand.

Most auction-level behaviors — like bid density shifts or bidder pullback — get completely washed out in monthly rollups.

7) How do price floors influence which demand partners and campaigns even enter an auction? Is it just about setting a minimum price?

It’s more than just having a minimum price.

In modern auctions, price is a signal — but a static price is a weak one. Without a reliable, real-time signal, DSPs have to guess the likely clearing price, which makes pacing and confident bidding harder, even when the audience match is strong.

Dynamic floors strengthen that signal. By adjusting in real time, they give buyers a clearer view of where the market is clearing right now, which makes them more willing to participate and bid at the true value of the impression.

Dynamic floors can also help unlock demand. When a higher, real-time clearing price is visible, it can influence which campaigns DSPs prioritize for that impression.

Finally, dynamic floors improve how inventory moves through SSP and DSP throttling systems. Requests that are priced in line with real demand are more likely to clear — and therefore more likely to pass throttling and access available budgets.

In short, dynamic floors act as a real-time market signal that shapes participation, routing, and budget access — not just a guardrail on price.

8) If a Head of Programmatic wanted to properly test whether a floor change improved performance, what would that test need to look like?

They would need a true real-time A/B test, not a before-and-after comparison.

That means:

● Splitting traffic into a floored cohort and a control cohort running simultaneously

● Measuring holistic RPM per ad unit across both groups

● Ensuring both cohorts see the same demand conditions

● Tracking bidder behavior (bid density, win rates, drop-offs) alongside revenue

Only with this setup can you isolate the true impact of pricing from normal market volatility.

9) What patterns have you seen when publishers shift from static to more dynamic floor strategies? What typically changes in their auction outcomes?

The most consistent pattern is that auctions become healthier.

Typically we see:

● Higher holistic RPM (often 10–16%)

● Slightly higher CPMs (around 7-11%)

● Stable or improved fill

In other words, pricing aligns better with real demand. Publishers capture more value without suppressing liquidity.

Perhaps most importantly, they stop seeing the wild performance swings that plague static floor setups.

10) Where do you see price floor strategies evolving as the programmatic ecosystem becomes increasingly algorithmic on both buy and sell sides?

On the web, pricing is becoming more holistic, unified, and visible inside the auction.

Two recent shifts are accelerating this:

● Amazon now participates in Prebid as a bidder, meaning Prebid floor decisions can directly influence Amazon demand instead of pricing it in a separate silo.

● Google now allows separate pricing for AdX demand in GAM, giving publishers far more control over how AdX is treated relative to other demand.

Prebid floors themselves aren’t new — they’ve existed since early header bidding. What’s changing is that more major demand sources now sit inside a common pricing perimeter.

As a result, I expect floors to become more holistic and consistent across channels, delivering clearer RPM impact and giving publishers greater control over how their inventory is positioned and monetized.