1. How can brands measure ROI in influencer marketing beyond metrics like likes and shares ?

Likes and shares are surface-level. The real value lies in conversions, brand lift, and cost efficiency. ROI should be measured by how many people actually take action-clicks, sign-ups, sales, or even brand recall over time. I’ve seen campaigns where the cost per engagement was low, but the impact was massive in terms of customer retention and long-term value. That’s the ROI that matters.

2. What role does data analytics and AI play in optimizing influencer campaigns for maximum impact ?

A huge one. I personally use AI tools to track engagement trends, audience behavior, and ad performance in real time. Data shows you what’s working and what’s just vanity. AI helps optimize the timing, placement, and creative elements so you’re not just shouting into the void-you’re speaking to the right people at the right time. It’s like having a digital sixth sense.

3. What are the best practices for selecting the right influencers to maximize campaign success ?

Relevance over reach, every time. The right influencer is someone whose audience trusts them, not just follows them. Look at engagement rates, comment quality, content consistency, and how aligned they are with your brand’s voice. And most importantly, ask: “Would this person actually use our product in real life?” If the answer is no, it’ll show.

4. What strategies can brands use to ensure long-term influencer partnerships instead of one-off campaigns ?

Build relationships, not just transactions. Treat influencers as creative partners, not ad slots. Involve them in the storytelling process, give them space to speak authentically, and focus on long-term value. Also, track their performance holistically-some influencers may not spike in numbers immediately, but they build loyalty over time. That’s gold for a brand. 

5. How can businesses use micro-influencers vs. macro-influencers to drive higher ROI ?

It’s not either-or-it’s about strategy. Micro-influencers drive hyper-targeted engagement, especially in niche markets. Macro-influencers bring scale and awareness. I recommend a hybrid approach: use micro-influencers for conversions and macro-influencers for brand storytelling. The key is coordination and consistency. When done right, it creates a powerful ripple effect.

6. What are the biggest challenges in influencer marketing today, and how can brands overcome them ?

Authenticity fatigue and inflated costs. Audiences are smarter now - they can spot forced content from miles away. Also, some brands overpay for reach without real results. The solution? Vet influencers properly, focus on genuine alignment, and use performance data to guide decisions. Influencer marketing isn’t dead - it just needs to be smarter, not louder.

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. Dana, can you share how your extensive experience in MarTech and AdTech has shaped your approach to leadership and innovation at Predactiv?

Absolutely! Prior to joining ShareThis, I was a Group Vice President of Global Partner Development at Acxiom, where I orchestrated the expansion of the company’s portfolio to include premier publishers such as Facebook (Meta), Twitter, eBay, Yahoo! (Verizon), and  Pandora. I led this portfolio and grew it to $100 million in revenue. Additionally, at Acxiom, I introduced the company’s first ever addressable television offering, leveraging data from industry giants such as DirectTV, Dish, Cablevision, and Comcast. I also cultivated partnerships with the leading advertising agencies including Starcom and Mediavest Group. As you can see, my extensive media, data and technology experience aligns perfectly with the Predactiv brand.  Predactiv is the unique combination of data and technology. A data platform powered by Gen AI. 

2. What challenges do you encounter in maintaining seamless data integration across platforms, and how does Predactiv address these challenges?

Predactiv invested significant time and resources in building direct integrations to all of the major platforms. We have “pipes” that can deploy data literally anywhere in the digital ecosystem — that includes activation partners such as DSPs, as well as clean rooms, client CRMs — anywhere our clients want to leverage data, we can support deployment in near real-time. All of this is enabled by our transparency in data management as well as our ability to operate outside of walled gardens. Our platform’s flexible infrastructure allows for the seamless integration and combination of multiple data sources, enabling a richer, more comprehensive view of consumer behavior. Fresh data, deployed anywhere, in a privacy centric manner, without being confined to walled gardens — this is the power of The Predactiv Data Platform.  

3. How does Predactiv balance the need for transparency with the necessity of protecting proprietary data and client privacy?

Transparency, data protection and client privacy work in tandem, not at opposing ends. At Predactiv, we are true to these ideals by putting privacy, compliance, security and consumer choice at the heart of everything we do. Our data collection, management and deployment are all handled with a privacy centric foundation. Similarly, all of our technology processes are both secure and compliant. We employ advanced artificial intelligence and machine learning algorithms that anonymize and aggregate data, thus safeguarding individual identities while still allowing clients to glean valuable insights.

Additionally, our recent product launch of our contextual audiences is an example where Predactiv is challenging the industry to do better. Our contextual audiences allow for precise digital targeting without the use of cookies. Even after the decision to deprecate cookies was delayed, Predactiv launched this product, with the intention of offering advertisers a more anonymous targeting approach. 

Finally, we operate on a global scale and strictly adhere to evolving privacy laws and regulations worldwide. This commitment consistently places us in the top 5% of data companies, as recognized in quarterly rankings by Neutronian—an industry leader in independent data verification, auditing providers for privacy compliance, data quality and transparency.

As I have described, you will find that privacy, compliance and the protection of consumer data is at the core of everything we do. 

4. What are the benefits of Predactiv operating within an open ecosystem, and how does this approach enhance data integration and collaboration across platforms?

Operating with an open ecosystem is a significant differentiator for Predactiv. For years, advertisers and marketers have successfully targeted audiences within the big platforms like Meta and Google. However, those platforms are likened to a black box, where data goes in but doesn’t come out. At Predactiv, we source data from over 3 million global domains, processing close to one trillion events per year. We can deploy this data anywhere, at any time, before or after campaigns, without confining the data to walled gardens. It is a very transparent data handling process that allows for multi-channel targeting, in-depth analytics, and campaign optimization. 

5. How does Predactiv plan to expand its capabilities and integrations in the future to continue providing value to its clients?

Predactiv will grow and evolve its Data Platform to meet client needs. The Predactiv Data Platform will be customized, as each client’s business is unique and different. In simple terms, The Predactiv Data Platform will be able to leverage our client’s first party data and integrate it with various data sources, including our proprietary, global, digital, near real-time behavioral and intent data. The combination of first and third party data assets will provide the whole picture of consumers, enabling more effective and efficient marketing and advertising.  In terms of efficiency, the platform will reduce the need for engineering resources, people and software, with the ability to still extract the most value out of big datasets. 

6. In your view, what are the most significant opportunities and challenges in the current MarTech and AdTech landscape?  

That is a great question. In thinking about the opportunities, I think it is best to start with the challenges. The industry is evolving at such a rapid pace; It is difficult for companies to keep up.  Clearly, the first challenge is in the evolving privacy laws and regulatory compliance. There always seem to be new laws emerging, and the lack of knowledge of “what is coming next” poses a risk for companies. We faced GDPR and CCPA, which changed the entire way companies operated. Data collection, management, and storage all needed to be reinvented.  The challenge of CEOs, like myself, is to try to get in front of the privacy legislation, as we want not only to follow the laws, but to lead the change. An opportunity in this area is to develop new products that don’t require cookies or consumer identifiers, such as we built with our contextual audiences. 

Another challenge is in the fragmentation of technology ecosystems as well as data.  Everywhere you look, today, data is being generated. We have smart watches, smart televisions, smart phones, connected fitness devices — the list is endless. The proliferation of available data is immense. Companies need to be smart about leveraging AI, data science, and technology to make the most of this data, in a privacy-centric way. The future is about connections — connecting various data sources together and, of course, connecting, or fostering interoperability between platforms and ecosystems. The connection point is significant for business results. 

The Predactiv Data Platform addresses these challenges. Powered by advanced data science and AI, the platform starts with connecting datasets. We combine, refine, and transform, readying the combined data for deployment to any “connection” point the client chooses. The Predactiv Data Platform is really a means to connect multiple datasets, and then be able to harness the value, and activate or deploy to the digital ecosystem. We are all about “connecting” to drive meaningful results.

Another challenge I will address is the dependence of advertisers on some of the big platforms, that in essence, are walled gardens. Data goes in but it does not come out. In addition to limiting the uses of the data, walled gardens limit transparency and carry high price tags for advertisers and marketers. 

Proudly, at Predactiv, we operate via the open web, and maintain privacy, transparency and the ability to return data, as opposed to locking it within a closed platform. 

Finally, just as privacy, data, and technology are all changing, so are the use-cases and monetization opportunities of data. CTV and retail media networks have emerged, and are driving up the cost of digital advertising. However, if companies can figure out how to leverage these opportunities, the returns will be worthwhile.