1. What measures are in place to ensure that your brand's messaging remains consistent and resonates across diverse channels and audiences?

In 2024, Beyond Trending underwent a thorough rebranding process where we analyzed our company values, vision, voice, and overall messaging. It was a big project with plenty of exploration to truly uncover who we are as a company, the types of clients we serve, and what makes us stand out in the crowded PR industry. Thankfully, when we emerged from that process, we felt crystal clear in who we are – and who we are not. We are the opposite of a big-city agency as we move fast and get stuff done. We are strategists who are confident enough to question our clients instead of just saying ‘yes.’ And we never follow a copy and paste model as we know that PR success is dependent on thoughtful and creative storytelling tactics. 

Just as we say to our clients, consistency in your messaging is critical – so we of course must practice what we preach! We don’t try to shift to fit a customer prospect that doesn’t align with our strengths, or offer services that don’t fall within our wheelhouse. We remain consistent across all marketing channels and have messaging guidelines and best practices to further enforce that approach. Because we work with three main categories of clients – tech companies, startups & VCs, and nonprofits – we use those sectors as a benchmark for the content we produce to keep our core audiences engaged. 

2. What technologies are currently employed to monitor and analyze media coverage, and how do they inform your PR strategies? 

In terms of monitoring to ensure we don’t miss any client coverage, we use a broad range of tools – from basic to more complex, as every monitoring service is a bit different. We have Google and TalkWalker alerts as well as more robust tools like Muck Rack and TVeyes. This helps us ensure nothing falls through the cracks. 

By utilizing the analytics capabilities within Muck Rack, for example, we can compare quarter-over-quarter or year-over-year results, conduct competitive analyses, and even see the top publications or writers who cover our clients. This provides us with a picture of where we have strong visibility and as well as areas of improvement.   

3. How does your organization track and measure the effectiveness of PR campaigns in achieving business objectives?

The big question for any company looking to hire a PR agency is ROI and that’s something we’re always seeking to measure in new ways. Some are basic measurements that are standard across all kinds of clients, whereas others are measured in more unique ways based on the specific KPIs that a client wants to achieve. At the start of every engagement, and with a review on a quarterly or half-year basis, we discuss those goals and KPIs with our clients to ensure we’re on track, and if the targets have shifted at all for the business, we can adapt accordingly. 

Then analyzing the effectiveness of a campaign and the media results, we’re tracking the number of media placements, potential reach (UVM), social shares, and advertising value equivalency (AVE). We also often analyze and track competitive share of voice (SOV) as well as review engagement and comments on article placements. 

The real magic happens when we sit down with our clients to learn of the uptick in website traffic, general visibility, and/or secured customers in response to one of our media campaigns. We’ve had many clients share feedback from an investor, partner, or customer who read or heard about them in ‘X’ media outlet and then engagement with the company. 

4. What strategies are being implemented to foster innovation and agility in your PR practices? 

Everyone’s leveraging AI and tools like ChatGPDT in some capacity – and it would be silly not to bolster your team with the ability to save time and accomplish more. But we’re selective in how we use these AI-supported tools. They serve as a great launching off point for brainstorming as well as outlining things like press releases, pitches, or byline articles. But we never use AI to draft full articles, for example, as the content is cookie-cutter and lacks tone; the human touch is 100% still necessary. 

We also use email tracking tools like Mix Max to determine the most opportune time to reach a reporter’s inbox or have visibility into how often they’re looking at our pitches. This helps us tailor and maximize our media relations efforts and ensure we’re giving reporters what they want. 

5. How is your organization adapting its PR strategies to align with evolving media landscapes and consumer expectations? 

The media landscape has changed dramatically not only over the last decade but even the last year, so in order to stay relevant, you must continuously adapt and evolve. On a granular level, we continue to get more targeted and tailored with key journalists we want to build relationships with, and are straightforward and succinct in our outreach to them. We know reporters are time and bandwidth strapped so we’ve learned to get right to it – no fluff. 

Perhaps most important are our efforts to approach communications strategy holistically. What we continue to do is bolster our services through strong partnerships. PR can’t work with a siloed approach, so if our clients are looking for social media, branding, growth marketing, etc., we can pull in the right partners to ensure we’re not only reaching a broad range of media targets, but also incorporating the various ways people consume and digest information. 

6. How are you ensuring that your PR approaches remain adaptable to changes in media consumption and public sentiment?

We continuously analyze traditional as well as non-traditional mediums to ensure our clients are being seen by their core audiences, the ones that drive revenue and growth. That means being familiar with all the ways people consume their news beyond just online, print and television – newsletters, podcasts, social media, and more. 

1. What role does strategic partnerships and technology innovation play in an organization’s growth plan?

At AdCellerant, strategic partnerships and technology innovation are two sides of the same coin regarding sustainable growth. Partnerships allow us to expand our reach, create better outcomes for our clients, and move faster with trusted allies by our side. Whether aligning with top-tier media companies or integrating with cutting-edge platforms, we view every partnership as a way to increase the value we deliver to small and medium-sized businesses.

On the technology side, our innovation engine is always on. We’ve built our Ui.Marketing platform to simplify complex advertising strategies, empowering businesses with automation, data-driven insights, and cross-channel execution at scale. When these two forces—strategic partnerships and technology—work in harmony, they become powerful accelerants of growth for us and our partners.

2. How does the role of a Chief Growth Officer differ from traditional executive roles in driving business expansion?

A CGO is uniquely positioned to examine the entire business and identify scalable, repeatable growth opportunities. While other executive roles might focus on operational efficiency, finance, or sales in isolation, the CGO’s role is to connect the dots across products, partnerships, marketing, and revenue.

At AdCellerant, that means constantly scanning the horizon for emerging technologies, market shifts, and ways to deliver more value to our partners. It’s not just about hitting growth targets; it’s about building the infrastructure, strategy, and culture that enable consistent expansion, without compromising quality or innovation.

3. What are the most significant trends shaping the future of advertising, and how do you plan to address them?

One of the biggest trends is the rise of AI and automation. Advertisers want to do more with less, and automation is becoming a necessity rather than a luxury. I also think it is important to mention the continued diversification of media consumption—Streaming TV, retail media, and first-party data strategies continue to play bigger roles.

We’re addressing these trends head-on by integrating AI-powered features into our platform, expanding our Streaming TV and retail media capabilities, and helping businesses future-proof their strategies in a privacy-first world. We aim to ensure that even the smallest business can access sophisticated, adaptive, and impactful marketing.

4. How is the digital advertising landscape evolving, and what new opportunities should businesses leverage?

The digital advertising landscape is evolving rapidly, with consumer behavior fragmenting across platforms and devices. What’s exciting is that this complexity is creating new opportunities for businesses to reach highly targeted audiences cost-effectively.

For example, small businesses can now advertise on streaming platforms, access premium inventory, and use dynamic creative tools that were once only available to big brands. With platforms like Ui.Marketing, these opportunities are more accessible than ever, helping business owners and lean teams do more with less. The key is to stay flexible, focus on measurable outcomes, and use tools that simplify execution without sacrificing performance.

5. How can businesses balance rapid expansion with maintaining service quality and innovation?

At AdCellerant, growth starts with listening. We stay closely connected to our partners to understand their challenges, goals, and evolving needs. Innovation isn’t about chasing the next shiny feature but solving real problems that drive meaningful outcomes. By keeping customer feedback at the core of our strategy, we ensure our roadmap delivers what truly matters.

From there, it’s about intentionality. We know growth without discipline can stretch teams thin, compromise service quality, and create friction. That’s why we’re actively building scalable systems—standardized workflows, flexible processes, and clear communication frameworks—to support our expansion while continually improving the experience we deliver.

6. What advice do you have for businesses looking to scale their digital advertising operations?

In digital advertising, adaptability is everything. What works today may not work tomorrow—so build a team and a mindset that can pivot quickly, test often, and evolve confidently.

That adaptability extends to the partnerships you choose. Just because something looks like a great idea doesn’t mean it’s the right fit. The right partnership should feel like an extension of your team—aligned in goals, transparent in communication, and built to grow together. Take the time to evaluate. The right partner won’t just check a box—they’ll accelerate your momentum.

Ultimately, clarity is what keeps everything grounded. When you understand your audience, define success, and recognize your gaps, you can make smarter decisions, choosing the right partner or investing in tools that scale with your business.

1. How is your organization adapting your products to capitalize on the growth of gaming as a primary channel for reaching digitally native consumers?

We believe that non-interruptive ad formats are the critical next step in the evolution of in-game advertising. When an ad delays or breaks gameplay, it takes away from everyone’s experience. That’s why we developed an in-game audio ad unit that can initiate alongside gameplay, giving advertisers the opportunity to reach players without harming their gameplay experience. As we look to the future, finding non-interruptive formats is our north star.

2. What role does in-game advertising—particularly non-intrusive formats like audio—play in your clients’ broader customer engagement and brand storytelling strategy?

More than ever, advertisers want to reach people where they know their ads have an opportunity to be seen and heard. In-game ads, especially non-interruptive units, maximize this opportunity for brands. For in-game audio, many brands who have already embraced streaming and podcasting are adding in-game as a way to maximize their reach and improve performance of the audio format.

3. How are your clients leveraging gaming environments to reach hard-to-access or ad-fatigued audience segments, such as Gen Z and millennial consumers? 

Mobile gaming is one of the broadest categories for reach, with almost 3 billion players worldwide. We have clients approach us for all of their target demos, whether it’s age, ethnicity, gender, or other behavioral segments. The beauty of mobile gaming is that no matter what kind of audience you’re looking for, you can find them within the gaming ecosystem.

4. How are your clients balancing innovation in ad formats with brand safety and user experience, especially in unmoderated or decentralized content environments like gaming?

There’s a big misconception that buying into gaming means operating in unmoderated environments. We have a multi-step process to ensure brand safety, and we don’t operate in UGC environments - we’re fully focused on mobile games from marquee global publishers in the casual and hyper-casual space. And we give our clients full visibility into our title list and the option to exclude any titles they feel are misaligned with their brands.

5. What performance benchmarks (e.g., listen-through rate, brand recall, engagement time) are most critical when evaluating emerging formats like in-game audio? 

We encourage our clients to focus on their key business results, especially since we’ve seen a lot of success with both top-of-funnel and lower-funnel campaigns. For brand advertisers we suggest running brand lift studies to see how powerful in-game audio can be for their brand health metrics. With performance advertisers, we encourage them to work with attribution partners to see how adding in-game audio can positively impact ROAS and acquisition costs.

6. What role do you see for in-game audio ads in clients’ long-term advertising strategy, particularly as gaming becomes an dominant form of digital entertainment?  

We’re optimistic that in-game audio will be as essential as podcasting or streaming audio for advertisers. We know the power of gaming from our experience in other formats, and we know that gaming is one of the few environments where ads really benefit the whole ecosystem. Early adopters are already seeing the benefits, and as programmatic audio becomes more widely adopted, we’re excited to see more advertisers embrace in-game audio.

1. How is the integration of AI-native advertising platforms transforming the landscape of retail and marketplace monetization strategies?

The integration of AI-native advertising platforms is fundamentally reshaping retail and marketplace monetization by shifting the focus from traditional impression-based models to outcome-driven advertising. Retail Media Networks (RMNs) are leveraging AI and machine learning to connect ads directly to transactions, offering brands measurable outcomes such as return on ad spend (ROAS) and cost per order (CPO). With macroeconomic uncertainty pressuring advertisers to prioritize certainty and performance, AI empowers platforms to optimize campaigns in real time using first-party data, personalize user experiences, and drive sales. As a result, retailers are evolving into sophisticated media ecosystems where ad spend is tightly linked to tangible sales results.

2. How can businesses ensure that AI-driven advertising solutions align with their brand values and customer expectations?

To ensure AI-driven advertising solutions align with brand values and customer expectations, businesses must prioritize personalization that enhances rather than disrupts the customer experience. The rise of commerce media emphasizes the importance of relevance; consumers are increasingly resistant to repetitive or poorly targeted ads. Retailers and marketplaces should implement AI technologies that leverage real-time behavioral signals and first-party data to deliver tailored, assistive ads that feel organic and helpful. Maintaining a customer-centric approach, where advertisements align with shopper intent and context. Crucially, businesses must retain transparency and control over how personalization is applied, ensuring it aligns with brand trust and evolving customer expectations.

3. What considerations should be made regarding data privacy and compliance when deploying machine learning models that utilize customer data?

When deploying machine learning models that use customer data, businesses must carefully navigate privacy regulations and compliance standards. As first-party data becomes a cornerstone of effective advertising, safeguarding this information is critical. Companies should ensure that data collection is transparent, consent-driven, and strictly limited to necessary use cases. Additionally, anonymization, encryption, and adherence to regional privacy laws (such as GDPR or CCPA) are essential practices. AI models should be designed to operate within these frameworks, ensuring that personalization does not compromise user privacy. Building trust through responsible data stewardship will increasingly distinguish leading retail media networks.

4. How does the use of AI in advertising impact the attribution models and the overall understanding of customer journeys?

AI is revolutionizing attribution models and deepening our understanding of customer journeys. Traditional models that heavily relied on last-click or basic touchpoints are being replaced by AI-driven, closed-loop attribution that ties ad exposure directly to transactions. Machine learning enables deeper analysis of browsing behavior, purchase patterns, and engagement across the funnel, allowing retailers to attribute value to multiple customer interactions more accurately. This enhanced visibility not only improves media optimization but also empowers brands to allocate budgets more effectively and design targeted, high-impact campaigns.

5. What metrics are most indicative of success in AI-driven commerce media initiatives, and how can organizations effectively track and interpret these metrics?

In AI-driven commerce media initiatives, outcome-based metrics such as return on ad spend (ROAS), cost per order (CPO), and incremental sales lift are becoming the primary indicators of success. Unlike traditional reach or click-based metrics, these measures directly link ad activity to business results, aligning media performance with revenue generation. Organizations can effectively track and interpret these metrics by employing real-time analytics platforms that integrate first-party data and AI-driven optimization to dynamically adjust campaigns based on performance. Continuous learning and real-time optimization enable businesses to adjust campaigns dynamically based on performance, ensuring that marketing investments consistently drive measurable value.

6. How should organizations prepare to adapt to the evolving technological landscape to maintain a competitive edge in digital advertising?

Organizations should prepare for the evolving technological landscape by investing in scalable, outcome-focused commerce media infrastructure and building specialized teams that bridge commerce and advertising expertise. As retail media matures, successful players will move beyond experiments  and develop robust self-serve ad platforms, automate campaign management, and leverage machine learning. Agility will be key, not only to adopt new formats like in-store digital activations and cross-channel integrations but also to accelerate the cycle of testing, learning, and iterating. By aligning technology investments with customer-centric strategies and performance-driven metrics, organizations can position themselves for sustainable growth and have a significant competitive edge.

1. How is the integration of GenAI reshaping the landscape of commerce media, and what implications does this have for traditional marketing strategies?

The integration of Generative AI (GenAI) is significantly reshaping the landscape of commerce media by introducing intelligent systems like Celeste AI that can analyze complex data, provide real-time insights, and automate tasks, ultimately leading to smarter and faster decision-making for marketers. Skai believes GenAI offers the opportunity to remove complexity from media and introduce a new future for marketers. For example, Celeste AI is designed to transform how brands and agencies navigate commerce media. It leverages Skai’s proprietary data intelligence, real-time signals from over 200 publishers, and cross-channel insights to deliver smarter, faster decisions. Skai’s platform now transforms the growing complexity of commerce media, with its surge in channels, publishers, ad formats, and data, into actionable strategies by unifying and interpreting information.

This transformation has several implications for traditional marketing strategies:

● Increased need for intelligent tools to handle complexity: As commerce media becomes more intricate with more channels, publishers, ad formats, and data, traditional methods of managing information become insufficient. GenAI capabilities are necessary to unify, interpret, and act on this complexity.

● Shift from manual data analysis to automated insights: Marketers will move from sifting through dashboards to interacting directly with data through natural language, asking questions and receiving immediate, actionable insights. 

● Focus shift towards strategic work: By automating routine tasks like reporting and performance monitoring, GenAI allows marketing professionals to focus on envisioning, strategizing, and creating. For example, Celeste can automate tasks that previously took hours or days, such as creating weekly, monthly, and QBR reports.

● Emphasis on faster decision-making: GenAI provides real-time insights and recommendations, enabling marketers to act more quickly and precisely. Speed to insight is crucial, especially in performance channels where trends shift rapidly.

● Importance of integrating and leveraging diverse data: Success in commerce media demands more than just access to information; it requires the ability to unify and interpret various data sources like first-party data, publisher insights, and competitive intelligence. GenAI helps connect the dots between insights and business outcomes by leveraging data.

● Need to adapt to AI-driven optimization: Traditional strategies might become less effective as AI continuously learns and improves campaign optimization based on historical performance, competitive trends, and channel synergies.

2. What challenges do organizations face in unifying data from multiple sources, such as first-party data, publisher insights, and competitive intelligence, to inform marketing strategies?

One of the biggest challenges in advertising today is the sheer volume of data generated across multiple platforms. Traditional advertising tools often struggle to process and interpret this data efficiently, leaving marketers bogged down in inefficient decision-making. While Skai's platform aims to seamlessly integrate omnichannel commerce media, combining first-party advertiser data, publisher insights, competitive intelligence, and digital shelf data, the general challenge remains for organizations using more traditional or less integrated tools. Many traditional platforms are siloed by channel, requiring separate logins and disjointed reporting, which leads to inefficiencies and missed opportunities. Unifying data from diverse sources requires the ability to connect disparate data points and transform them into actionable insights. Marketers often have to sift through complex dashboards and spreadsheets to make decisions.

3. What role does AI play in automating routine tasks, and how does this shift the focus of marketing professionals toward more strategic initiatives?

AI, particularly GenAI agents like Celeste, plays a significant role in automating routine tasks in marketing. Celeste can automate time-consuming tasks such as budget allocation, creative adjustments, and performance tracking. It can also handle tasks like generating reports (weekly, monthly, QBR) in minutes, which previously took hours or days. AI-powered systems can also automate bid management. By automating these mundane activities, AI frees up valuable time for marketing teams to focus on higher-level activities such as strategy development and innovation. This shift allows people to do what they do best: dream and envision, strategize, and create. With AI handling executional complexity, organizations can become more agile.

4. How can AI assist in transforming complex, fragmented data into actionable insights for more effective campaign planning and execution?

AI, with its ability to analyze vast amounts of data, can transform complex, fragmented data into actionable insights by aggregating signals from multiple advertising platforms. Celeste AI, for example, aggregates signals from over 200 publishers, competitive insights, and cross-channel performance to deliver tailored recommendations. AI can identify patterns and optimization opportunities that human analysts might miss. It can provide real-time, context-aware recommendations tailored to each campaign’s unique needs, such as adjusting budget allocations, optimizing bids, or tweaking creative strategies. Unlike traditional tools that might provide static reports, AI actively engages with the marketer’s workflow by delivering tailored recommendations, helping them act decisively and efficiently. Marketers can interact directly with their data by asking tactical and strategic questions and receive immediate, actionable insights. Celeste is designed to deliver real, actionable guidance: budget reallocations, keyword recommendations, SKU-level insights, anomaly detection, and channel-specific strategy prompts.

5. In what ways can AI-driven insights contribute to optimizing marketing performance and achieving higher ROI across different platforms?

AI-driven insights contribute to optimizing marketing performance and achieving higher ROI across different platforms in several ways:

● Improving budget allocation: AI can analyze performance data and recommend optimal budget reallocations across retail media, paid search, and social platforms to maximize ROI.

● Enhancing campaign optimization: AI provides granular insights into factors like keyword performance, audience engagement, and creative effectiveness, enabling marketers to fine-tune their campaigns for better results. 

● Identifying growth opportunities: AI can uncover hidden opportunities by aggregating insights from across the marketing stack and connecting dots that may otherwise go unnoticed, helping marketers refine strategies and identify new segments faster.

● Providing tailored recommendations: AI delivers tailored recommendations based on a brand’s specific needs, enabling marketers to act more quickly and precisely.

● Enabling real-time decision-making: AI provides real-time insights and recommendations, allowing marketers to make immediate, data-driven actions to seize opportunities or mitigate issues.

● Continuously learning and improving: AI is designed to continuously learn and improve with each interaction, ensuring it can make the best decisions possible based on historical performance, competitive trends, and channel synergies.

● Measuring incremental impact: AI-powered tools can help measure the true incremental impact of media spend, providing clarity on which campaigns are driving real growth and enabling more effective budget allocation.

6. What emerging trends do you foresee in the convergence of AI and commerce media over the next few years?

Several emerging trends are foreseen in the convergence of AI and commerce media:

● Evolution to multi-agent systems: AI agents like Celeste, currently acting as a single agent, will evolve into multi-agent systems, with each agent specializing in distinct tasks across planning, forecasting, activation, and measurement.

● Seamless integration with client-side AI: AI platforms will increasingly integrate and collaborate with clients’ own GenAI capabilities and custom AI agents, creating a truly intelligent commerce media ecosystem.

● Increased specialization of AI: AI tools will become more specific to different marketing roles within a brand or company, providing tailored results based on individual needs and objectives.

● AI as the new User Interface (UI): The primary way marketers interact with technology will shift towards natural language interfaces powered by AI, making complex systems more accessible and easier to use. As AI becomes the new UI, marketers need to focus on intelligence when selecting tech partners.

● Focus on enabling technologies: AI will be seen less as a standalone tool and more as an enabling technology that constantly evolves and improves with more data and user interaction, allowing clients to build more value in the future.

● Development of client-specific AI agents: Clients will increasingly want to create their own AI agents within platforms, introducing their own data types, processes, and artifacts to tailor the AI’s capabilities to their specific needs. For instance, Skai’s clients are asking to make Celeste “their” Celeste.

● Continued focus on efficiency, performance, and growth: The core value propositions of AI in commerce media will remain focused on driving efficiency and productivity, improving performance and optimization, and uncovering new growth opportunities for brands.

● The future is about intelligence, not just features: Commerce media won’t be defined by features alone, but by the intelligence driving them.

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.