1. How prepared is your organization to shift a portion of its digital ad spend from traditional search platforms (e.g., Google, Bing) to AI-native environments (e.g., ChatGPT, Perplexity, Gemini)? 

Intellibright is more than prepared—we’re already executing. We’ve partnered with Nexad, the market’s first end-to-end AI-native advertising platform, to pioneer this shift for our clients. As AI-native environments like ChatGPT, Claude, and Gemini emerge as meaningful traffic and conversion sources, we're evolving our strategy and tracking capabilities in parallel.

Our proprietary ROAS-focused reporting is built to adapt quickly, and we’ve already integrated AI-native sources as distinct channels within our client dashboards. This ensures full visibility into performance and spend, whether it's coming from Google or from the next generation of conversational AI platforms.

2. What internal or external capabilities (e.g., campaign management, creative optimization, AI expertise) would your organization need to adopt AI-native advertising at scale? 

Intellibright already has the internal capabilities in place to scale AI-native advertising. Our team actively uses more than a dozen advanced AI models across campaign strategy, creative development, audience targeting, and performance optimization. From prompt engineering to real-time creative testing, AI is already embedded in how we operate.

As AI-native advertising platforms mature, we’re well-positioned to scale with them. Our agile structure, deep technical expertise, and performance-first approach allow us to adapt quickly and execute effectively—ensuring our clients stay ahead as this next wave of digital advertising unfolds.

3. Would you be open to piloting managed AI-driven advertising services if performance reporting and optimization tools were comparable to current platforms like Google Ads or Meta? 

We’re not just open to it—we’re already doing it. Intellibright has partnered with Nexad, the first end-to-end AI-native advertising solution, and is actively running campaigns in AI-driven environments. Our team has integrated these channels into our proprietary ROAS reporting system, allowing clients to track performance from AI-native sources like ChatGPT and Claude alongside traditional platforms like Google and Meta.

We’re committed to helping clients invest where performance justifies the spend, and AI-native environments are already earning their place in that mix.

4. Do you see first-mover advantage in AI-integrated ad ecosystems as a strategic priority for your brand or business unit?

Absolutely. At Intellibright, we view first-mover advantage in AI-integrated advertising as a key strategic priority. Our partnership with Nexad positions us at the forefront of this emerging ecosystem, ensuring we’re ready to activate as these platforms evolve.

In parallel, our SEO strategies are already adapting to the rise of zero-click search experiences driven by AI. We’re optimizing for visibility and engagement within AI-generated results across platforms like ChatGPT and Perplexity—so our clients stay relevant even when traditional search clicks decline.

Being early means we’re learning, adapting, and preparing our clients to win in what’s next—not just what’s now.

5. Given sectors (e.g., e-commerce, financial services, travel, B2B tech), how relevant is hyper-personalized, AI-native advertising to your customer engagement and acquisition goals? 

Hyper-personalized, AI-native advertising is highly relevant to our customer engagement and acquisition strategy—especially as it expands across sectors like financial services, B2B, and e-commerce. While current AI-native ad inventory is limited, we’re prepared to scale as new opportunities emerge.

In the meantime, we’re already incorporating hyper-personalized AI experiences through advanced website chat deployments—both on our own site and for select clients. These are powered by a highly sophisticated AI chatbot developed over several years by a long-standing international partner, now launching in the U.S. through our exclusive collaboration. It’s a first step in delivering real-time, tailored engagement at scale—and it aligns directly with our commitment to data-driven performance.

6. Are AI-native advertising solutions currently part of your digital transformation roadmap or media planning discussions? Why or why not?

Yes—AI-native advertising is already part of our digital transformation roadmap and a growing part of our media planning conversations. As platforms like ChatGPT, Gemini, and Perplexity evolve from experimental to performance-ready, we’re preparing our clients to engage early and effectively.

Our partnership with Nexad ensures we’re aligned with the first wave of scalable, AI-native media solutions. And internally, we’ve built the infrastructure—from ROAS tracking to creative workflows—to seamlessly integrate these channels as they mature. It’s not a question of if AI-native advertising becomes a meaningful part of media planning—it’s when. And we’re making sure we’re ready.

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1. How important is adaptive content filtering over static blocklists for maintaining your brand’s integrity in digital advertising?

Content on social media moves incredibly fast.  Our evidence suggests that most of the views any piece of social media content will ever get,  occur in the first week, and on some platforms that is even faster.  If you’re not continuously updating your blocklists, you can very quickly either over or under block content.

2. How frequently do you adjust content exclusion parameters based on real-time events (e.g., geopolitical conflicts, social issues)? 

We make updates to block lists as fast as the platform allows.  On some platforms that’s once every hour.  We find that reacting fast to new trends on social media platforms is the only way to keep brands safe and suitable.

3. How do you integrate content suitability insights into your future ad placement decisions and media planning?

Our clients rely on us for fast reaction to ongoing changes in social media content.  We also provide longer time horizon analysis, such as trending content reports, which show information relevant to a brand on social media.  This is used as part of their media buying strategy to adjust media spend or to change first party suitability controls.

4. To what extent is your organization leveraging AI to automate brand suitability decisions in high-volume content ecosystems? 

AI is at the core of everything we do.  Social media content is both too voluminous and too nuanced to understand with any other technology.  We’ve been deploying AI content moderation strategies for over 7 years now.  The recent advancements in Large Language Models (LLMs) have super charged our ability to deliver highly performant, cost effective analysis of social media data at scale for our brand customers.

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1. How should marketing leaders balance innovation with ethical considerations to maintain consumer trust?

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1. How do you anticipate that economic pressures will influence your organization's strategy for evaluating and adopting performance marketing technologies?

In today’s environment, budget scrutiny is the rule, not the exception. Economic pressures have and continue to force us to look past shiny nice-to-have technologies and zero in on tech that delivers measurable results.

At AUDIENCEX, we’re evaluating third-party tools and simultaneously investing in building our own technology to fuel AXi, our integrated performance platform. This includes innovative predictive audience-building, customized algorithmic campaign decisioning, agentic AI tools, and analytics all working together to drive down CPAs and improve efficiency. Every product we develop - and every process we adopt - is measured against its ability to generate transparent, data-backed results and flexible scalability so we can adapt quickly on behalf of our clients to changing market realities.

If a tool can’t justify itself with transparent, data-backed results or direct time savings, it’s not moving forward, or we’re reinventing it ourselves. We’re also pushing for flexible, scalable solutions so we can dial investment up or down as market realities shift. 

2. What is the importance of having full control and transparency over omnichannel advertising, and how does AI optimization within such a platform align with your marketing objectives?

Full control and transparency across channels is imperative; without it, you create blind spots. Our objective is to maximize every dollar, so having line-of-sight into performance at the most granular level is non-negotiable. Our AXi Optimizer tool supercharges this approach. When we layer in AI, we’re able to not just react, but proactively allocate budget, test creative, and target audience segments in real time. That agility is the difference between hitting goals and missing them especially in a market that doesn’t sit still for long.

3. How would a guaranteed CPA model, which transfers risk from the brand to the agency, impact your organization's financial planning, budget allocation in scaling digital marketing efforts?  

A guaranteed CPA model definitely shifts the risk from the brand or agency partner to AUDIENCEX, but it’s not a shot in the dark. With our recently launched PriceFix - which sets a new standard for performance marketers - we leverage custom modeling based on historical and real-time data to set CPA guarantees grounded in reality. When done right, it’s actually a smarter, data-backed way to take on risk. For us, this lowers the perceived risk on both sides: the brand gets cost certainty, and we get to put our data confidence into action. That said, we also recognize that not all campaigns will fit a guaranteed model, so we’d approach this as one tool in our broader allocation strategy, ensuring it complements rather than limits our overall flexibility.

4. How do you envision intelligent automation reshaping the responsibilities of your marketing teams, and what challenges does this present for talent development?

Intelligent automation is an area that we are prioritizing as a company, with a major product launch scheduled for Cannes Lions around our new AXi Simulator product. Powered by synthetic personas modeled on real-world psychographic, behavioral, and cultural data, Simulator enables unlimited, risk-free testing across messaging, creative, and targeting variables before campaigns ever launch. By simulating real-world market dynamics with unmatched predictive accuracy and compressing validation cycles from weeks to just days, it allows us to uncover edge case risks, optimize cultural alignment, and fine-tune campaigns at scale.

While Simulator focuses on scenario planning, our other tools, such as Optimizer for in-flight adjustments, and Explorer for real-time analytics, ensure that our teams spend less time on manual tasks and more on strategy, creative thinking, and data interpretation. This shift requires us to hire and develop talent who can both leverage advanced tech and see the big picture. We’re investing in upskilling and encouraging hands-on experimentation so that our team evolves alongside our technology, not behind it.

5. How important is predictive insight into audience behavior before campaign investment, especially when aiming to identify potential cultural mismatches or message fatigue? 

Predictive insights are critical, especially now given the previously mentioned economic pressures. Before spending a dollar, brands want confidence that their message will resonate, not backfire or land flat. Once again AXi Simulator gives us that competitive edge: spotting potential cultural misses, audience saturation, or even timing issues before they cost us. It’s about shifting from reactive course-correction to proactive planning, saving budget and reputation in the process, and ultimately delivering significant performance gains for our brand and agency clients.

6. How does your organization plan to invest in and integrate AI and predictive technologies to ensure that marketing decision-making is data-informed and minimizes reliance on traditional, less agile processes? 

I recently heard a great perspective on this in The AI Breakdown podcast: companies shouldn’t build their whole strategy around AI just for the sake of it. Instead, the smarter move is to develop an AI strategy that actually supports and enhances what you’re already good at.

That’s exactly how we approach it. We start with a real business challenge or marketing objective, and then determine if AI or predictive analytics is the right tool to address it. Our investment in the AXi platform including custom algorithms, predictive modeling, and tools like Simulator and PriceFix is focused on giving teams real-time, actionable data and automating manual processes. Looking ahead, we plan to aggressively scale our investment in this critical growth pillar, accelerating our evolution from a services-led model to a fully integrated, technology-first performance platform. By embedding predictive simulation, autonomous optimization, and real-time intelligence deeper into every layer of our offering, we enable our clients to unlock durable competitive advantages and maintain precision marketing control in an increasingly volatile and algorithm-driven marketplace.

We’ll continue to integrate these technologies directly into our workflows so decisions are always data-informed and agile. Ultimately, it’s about moving away from legacy processes and making every decision faster, smarter, and more accountable. But we’re also realistic: it’s not just about installing new tools, it’s about building data readiness and making sure our people are trained and comfortable with these new capabilities. Like the podcast said, digital maturity with AI is a strategic journey, not a quick fix. For us, it’s about making smarter, more informed decisions, not just doing AI for the headline. 

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1. How are brands currently segmenting their marketing and engagement strategies to address differences in content consumption, commerce behavior, and support expectations? 

Link to the report Consumer Brand Social Engagement - https://emplifi.io/resources/consumer-brand-social-engagement-2025-survey/

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1. How important is achieving a unified customer profile, and what best practices ensure its accuracy and utility? 

A unified customer profile is a detailed, nuanced, and contextual understanding a customer, a business, a household or another entity that organizations use to provide a differentiated customer experience (CX) – or to power AI, analytics, and operations. It’s a critical part of a solid data foundation that turns a company’s customer data into business value.

A unified profile must be complete, accurate, and timely for marketers and business users to completely trust that it represents the customer they’re engaging with. A few best practices elevate a true unified customer profile over simply aggregating customer data from various sources, applying a simple match and calling it a day. 

The most important is to continuously apply data quality steps at data ingestion, not downstream. Accuracy depends on not only ingesting data from all possible sources, but also in applying normalization, standardization, data enrichment and advanced identity resolution as data enters the system. This prevents bad data from entering critical downstream systems and creates a strong data foundation that enables marketers to make more confident decisions and successfully execute campaigns.

Another key step to ensure the utility of a unified profile is to make it available and accessible across the enterprise – ensuring that all users have an identical understanding of a customer. For dynamic segmentation and real-time decisioning, a common understanding results in a consistent CX across every channel – as if the brand is speaking to the customer with one voice, regardless of the interaction touchpoint.

2. What are the key challenges organizations face in unifying customer data, and how can they overcome them?

Most companies have deep organizational silos that are difficult to overcome. They’re set up operationally with the mindset that each department needs data for its own purposes. Each department has a different idea for what constitutes business-ready data, leading to a lack of standardization. As a result, marketing teams and business users never develop a complete understanding of their customers – and can’t pull off an omnichannel CX. 

From a technology standpoint, the challenge in unifying customer data is that most customer data technology fails to prioritize data readiness, which includes making sure that data is complete, accurate and timely as soon as data enters the system. A basic match of customer data whenever there is a changing key, matching that is not tuned to the desired use case (overmatch, undermatch), or even failing to correct data inconsistencies are common when customer data technology approaches data quality as anything but a core capability. 

Technology that instead prioritizes data readiness in the building of a unified profile solves for the downstream problems associated with a lack of a single customer view, and it also is instrumental in helping organizations change their mindset for how customer data is used across the enterprise.

3. How can businesses balance the need for immediate insights with the challenges of data integration and system performance? 

Data integration and system performance challenges can often make it difficult to generate immediate insights needed to provide a great CX.  This problem gets at the heart of why data readiness is so important, and why so much customer data technology falls short of extracting value from customer data when it relies on third-party solutions for preparing data for business or CX use.

Because customer data integration has a direct bearing on delivering a real-time CX, data must not only be ingested in real time, but the various sources of customer data integrations mush also be updated in real time. If various business users accept different requirements for when data must be made ready for business use, then the result – just like having different standards for data quality – will be a poor CX due to a lack of a real-time customer understanding. Data readiness assumes that immediate insights are an indispensable part of a relevant, omnichannel CX.

4. What considerations should businesses keep in mind when adopting a composable approach to their data infrastructure? 

One consideration when assembling a composable martech stack is to understand if and when data quality processes occur. Many composable CDP vendors leave data quality to someone else, thus avoiding the consequences of having different components treating data quality with different approaches. But when a composable framework includes central ownership for making data ready for business use, marketers can trust the data they’re using to build segments and execute personalized campaigns – all without having to wonder if IT is returning the latest customer record.

Because a composability framework gives marketers direct access to a unified customer profile, they can independently build audience segments and launch personalized campaigns without having to rely on IT support, allowing them to more easily focus on strategy, executing and improving CX.

Ultimately, a composable infrastructure should ultimately make it easier – not harder – to power a differentiated CX, and that is only possible when data quality is a priority.

5. What metrics should organizations track to measure the success of their CDP implementations? 

Retention, loyalty and customer lifetime value (CLV) are three key metrics for measuring the success of a CDP implementation. A robust, enterprise-grade CDP should produce significant improvements in all three, the result of transforming raw customer data into actionable insights through having a deep customer understanding. In a McKinsey survey on CX, 76% of consumers said that receiving personalized communications is a key factor in prompting consideration of a brand, and 78% said that such personalization makes them more likely to repurchase. 

Higher retention, a more loyal customer base, and an increase in CLV are the direct result of implementing a CDP that gets data right – ensuring it is complete, accurate and timely – and makes it actionable for any business or CX use case. That means a unified profile is accessible for segmentation and real-time decisioning, that it is tunable depending on the desired use case, and that it is privacy compliant.

6. Looking ahead, what emerging trends do you believe will shape the future of customer data management and personalization?

Agentic AI will play an enormous role in the evolution of customer data management and CX. There is an expectation that brands will rely on agentic AI to manage and execute an end-to-end customer journey, essentially taking the familiar chatbot experience to another level with agents representing a virtual concierge for an individual customer. 

An expectation for personalization will harden into an expectation for agents to be responsible for a consistently relevant CX across channels. Because a chatbot can now easily handle questions about a company’s return policy, for example, customers will soon expect agentic AI to be able to execute a specific return – print a label, schedule a pick-up, apply a balance, etc. – and help guide the customer journey – show similar items in stock that match a customer’s stated preferences, find complementary items, show updated loyalty points, etc. 

 Successfully deputizing AI agents into customer data management and personalization will require agents to have access to the unified customer profile. As consumers become more comfortable with agentic AI as a CX tool, we may even begin to see a time when consumers create their own personal agents for different brands – with more trusted brands receiving more detailed data and preferences from the customers’ agents. Agentic AI may become a two-way street in other words. Brands that are open with how they use agentic AI to improve CX may be rewarded by customers providing more data through their own agents, which will then further improve CX. The key component in successfully integrating agentic AI into CX is high-quality data. Organizations that prioritize data readiness will discover that an accurate, real time understanding of a customer is the backbone to power any emerging CX use case.

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1. In what ways are you utilizing AI to create personalized customer journeys and content across multiple channels? 

At GrowthLoop, we’ve designed a system of specialized Growth Agents, each purpose-built to support the customer journey, with personalization at every touchpoint.

• The Data Agent continuously learns from your first-party data in your cloud, identifying key patterns and behaviors.
• The Audience Agent predicts the best-fit audiences and helps marketers build high-impact segments faster.
• The Journey Agent enables the creation of omnichannel journeys that adapt dynamically—whether across email, ads, SMS, or in-app.
• And the Insights Agent interprets performance data in real time, delivering daily recommendations to optimize what's working and fix what's not.
• Together, these agents form a Compound Marketing Engine, which empowers marketers to launch smarter, more personalized campaigns that improve every day and drive true business outcomes like revenue growth, conversion lift, and retention.

2. What challenges have you encountered in integrating AI-powered marketing solutions with your existing data infrastructure? 

One of the biggest challenges we hear from enterprise organizations is that they don’t see AI delivering value, and that’s often because the AI is pulling from incomplete or fragmented data.

We believe you don’t have an AI strategy if you don’t have a data strategy. 

And the truth is, your best, cleanest, and most valuable data doesn’t live in legacy marketing clouds—it lives in your enterprise cloud. That’s why traditional AI-powered marketing tools, which rely on syncing or copying data into their own environments, fall short before they even start.

At GrowthLoop, we were built from the ground up to run directly in the data cloud, so our Growth Agents can work with the full fidelity of your first-party data—securely, in real time, and without movement or duplication. 

This alignment between AI and cloud data isn’t just a technical preference. It’s the cornerstone of compound marketing. When AI can act on your best data continuously, marketers can iterate faster, improve performance daily, and create a compounding effect on growth.

3. What initiatives are in place to ensure that marketing strategies are directly linked to measurable business outcomes, such as sales and customer retention? 

At GrowthLoop, our goal is to make sure marketing isn’t just about launching campaigns—it’s about driving results that the business can see and measure. The Growth Agents work together to create a system that constantly learns from your data, activates personalized journeys, and improves outcomes over time.

And when it comes to proving impact, marketers can set up holdout groups with just one click for incrementality reporting. That means you can measure how much lift your campaign is actually driving, like increased conversions, higher revenue, or improved retention, compared to what would’ve happened with no action at all.

It’s this kind of continuous, provable feedback loop that powers compound marketing: a strategy focused on learning faster, and driving substantial, incremental improvements that compound over time.

4. How do you facilitate collaboration between marketing professionals and AI systems to optimize campaign development and execution? 

Our vision of AI is not to replace marketers, it’s to empower them. That’s why our interface is built like a studio, a space where marketers collaborate with AI agents powered by their first-party data. Marketers can start from AI-suggested audiences, build personalized journeys with AI recommendations, or edit directly with full control. 

It’s a human-in-the-loop system that makes good marketers great, and great marketers unstoppable. We’ve found this approach leads to faster iteration, more creativity, and ultimately better performance.

5. How do you measure the impact of personalized marketing efforts on customer engagement and satisfaction? 

Our platform allows marketers to directly track the effectiveness of personalization. All performance data is connected back to the data cloud, so we can slice performance by segment, journey, and channel.

One of the benefits of connecting to a single source of truth in the enterprise cloud is that marketers can benefit from the full breadth of company and customer data — that includes information on customer engagement and satisfaction. 

The result is a full picture of how personalization drives not just engagement, but business value. And the AI agents within GrowthLoop are constantly analyzing those insights to improve future campaigns.

6. How do you prepare for future trends in AI-driven marketing to maintain a competitive edge?

We believe the future belongs to marketers who can move fast, learn continuously, and execute across every channel with precision. That’s why we’re investing heavily in agentic AI that adapts to your business, not the other way around. 

We’re also building a community of forward-thinking marketing teams who embrace compound growth via small improvements that add up over time. Our roadmap is shaped by these customers and the belief that the next era of marketing will be driven not just by technology, but by how fast teams can act on their data with confidence.

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1. How has the adoption of CTV advertising influenced your organization's ability to reach high-intent audiences across multiple channels? 

CTV is generally trending in ways that benefit AdRoll immensely. You have the big media companies shifting toward hybrid ad-supported models that are really attractive to consumers, which is bringing in lots of new audiences. There was an 8% jump this year in traditionally subscription-focused consumers watching at least one ad-supported outlet. It’s not just the young, budget-conscious people anymore, and that gives us more reach, especially amongst audiences that appeal to B2B advertisers. 

2. How do you ensure seamless integration of CTV campaigns with other digital marketing efforts to create a cohesive customer journey? 

We really like to think of CTV as analogous to practicing before a big game or other kind of “in the moment” event. CTV prepares the consumer for an easier decision-making process that is more likely to happen away from the TV screen. For that reason, we have cross-channel capabilities as core to our offering. For example, you can target your CRM lists with CTV ads, then run display ads to those CTV ad viewers. Bringing all channels into a single campaign view makes it easy to track holistic performance while still allowing advertisers to pivot, isolate screens, and pinpoint what’s working.

3. What challenges have you encountered in implementing AI-driven CTV advertising solutions, and how have they been addressed? 

We have a tremendously powerful AI bidder, BidIQ, which works for any impression on any screen. It helps us proactively optimize price without sacrificing performance. For display, it optimizes for performance proactively on engagements like clicks and conversion probability, which is much harder to do with CTV. A big focus for us this year is identifying more actionable key performance indicators for CTV that make sense when training an “intelligent” bidder. 

Another area we’re thinking carefully about is creative generation and optimization. Many of our customers are finding the cost of ad creation to be a cause for hesitation when investing in a new channel. AI can help make video ad creative affordable, but it can’t be at the expense of both the brand and the user experience. Overly artificial feeling ads come across as disingenuous, so we’re thinking of these solutions as facilitators rather than outright creators.

4. What systems are used to map personal devices to households to enhance targeting precision in CTV advertising? 

We’ve found a great partner in Experian for this purpose. They’ve been doing enhanced targeting for a while now and underpin so many major components of CTV ads, so it’s great to leverage their technology. This is a major complement to our household identity solution, which has traditionally focused on resolving identity on the web with things like CRM lists, hashed email addresses (HEMs), and cookies.

5. What ethical guidelines govern the use of customer data in your CTV advertising strategies? 

We’ve leaned in hard on developing privacy-forward workflows for several years now and will continue to do so, even as Chrome walks back its deprecation of third-party cookies. This aligns well with the CTV ecosystem and the use of user-resettable device IDs. We still work directly with so many brands and have integrations with their customer systems, so we work with a lot of CRM data and personally identifiable information (PII). These items are pseudonymized the moment they hit our system, so we’re never holding any personal data. Keeping brands and their customers safe is one of our top priorities.

6. How is your organization preparing to adapt to the projected growth in CTV ad spending and evolving consumer viewing habits?

We feel the industry is largely acclimatizing users to mid and bottom funnel ads. You see more QR codes, more B2B/B2C ads prompting users to download a coupon or take advantage of seasonal offers. That’s good news for AdRoll, because we can build trust between brands and viewers when they understand why they’re seeing certain ads. That familiarity means delivering consistent performance, and we’ll be there with an offering that allows our customers to reach those audiences whenever they want. We’re running at full speed to deliver quality supply, the best service, comprehensive audiences and accurate targeting, alongside insightful, unbiased outcomes. 

Get in touch with our MarTech Experts.