1. What specific areas of demand generation do you believe offer the most growth potential for Gigamon under your leadership?

I see several high-impact opportunities for demand generation growth at Gigamon. There’s enormous potential in more deeply integrating brand and demand functions. Historically, these have operated in parallel tracks. By tightly aligning them, like we did with the 2025 Hybrid Cloud Security Survey, where we explored hybrid cloud security in the age of AI, it wasn't just a brand builder; it became a powerful demand-gen engine with legs across campaigns, channels, and customer touchpoints.

Artificial Intelligence-powered marketing also presents significant growth potential for Gigamon. AI is rapidly finding its way into global workplaces due to its ability to enhance productivity, automate processes, and enable smarter, faster decision-making. The marketing industry is no exception. There’s a real need to harness AI to drive smarter, more process and productivity in content development, which ultimately delivers more productive execution.

Another key area for us is the intentional focus on Ideal Customer Profiles (ICPs). By clearly defining and prioritizing our most valuable customer segments, we ensure that our efforts and resources are aligned with those who drive the greatest return for the business. This enables us to craft tailored use case messaging and develop targeted go-to-market campaigns that resonate deeply with high-potential buyers. We've found this results in higher-quality leads, stronger conversion rates, and accelerated revenue growth.

Finally, I see growth in continuously adapting how we use our channels like field marketing, digital presence, media, analyst relations, and beyond. The “what” may stay consistent, but the “how” must evolve with the data and buyer behavior, meeting customers where they’re at. That requires a team culture that’s agile, data-driven, and willing to experiment.  

2. In your experience, what role does marketing play in creating trust and credibility with CISOs and IT decision-makers?

Marketing plays a foundational role in establishing trust and credibility with CISOs and IT decision-makers, especially in cybersecurity where relationships and reputation matter deeply. To build that credibility, we’re focused on meeting CISOs where they are, with relevant, data-backed insights, peer-led narratives, and use cases that reflect real-world impact. Tailoring content to their specific needs and pain points is critical. For example, we took the broader survey mentioned above and created a focused piece, CISO Executive Summary, further focusing the data on CISOs’ specific challenges and priorities.  This is a great example of how we can emphasize clarity, precision, and consistency when communicating to key stakeholders. We are delivering value in the form of data and insights for security leaders, while establishing Gigamon as a leading authority..

3. From a strategic standpoint, how critical is deep observability to an organization’s hybrid cloud security strategy?

Deep observability provides complete visibility into all data in motion, including encrypted and complex AI-driven communications, enabling organizations to detect and respond to security threats proactively. This visibility is essential for securing hybrid cloud environments, where traditional monitoring tools often fall short, especially with the rapid growth of AI-generated traffic and shadow IT applications.

Gigamon serves more than 4,000 organizations worldwide with the Gigamon Deep Observability Pipeline, including more than 80% of Fortune 100 enterprises and 9 of the 10 largest mobile network providers.

4. How do you see the role of marketing evolving in response to the growing complexity of hybrid cloud security challenges?

As hybrid cloud environments grow more complex, the role of marketing must evolve from simply generating leads to deeply articulating value, especially in a space where deep observability isn’t always immediately understood or budgeted for. Our job is to educate, contextualize, and elevate the strategic importance of deep observability. That means clearly communicating not only what we do, but also how we help CISOs and their teams improve ROI, reduce risk, and drive operational efficiency.

We also have to account for a shifting audience. Gigamon has traditionally sold into network teams, but as we expand into cloud and security, we’re increasingly engaging new buyers who often don’t talk to each other internally. Marketing has to bridge those silos, tailoring messages by persona, and helping unify stakeholders around shared outcomes. Our success depends on how well we can translate complex technical value into compelling, role-specific narratives that resonate across the decision-making unit.

 5. In your view, what are the key marketing KPIs that should align with revenue-generation goals in cybersecurity-driven enterprises?

Modern marketing KPIs must reflect the full spectrum of today’s buyer journey. Think about segmenting your KPIs into three key categories: brand, engagement and intent, and pipeline generation.

Brand focuses on signals that indicate growing awareness and trust, such as branded search volume, direct traffic from target accounts, website interactions, social media or community engagement, and the impact of analyst and public relations. AR and PR can be measured through inclusion in analyst reports and inquiries, media coverage, interviews, and share of voice.

Pipeline metrics should center on both sourced and influenced pipeline to bookings. Teams should track marketing qualified accounts (MQAs), opportunity conversion rates, and marketing’s contribution to sales velocity. While we are not completely walking away from marketing qualified leads (MQLs), the shift toward MQAs acknowledges that buying decisions are made at the account level by multiple stakeholders. MQLs can still serve a role in lead-level nurturing, but MQAs should be integrated into your metric system as a more accurate and strategic measure of pipeline potential.

Engagement and intent, traditionally considered softer metrics, are critical indicators of buyer behavior. Engagement encompasses meaningful interactions—such as content consumption, return visits, anonymous visits, multi-persona activity within accounts. Intent signals indicate buyer readiness, including third-party intent data, website visits, and self-reported attribution—giving marketing teams a lens into where, how, and when to activate demand.

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1. How does the ACE generative AI tool differ from traditional ad‑optimization tools, and what measurable impact has it made on advertiser ROI so far?

The ACE (Adaptive Content Engine) generative AI tool represents a step-change in creative optimization for performance marketing within the Rokt platform. Unlike traditional ad-optimization tools that rely heavily on manual A/B testing or rule-based systems, ACE leverages advanced generative AI models trained specifically on Rokt’s proprietary Transaction Moment dataset. This enables it to generate and adapt ad creatives in real time, tailored to highly specific transactional contexts and consumer intent signals.

Whereas traditional systems typically iterate on a limited set of static variants, ACE dynamically crafts new creative variations that reflect subtle but impactful differences in tone, format, copy, and visual elements. It uses natural language generation, machine learning, and experimental design to systematically find the wording that resonates best with each audience, often yielding substantial conversion lifts (on average ~21% lift in conversion rates). It does so with a deep contextual understanding of what performs best on Rokt.

Advertisers using ACE have seen measurable performance gains, with the tool consistently delivering improvements in click-through rates, conversion rates, and ultimately return on investment (ROI). In benchmark testing, ACE-generated creatives have outperformed legacy variants by +20% conversion rates across key verticals. These gains translate into more efficient customer acquisition and higher revenue per impression, particularly for brands focused on maximizing the impact of performance marketing budgets.

2. What synergies are you seeing post‑acquisition of Aftersell, particularly in terms of upsell opportunities during cart, checkout, and post‑purchase moments?

Aftersell’s native “Upsells” capability has already lifted average-order-value by 15 %+ for thousands of Shopify merchants. By merging that engine with the Rokt Brain, we’re now deploying the same bespoke cross-sell logic to enterprise-scale retailers on Rokt’s network, unlocking materially larger revenue pools. Integrating Rokt Thanks into Aftersell lets merchants convert what was once a passive confirmation page into a high-intent engagement surface, delivering fresh incremental profit while preserving a premium shopper experience. The enhanced commercial model from this gives us reinvestment firepower to keep building first-party tools that compound those gains for merchants.

And, with Aftersell anchoring us in the DTC ecosystem, Rokt Ads can now reach a new segment of brands while simultaneously giving the entire network access to a broader pool of high-quality advertisers. Ultimately, this leads to more ad demand diversification and better results for our ecommerce partners and advertisers. 

3. You grew revenue by 40 % YoY to reach $600 million. What were the key levers of that growth, and what does 2025 look like from a revenue‑strategy perspective?

In 2024, we drove a 40 % year-over-year lift to $600 million by landing a wave of new ecommerce partners—vastly expanding supply—and by unlocking higher spend from existing advertisers across the network. For 2025, our plan is to compound that momentum by focusing on four priorities: first, elevating relevancy so every impression shown to shoppers performs better; second, broadening demand by winning new client segments and verticals; third, rapidly scaling Rokt Pay+, which turns the payments page into a profit engine; and fourth, integrating mParticle to deepen our CDP capabilities. Alongside these initiatives, we will keep pushing both supply and demand growth aggressively, onboarding bigger brands and powering transactions at an even faster clip.

4. What are the most overlooked moments in the ecommerce transaction journey where you are delivering hidden value for clients?

Ecommerce teams usually fixate on acquisition and product pages, but the quiet profit is hiding in checkout. For example, Rokt turns what merchants see as a cost center (the payments step) into a revenue engine by letting payment providers and BNPL partners compete for a placement, so every click monetizes itself without adding friction. In other placements, such as the cart, our ML can surface in one-click add-ons that lift AOV before the order even finalizes, while the order-confirmation page surfaces premium first- or third-party offers that convert at engagement rates multiples above standard ads. By optimizing these moments, Rokt consistently unlocks incremental dollars per transaction and upgrades overall unit economics that most brands never realize are possible.

5. What advice would you give to ecommerce brands looking to drive more value at the point of transaction without compromising customer experience?

To drive more value at the point of transaction without compromising the customer experience, it’s critical to start by letting data guide every decision. Treat each checkout change as a hypothesis: instrument the funnel end-to-end, set clear success metrics such as incremental revenue per session or attachment-rate lift, and test relentlessly. Evidence-backed micro-optimizations always win over sweeping, unvalidated redesigns and protect the shopper experience while surfacing new revenue. Equally important, ensure strong foundations, especially identity resolution. When you can reliably recognize a shopper across devices and sessions, you can present tailored offers, payment methods, and loyalty nudges that feel helpful rather than intrusive, which ultimately drive more incremental revenue. A strong data foundation turns checkout into a moment of relevant value instead of an upsell that clutters the journey.

6. How does your recent appointment—and those of your fellow leaders—support Rokt’s broader goals for growth and innovation?

My role as CPO is to integrate our products—Rokt Ads, Pay+, Thanks, and Aftersell—into one cohesive, AI-powered platform that helps brands and retailers deliver more relevant experiences at the moment of transaction. It’s about turning our data and AI advantage into products that are simple to use, drive real results, and become essential to our partners.

Claire, as Chief AI Officer, is making sure our models continue to get smarter, faster. Her team is building the AI capabilities that power everything from relevance to creative generation—like our new ACE (Adaptive Content Engine) tool—and making sure that innovation is responsibly applied and easy to integrate into our products.

Pete, as SVP of Advertiser Partnerships, is focused on growing and strengthening our advertiser ecosystem. He’s helping us bring in new categories and deepen relationships with top brands, which feeds even more data and demand into the platform.

Together, we’re tightening the loop between product, AI, and partnerships—so Rokt can keep pushing the boundaries of real-time relevance and fuel the next phase of our growth.

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1. What do you look forward to about combining audio ads and ID-less, AI-powered audience targeting in the gaming space?

The intersection of ID-less targeting and immersive audio unlocks a powerful, privacy-first channel for brand storytelling. We’re excited about how AI models trained on contextual (like the game category, genre affinities and related in-game contextual elements) and behavioral signals (like gameplay patterns, game device type, time-of-day, and even specific game moments) can drive meaningful ad experiences without the direct need to track specific users. Audio, by design, doesn’t require screen interaction for engagement, so when it’s combined with smart segmentation, it delivers high-scale, low-friction user engagement. This is the future of privacy-aligned performance.

2. How are you going to approach the effectiveness of contextual or behavioral AI signals over ID-based tracking for achieving campaign precision?

We’re investing heavily in building solutions for our advertisers to be able to reach and understand gamers based on contextual signals for mobile games. Partnerships like the one we have launched with NumberEight is a great example for that. Being able to understand who gamers are based on what, when and why they play is what we believe to be the best way for advertisers to tap into the massive audience of gamers, without compromising on relevant audience targeting. Pairing that with in-game-session behavioral indicators like level progression, game experience and flow, session depth, and engagement rhythm will bring this capability to the next level, allowing advertisers to not only understand who gamers are, but to target them at the right moment and state of mind. 

Instead of targeting "who the user is," we focus on where they are in their experience and how they’re playing, which is more actionable in real time.

3. Which of the Audiences mentioned (e.g., Gen Z, Affluent Consumers, Sports Fans) align most closely with your brand’s current targeting priorities? 

The honest answer is - it really depends on the brand. The magic of reaching users in games, is that you can basically reach EVERYONE, since today, everyone’s a gamer. Different brands look for different audiences, and the power of being able to break down the different games based on their affinity clusters (not 3P or 1P IDs) is what makes our games-network so appealing for brands. We can today identify within our games Gen-Z’s, Parents and also Senior Citizens - and they can all be relevant for different brands, depending on what they want to get out of their campaign. 

4. How do you plan to leverage seasonal and moment-based audiences like Black Friday or Super Bowl activations in immersive channels such as gaming? 

We’re definitely leaning into moment-based audience packaging, especially for high-spend tentpole moments like Back to School, Black Friday/Cyber Week, Holiday Season, Super Bowl & other major global sporting events. 

We use general real-time triggers (e.g., device locale, device type, time of year etc.) and also game real-time triggers (what we call ‘in game E-motions’ like in-game calendar events, in-game challenges, levels and so on) to surface themed and moment-relevant creative and segment players based on their current state of mind and context - ensuring timely, relevant delivery.

What’s powerful in audio, and made possible with AI, is that we can tailor the message dynamically, like a radio spot- without any creative fatigue or layout disruption.

5. In-game audio is praised for being non-disruptive. How important is it for your brand to balance creativity with user experience in new media formats? 

It’s everything. Our entire product strategy at Odeeo is built around the principle of "audio as additive, not intrusive." We don’t pause the game or hijack the screen. That means we aim to nail the tone, pacing, and relevance of the message because users stay in the experience while the ad plays.

We work closely with brands on creative adapting, ensuring the voice, script, sound and even length fit the gaming vibe and pace. This is a huge benefit for both gamers, game developers and brands. 

I think for brands this is a huge added value. No brand wants their ads to be consumed when users are in a bad state of mind or feel like the brand ad is interrupting them, so being able to get exposure while the consumers are receptive and not intruded, is a real game changer. 

6. Your Affinity Audiences are built without personal identifiers. How critical is ID-less targeting in your company’s future-proofing strategy for privacy regulations?

It’s absolutely core to our roadmap. We’ve accepted that persistent IDs are a legacy tool, not a future asset. Regulations like GDPR, CPRA, and similar in other regions, alongside the evolving stance from Apple and Google signal a clear direction: privacy by default.

We’re building all our audience strategy to be based on non personalized IDs leaning on on-device signals, contextual intelligence, and AI models that adapt in real time. 

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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.

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1. You have redefined yourself around a “systems-first” model. What prompted this shift, and how does it reflect the needs of modern businesses?

• Strategy sets the purpose and market direction.
• Branding expresses that vision in a way people can connect with.
• Technology builds the infrastructure to support and scale that experience.
• Growth ensures it reaches the right audience with measurable results.

• OKRs (Objectives & Key Results)
• Customer Journey Architecture
• North Star Narrative Alignment

1. In what ways do you define success in online brand protection today, and how does that differ from older models?

Success in brand protection is no longer about playing whack-a-mole with takedowns. The old model was a volume game—counting how many listings you could manually remove. It was reactive and inefficient.

Today, we define success as achieving mastery over a brand’s online channels. This is a fundamental shift from a manual-hour-based approach to a strategic, data-driven one.

Success is a metric that is unique to each brand. For one, it might be reclaiming lost revenue. For another, it's about preserving brand equity or enforcing distribution policies. Our approach is to provide the data and tools to achieve that specific goal. If the goal is anti-counterfeiting to clean up online marketplaces, we will then measure success by how "clean" a brand's channels are, how cooperative platforms are, and the overall visibility of both authentic and counterfeit content. It’s about moving from simply chasing infringers to strategically controlling your online presence.

2. Can you explain how the Cleanliness Score™ is calculated and how brands can use it to assess their online health?

Think of the Cleanliness Score™ as a daily credit score for your brand's online health. It's a simple, powerful KPI that transforms an abstract problem into a measurable one. 

The calculation is the result of six years of focused R&D.

For brands, this score provides immediate clarity. They can see if their channels are 99% clean or 50% clean, track progress over time, and use this objective data to hold marketplaces accountable and focus enforcement where it's needed most.

3. How does the Deep Semantic Detection capability improve the detection of disguised or non-textual infringements?

Traditional search technology is like looking for a needle in a haystack by only searching for the word "needle." Our Deep Semantic Detection is like a bloodhound—it follows the scent of an infringement, even when the sellers are trying to cover their tracks.

It works by mimicking the complex path a determined buyer uses to find fakes. They don't just search "counterfeit Brand X watch" on a marketplace. They start on Google, find a discussion on Reddit, follow a link to a seller’s page, and then browse related items on a platform.

Our technology automates this "graph traversal" process. This approach excels for two key reasons:

1. It uncovers hidden networks of infringers who intentionally avoid using trademarked terms.
2. By following these intelligent pathways, itidentifies infringing content much faster and more accurately than traditional searches, cutting through irrelevant noise.

So while they might use vague phrases like "clover-style jewelry" instead of "Van Cleef & Arpels Alhambra," our system connects the dots and finds them anyway.

4. Can you walk us through how risk clustering and SKU detection improve threat prioritization and resolution?

When you're facing thousands of potential threats, you can't treat them all equally. Our strategy for intelligent prioritization relies on two core pillars: a sophisticated scoring system for ranking threats and granular data for precise, automated actions.

1. Risk Clustering: We move beyond a simple "high risk" flag with a more advanced, two-part scoring system. This ensures our clients' resources are focused on the threats that deliver the fastest, most significant impact.
2. SKU Detection: This provides the critical layer of data granularity needed for modern enforcement. This capability is especially powerful for managing grey market distribution and executing highly targeted enforcement strategies.

5. How customizable is the Corsearch Zeal 2.0 platform for brands with different risk profiles or industry-specific needs?

Corsearch Zeal 2.0 wasn't built with customization as an add-on; it's foundational to its architecture. The core logic engine is tailored to each brand's unique risk profile from day one.

This customization is both deep and practical. The Risk Score is calibrated using a "brand bible" we develop with each client, defining what constitutes an infringement for their specific products. The Enforceability Score is tuned based on the brand's exact enforcement rules and the known policies of the platforms they need to police. This means the sorting and prioritization of threats isn't based on a generic, one-size-fits-all algorithm. It’s a bespoke enforcement engine configured for a brand’s unique needs, whether they're in luxury goods, pharmaceuticals, or fast-moving consumer goods.

This deep adaptability extends beyond the core logic and into the entire workflow. Brands can configure everything from product categories and custom data labels to reporting dashboards. The platform adapts to the client's team structure and objectives, not the other way around. We provide a powerful, configurable engine; our clients build their ideal command center on top of it.

6. How does Corsearhc Zeal 2.0 adapt to evolving threats, such as generative AI content misuse or new marketplace behaviors?

Our defense against emerging threats is a proactive, data-driven feedback loop, not a static rulebook.

For new marketplace behaviors—like infringers using new visual tricks to hide logos—we constantly monitor platform data. Our Cleanliness Scores and platform cooperativeness metrics act as an early warning system. Because our AI models are designed for rapid retraining, we can quickly adapt our detection capabilities to recognize and neutralize new tactics at scale.

Regarding Generative AI, we see it as another vector of attack, but not an unbeatable one. AI-generated fakes are often trained on flawed or "dirty" data, as counterfeiters lack access to official brand assets. This process inevitably creates subtle but detectable errors—mistakes in packaging details, incorrect logo placement, or flawed product renderings.

Essentially, we fight AI with more sophisticated, specialized AI. Our systems are trained to spot these tell-tale imperfections. By maintaining this agile, data-centric approach, we ensure we are always prepared to analyze and counter new threats the moment they emerge.

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