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. What challenges have you faced in integrating identity solutions into your audio advertising platforms?

The most complex challenge is creating the right configurations that consider the large spectrum of devices and playback mechanisms. By that, I mean if you are listening to a podcast on your iPhone, you are likely within the native player Apple provides with the phone. In this case, there is no data outside of an IP address and the content you are listening to if it’s delivered by one of Triton Digital’s platforms, Omny Studio or Spreaker.

Faced with multiple different solutions for podcast delivery, live streaming, and other methods used to insert and deliver audio advertising, the complexity grows very quickly. There simply isn’t a “one size fits all”, making it almost impossible to apply the same recipe or solution to everything.

Additionally, within the publisher’s applications, the limitations and level of integration are all different making this integration more challenging but just as necessary. We have found ourselves often adapting and re-implementing various SDKs to enable the complete audio ecosystem to maximize ad opportunities for our publisher clients.

Furthermore, when it comes to identity, finding the right solution for different types of delivery methods is essential to achieve maximum coverage.

Existing methods and tools, like IP+UA fingerprinting and external datasets, must be combined with other techniques, such as building a listener profile at an entry point that can then be used along the advertising chain. Ultimately, we take the best information available coming in and extend the listener profile to include as much information as possible; at the end, when we have identifiers to insert into the programmatic bidstream, we know we accomplished the enrichment of that profile to the best level possible.

2. What role does user identity data play in enhancing the effectiveness of programmatic advertising?

At a high level, it helps increase addressable audience ultimately providing more value to advertisers.

To break it down more, it’s important to understand the different types of identity that apply and are available.  Most boil down to deterministic identity and probabilistic identity, each with different degrees to them.

Probabilistic Identities are a “best guess” approach. Meanwhile, cookies, MAIDs (known as mobile advertising IDs), and IP addresses are deterministic in nature but are still probabilistic because you cannot uniquely identify an end user, a household, or any true uniqueness to them. A listener, for example, could be listening in the car going from 5G to Wi-Fi as they stop to grab coffee and then on another Wi-Fi network at work.  The goal of leveraging both types of identities is to build a profile that is consistent and can help find the common thread in that listener's journey.

Using solutions like ID5, we will capture the IP along with any other identity details captured by the cookie or MAID, which will then be used to determine the best set of IDs to insert into the bidstream that would represent the consumption pattern of a listener. DSPs do the actual identification part and determine most properties of a listener, while our role is to make sure they have the best “passport” for the listener.

Deterministic identifiers on the other hand are based on generally identifiable information, like an e-mail address or other information. This requires a completely different process and method that ensures the underlying data is protected and private, and that user consent was provided, even though the resulting profile is derived from that information.

Both of the identifiers play an essential role in rendering the listener profile much more addressable and valuable to advertising buyers. Highly deterministic identifiers are applied to publisher applications with logged-in listeners and the probabilistic identifiers are applied in all other situations. Both of them cover different portions buyers are trying to reach and the CPMs they are willing to pay to do so.

3. How has the implementation of identity management solutions impacted your programmatic ad revenue?

I won’t dive into figures, but generally, increasing addressable audiences through the use of identity management solutions increases bid confidence which can yield better results, “The more you seed, the more you can harvest.”

To break it down more, probabilistic IDs boost general addressability, while deterministic identifiers play a much bigger role in bid confidence. This means the frequency of bidding is much higher than the addressability boost as these only apply to a much smaller subset of listeners (those who use the publisher’s applications, have a login, share their e-mail, and consent to share their information).

A DSP might privilege confidence vs addressability depending on the different buying profiles in their systems. This causes different DSPs to bid more constantly on different types of inventories, which widens the spectrum of possibilities.

4. What measures are in place to ensure compliance with data privacy regulations when managing user identities or user data used to enhance addressability? 

We always strive to exceed industry standards for data privacy and compliance whenever possible, while still adhering to established compliance guidelines. This is particularly true when it comes to dealing with user identities and user data.  Notably, for many solutions, an identifier cannot be created without having some signal representing the consent of the end-user.

Furthermore, any identifier that is based on deterministic data like e-mail, passes through a more vigorous process where we ensure we don’t share any emails or hashed emails (otherwise known as a simplistic encryption), nor access emails.

For this particular process where we’re integrating identity solutions, we use a 3^rd party cleanroom made possible through our partner Optable, which allows the publisher to directly encode deterministic data into a UID2 token. This adds an extra layer of protection for the publisher’s data.

5. What best practices do you follow to navigate the complexities of data collaboration in the audio advertising industry? 

When it comes to navigating the complexities of data collaboration, I always try to stay open and ahead of the curve. Cleanrooms for, example, are still fairly new to the audio industry but it's ideal for data collaboration, data matching, and other types of operation. We believe the use of cleanrooms will continue to grow over the next few years, as advertisers and publishers continue to look for ways to improve targeting and retargeting at scale.

One of the growth drivers for the adoption of cleanrooms we see is the promising use in retail, where e-mail-based identifiers are becoming more sought after. For example, a retail business has registered accounts from users who buy online on their website.  An audio publisher also has registered accounts that consume audio on their media players. Using cleanroom data matching it’s possible to compare and match the different e-mails and extrapolate identifiers that represent the common audience in a privacy-safe manner where the other's information isn’t visible to the other party. This is known as a zero-knowledge proof.

In the end, you get a list of UID2, for example, and can use that list as targeting criteria and no party (not the advertiser, publisher, or Triton) transferred any e-mails over an unsecured connection, nor were any e-mails shared.

Sounds like magic? Yes, it’s the magic of mathematics; ZKPs (Zero-Knowledge proofs) are an amazing tool at our disposal that allows for a more privacy-safe advertising industry.

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. What strategies can be implemented to update and maintain product information to reflect changes in inventory or specifications?

Ensuring that you are using a centralized and automated approach is key. A strong product information management system (PIM) provides businesses with the ability to centralize all product data and make it easier to update and distribute across every channel in real-time. Also, the integration of AI tools is also essential. Not only can AI help speed up and streamline the processing and sorting of data, but it can also work to ensure all the data is as current and complete as possible. Reflecting the most accurate inventory information, sizing, availability, etc. is imperative in order to create a good CX, and at the end of the day, if the customer experience is lacking, companies will feel the impact.

2. What measures are taken to verify that product images and descriptions accurately represent the items being sold?

Ensuring that product images and descriptions accurately reflect the items being sold is crucial, as the product experience plays a vital role in the customer journey. While a strong PIM system is essential for maintaining accuracy, brands must go beyond that by focusing on consistency across all omnichannel distribution channels. Implementing data quality audits, collaborative workflows, and robust data validation and enrichment processes creates a structured approach to delivering the most precise, reliable, and engaging product information to buyers.

3. What strategies are in place to regularly update and maintain product information to reflect changes in inventory or specifications?

There are a number of different ways that companies can go about ensuring information is updated with the most accurate and relevant information. AI and machine learning are often used to detect any inconsistencies and outdated information, and API integrations with a PIM ensure that supply chain systems and e-commerce platforms changes are updated as soon as possible. AI can also monitor and sort through custom reviews to identify any themes appearing in the content such as complaints about products or sizing issues that can be flagged to the team immediately.

4. What role does technology, such as AI or machine learning, play in enhancing the precision of product data?

Technology plays a critical role when it comes to enhancing product data and ensuring that it is up-to-date. By integrating the use of technologies such as machine learning or AI, we can not only speed up the rate at which we are sorting through and updating data, but we can also ensure that the collection, consistency, accuracy, and validation are as precise and accurate as possible across every channel. By using automated technology, we also have the ability to translate into different languages and identify any duplicate information at scale.

5. What initiatives are in place to encourage customers to make informed purchases, thereby reducing the likelihood of returns?

Product information and accuracy are the crux of reducing returns. We cannot control if a customer ultimately decides to make a return, but we can ensure that we reduce the chance; 62% of consumers believe having more accurate product information upfront would reduce their likelihood of making a return. So, by providing the most updated, accurate, and real-time information on crucial decision factors such as sizing, colors, stock availability, etc., customers can feel confident that they are making a purchase and receiving what they ordered as opposed to a misrepresentation online. Doing this will reduce the chance of returns and enhance customer trust and loyalty.

1. How does your organization leverage data analytics to enhance marketing effectiveness and ROI?

At mr.Booster, data analytics is at the core of everything we do in marketing. From the early stages of any campaign, we focus on how we will measure success, which data sources will be available, and whether the expected results align with client expectations. Whether it’s performance marketing, brand awareness, or retargeting, we tailor our analytics and KPIs to match the specific goals of each campaign.

Modern tools enable us to track campaigns from the moment a user first views an ad, capturing data with precision. For example, in February, our team analyzed over 1.3 billion impressions, assessing user interaction at each second after they saw the ad. This level of detail helps us pinpoint where, when, and how users engage with the ad and what actions they take.

With these insights, we can immediately shut down non-performing traffic sources, optimizing ad spend and ensuring our clients receive value. After initial tests, we continuously refine our KPIs, always striving for better results. As we move forward, we focus on improving ROI beyond the initial engagement by guiding users through the entire funnel. For example, in iGaming, we focus on nurturing users from their first deposit through to multiple deposits, optimizing retention and engagement.

"Data opens doors where they are usually closed." – mr.Booster.

For instance, in the CIS market, we spent €62,000 to acquire users who hadn’t made their first deposit. The post-click cost for a deposit was less than €3, while the post-view cost was only €0.40.

2. What role does real-time data play in assessing and optimizing the performance of display advertisements?

Real-time data plays a critical role in the optimization of display advertisements. In media campaigns, where CAC (customer acquisition cost) can be high, relying on real-time data is a must. Without effective analysis, campaigns can result in a high cost per user, and users may churn shortly after being acquired, turning a campaign into a costly failure.

We divide our analysis into post-view and post-click types, each with its own set of KPIs. With post-view data, we look at attribution windows as small as one minute, enabling us to assess campaign performance even without a solid post-click baseline. By analyzing these early indicators, we can make quick adjustments to reduce CAC and boost LTV (lifetime value).

In addition to improving KPIs, real-time data helps us spot issues with traffic or product alignment early, allowing us to address problems before they become significant. It’s essential for continuously improving ROI and driving campaign effectiveness.

3. What challenges have you encountered in implementing data-driven approaches, and how have you addressed them?

One of the main challenges in implementing data-driven approaches is dealing with large volumes of data and ensuring seamless access to the right data from the product side. Early on, clients may hesitate to share sensitive user data, but as we demonstrate the power of data and show tangible results through real-world cases, this trust builds over time.

We’ve also found that it’s not enough to simply collect data; accessing and processing the right data is crucial. To address this, our team’s expertise comes into play. We work closely with clients and product teams to identify and gain access to the necessary data. In cases where data flow is incomplete or fragmented, we proactively develop custom solutions to bridge the gaps.

4. What metrics are most indicative of success when evaluating new digital marketing tools and platforms?

When evaluating new digital marketing tools, the metrics we focus on depend on the type of campaign and the product. For performance-based campaigns, we prioritize metrics such as:

●     Ads volume, CTR (Click-through rate), CR (Conversion rate), and Reach

●     First-Time Deposits (FTD) post-view/post-click CAC for 1-24 hours

●     Deposit post-view/post-click CAC for 1-24 hours

●     FTD/Deposit sum

●     LTV, NGR (Net Revenue)

●     Reactivation rate

If we’re working with an active user base, the metrics shift depending on the campaign type. For example, when our clients take part in a tournament, we analyze engagement and participation metrics. In general, our approach involves deeper, product-driven metrics that allow us to align performance with business goals.

5. What emerging technologies are you integrating into your digital marketing strategies to stay ahead of industry trends?

At mr.Booster, we use every available tool on the market to stay ahead. But, to be honest, our team’s talent is one of our greatest technological assets. If there’s something missing in our tech stack, we develop our own solutions.

One exciting technology we’re using is post-view analytics. We’re conducting tests across various social and native ad networks, where we place branded ads without direct product links, using promo codes instead. This method avoids moderation issues while still driving significant user acquisition. By analyzing such campaigns, we gain insights into how users engage with brands organically, often resulting in positive ROI. For example, in a test run in Kazakhstan, we spent just $400 and gained 39 FTDs (first-time deposits), generating over $1,200 in deposits at a CPA of $10 per FTD.

We also utilize AI to generate creative assets tailored to specific audience segments, ensuring better ad performance while reducing production costs.

Moreover, we're exploring the use of AR and VR for creating immersive ad experiences, particularly in industries like gaming where visual appeal and interactivity are key to engaging users.

By continuously adopting new technologies, we can offer our clients cutting-edge solutions that keep them ahead of industry trends and drive measurable results.

Leonid Pudov, CEO Speech at Sigma Dubai: https://www.youtube.com/watch?v=fJucQhU3VSI

1. In what ways does leadership collaborate with marketing and sales teams to refine GTM strategies?

At SEON, we embrace a startup culture where everyone works together to grow the business. Leadership maintains open communication through various means such as regular all-hands meetings, in-person kickoffs and Slack. Multiple Slack channels serve as hubs for raising issues, sharing feedback and leveraging field insights to refine strategy and prioritize use cases. Most importantly, leadership has an open-door policy and listens and takes action in the best interest of both customers and the business.

2. How does leadership ensure alignment between innovation initiatives and the company's strategic objectives?

Great leadership is all about prioritization—distinguishing between true force multipliers and mere shiny objects. No matter how big the business, tradeoffs are inevitable.

3. What role does leadership play in ensuring the successful execution of GTM initiatives across different regions and sectors?

Leadership plays a crucial role in guiding teams to research, collaborate and develop market-driven strategies across industries and regions. They make strategic decisions on investments, resources and budgets while cutting through distractions that hinder execution and learning. Success hinges on aligning technology, data and talent with clear objectives, as too often, ambitious visions fail due to misaligned investments.

4. How are market trends and customer feedback incorporated into the development GTM plans?

Listening is an ongoing process, and where you tune in must evolve. Market conversations and customer expectations have shifted, with self-guided research now making up 70% or more of the buying journey before a salesperson is even engaged. That means businesses can’t afford to wait for feedback; it requires actively seeking insights–from customers, field teams, surveys and industry conversations–both online and offline. With more voices influencing buyers than ever, cutting through the noise to find meaningful signals is crucial.

5. How does your organization's leadership team drive and sustain innovation to maintain a competitive edge?

We listen to customers, frontline teams, industry leaders and even our own data to understand the challenges businesses face. Innovation only matters if it solves real problems or improves existing solutions. Leadership keeps the customer at the center of every decision, placing smart bets and learning quickly from early signals of impact.

1.  How is the rise of voice search changing the way users interact with search engines and digital assistants?

The rise of voice search is profoundly changing how users interact with search engines and digital assistants, transforming the digital landscape in several key ways:

Shift to Conversational Queries

Voice searches use natural language processing because users tend to ask questions in conversational speech which requires search engines to heavily depend on NLP for effective response ranking.

Users express their search needs through longer specific phrases when speaking instead of typing so content needs optimization for extended queries.

Increased Use of Smart Devices

Smart Speakers and Virtual Assistants have become integral household tools through daily life which made voice search a common behavior among people.

Voice search appears regularly on mobile platforms which requires optimizing mobile search results because users need fast accurate solutions.

Local and Question-Based Searches

Inside local SEO strategies voice search stands out because users use it to locate nearby businesses thus requiring optimized local search profiles.

The implementation of direct questions through voice search requires content which offers easily digestible answers to achieve top positions both in featured snippets and "Position Zero."

Impact of AI

The evolution of voice search depends fundamentally on Artificial Intelligence (AI) because this technology improves NLP capabilities and produces voice assistants that adapt better to individual preferences.

AI allows professionals to develop content that adheres to voice search user requirements which results in enhanced visibility and interaction.

2. How is NLP improving voice search accuracy, and what impact does this have on content optimization?

Natural Language Processing (NLP) delivers important enhancements in voice search accuracy through its ability to properly process language nuances in speech. The enhanced performance of voice search technology through NLP affects content optimization in multiple essential aspects.

NLP technology enhances voice search accuracy through better understanding of spoken language patterns.

Voice assistants utilize NLP to understand the full context of queries regarding purposes along with emotional aspects which results in better relevant accurate replies.

Suitable NLP methods facilitate improved accuracy in speech recognition systems because they handle speech variations resulting from noise and different accents and dialects.

The ability of NLP to detect important entities including names along with locations and organizations makes result accuracy more precise.

Impact on Content Optimization

Proper NLP implementation requires optimizing content through conversational keywords that include question-based phrases structured like natural human dialogue.

Content optimization through NLP technology means users receive personalized information which understands their search history.

The trend of voice searches carried out on mobile devices requires content to receive both mobile-friendly design adjustments and local search engine optimization so it can focus on targeted geographical queries.

The content needs to deliver straightforward answers to typical questions because voice searches begin with "how" "what" "where" "when" and "why".  

3.  How should brands adapt their keyword strategies to align with the more conversational nature of voice search queries?

Brands need to implement these three strategies to transform their keyword strategies for the conversational voice search queries:

Use Conversational Keywords

Voice searches replicate human speech patterns because they function with full sentences and questions. Brands need to incorporate conversational keywords into their content because this helps their content match the way voice queries are structured.

Question-Based Phrases should include start phrases like "how" "what" "where" "when" and "why" because voice searches operate through question-based protocols.

Incorporate Long-Tail Keywords

Voice searches produce longer detailed inquiries that exceed the length of text-based queries. Brands need to adopt long-tail keywords which match detailed search inquiries because they attract specific search volumes.

Natural Language should be used to create long-tail keywords which follow the natural patterns of user conversational queries.

Conduct Voice Search Keyword Research

Tools should be used to analyze customer interactions while identifying conversational phrases that match natural speech patterns. The creation of content which connects with voice search users becomes possible through this approach.

The research process for voice search keywords demands creative thinking about user questions instead of using traditional keyword tools as the sole method.

Optimize for Local SEO

Voice searches frequently contain requests that need location-specific responses. Brands should adjust their content to focus on local search terms so they can better appear in results for users in specific areas.

A Google My Business listing is needed to stay current to enhance local search results visibility.

4.  How can brands ensure their content is easily discoverable in an era where more searches are conducted through smart speakers and virtual assistants?

Brands must implement these following approaches to increase the discoverability of their content when voice assistants and smart speakers control search discovery:

Optimize for Conversational Queries

The use of natural language proves more beneficial for voice searches because they imitate spoken human dialogue. The content produced by brands should adopt natural flows of speech together with question-based keywords which reflect typical user interactions with voice assistants.

The implementation of longer specific phrases which match spoken language should become part of your keyword strategy such as "What are some fun things to do outside in Santa Fe?"

Enhance Local SEO

Voice search queries frequently contain local intentions which makes it essential to maintain updated and locally optimized details on your Google Business Profile for search terms like "best latte near me.".

Positive reviews should be actively pursued since they improve businesses' presence in local search engine result pages.

Focus on Featured Snippets

Voice assistants retrieve their answers through featured snippets which they extract directly. The structure of your content should deliver quick answers to frequently asked questions because this strategy optimizes your chances of becoming featured in voice search results.

The use of direct answer headings as well as clear headings that match specific questions leads to better snippet eligibility.

Ensure Mobile and Speed Optimization

Since voice search mainly takes place through mobile devices you must implement both mobile-friendly design combined with fast page loading to deliver uninterrupted usability.

You should use PageSpeed Insights from Google to detect and resolve speed problems on your site.

Brands implementing these strategies enable better discoverability and user-friendliness of their content in the voice search period.

5.  How will AI and voice search contribute to the rise of zero-click searches, and what does this mean for organic traffic?

The combination of AI technology with voice search functions as a significant driver of zero-click searches which produces multiple effects on organic traffic patterns.

Contribution of AI and Voice Search

The development of AI-driven direct answers through search engines eliminates user necessity to visit websites because answers appear on the search results page. Voice searches demonstrate this phenomenon because voice assistants such as Siri, Alexa and Google Assistant extract their answers directly from search results.

Through voice searches users tend to ask longer conversational questions that voice assistants directly answer which produces zero-click search behavior.

Impact on Organic Traffic

Search engine results pages (SERPs) provide direct answers to users which reduces the number of clicks website visitors make. The zero-click search phenomenon affects multiple population segments and results in substantial search termination without user interaction.

The necessity of zero-click searches requires businesses to modify their SEO approaches. Brands need to optimize their content for featured snippets while also creating "People Also Ask" sections and delivering instant value to users who stay on the SERP pages.

6.  How can businesses better align their content with AI-driven search intent detection to improve visibility?

Businesses can improve their content perception by AI search intent detection through the following methods:

Leverage AI for Intent Analysis

The application of Natural Language Processing allows businesses to assess user query content and semantic meanings that direct content alignment with specified search intentions.

Businesses should deploy machine learning algorithms to identify intent categories through behaviors and choices of users by predicting their actions such as requesting information or navigation or transactions.

Create Hyper-Specific Content

The use of AI tools detects real-time hyper-specific intents which enables the creation of custom content that perfectly meets user demands.

The delivery of content must be both contextually important for users and provide quick practical value to improve user involvement as well as search engine rankings.

Optimize for Conversational Search

The content should match conversational search patterns through natural speech and question-based keywords which match user interactions with voice assistants.

Users can benefit from sentiment analysis since the technology enables systems to detect emotional cues in their queries thus enabling tailored sympathetic responses.

Utilize Predictive Analytics

The application of AI-driven predictive models predicts future search trends alongside intent changes which allows businesses to plan their content strategies ahead of time.

The analysis of previous user interactions enables organizations to enhance their predictions about future user intentions alongside building content that satisfies reoccurring requests.

7.  What are the biggest technical and strategic challenges businesses face when optimizing for voice search?

Businesses face multiple technical along with strategic obstacles when they optimize their operations for voice search capabilities.

Technical Challenges

Voice search technologies currently have problems correctly understanding spoken queries especially when these queries come from users with different accents or dialects or non-general languages. Search results become incorrect because of this inaccuracy resulting in user dissatisfaction.

Voice search queries have insufficient data available for successful optimization thereby creating obstacles for businesses to properly measure user behavior along with conversion statistics.

Technical SEO optimization involves ensuring fast website loading speeds and mobile compatibility because users need a seamless experience when optimizing for voice search.

Strategic Challenges

Voice search queries demand businesses to modify their content approach toward natural speech patterns by using long-tail keywords.

The competitive nature of securing top positions in voice search results has intensified because featured snippets appear only in a few selected results. This drives businesses to focus on optimizing for featured snippets.

Local businesses experience difficulties with voice search because these queries typically show preference for local results. The optimization of voice search brings enhanced visibility to local businesses.

8.  What emerging AI trends will have the biggest impact on SEO and voice search over the next five years?

Multiple emerging AI trends during the upcoming years will greatly affect SEO practices and voice search operations.

Impact of AI on SEO

AI tools like ChatGPT and Google Gemini will enhance content creation during the next few years by using their AI capabilities to accelerate repetitive procedures while supplying user conduct data which produces customized and suitable content.

Advanced Natural Language Processing systems will achieve better results in voice assistant understanding thus increasing voice search accuracy and user satisfaction.

AI in Voice Search Optimization

The evolving trends of conversational AI systems will improve voice search optimization by allowing users to naturally interact with voice assistants so they receive more pertinent search outcomes.

AI technology will analyze search intent better to enable businesses to develop content which meets distinct user needs while enhancing their search ranking position.

Strategic Opportunities

Organic search performance benefits from the combination of AI technology with emerging VR and AR systems to produce engaging immersive user experiences.

The application of AI produces personalized search results basing them on contextual information which companies can use to raise user satisfaction rates and convert sales.