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.

1. What are the biggest challenges in bridging the gap between product capabilities and user adoption?

Most users only utilize about 40% of a product's features. Product analytics tools are helpful in identifying which features are being underused. Typically, the lack of adoption stems from users being unaware of the features available or not understanding how to apply them. For example, during my time at Omniture, we had a powerful but complex product. I noticed many users weren't fully leveraging it, so I began writing blog posts about each feature, illustrating how they could bring real-world value. The blog gained popularity and helped significantly boost user adoption. Educational content like this, along with customer success teams showing real-world use cases, can play a crucial role in increasing adoption.

I believe the best way to bridge the gap is to identify underused features, educate users about their value, and continuously monitor analytics to gauge if education efforts are driving better adoption.

2. How do you approach educating the market about a new product category?

Creating a new category is one of the most difficult challenges in marketing. Humans naturally group things into familiar categories, so introducing something entirely new requires a lot of work. When Hightouch first introduced the concept of building customer audiences and activating them directly from cloud data warehouses, it was hard for people to grasp because the concept didn’t fit into any existing category. Initially, we referred to it as "Revense ETL" because consumers were familiar with ETL, and our product was essentially the opposite. As we expanded the functionality, we built a new category, the "Composable CDP," which was different from traditional Customer Data Platforms (CDPs). However, the creation of this category took time, effort, and many customer conversations before the industry accepted it. Throughout this process, we had to work around consumers' existing mental models to help them understand this new concept.

If you're trying to create a new category, it's crucial to spend a lot of time educating the market about its need, benefits, and how it connects to existing product categories. Though it’s tempting to dominate the category without competition, it’s often more advantageous to have competitors emerge, as it validates the existence of the category.

3. What role does data play in driving innovation and competitive advantage?

In today’s digital age, most user interactions take place on digital platforms, making behavioral data an invaluable resource for understanding what works and what doesn’t in your products. Data is the new way to “listen” to users, and the better you are at listening, the more likely you’ll be able to turn those insights into innovation.

Since most companies use similar platforms to build their products, the key to gaining a competitive advantage is learning faster through behavioral data. However, it's not as simple as it sounds. You need to gather the right data at the right moment, have the right people to analyze it, and then act on the insights to innovate. Those innovations, when implemented effectively, can help you outpace competitors.

4. What common mistakes do companies make when implementing data-driven decision-making?

One of the biggest mistakes companies make is failing to first define the questions they want their data to answer. Many jump into data collection without considering the broader questions that can have a real impact on revenue or user experience. Another mistake is letting data drive decisions too much. Data alone can't reveal the reasons behind customer struggles; you also need to understand the customer journey through tools like surveys or session replays. Data is most useful when it quantifies known problems, but identifying those problems based solely on data can be a misstep.

5. How do you approach mentorship and knowledge sharing within your industry?

The data industry, despite its prominence today, is still relatively young. Many people in data roles today didn't study it in school and instead fell into the field as the world digitized. Given the industry's youth, mentorship and knowledge sharing are essential to help everyone grow. When I started in the data space, few people had written or spoken about data best practices, so I began sharing what I learned through blog posts. I continued this trend at Salesforce and in consulting, always eager to pass on what I had learned. At Salesforce, my team and I would document what we did in blog posts so other companies could benefit from our experiences.

Mentorship is also a key part of my approach. I regularly schedule calls with people in the data industry to discuss trends and help guide their careers. It’s incredibly rewarding to hear that my blogs and books have helped people launch or grow their careers. I believe that knowledge sharing and mentorship are mutually beneficial: the more you give, the more you get.

1. Your career spans the fast-evolving landscape of commerce advertising. If you had to describe your journey in three words, what would they be and why?

Connections: Success in this space is all about relationships. Whether with advertisers, publishers, platforms, or tech partners. Being deeply connected to industry trends, networks, and the right people has been key to driving growth and staying ahead. It's also been enormously rewarding meeting the individuals shaping the industry.

Innovation: Commerce advertising is a space where innovation never stops. Whether it's testing new performance models, scaling partnerships, or navigating platform shifts, there's always something exciting happening, interesting conversations to be had and decisions to be made. The dynamic nature of the industry keeps things fresh and engaging.

Entrepreneurship: The industry rewards those who move and act fast. From launching new business models to optimizing performance strategies, there's always an edge to find and always an opportunity to innovate and build something that delivers value.

Strong relationships are the foundation. When paired with an entrepreneurial mindset, an innovative spirit, and a touch of humour, we can achieve the best results.

2. mrge is shaping the future of intelligent commerce advertising. What’s the one misconception people have about the industry that you’d love to, correct?

The conception that commerce advertising is just about last-click conversions. Many assume it’s a lower-funnel tactic only, but in reality, commerce advertising influences the entire customer journey. The right data can drive brand awareness, discovery, and consideration, as well as transactions, sign-ups, or leads.

E.g. a creator paid per sale will generate plenty of "free" branding and should not be evaluated purely by the sales generated. Advertisers need to explore and examine those partnerships more actively and reward those activities with higher CPA/CPCs, better attribution or tenancy payments.

3. Your 2025 Commerce Advertising Report positions influencer marketing as a top growth channel. Is this just another trend cycle, or are we witnessing a fundamental shift in how brands drive commerce?

It’s a fundamental shift. We’re seeing commerce move to where people spend their time, and creators are at the centre of digital engagement. Look at tiktoks affiliate strategy that empowers millions of creators to start earning on each sale they make. Creator marketing isn’t just about brand awareness anymore, it’s a performance channel that drives measurable revenue.

4. With brands obsessing over reach, engagement, and cost efficiency, what separates an average influencer campaign from a game-changing one?

You have to adopt a long-term partnership mindset. Which creator is a good fit and could keep posting for years? Who do you want to associate with and who can represent you authentically? It takes good communication to make sure the content fits the purpose and lastly, it takes trust to let the creator produce authentic content.

A smooth, easy integration is also key, making it simple for interested followers to purchase and check out quickly, ensuring a positive experience with your brand from start to finish.

5. There’s a fine line between automation and authenticity. Can AI really help brands build trustworthy influencer partnerships, or is there a risk of losing the human touch?

I like the "human-in-the-loop" concept. AI is incredibly helpful with repetitive tasks, but when it comes to content it often misses the spark and the authenticity. AI learns from the internet, Reddit, etc. and unless you have trained a model to fit exactly your personal tone, it will struggle to convey your message. For some creative tasks, AI is good but not great. If you want a winning creator strategy or converting content, you need to be better than the cut.

6. Shoppable video and niche platforms are on the rise. Are we entering a future where influencers become entire marketplaces themselves?

Yes, and it’s already happening. Think about fitness creators and their often highly trusted product recommendations. Every day creators are launching their own storefronts, selling through livestreams, and integrating affiliate marketing into their content. Some advertisers are investing in dedicated high-quality shops for influencers to ensure exclusivity. Platforms like TikTok Shop and Instagram Checkout are definitely turning influencers into mini-marketplaces. Outside of the large platforms, LTK (one of the largest platforms intersecting affiliates and influencers) is making it easy for influencers to create shoppable content and everyone knows “Link in Bio” products.  Interestingly enough the first fitness affiliate influencer was Michael Jordan with the Nike Air Jordan, earning him more than 100million USD in commissions per year.

7. The industry is evolving fast, but what’s the one blind spot brands aren’t paying enough attention to in influencer marketing?

A well-curated group of creators with an engaged audience can outperform an entire ad campaign. Inspiration is now coming from creators more than traditional media. And contrary to traditional media, creator content can easily be made shoppable and tracked. This is a huge opportunity for performance marketing. As the impact of the influencer can be amplified with smart ads and targeting. Think of combining the creator content with on-platform retargeting and targeting on the open web.

8. If you had to bet on one influencer marketing strategy that will dominate 2025 and beyond, what would it be?

The growth of influencer marketing within commerce advertising at scale. Performance-based influencer campaigns will take over. More brands will shift to CPA, and CPC models in combination with flat fees, leveraging influencers as both media buyers and sales partners.

1. Your career has spanned multiple industries, from startups to global brands. What key moments or challenges shaped your leadership style and approach to driving digital transformation?

This answer is really driven by two different paradigms: failure, and success.

When I was 26 years old I became the head of eCommerce for Lucky Brand Jeans – and I was fairly certain as a successful 26 year old that I was in fact god’s gift to the internet. However, I had never worked with creative people - and like most 26 year olds - I was not nearly as smart as I thought I was. We crushed it from metrics perspective, but culturally I was a fundamental disaster. However, it did teach me how to work with mentally diverse teams.

A few years later, I had the opportunity to work at Schiff Nutrition under a gentleman by the name of Tarang Amin. In one of our first meetings, Tarang, who had been the global head of Clorox and was infinitely more experienced and successful than myself, said: “You don’t know anything about the CPG world, and I don’t know a lot about the digital world. Let’s learn from each other.” Tarang transformed me into the leader I am today, and I am forever grateful to be able to call him a mentor.

The moral of the story: Digital Transformation is all about how you influence and transform a culture as much as it is the 1’s and 0’s of a P+L. If you don’t make the entire company a part of the transformation, you are doomed to fail regardless of immediate financial results.

2. eCommerce is evolving at an unprecedented pace. What are the biggest technological gaps you see today, and how can the industry innovate to bridge them?

For a function that is so technologically enabled, it’s shocking to me how little AI, particularly Generative AI, has influenced the actual shopping experience. Many companies have done things such as AI-driven chatbots in customer service, but very few are actually changing and optimizing how the customer experience adapts. The fact that many shoppers still experience ‘0 Results Found’ when searching for products is an indicator of just how far behind the industry is, because AI solves that problem in an instant. Or let’s look at recommendations. So many brands are using decades old segmentation methodology or content-based filtering to generate recommendations. Yet there are generative AI solutions on the market that understands a shoppers behavior in real-time and make hyper-relevant recommendations. This is the core reason why I joined XGEN — I want to make generative AI a key part of every eCommerce leader’s strategic roadmap.

3. With AI revolutionizing online retail, where do you see the biggest impact in the next five years—personalization, automation, or something entirely new?

The promise of ‘personalization’ has largely been a fallacy in the eCommerce world. The large asterisk besides personalizations is: as long as we have data on what you did in the past. 

Let’s look at shopping recommendations as an example. You go to your Amazon results, and what do you see for recommendations? You need more dog food. More diapers. More chicken strips. Why? It’s what you bought last time, and it has a fairly predictable consumption cycle, so the recommendation algorithm starts to react to your past purchase history.

For true personalization to happen, recommendations need to move away from reacting to a shopper’s historical data to predicting their next move. AI, especially Generative AI, can handle this with ease. The models are continuously learning from real-time user interaction. Every click, hover, and page view is a signal that helps the AI understand a shopper’s preference and predict a future need. 

The real beauty of AI though is that it’s engineered to handle this level of personalization at scale. Every shopper will encounter an experience that is truly unique to them. That’s a game changer for an industry that’s struggled to deliver this to-date.

4. Customer expectations are constantly shifting. How should brands adapt their digital strategies to stay ahead without over-relying on trends?

There is an underlying paradox here that needs to be addressed — customers want two things:

1. Hyper personal shopping experiences
2. To not give up more data than is absolutely necessary

Crafting personal experiences when knowing nothing about a user is hard - so what brands need to do is give customers a REASON to give them data and then deliver on the promise of hyper personal experiences, all while ensuring their security and making them feel safe.

Staying on top of customer expectations isn’t difficult when you make customers the center of what you do as a brand. I’m going to sound like a stuck record here, but GenAI really is the solution for this. Let’s look at the way search is currently deployed. If I want to find something in a brand’s product catalog, I have to input some form of keyword to get a result. If that keyword doesn’t exist in their product feed, I get a ‘0 results found’ message. Maybe I’ll get some recommendations. 

When brands deploy a GenAI search, the results are no longer tied to the data in the product feed. For example, I asked my 6-year old daughter to tell me a “look” she’s always wanted for me to look up in one of our brand’s eCommerce sites. She said (as most 6-year old girls do): I want to look like a unicorn. 

So I typed that into the search bar, and I got dresses that all had elements to them that resembled unicorns. My daughter was beyond thrilled! If you know the brand I’m talking about, you’d know that not a single person on their merchandising team would have thought to put a ‘unicorn’ tag on any of their exquisite dresses. 

The AI model intuitively understood a few fundamental things:

1. It knew what a unicorn was (hello trend of the day)
   
   
2. It knew what products closely aligned to the “look” of a unicorn (glittery, sequenced, colorful, etc.)
   
   
3. It did not need any additional detailed information to deliver the results that delighted my daughter 

When you’ve got a powerful GenAI solution like that in place, you can continually stay ahead of the curve. 

5. AI-driven automation is streamlining everything from inventory management to customer service. What’s the next frontier for AI in eCommerce?

Generative AI. Mic drop.

6. Supply chain disruptions have forced retailers to rethink logistics. What innovations do you believe will define the future of resilient and agile eCommerce supply chains?

Such a hard question because macro things out of our control can throw a wrench into this, but I think that is at the core of what retailers need to think about more and more — flexibility. We have to be able to make, distribute and support our products as location agnostically as possible, and build automated systems to allow for the macro disruptions we can’t control.

7. DTC brands are disrupting traditional retail models. What strategies should legacy retailers adopt to stay competitive in this evolving landscape?

Well, I think true DTC brands have a huge advantage and disadvantage at the same time. The advantage? If you are truly DTC you have a remarkable amount of control.  Control over the narrative, the pricing, the promotions, the product distribution.  However, you are also LIMITED by the fact that you can only market in a direct to consumer way.  

A legacy retailer needs to do everything they can to have that same level of control.  How are you making sure your wholesale partners and driving your narrative?  Featuring the right product? Not discounting you to death?

In a world where the internet has democratized the ability to FIND a product - controlling your brand becomes infinitely more important — and gets more important everyday.

8. With the rise of sustainable shopping and conscious consumerism, how can retailers balance profitability with environmental responsibility?

This is a tricky question.  A number of studies have come out with two factors:

1. Consumers want sustainability/conscious consumption
   
   
2. They won’t pay a significant premium for it

Brands that engage in sustainability as part of a key messaging need to embrace the fact that it is a reason to believe in a brand, not one that consumers in general will pay a massive upcharge for.

9. Charlie, as President and a leader in digital, data, and marketing, what are the key leadership principles that have guided your success in transforming businesses?

1. Build diverse teams — people that think differently and thus allow you to think of the problem differently.
   
   
2. Best idea wins - low ego, metrics driven optimization
   
   
3. Have fun. We are spending 10+ hours a day with each other, we might as well enjoy ourselves.
   
   
4. Winning matters. We are judged on results.
   
   
5. Innovation is a constant as opposed to one-off implementations.
   
   
6. Transparency is a full time job. Be honest with one another - even when it’s hard.

10. Looking ahead, what’s the one eCommerce trend you believe industry leaders can’t afford to ignore in 2025 and beyond?

If you are not utilizing generative AI in your day to day operations - ALL your day to day operations - you are going to fall behind. We have embraced it in a customer service/post sale world - but we haven’t even started thinking about what it can do on the front end of a customer experience, or even as a customer acquisition tool. I truly believe we all need to help solve this problem together and we will make our profession that much farther ahead of everyone else technologically and even more customer centric than it is today.