1. How can organizations transform siloed data into unified, AI-ready data products to enhance decision-making and operational efficiency?

The DataOS platform transforms fragmented data into AI-ready knowledge through these key capabilities:

Connect data: DataOS eliminates silos by linking data across systems in real time, ensuring teams have a unified view.

Add meaning: Metadata, ontologies, and relationships provide context so AI can generate insights faster and more accurately.

Built-in governance: AI observability and compliance ensure decisions are based on trustworthy, transparent data.

Knowledge-first approach: Transforms raw data into reusable, AI-ready products that accelerate analytics, automate workflows, and drive better business outcomes.

2. How can we ensure that our data products are trustworthy and scalable to support AI initiatives effectively?

DataOS ensures trustworthy, scalable AI data products through an integrated approach to governance and quality. Our end-to-end governance tracks lineage, ensures compliance, and maintains explainability for all AI models. We employ semantic modeling to add essential business context and relationships, providing AI with trusted, high-quality data. Our architecture connects and composes data on demand, avoiding duplication and performance bottlenecks. Additionally, automated quality checks continuously validate data, keeping AI-driven decisions accurate and consistent across the enterprise.

3. How can we eliminate data silos to improve cross-platform compatibility and seamless data access?

DataOS eliminates data silos by transforming how organizations structure and share data. Instead of isolated datasets, we create standardized, reusable data products that are logical constructs capable of being mapped to data across multiple systems and clouds. Our Data Product Hub provides a single destination for all teams to discover and access data without IT bottlenecks. With built-in interoperability, DataOS connects structured, real-time data from any system, making cross-platform integration seamless. Our knowledge-first architecture ensures data is contextualized upfront, so every department operates from the same page with AI-ready data that maintains consistency across all access points

4. What best practices should we adopt to maintain data quality and integrity when developing AI-ready data products?

Creating truly AI-ready data products requires disciplined best practices that ensure quality and integrity throughout the data lifecycle:

Data contracts: Define schemas, SLAs, and validation rules upfront to keep data consistent and prevent breaking changes.

Strong governance: Track lineage, version control, and compliance for transparent, explainable AI models.

Data lifecycle observability: Detect anomalies, prevent drift, and maintain data consistency.

Unified access layer: REST APIs, SDKs, and query interfaces ensure AI-ready data products support diverse workflows without duplication.

5. What steps should we take to ensure our data infrastructure is prepared for future AI-powered applications?

How DataOS enables AI in enterprises with minimal engineering effort:

●      Moves from storing data to activating knowledge: AI needs meaning, not just tables.


●      Auto-generates metadata-rich, contextualized data products: Removes the need for manual data prep.
 

●      Eliminates the need for migrations & manual ETL: Works across cloud, on-prem, and hybrid stacks.

●      Composable architecture delivers real-time, AI-ready insights: No waiting for batch processing.


●      Built-in AI-native governance & observability → Ensures enterprise-grade security & compliance.

6. How can we leverage partnerships with data consulting firms to address complex data challenges? 

Partnerships with data consulting firms can accelerate our customers' success with DataOS. These partners help organizations implement DataOS faster, integrate with existing systems, and navigate complex data environments specific to their needs. They fill critical skill gaps where in-house data teams may lack AI-readiness capabilities, providing expertise exactly where it's needed. Perhaps most importantly, partners bring valuable industry-specific domain knowledge that helps adapt DataOS to sector-specific challenges in finance, healthcare, manufacturing, and other verticals, ensuring solutions are both technically sound and business-relevant.

1.       Alorica had a ground-breaking 2024. If you had to sum up the company’s success in one key lesson, what would it be?

Mike: In this industry, hesitation is the fastest way to fall behind. It’s in our DNA to be the catalyst for change since Alorica began over 25 years ago. That’s why in 2024, we executed on a bold, long-term vision, launching game-changing AI solutions like Alorica ReVoLT for real-time voice translation, expanding into new global markets, and strengthening our CCaaS and automation capabilities. The takeaway is that being reactive isn’t an option—being ahead is the only way to win. That’s why Alorica is shaping the future of CX and giving our clients that competitive edge to stay ahead.

2.       AI, automation, and human connection, how do you see these elements working together in the future of customer experience?

Mike: The future of CX is not AI vs. humans—it’s AI with humans. We’re designing AI-powered solutions that enhance, not replace, human interactions. AI lifts the workload by automating repetitive tasks, speeding up resolutions, and personalizing engagements, while human agents bring empathy, creativity, and critical thinking. Our AI innovations like Alorica ReVoLT and conversational AI allow brands to scale CX without sacrificing genuine, meaningful customer connections.

3.       AI-powered tools are now predicting customer needs better than ever. How far do you think we are from AI delivering a truly personal customer experience?

Mike: At Alorica, AI is already delivering hyper-personalized experiences through real-time speech understanding, sentiment analysis, and predictive automation, ensuring our clients stay ahead with the best AI-powered solutions. Our newest solution—a next-generation conversational AI platform—accelerates resolutions with empathetic, context-aware dialogues, proactively anticipating customer needs to build trust and brand loyalty. The reality is simple—customers who feel heard stay engaged, driving long-term business success. By handling up to 50% of call volume, our conversational AI frees human agents to focus on high-value tasks, reducing wait times and improving operational efficiency. And the impact is real—a 20% increase in customer conversions and a 40% reduction in agent handling time.

4.       With a 368% surge in CCaaS deployments, what’s driving businesses to make this shift, and what’s holding some back?

Mike: Flexibility, efficiency, and cost savings are driving CCaaS adoption. Brands need scalable, cloud-based solutions to handle fluctuating demand while reducing operational costs. However, legacy infrastructure and integration challenges, hold some companies back. We address these barriers with seamless solutions that allow brands to transition smoothly and future-proof their operations.

5.       The rise of AI-driven chat and digital assistants is changing how brands interact with Alorica helped Aer Lingus develop ‘Kara,’ a meta-human assistant. Are digital personas the future of brand interaction, or will people always prefer talking to humans? 

Max: Digital assistants are the future—but human connection will always matter. AI-driven personas like Kara elevate customer service by enhancing speed, efficiency, and accessibility. However, for complex, high-value interactions, customers still want a human touch. The winning strategy is a hybrid model—AI handling routine inquiries while human agents provide empathy, creativity, and problem-solving where it matters most.

6.       Alorica’s customer satisfaction scores are soaring. What’s one underrated factor that makes a CX strategy truly successful?

Max: Empowered employees. Happy, well-trained agents deliver better experiences and build stronger customer relationships. At Alorica, culture isn’t just something we talk about…it’s at the core of our success. We’ve built a diverse, inclusive, and family-like environment where employees feel valued, empowered, and connected. We hire talent from all backgrounds and invest in award-winning training, career development, and engagement programs to help them grow. Beyond work, our employee-led nonprofit, Making Lives Better with Alorica (MLBA) allows them to drive real change in the communities where we operate. As leaders, we also play an active role. That’s why Mike and I launched the ‘Double Take with Mike & Max’ podcast—to share career advice, leadership insights, and real conversations about building a meaningful career. When we invest in our people, we create more than just jobs—we create opportunities, drive innovation, and build a culture where everyone thrives.

7.       Alorica expanded into Paraguay, South Africa, and Egypt. What’s the biggest insight you've gained about building a global CX presence?

Max: Local expertise is key to global success. Expanding into Paraguay, South Africa, and Egypt has reinforced the importance of cultural adaptability, language diversity, and regional CX strategies. Our global expansion isn’t just about scaling—it’s about delivering CX that resonates locally while maintaining global excellence.

8.       If you had to predict one major shift in customer experience for 2025, what would it be—and how is Alorica preparing for it?

Max: The biggest shift will be the rise of conversational AI as the primary interface for customer engagement. AI-driven chatbots and voice assistants are moving beyond scripted responses to real-time, context-aware conversations that feel natural and intuitive. As brands seek hyper-personalized, multilingual, and emotionally intelligent AI interactions, Alorica is already leading the way with conversational AI and Alorica ReVoLT—solutions that enhance real-time speech understanding, sentiment analysis, and predictive automation to create natural interactions at scale.

1. Skydeo has been at the forefront of deterministic audience data for nearly a decade. Looking back at your journey as CEO, what has been the biggest lesson in balancing innovation, data accuracy, and ethical advertising?

Ethical responsibility and innovation have to come hand in hand. We have enhanced the usage of data to segment audiences, but user privacy and ethics should be the main priority, ensuring trust for future success. Ensuring a balance means adapting technology and policy to serve customers responsibly while remaining competitive.

The biggest lesson? Trust beats tech every time. You can build the most advanced audience data platform on the planet, but if you don’t operate with transparency and ethical responsibility, it won’t matter.

At Skydeo, we’ve had to push the boundaries of innovation while making sure our data is accurate, actionable, and compliant—because the second you lose accuracy, your insights become noise, and the second you lose trust, your platform becomes obsolete.

One of the biggest shifts is that brands now demand full transparency—they want to know where their data comes from, how it’s modeled, and how it’s used. We’ve leaned into that. Instead of black-box algorithms, we show exactly how predictive audience data works, helping brands cut waste, improve targeting, and do it in a privacy-safe way.

2. How has AI changed the way businesses approach audience segmentation and targeting in digital marketing?

AI has transformed audience segmentation as it allows companies to process large volumes of data and identify patterns that were not accessible before. It enables greater accuracy, making it possible to develop highly targeted campaigns. This implies that companies are no longer targeting demographics but behavioral and intent signals, which translates to improved ad performance and stronger customer relationships.

AI has flipped audience segmentation on its head. Five years ago, marketers were still using broad demographic buckets—“Men, 25-45, in urban areas.” That’s prehistoric by today’s standards. Now, AI enables hyper-personalized, real-time audience building based on behavior, intent, and predictive modeling. It’s not about static segments anymore—it’s about dynamic audiences that evolve as behaviors change.

For example, instead of just targeting “fitness enthusiasts,” AI can identify who’s actively looking for a gym membership versus who’s just casually watching home workout videos. That level of intent-based targeting is what makes AI a game-changer—it helps brands reach the right people at the right moment, not just the right general group.

3. How can predictive audience management improve personalization without compromising user privacy?

Predictive audience management utilizes anonymized data and machine learning to identify macro behavior and patterns, basically examining aggregated insight for personalization without invading user privacy. This approach can be further enhanced using privacy-first models like differential privacy.

Predictive audience management doesn’t need creepy tracking or third-party cookies to work. Instead, it looks at pattern-based behavior and anonymized signals to anticipate user needs.

Let’s say a consumer starts searching for baby strollers—they haven’t explicitly told a brand they’re expecting, but AI can recognize those signals and build a privacy-safe audience of “new parents.” The difference? No personal data is exposed—it’s just behavioral patterns at scale.

When done right, predictive audience management delivers better experiences for consumers while keeping brands on the right side of data privacy regulations.

4. What are the biggest challenges businesses face when leveraging customer data for AI-driven audience management?

A few of the most notable challenges include data quality, meeting strict privacy regulations, and ethics mapping of AI output. Some companies also find it difficult to integrate new AI solutions into their technology stack, which affects AI-based audience management adoption.

There are three big challenges:

Bad data = bad AI. AI is only as good as the data it’s trained on. If your audience data is inaccurate, outdated, or incomplete, AI won’t magically fix it—it’ll just make bad predictions faster. Brands need clean, verified data before they even think about AI.

Data silos kill efficiency. Too many brands still have disconnected data across marketing, sales, and customer service. If your AI can’t access the full customer journey, you’ll leave money on the table.

Privacy & compliance risks. AI can optimize targeting, but if brands aren’t respecting privacy laws (CCPA, GDPR), they could be setting themselves up for big legal headaches. The best AI solutions prioritize privacy-first modeling to stay compliant.

5. In what ways does AI-powered audience management improve ad spend efficiency and campaign performance?

AI-driven solutions quickly process data to recognize high-value segments, where ad budgets can be allocated to probable converters. Real-time optimizations enable dynamic campaign adjustments that minimize waste. Better targeting means relevant ads and improved performance in metrics like click-through rates and ROI.

AI saves marketers from themselves. A lot of brands still rely on gut instinct when building audiences. They assume they know their ideal customers, but AI proves otherwise.

AI delivers Higher ROAS, lower CPA, and smarter marketing decisions because it can:

Find high-intent users. AI identifies which customers are most likely to convert, so you’re not wasting money on broad targeting.

Predict lifetime value. Instead of chasing clicks, AI helps brands prioritize high-value customers that drive long-term ROI.

Optimize in real-time – AI doesn’t “set and forget”—it constantly adjusts targeting based on performance.

6. What are the key metrics businesses should track to measure the effectiveness of AI-driven audience segmentation?

Businesses must track conversion rates, CAC, LTV, and ROAS. Engagement metrics like CTR and bounce rates reflect the audience's resonance with the messaging. Analysis of accuracy of data and relevance of audience delivers greater insight into effectiveness.

If you’re running AI-driven audience segmentation, here’s what to track:

Conversion Rate (CVR). Are AI-powered audiences actually buying?

Customer Acquisition Cost (CAC). Are you spending less to acquire high-value customers?

Audience Match Rate. Is AI helping you reach more of the right customers?

Return on Ad Spend (ROAS). Is AI driving higher revenue per ad dollar spent?

Customer Lifetime Value (CLV). Are AI-targeted audiences sticking around longer?

Bottom line, if these aren’t improving, your AI audience segmentation isn’t working.

7. What ethical considerations should companies keep in mind when using AI for predictive audience targeting?

Companies must prioritize user consent and transparency when collecting and using data. Bias in AI models is another critical issue to address, as it can lead to unfair targeting. Additionally, businesses should ensure their methods remain compliant with privacy laws like GDPR or CCPA and consider implementing measures like anonymized data processing to uphold high ethical standards.

Ethical AI starts with three things:

Privacy-first modeling – Don’t use personally identifiable information (PII). Build models off behavioral insights without tracking individuals.

Bias detection. AI inherits human bias if it’s not trained properly. Brands need to audit AI models to ensure they’re not reinforcing stereotypes.

Transparency & opt-outs. Consumers should know how they’re being targeted and have the ability to opt-out. If you wouldn’t feel comfortable explaining your AI’s decision-making to a customer, it’s probably not ethical.

8. How do you see AI evolving in audience segmentation and predictive modeling over the next five years? 

AI for audience segmentation will be enhanced with better real-time data processing and deep learning. Predictive algorithms will power customer need forecasting, and hence hyper-personalized campaigns. More uses of AI models that deal with ethics and privacy are expected to be compliant with global regulations. NLP has the potential to enable greater understanding of nuanced customer interactions.

AI-driven audience segmentation is just getting started.  Here’s where we’re headed:

Real-time, adaptive segmentation. AI will continuously refine audiences in the moment based on new data.

Gen AI for audience creation. AI will automatically build custom audiences based on brand goals + real-time consumer behavior.

Predictive commerce. AI will anticipate what customers want before they search for it, making marketing proactive, not reactive.

Goodbye third-party cookies, hello AI-first data strategies. Brands will rely entirely on first-party data + AI models to personalize marketing without tracking users.

1. What strategies can be implemented to maintain the original tone and emotional depth of content during AI-powered translations?

Preserving tone and emotional depth in AI-powered localization goes beyond words alone. It’s about capturing the full human performance. Traditional dubbing often ignores pauses, inflections, and gestures, making speech feel unnatural or disconnected.

At Panjaya, BodyTalk ensures that voice, lip movements, and full-body gestures stay synchronized, preserving the authenticity of the speaker’s performance. This allows translated content to retain its natural rhythm and emotional impact, ensuring that audiences connect with it just as they would with the original.

Our latest innovation, Pod Pro, extends this to podcasts and interviews, where speech modulation and contextual adaptation help maintain the flow and nuance of spoken content. By using AI-driven adjustments, creators can ensure that translated versions sound as natural and compelling as the original, without the disconnected feel of traditional dubbing.

2. What are the technical considerations when implementing AI-powered dubbing solutions into existing content management systems?

For AI-powered dubbing to be scalable and effective, it needs to work seamlessly within existing content workflows, without adding complexity for creators. Traditional localization requires expensive, time-intensive studio recordings, but Panjaya eliminates these inefficiencies with an automation-driven approach that adapts to real-world content workflows.

Our solutions handle complex real-world scenarios like multi-speaker dialogues, shifting face angles, and even occlusions (e.g., microphones or hand gestures covering the mouth). This ensures that dubbed content stays synchronized and looks natural, even in dynamic video settings.

Additionally, human-in-the-loop precision tools give publishers the ability to fine-tune translations, adjust tone, and refine performance nuances, ensuring that localized content aligns with brand voice and audience expectations. Whether integrating into a corporate video platform, media library, or podcast workflow, Panjaya makes AI-powered localization effortless, scalable, and high-quality.

3. What metrics should be used to evaluate the effectiveness of AI-powered dubbing in maintaining content integrity across languages?

At Panjaya, we measure success through engagement, audience growth, and time and cost efficiencies.

Two key indicators of engagement are completion rates and share rates. TED saw a 2X increase in completion rates and a 30% increase in shares rates when switching from subtitles to Panjaya’s AI dubbing, proving that audiences prefer a natural, immersive experience over reading text on screen, and are far more likely to share that experience with others

Beyond retention, audience growth is another major measure of success. TED Talks localized with Panjaya saw a 115% increase in international viewership, proving that high-quality localization expands reach and strengthens audience connections.

Finally, time and cost efficiencies are critical metrics. Traditional dubbing takes weeks to months to complete, but our AI-powered solutions deliver high-quality translations in minutes to hours. This means enterprises can localize content at a fraction of the time it used to take. Additionally, our approach reduces costs significantly. Our AI dubbing solutions operate at around 1% of the cost of traditional dubbing methods, allowing companies to scale their multilingual content strategy without budget constraints.

4. How can AI dubbing solutions improve the localization of audio content to engage a global audience?

Expanding global reach requires more than just translation. It demands cultural and emotional resonance. Traditional subtitling and voice-actor dubbing often fail to capture the essence of the speaker, leading to disengagement from non-native audiences.

Panjaya solves this by ensuring that translated content retains the natural rhythm and delivery of the original. With multi-speaker support, adaptive speech modulation, and contextual adaptation, our AI makes translations feel just as engaging as the source material.

For example, 60% of Spotify’s podcast listeners are non-English speakers. That means podcast publishers relying only on English are missing a massive global audience. With Pod Pro, publishers can instantly translate episodes while preserving the speaker’s unique tone and storytelling style, allowing them to connect more deeply with international listeners.

By making content feel native in every language, AI-powered dubbing removes the barriers to global engagement.

5. How can reallocating resources from manual translation efforts to AI-powered solutions impact overall operational efficiency?

Manual dubbing is time-consuming, expensive, and difficult to scale. It requires studio time, professional voice actors, and intensive post-production work—all of which add costs and delays.

AI-powered solutions eliminate these inefficiencies by automating the dubbing process while still allowing human control where needed. Instead of taking weeks or months to localize content, organizations using AI-powered dubbing can generate high-quality translations in just minutes to hours. This time reduction translates directly into cost savings. Our AI-powered dubbing operates at just 1% of traditional dubbing expenses. For organizations like TED and JFrog, this shift has dramatically increased audience engagement while significantly lowering the cost of expanding their multilingual reach.

6. How can organizations balance the efficiency of AI translations with the need for human oversight to maintain content quality?

AI-powered dubbing is incredibly efficient, but human oversight remains essential for accuracy, cultural sensitivity, and maintaining brand voice. The key is to combine automation with human refinement, ensuring speed, scalability, and authenticity all at once.

At Panjaya, we believe the future of localization is a hybrid approach. AI delivers efficiency, while human oversight ensures accuracy, nuance, and cultural alignment. AI takes care of translation, lip-syncing, and body synchronization, while creators refine tone and style using precision tools.

This balance makes Panjaya’s solutions not just fast and scalable, but indistinguishable from native-language content, ensuring that every story is told authentically, no matter the language.

1. Your career spans industry giants like Accenture, Cisco, and now Five9. What drew you to the world of AI-powered CX, and how has your journey shaped your vision for the future?

I have had the opportunity to work with very talented people at some very influential companies. A common theme in my roles has been the power of partnerships, and Five9 really takes this to heart. The importance Five9 puts on doing the right thing and putting people first -- employees, customers, and partners -- was a big draw for me, along with the chance to work in an industry that so directly aFects everyday consumers.

I was also interested in working with a company that prioritized learning and nurtured innovation. AI is a great example. Five9 isn't just getting started there – we've been doing AI since 2018, and I wanted to be a part of bringing AI-elevated oFerings to the customer experience. While the industry has built a legacy of successful contact center solutions, the market is asking for modern capabilities to improve and deliver great customer experiences. Companies today need tighter integrated workflows and comprehensive capabilities that bring together the best of multiple platforms to future-proof their technology investments.

This leads me to the development of our global partner program. We believe that working with partners empowers our customers to do so much more – and do it all faster, better, and more eFectively. It's an exciting time to work with Five9!

2. You've led global partner strategies at top organizations. What's the secret to building partnerships that aren't just transactional but truly transformative—like Five9's with Google Cloud?

It always comes back to having a mutual and sincere desire to see customers succeed. It's that simple. When customer success is top of mind, you design solutions that address real customer needs, create processes focused on ease of doing business, and choose partners that share your values and drive for innovation and excellence. That's what guides us when we look for partnerships and, ultimately, what makes our partnership with Google so successful. 

3. Five9's launch on Google Cloud Marketplace simplifies access to AI-powered CX solutions. What makes this partnership a game-changer for enterprises worldwide?

The CX market is evolving at an incredibly rapid pace, with a big focus on AI. Ultimately, we strive to help our customers find success by providing hyper-personalized experiences that drive growth. We also recognize that customer organizations, including ourselves, around the world are increasingly engaging with Google Cloud & leveraging Google Cloud solutions to accelerate their growth. Five9's global availability on Google Cloud Marketplace means we are easy to find, purchase, and deploy for an even broader set of customers. This becomes a game changer for businesses looking to diFerentiate how they can serve their customers, potentially burn down some Google Cloud spend, and future-proof their technology stack for long-term scalability and innovation with a company that also is aligned to Google Cloud.

4. Five9 AI Agents are now globally accessible. How does this expansion empower businesses to scale AI-driven customer engagement effortlessly?

We launched AI Agents in November 2024, and it empowers businesses to create chat and voice bots that combine the conversational abilities of a human with the speed and extensive knowledge of AI -- and our recent announcement unveiled a new version of Five9 AI Agents specifically for Google Cloud. This stand-alone solution lets customers take advantage of the ease of Google Cloud Marketplace and utilize Google Cloud technology, coupled with Five9 AI-elevated CX, to create exceptional customer experiences. Designed to deliver the power of AI self-service for an elevated customer experience, we see the combination of the Five9 Intelligent CX Platform and the scalability and security of Google Cloud as a recipe for CX success.

5. The CX landscape is shifting rapidly. What's one customer expectation trend that businesses must adapt to in 2025 and beyond?

Without a doubt, AI is the biggest trend affecting all companies today, and CX is no exception. Its potential is endless, giving us the ability to help customers move confidently to a new era of CX. We believe in empowering everyone whose role can be supercharged by it. We harness AI to unlock organizational insights, making them accessible, actionable, and transformative.

In this AI-driven era, we believe that contact center agents will operate more like brand ambassadors. Armed with relevant and contextual business information, they will remain the backbone of customer relationships, fostering loyalty and trust. In terms of saying it as "one customer expectation": customers want real AI that delivers real outcomes, not just discussions.

6. AI is often measured by ROI and operational impact, but can you share a human-centric success story that highlights the real-world impact of Five9's AI solutions?

An AI-driven CX success story I love is US Radiology Specialists, one of the country's premier providers of diagnostic imaging services. But they aren't just a leader in diagnostic imaging - they're transforming patient care with tech.

With over 5,000 team members and more than 175 outpatient imaging centers across 13 states, their team conducts more than 7 million studies annually. They came to a place where they were experiencing significant wait times, misrouting calls, experiencing rising costs in their 10 contact centers, along with using outdated technology and ineFicient manual processes.

We were honored to partner with them, and they are now automating 75% of outbound calls and cutting call times by 4.5 minutes. With agents freed up for complex cases, conversion rates jumped by 24%, adding $4M in revenue.

But aside from great stats, the speed of getting through to a live person and getting the right treatment can make a real diFerence and save lives.

At Five9's recent annual sales & services kickoF, US Radiology shared a story about one of their physicians' family members experiencing severe head pain. They were worried about a hemorrhage. They called in, were routed to an agent, and they were able to schedule and see the patient within an hour to get a scan. Turns out it was indeed a brain hemorrhage – and they were able to bring the patient in for surgery, which ultimately saved that person's life. That's the kind of impact we are helping to enable – and you can't help but be inspired by that.

7. With Five9's centralized procurement through Google Cloud, how does this help companies stay agile, future-ready, and innovation-driven?

There are some very practical benefits to procurement of Five9 via Google Cloud Marketplace, including simplified billing and management and the ability to use existing Google Cloud budget and retire cloud-committed spend. The Google Cloud Marketplace model we deployed also makes Five9 an accessible solution for organizations of all sizes. At the end of the day, we want to be easy to access and easy to do business with to help drive innovation, collaboration, and sustainable value for our customers as the CX landscape continues to develop.

8. If you had to predict the next major breakthrough in AI-powered CX, what would it be—and how is Five9 positioned to lead that transformation?

We believe that we'll see the evolution from AI as primarily a chatbot or automation tool—to becoming a real-time assistant for human agents. We need to rethink AI—not as just a technology tool, but as a co-worker. Imagine if AI could sense tone, emotion, and frustration levels, allowing for human-like interactions. Analyzing voice and even facial expressions, what if AI could detect when a customer needs human escalation or when reassurance and tailored messaging could solve their concerns?

AI innovation is moving fast, and we believe that it can make the customer experience better. Five9 is taking that seriously by looking at the entire customer journey. We are one of the leaders redefining CX within and beyond the contact center and approach it with both practicality and 'sky-is-the-limit' ambition, remaining business-oriented in our innovations. As we look to the future, you will continue to see us invest heavily in our AI, our platform, and our people for success. 

1.       How do CDPs enhance customer segmentation and improve engagement across different channels?

The real power of CDPs lies in their ability to unify diverse data sources - from DMS and service records to digital interactions - creating a unified profile for each customer. What's particularly exciting about real-time platforms like Tealium is how they enable automotive businesses to segment and respond to customer behavior as it happens. For instance, when a customer is researching specific models on a website or approaching the end of their lease, the dealer can immediately tailor their approach. However, what really drives ROI is how CDPs orchestrate consistent messaging across all channels - whether a customer is receiving an email, seeing a digital ad, or walking into the dealership, they experience a cohesive journey allowing sales and marketing teams to engage proactively and significantly boost acquisition and retention rates.

2.                   What are the biggest challenges in integrating first-party, second-party, and third-party data within a CDP? 

For the automotive industry, the biggest challenges in integrating different data types stem primarily from data quality and standardization issues. This is because dealerships deal with multiple systems that often speak different languages - from dealership management systems to OEM databases, third-party sources, and more. 

Another challenge is privacy and compliance, particularly with regulations like CCPA and GDPR, as proper consent management and data handling is critical, all while maintaining complete transparency about how customer information is being used.

From a technical standpoint, most automotive companies struggle with legacy system integration and real-time data synchronization, which can impact their ability to deliver timely, personalized customer experiences. Equally challenging on the business side is the need to justify the significant investment in data integration while managing complex data-sharing agreements with manufacturers and partners. Finally, don’t overlook the organizational challenges - in my experience, getting different departments to break down their data silos and embrace a unified data strategy often requires significant change management and clear demonstration of ROI.

3.                   What strategies do you use to ensure customer data collected from various sources is unified and actionable? 

Tealium employs several key strategies that have proven highly effective for automotive clients. At the core is our real-time EventStream technology, which captures customer interactions across touchpoints and immediately makes them available for activation. What sets Tealium apart is our identity resolution framework that uses both deterministic and probabilistic matching to create a unified customer profile regardless of whether interactions happen on the website, mobile app, in the service department, or at the dealership.

Tealium’s data layer approach has been particularly valuable - it standardizes all incoming data into a consistent format before it enters our ecosystem, solving one of the biggest challenges in the automotive space where we're dealing with disparate systems like DMS, CRM, and digital platforms. Tealium's pre-built connectors to over 1,300 martech systems mean customers can quickly integrate new data sources without extensive development work.

Another powerful solution is Tealium’s server-side integration capabilities, which allow us to enrich customer profiles with additional data and execute complex segmentation rules before sending information to downstream systems. This ensures we're not just collecting data, but making it immediately actionable across our entire tech stack, enabling the personalized experiences our customers expect whether they're shopping online or visiting the dealership showroom.

4.                   How do you ensure that customer data collected and processed through a CDP complies with evolving privacy regulations? 

Privacy and consent are core tenets of Tealium’s value proposition. Our new Consent 2.0 Suite fundamentally changes how organizations manage privacy. With Consent 2.0, users get a new consent manager with granular preference controls and a zero-latency consent orchestration engine that automatically enforces preferences across all touchpoints. What’s unique is that consent validation and enforcement is executed real-time to minimize risk of non-compliance. We've embedded privacy-by-design into Tealium’s workflows by implementing data minimization principles and automatic purging procedures when data is no longer needed. 

5.                   What key performance indicators (KPIs) do you use to measure the success of a CDP-driven marketing strategy?

Our automotive customers have developed sophisticated KPI frameworks that demonstrate the true value of their CDP investments beyond traditional metrics. They measure acquisition efficiency through reduced cost-per-qualified-lead and improved source attribution. For engagement, our automotive clients closely track indicators like VDP (Vehicle Detail Page), view-to-lead ratios, and personalized offer response rates, which can increase significantly when leveraging real-time behavioral data. 

On the conversion side, they monitor sales cycle compression and multi-touch attribution across digital and in-dealership touchpoints, with many reporting significant improvements in attribution accuracy and faster closing times. Retention metrics reveal the most compelling ROI story, with service department utilization, lease renewal rates, and customer lifetime value showing how personalized experiences drive long-term loyalty. Finally, operational efficiency metrics demonstrate how our platform enables dealerships to execute sophisticated multi-channel campaigns without expanding their marketing teams.

6.                   How does your organization leverage CDPs to create more personalized and targeted marketing campaigns? 

Automotive brands use Tealium’s CDP to create highly-personalized and data-driven marketing campaigns and experiences. By unifying data from dealership websites, CRM systems, and offline interactions, Tealium clients can build 360-degree customer profiles that reveal buying intent and preferences. When a customer browses SUV models online, for example, Tealium can trigger real-time targeted ads or personalized emails showcasing relevant offers. AI-driven insights also help predict behaviors, such as when a customer may be ready for a trade-in, enabling proactive engagement. With seamless omnichannel execution, brands can synchronize messaging across email, social media, and paid ads, ensuring a consistent experience.  

One of our customers is a global luxury sports car and SUV manufacturer who uses the data collected from the online car configurator to build unique customer profiles for each visitor. Tealium collects and unifies all the first-party data that website visitors share - starting from the initial information they provide during the car configuration tool, and constantly updating the profile with future interactions and engagement with the manufacturer. These engagements can range from surveys, challenges, and even eBooks filled with information that is tailored to the individual customer.

1. In what ways can natural language processing capabilities in audience segmentation tools improve the efficiency of market research?

Natural language processing allows users to analyze and segment large datasets based on user intent in short periods of time, reducing the need for manually filtering audience attributes. NLP tools can generate complex audience expressions, saving marketers time while also improving precision and efficiency. This way, users can uncover consumer insights faster, leading to high-quality data-driven marketing strategies.

2. What are the implications of rapid audience segmentation on real-time marketing campaigns and customer outreach efforts?

By utilizing audience segmentation within seconds, marketing efforts become more agile with real-time data. Marketers are given the opportunity to react to rapidly changing consumer behaviors to launch more targeted campaigns and better align their messaging. The speed of the segmentation efforts allows brands to engage with the right audiences at the right time to encourage customer engagement.

3. How can organizations ensure the ethical use of AI-powered audience segmentation tools in their marketing practices?

Organizations need to prioritize data minimization, which uses only necessary data, pseudonymized and cached where possible, following compliance regulations, and maintaining some form of human oversight. When using AI-powered tools for marketing, transparency and consumer consent remains a key factor in data sourcing along with proper licensing to build trust. AI guardrails have been installed in the U.S. to prevent the misuse of personal data. Each state’s data privacy law is considering AI now as more organizations adopt these tools, so marketers need to leverage AI responsibly to avoid going down a slippery slope.

4. What training and skill development are necessary to effectively utilize AI-driven audience segmentation tools?

Building audiences can be extremely time-consuming and usually requires an understanding of Boolean terms and how to find exactly what you’re looking for using them. Ideally, AI-driven audience segmentation tools should be intuitive and simple to learn for marketers of all different skill levels. We made sure that Resonate’s rAI-powered Audience Builder gives users simple natural language prompts so that anyone can quickly and seamlessly create custom audience personas. Users can simply type in plain English what audience they want to create and rAI-powered Audience Builder will select the most appropriate attributes and build the Boolean logic-based audience expression for them with a single click.

5. What are the potential challenges and solutions in integrating AI-powered audience builders into existing data analytics frameworks?

General concerns about integrating AI-powered audience builders with existing data analytics frameworks include data compatibility, scalability and integration complexity – but all have simple solutions. For example, integration complexity can be reduced by using middleware or APIs to help facilitate communication between two different systems.

AI tools, including AI-powered audience builders, are only as strong as the data they are given. Resonate has been perfecting its AI-powered models for more than a decade to be able to predict and deliver the most comprehensive, updated understanding of consumer behavior. That’s why implementing practices like data minimization, hashing and pseudonymizing data are so important to start with. They lower levels of data bias and give marketers confidence in the ethical nature of the data they receive.

6. How can AI-powered audience segmentation tools be customized to meet the unique needs of different sectors?

The beauty of tools like rAI-powered Audience Builder is that it allows users to build complex, tailored audiences quickly and efficiently. Building complex audiences used to take hours, but rAI-powered Audience Builder creates them in seconds. This type of tool allows marketers across industries to dive deeper into strategy and the audiences they want to reach, creating potential to reach untapped or nuanced audiences and markets. It can also help find aspirational audiences to use in developing messaging strategy by using broad audience descriptors such as, “build an audience of people who like a lot of excitement in their lives.”

SurveyMonkey, the world’s most popular platform for surveys and forms, recently announced a series of updates to its market research solutions designed to help organizations of all sizes conduct more advanced research faster. We connected with Scott Monroe, SurveyMonkey’s Director of Product Marketing, to discuss how these newest advancements will help end-users make more informed, data-driven decisions:

1. How can organizations leverage expanded global survey panels to gain deeper insights into diverse consumer behaviors and preferences?

If organizations want to make smarter, more data-driven decisions, leveraging global survey panels is a great way to start. Getting feedback from the right people at the right time can help uncover market trends and nuances in customer buying habits or preferences. Additionally, broader audiences provide broader insights, which can help with building the right product, validating a new marketing strategy, or improving customer experiences online and in person.

2. How does access to a more extensive and diverse respondent pool impact the reliability and validity of market research findings?

When feedback is needed on an idea or concept, a larger, more diverse panel provides more trustworthy insights. A bigger audience means you’re hearing from a true mix of people, not just a small, skewed group. Plus, it helps reduce bias, so your results aren’t accidentally leaning too much in one direction. The bigger and more varied your sample, the clearer and more reliable your insights will be.

3. What strategies can businesses use to integrate enhanced market research capabilities into their decision-making processes?  

Sometimes feedback from a small sample, or even a hunch, can drive big, strategic decisions that impact business. Hunches are good, but bolstering them with data is better. Organizations can use market research to validate those new ideas and determine whether they are viable or not. Even if a company is already very data-driven, market research adds another level of insight that can inform building new products, creating new services, or better serving customers. 

4. What are the potential challenges and solutions in adopting new market research technologies within various organizational structures?

Anytime new technology is introduced to a company, some people may be slow to adopt–or even to use it at all. There may also be a preconception that it’s going to be slow and expensive. But it doesn’t have to be that way, because today’s market research is simpler to conduct and faster to insights. A solution to these potential challenges is to choose a market research provider that is easy to set up, intuitive to use, and able to provide powerful insights affordably. Ensure they have proven expertise and experience, as well as proof points to back it up.

5. What role does real-time data collection and analysis play in shaping marketing and product development strategies? 

Most marketing and product teams want feedback from target markets quickly, so timing is crucial. Marketing teams may be looking to launch a new campaign, and they want to validate a couple of different creative concepts to see what resonates. Having the ability to get close to real-time feedback like this helps them decide quickly on creative direction and get their campaign in the market faster.

6. How can businesses measure the return on investment (ROI) when implementing advanced market research solutions?

There are several metrics that can help determine the ROI of market research initiatives. Let’s say an organization conducted market research on a concept for a new product to add to its portfolio. And, in this case, the research validated the idea and indicated that consumers were willing to buy it. After the product is launched, there are some questions to answer that will provide insight into the ROI: How much new revenue was created from sales? Did the business improve market share? What was the customer satisfaction score for the new product? To truly measure the ROI of market research, organizations can also track results such as cost savings and revenue growth, as well as benefits like time-to-insights.