1. What measures are in place to ensure that voice-of-customer data from various channels (e.g., reviews, social media, surveys) is effectively utilized to inform business decisions?

Brands today are inundated with Voice of Customer data from every direction. To turn this chaos into clarity, data quality must come first. It is the foundation, the building blocks, that drive informed decisions across every industry. At our core, we are focused on delivering the highest-quality insights to brands.

We started with review data, a post-purchase source making it highly credible but often challenged by duplication due to syndication. Once we perfected that, expanding our AI engine to handle social media and survey data was a natural next step.

Revuze does not stop at analysis. Our AI goes further by offering data-backed recommendations and activities that help brands move confidently from insight to action.

2. What technologies are currently employed to process and analyze large volumes of unstructured consumer feedback, and how do they integrate with your existing systems? 

Since 2011, Revuze has been investing and optimizing its own proprietary Generative AI and LLMs, well before ChatGPT took the spotlight. Over the years, we've focused on refining and optimizing our large language models to deliver unmatched precision in analyzing VoC data.

Our GenAI engine detects tone and context with remarkable accuracy. Take battery life, for example. It's important across many industries, but context matters. A one-hour battery life is unacceptable for a smartphone, yet impressive for a drone. Our AI is trained to understand these nuances based on the product category, ensuring insights are both relevant and accurate to support brands’ decision-making.

This deep expertise powers best-in-class topic extraction and sentiment analysis across more than 1,500 product categories. In addition, Revuze integrates advanced "off-the-shelf" LLMs, such as Anthropic’s Sonnet, for specific tasks like generating review summaries, further enhancing our platform's capabilities.

3. In what ways are you streamlining cross-functional collaboration between departments (e.g., marketing, product development, customer service) to act on AI-driven recommendations?

One of the core challenges many companies face is siloed data, where different teams rely on separate sources. This often results in inconsistent insights and misaligned decisions. ActionHub addresses this by bringing all teams together, marketing, product, eCommerce, and consumer insights, on a shared foundation. While each team has a customized hub tailored to their needs, everyone works from the same unified data set. This includes review and rating data, social media, and survey responses, all accessible in one place.

The platform combines quantitative and qualitative data in a visual format, making it easier to uncover not just what consumers are saying, but why they are saying it. Regardless of role, users can explore the drivers behind consumer behavior in a way that is both intuitive and consistent.

To encourage collaboration, we have developed cross-functional use cases that help teams align on opportunities and next steps. For instance, product teams can use insights from the ProductHub to identify purchase motivators and align them with product strengths, guiding marketing on which messages are most likely to resonate. Similarly, marketing can collaborate with eCommerce teams to refine product detail page (PDP) content based on consumer feedback, improving online performance.

We have also prioritized integration with enterprise tools such as Microsoft Copilot and Claude. This allows organizations to combine VoC insights with internal sales data, enabling a more holistic view of the customer and supporting advanced querying.

4. How does your organization track and analyze the effectiveness of AI-generated insights in improving product features, marketing campaigns, and eCommerce performance?

At the core of the Revuze platform is a recommendation engine powered by proprietary large language models, which is also supported by external LLMs for tasks such as summarization and validation. The system is designed to turn VoC data into practical, tailored recommendations across a wide range of use cases.

In the MarketingHub, this includes support for content creation, whether it’s creating social media posts that resonate, enriching product detail pages, or creating influencer kits and data-backed video scripts - all based on the VoC data. On the product side, the ProductHub offers data-driven suggestions, ranging from addressing specific product pain points to effective innovation planning.

A key focus of the platform is helping teams explore new ideas. For example, product managers can begin with addressing unmet needs that are based on consumer review data, and the AI engine surfaces relevant trends from social media, drawing inspiration from entirely different industries. A concept in footwear might emerge from patterns identified in automotive or electronics. The data sets are being used in different ways, ways that always leverage their strengths.

These examples illustrate how the platform is built to support cross-functional teams in turning consumer feedback into actionable insights and creative exploration.

5. How is your organization preparing for emerging trends in consumer behavior analysis, such as the use of large language models (LLMs) and advanced AI in deriving insights?

Our organization is closely engaged in the advancement of AI and LLMs, with a focus on how they enhance consumer behavior analysis. We see LLMs as tools that complement traditional research methods by automating tasks like sentiment analysis, theme detection, and text summarization. This speeds up the research process and allows teams to access deeper insights with agility.

We use a combination of proprietary and third-party models, applying each where it adds the most value. Internal models help with contextual understanding in specific product categories, while external models support tasks like content summarization.

Our approach remains flexible and research-driven, allowing us to adapt quickly to new technologies while maintaining accuracy, relevance, and responsible AI use. As consumer data sources evolve, we are committed to evolving with them to support timely, data-informed decision-making.

6. How are you ensuring that your approach to consumer insights remains adaptable to evolving technologies and competitive pressures?

We stay informed about technological developments and evolving industry trends, continuously exploring new tools and methodologies. By maintaining an open and collaborative approach with partners and actively testing emerging technologies, we strive to remain adaptable to a wide range of research needs.

Our platform is designed to support both quantitative and qualitative data analysis and to accommodate various research approaches and needs. This flexibility allows us to work effectively with organizations across different sectors, helping them access and interpret the insights most relevant to their goals.

1. How is your organization adapting its media investment strategies to leverage the rapid growth and innovations in CTV advertising? 

Kargo is adapting media investment strategies for its advertisers across three key pillars of innovation: context, commerce, and creative. These three pillars represent opportunities for advertisers to do more with CTV advertising than they ever did with traditional TV advertising. CTV is a medium where advertisers can use data-driven ad creation, targeting, dynamic ad serving, and interactivity to create more engaging, relevant experiences for audiences. Tapping into the context of the content around an ad, with an opportunity to sell products and measure the impact of advertising on sales, and to reinvent creative formats is not only exciting, it’s been shown to deliver higher returns for advertisers. 

For context we are using scene-level targeting by analyzing streaming video content to serve ads based on specific scenes, emotions, dialogue, or on-screen elements. This has been used by brands like Merci Chocolate to run their ads strategically timed with “giving” moments within a show.

For commerce, Dynamic Product Ads (DPAs) allow advertisers to customize big-screen ads using SKU-level data, geo-targeting, and behavioral insights, producing highly personalized creative variations. Mondelez is an example of a brand that has created ads that allow a viewer to click and purchase directly from the creative. American Eagle Outfitters used CTV commerce ads to pull in different product imagery to personalize ad creative for specific target audiences. 

On the creative front, high-impact ad formats—including Squeezeback, Glass and new offerings such as Mirage, Flipbook, and Tiles—maximize viewer engagement by integrating unique ad experiences seamlessly into streaming content, Smart TV interfaces, and standard ad pods.

2. How are you integrating advanced targeting techniques, like scene-level contextual targeting, into your media planning to enhance relevance and engagement?

Traditionally CTV contextual targeting has been performed at the level of an entire TV show, film, or live event. This approach only allows for targeting CTV ads in the stream based on the genre or category for the entire content. Although this is useful in specific scenarios, there are limitations due to the broadness of the method. With scene level targeting on CTV, there are more granular opportunities to drive high levels of relevance and engagement. For example imagine targeting moments for scenes in a video stream as follows: a “credit card swipe” for a card provider, a sports car driving along a scenic mountain route for a new fast car brand, or a happy scene with newborns for a baby clothing advertiser. Every scene in a show, movie, or live event, labeled by keywords, can be a powerful opportunity to reach consumers.

We are also incorporating AI to evaluate content on a much deeper level. Rather than relying only on a show description, we can understand many different aspects of the content including dialogue, what’s present in the scene, even sentiment and emotion. 

3. What technologies are currently employed to facilitate dynamic product ads and real-time customization in your CTV campaigns? 

Kargo has developed a video technology platform that can dynamically render thousands of variations of a high-definition high quality CTV ad based on unique attributes such as geo-location, audience segmentation, and/or product catalog items. The technology is able to extract only those specific product catalog items from large-scale product catalogs which are relevant for a user in a specific location based on proximity to a store, and then composite the visual image of that product into video content. These dynamically rendered creatives bring an optimal experience to the big screen in the living room, and so can be considered “Television Creative Optimization” or TCO, a next evolution of dynamic creative optimization. In addition to CTV, the technology extends to cross-channel experiences for the open web and social platforms, so the same consumer is reached across several touchpoints for maximum impact. For retailers, this is a game-changer, enabling highly targeted creative variations at scale that would previously have been impossible to produce.

Our Narrative product helps advertisers customize creative even if they do not have high quality video assets. Our AI-based technology uses existing content including display ads and website content to build CTV-ready campaigns that help many more advertisers tap into this channel. 

4. In what ways are you streamlining the creative development process to produce high-impact CTV ads without relying on extensive pre-existing assets?

We are truly a company that combines creative and technology in everything we do. Kargo has a global team that includes more than 50 creative designers. These visual designers bring a high level of artistic talent to creating powerful brand experiences across screens. This includes work we do for unique ad formats such as Squeezeback, Commerce, and Glass ads that require different creative designs vs. traditional TV creative. As part of the designer workflow they are using generative AI tools to complement and extend their work.. With Narrative, our team uses Generative AI to create a high-definition high quality TV ad using models for audio soundtracks / voiceovers and a dynamic composite of the still images resulting in a video stream. Another example is the use of generative AI to produce full motion highly realistic video which can be composited into custom creative CTV content built out for advertisers.

5. How does Kargo’s multichannel focus help you elevate CTV performance for your advertisers? 

Kargo offers measurement capabilities for CTV that mirrors what programmatic advertisers have had for years for more traditional display and video, effectively extending a depth and breadth of metric driven methodologies onto the big screen in the living room. For example, an extensive set of measurement attributes such as brand preference, purchase intent, consideration, brand awareness, brand lift, ad recall, brand favorability, attention, brand familiarity, ad memorability, store visits, and more are all available when working with CTV. This type of multichannel measurement capability allows advertisers to validate the performance benefits of context, commerce, and creative, while blending branding with performance in an integrated package.

6. How are you ensuring that your CTV advertising approaches remain adaptable to changes in consumer behavior and media consumption patterns?

Kargo is pursuing a multi-pronged strategy to ensure that its approach to CTV advertising is flexible and fluid, aligned tightly with consumer trends and behavior. First, Kargo brings art and technology together to drive results, innovating with high-impact omnichannel ad formats, delivering creative that is literally and metaphorically “out of the box”. Second, Kargo takes social assets like short form video in portrait mode, and repurposes these onto the big screen in the living room, fusing social and CTV into a new experience. Third, Kargo provides contextual targeting at the scene level which is privacy compliant respecting consumer preferences while still delivering a high degree of relevance to what the consumer is streaming. This combination of creative and context platforms honors consumer behavior trends and media consumption patterns in a new way.

1. What are the biggest challenges in market research today, and how are you adapting to them?

The challenges depend on whether you're looking at survey research or qualitative research. For surveys, the big one is data integrity. Artificial intelligence and bots have introduced ways for respondents to cheat, so quality control has become more important than ever. We’ve implemented very rigorous quality control protocols, including safeguards like disabling copy/paste functions in our surveys and flagging suspicious behavior. Even if someone uses AI to generate responses, we make it harder for them to directly plug that into the survey.

On the qualitative side, recruiting knowledgeable respondents is getting tougher. There are so many third-party recruiters out there now that the audiences we used to rely on are fatigued they’ve become somewhat blind to outreach and more difficult to recruit. Cold calling is nearly obsolete. Nobody answers a call from a phone number they don’t recognize anymore.

To adapt, we’ve embraced smarter targeting and leaned into select third-party recruiting platforms. We’re also meeting a new challenge: client expectations have become more sophisticated. That’s a good challenge—it forces us to get sharper and deliver more precise insights.

Of course, incentives are more important than they were a decade or so ago. In the past, we conducted thousands of interviews with no incentives—just cold calls and a clear explanation of what we were doing. That doesn’t work anymore. Time is our most valuable commodity, and we now compensate nearly every type of participant, whether it’s a survey respondent or an interviewee. But incentives come with their own challenges. When money is involved, there’s a stronger motivation to game the system. That’s why we’ve evolved not only how we recruit but also how we verify participants and their responses. Data quality and incentive strategy are now tightly intertwined.

2. What role does innovation play in market intelligence, and how do you plan to stay ahead?

Innovation is everything. We’re using methods today that perhaps didn’t exist even five years ago. Take willingness to pay research—it’s becoming more common, and we’re also seeing a resurgence in conjoint analysis, which had gone quiet for a while.

Sometimes we have to invent new methodologies altogether. One example is our “Benefit-Value Analysis” methodology, which we created to help a client to get information about a proposed product iteration without revealing what the product actually was. We essentially devised a way to survey the market to determine the value customers placed on various product benefits, honing in on an actionable calculation of market value and, therefore, pricing strategy. It worked incredibly well, and now it’s part of our toolkit to apply to other research projects..

We’re also investing in an area we pioneered known as “Emotional iIntelligence”—understanding how emotions drive buying decisions, both for consumers and B2B audiences. Our linguistic-based emotion analysis can be applied to any survey or even retroactively to existing research. Proprietary to our firm, we developed this methodology in partnership with Dr. Tom Snyder, a noted psychiatrist with a PhD in neuroscience, who had become fascinated with the human expression of experienced emotions. The tool is AI-assisted, but always human-guided—we never rely on AI alone to interpret emotions. Context is everything. We are fond of saying, Artificial Intelligence + Human Intelligence = Augmented Intelligence.

3. What industries are seeing the most disruption in consumer behavior, and how does research help brands stay agile?

Honestly, it’s easier to ask which industries aren’t seeing disruption. That said, automotive is a great example of an industry in which consumer preferences and market adoption has undergone significant disruption in recent years. Specifically, the EV surge hit hard initially and then eventually pulled back. As a result, many of our clients in that supplier space—especially those tied to battery production—are over-capacitized. That’s created a lot of uncertainty. As consumer demand has shifted, so too have their manufacturing and supply chain strategies. Understanding how disruption is moving markets is key to innovation and planning.

Inflation has also had a major effect across industries. Food service, consumer electronics—pretty much any industry that touches consumers—are seeing shifts. Political and global economic factors add even more variability.

This is where market sizing research plays a huge role. We help clients see where their markets are going, not just where they’ve been. For one industrial manufacturer, we’ve done global market sizing to understand how their opportunities have shifted due to macro changes. Businesses need to know “where the hockey puck is going” (not just where it is today)—and we help them see that so they can make informed decisions about where, when and how to pivot, if appropriate.

4. In the era of privacy, how can companies ensure ethical data collection while still gaining valuable insights?

Ethical data collection is a huge priority, especially with privacy concerns growing. We use multiple layers of quality control—some are traditional, like attention checks within surveys, but we also use tools to detect bots and AI-generated responses. We track time-on-task and look for patterns in the data to make sure it's human and thoughtful.

Because we use third-party panel providers, there’s a built-in layer of anonymity. We don’t know who the respondent is, and neither does our client. That’s why our QC has to be rock-solid—we can’t go back and ask someone to clarify. It also means our respondents participate voluntarily and with full awareness. Multiple levels of isolation between client and respondent and researcher and respondent help to ensure that responses are authentic and complete, with no influence by those conducting the survey or commissioning the project.

5. What new trends in market research are emerging, and how should businesses prepare for them?

Of course, AI is the obvious trend, but it’s not the silver bullet people might think. We use it cautiously and mostly for support, not for decision-making itself. Privacy issues prevent us from using generative AI tools like ChatGPT for client-specific questions—those can’t become part of the public domain, which would happen if we entered them into publicly accessible large language models.

The less obvious but equally important trend is the rise of strategic pricing research. Since COVID, we've done more pricing work than we did in the previous 20 years combined. Businesses are trying to figure out not just what the market will bear, but what’s a fair price based on the value they provide.

There’s been so much uncertainty—pandemic, supply chain chaos, inflation, tariffs—and now many organizations are creating new roles internally just to address pricing strategy. They want to know how they stack up against competitors and whether their value proposition matches the price. That’s where we come in with tools that go well beyond guesswork.

Final thought: As market research continues to shift in response to new technologies, economic forces, and evolving consumer behaviors, staying ahead requires a mix of innovation, adaptability, and rigor. Whether it’s navigating evolving technologies, tightening privacy regulations, or responding to the ripple effects of global economic shifts, market research today is as much about adaptation as it is about insight. Success will come in constantly refining the tools, asking smarter questions, and never settling for surface-level answers.

1. How does modular technology enhance flexibility and scalability in retail media networks?

Modular technology replaces the rigid, all-in-one retail media stack with a flexible architecture designed for change. Instead of locking retailers into a single solution for both sell-side and buy-side needs, modular systems decouple those layers so you can plug in a new DSP or update your ad server without rebuilding your entire tech 

For example, when The Home Depot launched Orange Apron Media, they didn’t settle for an out-of-the-box platform. Instead, they built a modular retail media stack around Pentaleap’s technology prioritizing interoperability, customization, and control.

This kind of setup makes it easy to scale. Need to add a new DSP? Add a new offsite partner? You can do it, and without disrupting everything else. That’s the power of a modular approach: faster upgrades, smoother integrations, and future-proof flexibility.

2. How does this approach compare to traditional retail media platforms, and what advantages does it offer?

Traditional retail media platforms were built as closed ecosystems—bundled tools, limited interoperability, and one path to monetization. That model may have been serviceable early on, but it limits innovation and control.

A modular approach changes the equation.

• Best-in-breed components: Retailers can select the strongest tech for each part of the stack.

• Reduced lock-in: No dependency on a single vendor’s roadmap or timelines.

• Lower total cost of ownership: Competitive pressure drives down costs and keeps margins healthy.

Again, when The Home Depot launched Orange Apron Media, they didn’t buy a monolith—they built a modular platform with Pentaleap at the core. This allowed them to customize the UI, optimize for supplier experience, and scale onsite and offsite offerings at their own pace.

With modular retail media, retailers aren’t limited to a fixed toolkit they build the platform that fits their business, not the other way around.

3. How can advertisers and brands maximize their ROI through modular retail media solutions?

With modular retail media, more of a brand’s budget reaches actual media, not middlemen. Lower ad tech fees = higher media efficiency and ROI.

It also opens the door to tighter integration with a retailer’s own site; search, personalization, and AI can all feed directly into ad delivery. 

That means more relevant placements, better shopper experiences, and higher conversion rates. It’s performance marketing with native intelligence built in.

4. How does this technology improve targeting, attribution, and measurement in retail media?

Retail media thrives on search—because intent is clear. Someone searching “dog food” is already halfway to checkout. That kind of context-based targeting consistently outperforms audience-based methods that guess at intent.

But targeting alone isn’t enough. What really matters is how ads are served.

Most platforms disrupt the shopper's journey with ads that feel bolted on. Their strategy is to push more, better, above-the-fold placements, but often at the cost of relevance.

Pentaleap’s Fluid Ad Server takes a different approach.

If a sponsored product is relevant and likely to convert, it earns top placement. If it’s not, it doesn’t show. Simple as that.

Some clients have told us that this approach makes it feel like your search algorithm was built to handle ads.

And the result is a native, seamless experience where paid content feels like part of the journey—not an interruption.

And because Fluid understands both organic and paid signals, high-quality ads naturally rise to the top. Poor performers are filtered out; not just by bids, but by relevance.

This isn’t about cramming in more ads. It’s about serving the right ones, in the right places, at the right time.

That’s how retailers increase yield without compromising experience, and how brands get attribution they can trust.

5. What role will privacy-compliant data strategies play in the success of modular retail media?

Privacy is foundational. Retail media already has a major advantage here: it runs on first-party data. Add in modular infrastructure and you get even more control; particularly around how that data is used.

We double down on contextual targeting rather than relying on identity graphs or third-party cookies. That’s good for privacy, and it’s future proof by design. The result: sustainable advertising that respects the customer and scales responsibly.

6. What trends are shaping the future of retail media networks and their role in the broader digital advertising ecosystem?

As usual, the trends are predicted by the biggest obstacles. One of the biggest challenges right now for both retailers and brands is fragmentation. Many retailers build their own walled gardens, which makes it harder for brands to buy and harder for retailers to sell.

Real-Time Bidding (RTB) is a huge breakthrough trend positioned to solve fragmentation—but it’s not the RTB you remember. This isn’t about cheap remnant inventory, opaque auctions, or poor targeting.

This is a new wave of RTB, built specifically for on-site Sponsored Products with:

• Ultra-low latency (under 30ms response times)

• Cloud-native, co-located infrastructure for performance

• Relevance-first, dynamic ad serving tech

RTB is democratizing retail media in a few ways:

• Brands will access retailer inventory more easily, from platforms where they already spend.

• Retailers will unlock demand from a broader set of advertisers, including smaller brands that couldn’t access them before.

• And the entire ecosystem will move closer to a more open, standardized landscape—finally breaking through the walled garden deadlock.

Pentaleap has been RTB-native from day one. We’re already live with 17 retailers—and what we’re seeing is clear: this model works. It’s faster, fairer, and more flexible

1. What measures are in place to ensure brand safety and suitability when utilizing AI-powered platforms for podcast advertising?

Sounder is the cornerstone of our brand safety and suitability framework. Sounder's AI transcribes, analyzes, and categorizes conversations within podcasts, creating detailed topic maps and IAB analysis for each episode. This goes beyond basic keyword identification to understand context and appropriateness of content.

Sounder's brand suitability system also allows advertisers to establish customized parameters that align with their specific values and messaging guidelines, ensuring ads only appear in contextually relevant and brand-appropriate environments. Since acquiring Sounder in 2024, we've integrated the system into Triton’s full ad stack for direct sold and programmatic podcast advertising using pre-bid targeting – meaning that instead of simply observing and reporting on content once published, we’re able to ensure that content is classified before publishing and avoid targeting leakage or errors.

2. How has the integration of AI-driven contextual targeting impacted your organization's podcast advertising revenue streams?

While we don’t share specifics, the integration has positively transformed our revenue landscape in several ways. We've seen substantial inventory expansion as previously untapped content becomes viable for advertising. Content that might have been overlooked in traditional genre-based targeting can now be monetized at an episodic level when they contains relevant conversations, creating new advertising opportunities without requiring additional content production. We've also seen increased advertiser confidence and spending attributed to their use of tools like Sounder. Buyers appreciate the precision targeting capabilities and brand suitability assurances, leading to higher investment levels through our Audio Marketplace. For our podcast creators, this has translated to increased revenue streams as their content attracts a broader range of advertisers beyond their primary category.

3. How do you evaluate the potential of AI-enhanced podcast advertising in reaching new markets and audience segments?

Through Sounder, advertisers can reach relevant audiences regardless of podcast genre, significantly expanding their reach. For instance, an automotive brand can target tech podcasts discussing transportation innovation. We're also analyzing how contextual targeting helps advertisers connect with niche interests that transcend standard demographics. This creates opportunities to reach underserved audience segments with highly relevant messaging. Recently, Sounder expanded its capabilities beyond English to include Spanish content, opening new doors for advertisers looking to connect with the rapidly growing Spanish-speaking audience. We continuously measure Sounder’s impact through performance metrics, advertiser feedback, and market expansion indicators to refine our approach and maximize the potential of this technology, always keeping an eye toward identifying untapped opportunities where AI can connect advertisers with receptive audiences they might otherwise miss.

4. How does your organization assess the effectiveness of AI tools in maintaining brand integrity across diverse podcast content?

Sounder provides deep contextual understanding beyond simple keyword matching, which has proven particularly effective for content from diverse creators whose conversations might be misclassified by less sophisticated systems. The effectiveness of our approach can be seen in our partnership with Urban One, where our contextual AI analysis reduced content restrictions from 92% to just 11% of their catalog, dramatically increasing monetizable inventory while maintaining brand safety standards.

5. What role do you foresee AI playing in the evolution of your organization's podcast advertising strategies over the next 3-5 years?

AI will fundamentally transform advertising strategies over the coming years. We'll move beyond topic identification to more sophisticated analysis of conversation dynamics, emotional context, and cultural relevance. This will create even more nuanced targeting opportunities for advertisers.

AI also opens opportunities for better podcast advertisement creation, in response to briefs, or creation in other languages. It can streamline the production process, enabling rapid generation of localized or tailored ad creatives that resonate more deeply with diverse audiences.

Throughout this evolution, we remain committed to balancing technological advancement with human oversight to ensure responsible and effective deployment of AI in advertising while maximizing returns for both advertisers and content creators.

6. How is your leadership team preparing for emerging trends and innovations in AI-powered podcast advertising?

Our leadership team is focused on strategic expansion of our AI capabilities through targeted acquisitions and partnerships. Our acquisition of Sounder in March 2024 represents a cornerstone of this strategy, bringing sophisticated AI-powered audio intelligence to our ecosystem where Sounder is also being integrated into our CMS offerings for workflow enhancements.

We're continuously enhancing these capabilities, as evidenced by our recent integration of Sounder with Spreaker in March 2025, enabling advanced contextual targeting across our network of 260,000+ independent creator podcasts. Additionally, our expanded partnership with Audioboom to implement Sounder across their extensive catalog demonstrates our commitment to scaling these innovations industry-wide.

As we look ahead, we're continuing to invest in enhancing our audio intelligence platform while expanding partnerships across the industry, ensuring Triton Digital remains at the forefront of AI-driven podcast advertising innovation while creating value for both advertisers and content creators throughout our ecosystem.

1. How is your organization adapting its content marketing strategies to leverage AI-powered platforms ensuring alignment with brand voice and business objectives? 

At Skyword, we believe AI should work for your brand not the other way around. As more content starts to sound the same, standing out with a clear, authentic brand voice and a focused topic ownership strategy is more important than ever. So, when we think about using AI in content marketing, our priority is making sure it reinforces what makes our own brand and each of our clients’ brands unique its voice, tone, goals, audiences, and focus areas not just churning out generic content.

We engineered our AI-infused content marketing engine, Accelerator360™, to do this with our Configure tool. It’s designed to take in all those brand-specific inputs such as voice, tone, business goals, audience personas, and messaging themes and apply them across every AI-enabled function in the platform so that every piece of content aligns with the brand's identity and strategic objectives and the data that’s used to inform things like AI-generated assignment briefs and campaign plans is relevant and specific to the brand.

To make it easy, brands can simply input their website URL, and Accelerator360™ will automatically pre-fill their Configure fields. It analyzes how the brand is currently presenting itself, identifies inconsistencies or opportunities for improvement, and surfaces where conversations are happening around the topics the brand wants to influence. It also maps out key themes and offers strategic guidance on how the brand should show up in those conversations to lead, not just participate. All of this information is editable, but it’s another way we can use AI-powered insights to actually improve and refine brand identity and strategy rather than copy/pasting inputs from a brand book that might be dated and lack context.

We’re really focused on removing the friction that often comes with getting AI tools to stay ‘on-brand’. If your team has to spend more time fixing the output than it would’ve taken to create it themselves, the tool’s not doing its job. So our goal is to make brand alignment not just possible, but effortless. That way, marketers can spend less time rewriting or re-prompting and more time actually moving the strategy forward.

2. How are you integrating cross-channel content atomization into your marketing efforts to maximize ROI and maintain consistency across platforms? 

This is where the right AI can be a game-changer for marketing teams. It all starts with strategy. We don’t treat cross-channel content atomization as an afterthought it’s something we plan for from the beginning. We identify the high-value, “hero” content we want to lead with, knowing it’ll serve as the foundation for an entire ecosystem of supporting content.

Historically, scaling that ecosystem required a lot of manual effort from marketers and content creators. But with Accelerator360’s Atomize tool, we’ve dramatically changed that. Once we have expert-created, high-quality source material, we run it through Atomize, which instantly generates platform-native content tailored for social, email, blogs, PR, sales enablement, and more. It even adapts tone and messaging based on different audiences and use cases.

What really sets this apart is that it’s not just about automation it’s about relevance. Because we’ve baked in brand-specific inputs and understand how each channel behaves, every asset still feels true to the brand and the environment it lives in.

The impact on ROI is huge. You’re getting more like 10 to 20 times the output from a single asset, and it’s hitting the market faster without needing multiple teams to manually recreate content for each touchpoint. It’s more efficient, more consistent, and ultimately, more effective.

3. How does your organization track and analyze the performance of content assets, and how are these insights used to inform future content strategies? 

At Skyword, performance tracking is baked into everything we do, and it starts well before anything goes live. We pull from a mix of audience insights, channel data, and client-specific intelligence to shape our content strategies from the outset so we’re not guessing what will resonate, we’re informed by where there’s actual interest and intent. Some of that data comes from us and some from analyzing our clients’ existing analytics. 

Once content is in market, we track performance across all key touchpoints SEO and AIO rankings, social engagement, web traffic, email metrics, and how content is converting and influencing sales. This gives us a clear view of how the full content journey is working, from awareness to conversion. We like to track how content is driving key events in this process—so specific audience actions we set out to impact—which tends to help brands stay more focused on the big-picture than if we were to get stuck on optimizing for leading indicators. 

Those insights directly inform the next wave of strategy. We look at what topics and formats are landing, which channels are converting, how seasonality impacts engagement, and where we can fine-tune the message to better speak to the audience’s needs. All of that data gets fed into our system, so that the system and the strategy keeps getting smarter and more effective over time.

4. In what ways are you leveraging AI-generated briefs and performance-enhancing recommendations to improve content quality and efficiency? 

At Skyword, we’re using AI not just to create content faster—but to make it smarter from the start. And we do that with AI-generated briefs and recommendations in a few ways beyond initial content planning and ideation: 

With Accelerator360™, every AI-generated brief is fueled by a mix of the brand-specific intelligence I mentioned earlier and real-time competitive analysis. As the brief is being built, the AI scans the top-performing content for the topic, then crafts a brief designed to outperform it—while automatically adjusting the angle and tone to match the brand’s voice, audience, and strategy. It’s not just about ranking; it’s about standing out and being more helpful than what’s already out there.

Marketers can also access AI-powered suggestions in the content editing and review process—whether it’s for improving quality, adjusting tone, or fine-tuning formatting. And when it’s time for a final polish, our AI Copyedit option is available to give the content that last layer of refinement.

We’re just as focused on helping teams get more from the content they already have. In real-time, Accelerator360’s Audit & Optimize tool can scan all the content across a domain, subdomain, or specific URL, analyze its performance, and make both technical and qualitative optimization recommendations, based on the latest SEO best practices and what’s currently top-ranking on the topic. Our AI can then optimize the content for you automatically or you can choose to implement changes manually. So, even content refreshes are data-informed to ensure content is more competitive and aligned to what audiences are searching for at the moment. 

5. How is your organization preparing for emerging trends in content marketing, such as the integration of interactive and video content formats? 

At Skyword, we’re keeping a close eye on how AI can further enable and improve the creation of content formats like interactive media and video, but we’re doing it with a disciplined, client-first approach. While AI has made incredible strides, there are still some quality and governance hurdles when it comes to fully generating interactive and video content. We govern our adoption of this technology based on the quality of output, governance protecting our clients’ data, and ensuring what’s produced is authentic and ownable.

Right now, we use AI to support the content creation process for interactive designs and video where it makes the most sense. For example, we leverage AI and data to develop video scripts and design guidelines, which are then handed off to our global talent network of expert human creatives to bring to life. This hybrid approach ensures the final product meets the high standards our clients expect.

Looking ahead, we're preparing to accelerate this process even further. Soon, we’ll be able to atomize human-crafted source content into short-form visual stories instantly—giving brands even faster ways to amplify campaigns across multiple channels, without sacrificing quality or originality.

In short, we’re ready to adopt emerging formats as soon as the technology meets our standards and in the meantime, we’re making sure our clients have the best of both worlds: the efficiency of AI plus the creativity and craft of top human talent.

6. How are you ensuring that your content marketing approaches remain adaptable to changes in consumer behavior and technological advancements?

Adaptability is at the core of our content marketing approach. When we built Accelerator360™, we didn’t just design it to work with a single AI model or a fixed creative workflow—and we certainly didn’t leave out the human element. We intentionally built in the flexibility to tap into our global network of expert creative talent wherever it's needed. Having both AI and human-driven options isn’t just nice to have—it’s critical to staying adaptable in today’s environment.

On the AI side, Accelerator360™ leverages a dynamic mix of large language models currently more than nine each selected for their strengths in specific applications. This "plug-and-play" approach allows us to constantly match the right technology and talent to the task, whether it’s boosting creativity, ensuring brand consistency, speeding up production, or responding quickly to shifts in consumer behavior. As models improve or new ones become available, we are ready to make them available to our clients.

We understand that modern content marketing isn’t one-size-fits-all. It’s about balancing creativity, differentiation, quality, speed, modularity, audience insights, and brand authenticity while also managing time and budget realities. We are committed to giving our clients access to the best and latest technologies, but we always keep creativity and brand integrity at the center of everything we do.

1. How do you see AI-powered search evolving in the next 3-5 years?

AI-powered search is evolving fast, and we’re just getting started. The way people search is already shifting, thanks to AI models that don’t just return links but summarize, expand, and refine queries in ways that make searching way more intuitive.

Over the next 3-5 years, we’ll likely see search engines becoming more specialized. Google has a massive advantage in real-time indexing and location-based search, while newer players like Perplexity are leaning into research-heavy use cases with built-in citation tools. Just like we already turn to different platforms for different needs Google for general searches, Amazon for products, YouTube for videos we’ll see AI-powered search engines carve out their own niches.

For businesses and marketers, that means adapting. AI-driven search engines prioritize authority, so it’s more important than ever to be a trusted, go-to source in your domain. High-quality, insightful content that establishes expertise is key because AI models will be surfacing and citing the most credible sources.

That said, AI-driven search also brings challenges. With platforms licensing data from sources like Reddit to train models, questions around content moderation, bias, and misinformation will only grow. AI engines aren’t just retrieving facts anymore they’re presenting perspectives, which means credibility and accuracy are critical.

The bottom line? The next generation of search will be smarter, more conversational, and more fragmented across different AI platforms. If businesses want to stay ahead, they’ll need to focus on authority, adaptability, and making sure they show up where it matters.

2. What are the biggest challenges organizations face in adopting AI-driven search solutions?

In my view, AI-powered search is changing the game just like mobile did years ago. The way people find information is shifting from traditional search to AI-driven experiences, where AI agents are doing the searching for them.

For brands, this means a new playbook. It’s no longer just about ranking on Google; it’s about showing up where AI models pull their information from AI Overviews to ChatGPT and Perplexity. In the past, if you ranked well, you had a shot at telling your story when someone clicked on your site. But now, AI decides which brands to feature, how they’re ranked, and sometimes even what to buy. The real challenge for brands today is figuring out how to adjust their marketing strategies for this new AI-driven world.

Enters BrightEdge’s new tool, AI Catalyst, to help brands see how they’re showing up across AI platforms like ChatGPT, Google AI Overviews, and Perplexity—and gives them the insights they need to stay visible and relevant. Everything’s in one place, so teams can stop guessing and start acting.

Just like we optimized for mobile, now we have to optimize for AI.

3. What are the key data security and privacy challenges associated with AI-powered search tools?

When I look at the AI search industry and speak with brands, user privacy and data collection consistently emerge as top concerns. Another major challenge is the need for sensitive company data to train AI models, which makes many businesses hesitant to adopt these technologies due to security, accuracy, and compliance risks. 

AI is still evolving, and we are actively refining solutions for content moderation and bias to ensure information remains accurate and credible. While progress is being made, there’s still work to do to get it right. Platforms like DeepSeek are introducing more transparency into how they generate answers, but many AI models still operate as black boxes. Brands need to prioritize AI-driven search solutions that provide clarity on how data is processed and surfaced.

4. How do organizations differentiate themselves in a market increasingly influenced by AI-driven search capabilities?

There are a couple of key ways organizations can stand out in an AI-driven search landscape, starting with building authority and trust. AI-powered search prioritizes credible sources, so brands that consistently publish expert-driven, well-structured content will surface more often. 

Second is optimizing for AI-driven search. Unlike traditional SEO, AI search engines rely on contextual understanding. Structured data, schema markup, and multimedia formats help AI interpret and categorize content effectively. The goal isn’t just ranking but ensuring AI can synthesize and serve your insights in meaningful ways.

Finally, search is fragmenting. Different AI models prioritize different search behaviors, so brands must ensure visibility across platforms like Google, ChatGPT, and Perplexity. Optimizing for one platform isn’t enough anymore. In sum, it's not just about ranking—it’s about becoming the source AI trusts.

5. How can AI-driven search be effectively integrated with other technologies like automation, analytics, or personalization engines?

The future of search is deeply interconnected with automation, analytics, and personalization and can be extremely powerful when well-integrated into these other areas of a business. For example, automation streamlines SEO workflows by handling tasks like keyword research and performance tracking, freeing up marketers to focus on strategy. And personalization engines use AI to understand user intent and deliver tailored content, experiences, etc., which boost engagement and conversions. By blending automation and analytics, businesses can refine their strategy dynamically, which in turn creates hyper-relevant, hyper-personal experiences that drive engagement and conversions.

6. What are the key metrics businesses should track to evaluate the effectiveness of AI-powered search?

You can start with the basics how you're performing in traditional search. Then, look at how visible you are in AI-generated responses and how people engage with them. Forget just tracking clicks; with AI doing most of the answering, that doesn’t tell the full story. Focus on metrics like impressions, share of conversation, and sentiment in those AI results. The first step is understanding where your brand shows up in AI responses, what the sentiment is, and how it compares to competitors. 

While clicks still matter in traditional search, with AI, it’s about how your brand is reflected in the answers. Tracking query refinements—whether users adjust their searches after an AI response—could be useful, but it's tough since only AI engines have that data. By understanding which metrics are available and then focusing on the ones that directly impact your brand presence, you can refine your strategy and stay ahead in the AI search landscape.

7. Do you believe traditional search engines will remain dominant, or will AI-driven alternatives redefine the search landscape?

With new AI-powered search platforms emerging every week, it may seem like AI-driven alternatives could overtake traditional search engines. But it’s still too early to tell. In reality, I believe traditional search engines and AI-driven alternatives will coexist, each serving distinct and evolving user needs. We’re already seeing this unfold with the rise of specialized, vertical search engines designed for specific industries and content types. Our latest research highlights the potential of DeepSeek’s "do it for me" approach where AI thinks first, searches second to redefine how users engage with search. There’s no question that AI-driven search is fundamentally reshaping the landscape, impacting both brands and consumers. Yet, Google still commands 92% of the market, and AI-powered search continues to rely on established SEO principles. As this transformation accelerates, the key for brands is to stay agile and optimize for visibility across all AI-powered search experiences.

1. How are you leveraging insights from industry events like the Marketing Procurement iQ Conference to inform your agency management practices?

Our industry is in a transformative learning phase an era defined by partnership capital. We’re no longer operating in silos; strength now lies in how deeply we collaborate. That means forging tighter bonds not just between brands and agencies, but also between marketers and procurement teams. The energy is palpable at every industry event, where real-world practitioners take the stage to share bold ideas and hard-won lessons. Their insights are the compass points guiding us forward. Because in this business, if you’re not learning, you’re not evolving—and if you’re not evolving, you’re falling behind. Collaboration is the new currency. Growth is the reward for those willing to invest.

2. In what ways are you redefining success metrics in agency partnerships beyond cost and delivery timelines? 

Delivering work on time and on budget is table stakes a tactical necessity, not the finish line. It’s merely the tip of the iceberg when it comes to measuring true success in client/agency partnerships. Today’s brands demand more. They look to agencies not just for execution, but for strategic firepower, bold ideas that cut through the noise, and breakthrough innovation that transforms how they engage their audiences.

With such a broad spectrum of agency contributions, the definition of performance is expanding—and so is the list of KPIs. From effectiveness and efficiency to creativity and collaboration, we must measure what we truly treasure. Because in modern partnerships, value isn't just delivered—it's demonstrated.

3. What innovations have you implemented that enable your brand to lead rather than follow in agency collaboration? 

In today’s fast-paced economic landscape, innovation isn’t optional it’s a mindset, a method, and a mandate. Brands and agencies alike are in constant pursuit of smarter, faster, and more impactful ways to operate. Whether it’s harnessing technology to streamline workflows, reimagining processes to unlock efficiency, or driving productivity through more structured and collaborative team dynamics—innovation is the fuel that powers progress. As a leading software platform provider, we don’t just follow innovation—we build it. Our solutions are designed to elevate partnerships by enhancing collaboration, sharpening communication, and simplifying reporting. Now, we’re going a step further—embedding AI to supercharge these tools for marketing, procurement, leadership, and agency teams alike. Because true innovation doesn’t just improve how we work it transforms what’s possible.

4. How are you balancing central oversight with regional autonomy in managing diverse agency ecosystems across geographies? 

Today’s brand advertisers navigate extraordinary complexity spanning multiple business units, brands, regions, and audiences. Some operate with a centralized command, others empower local markets with greater autonomy. This intricate landscape demands an equally sophisticated agency model one that flexes, adapts, and scales with precision. To succeed, the agency ecosystem must be carefully calibrated to reflect these diverse realities. Often, brand strategy is centralized for alignment, while execution is decentralized to honor regional nuance. In this model, a lead agency may serve as the strategic anchor coordinating across markets and agency partners to ensure a unified brand voice, wherever the message lands. Because in a world of complexity, clarity—and the right collaboration model is everything.

5. With the growth of agency relationships in Europe and other international markets, how are you tailoring your agency strategies regionally while maintaining global consistency? 

Every advertiser must architect an operating agency model that aligns global vision with regional realities and local ambition. It’s a delicate balance—where the global team sets strategic direction, while regions are empowered to adapt and execute within tailored guardrails. The degree of flexibility varies by market and mandate, but the goal remains the same: relevance without sacrificing consistency. In response, agencies must flex with purpose adapting their approach, structure, and team composition to mirror the client’s organization. By doing so, they ensure alignment, responsiveness, and impact at every level. Because true partnership isn’t one-size-fits-all it’s custom-built to serve global goals and local genius.

6. How is your organization ensuring continuous improvement in agency relationships to drive long-term marketing ROI?

Feedback is the heartbeat of every high-performing relationship. It fuels growth, fosters trust, and drives continuous improvement. The most effective partnerships embrace it—not just at the finish line, but throughout the journey. That’s why most leading brands conduct year-end client/agency performance reviews, with many adding mid-year check-ins to ensure alignment and avoid surprises. These touchpoints open the door for honest reflection on what’s working, what’s not, and how the partnership can evolve. But feedback doesn’t stop there. Quarterly business reviews offer a regular pulse check on performance, while post-mortem meetings provide space for candid conversations and lessons learned. Because when feedback flows, relationships grow—and great work follows.