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. Why is data collection still such a challenge for organizations ?

Organizations are collecting more data than ever-captured by different teams, using different tools, for different purposes. For many, spreadsheets and manual data tasks are just as common as digital tools and automated processes.

As more teams collect more data and introduce more tools, integration, quality, and security challenges mount. When data gets stuck in silos, errors go unnoticed, compliance risks increase, and teams struggle to maintain a reliable source of truth. That’s why a structured approach to data collection is critical-ensuring systems work together to provide accurate, accessible insights.

2. How does all of this incoming data impact quality ?

Integration challenges make it difficult-if not impossible-to establish and maintain a single source of truth. What often begins as a quick fix-adding a tool here, a plugin there-can quickly turn into a patchwork of disconnected solutions. Each tool may solve an immediate problem, but together, they create inefficiencies, manual workarounds, and increased security risks. Instead of centralizing data, organizations end up with fragmented systems that prevent teams from accessing a complete and accurate picture of their customers.

These inefficiencies lead to poor data quality, which affects every stage of decision-making. Clean, accurate, and timely data is essential for identifying both roadblocks and opportunities, yet many organizations unknowingly operate on outdated, inconsistent, or incomplete data. Without a single source of truth, they may not even realize the extent of the issue, why it’s happening, or the true impact on their operations.

3. You’ve mentioned security and compliance a few times. What’s leaving organizations vulnerable ?

We recently surveyed 400 data professionals, and 91% said security keeps them up at night. And for good reason-IBM estimates that the average cost of a data breach in 2024 was $4.88 million.

One of the biggest vulnerabilities with data is a lack of clear ownership. When there’s no alignment on who owns data and how it should be managed, teams operate in silos, policies become inconsistent, and accountability slips—creating an environment where security and compliance risks thrive.

Moreover, more than half of the professionals we surveyed reported challenges with data integration and analysis. Without a clear data flow, employees resort to workarounds-exporting sensitive information into spreadsheets and sharing it via email. This disrupts workflows, increases compliance risks, breaks the chain of custody, and makes audits nearly impossible.

4. Organizations want to automate more processes, but getting there can be a real challenge. Why ?

System limitations, knowledge gaps, and budget constraints are common roadblocks. But even when automation is in place, broken integrations or homegrown solutions that weren’t built to scale can prevent data from flowing correctly into CRMs and other core systems.

Ultimately, it always comes back to the data. A structured approach to data collection is the foundation of any successful automation strategy. Once that’s in place, organizations can integrate their systems more effectively, eliminate redundancies, and reduce inefficiencies. From there, automation can be implemented intelligently-replacing manual processes where they make the most impact while maintaining human oversight where it’s needed.

5. Data quality is key to effectively adopting AI solutions as well, right ?

Absolutely. AI is only as good as the data it processes. Without structured, accurate information, AI doesn’t enhance efficiency-it amplifies mistakes. When AI-powered tools are fed incomplete or inconsistent data, they generate unreliable outputs.

AI thrives on high-quality inputs. Clean, well-structured data enables AI to automate tasks effectively, deliver meaningful insights, and optimize decision-making. But without a strong data foundation, even the most advanced AI tools will struggle to deliver real value.

1. How can integrating a DSP-agnostic platform streamline operations and reduce costs for advertisers and brands ?

Day to day, a DSP-agnostic platform simplifies operations by allowing marketers to activate all biddable media from a single UI and workflow. This saves hours of time and frustration for teams that are used to juggling multiple platforms. Instead of logging into different systems, managing various interfaces, and reconciling separate reporting structures, everything is streamlined into one place. More strategically, a DSP-agnostic approach makes it easier to test new platforms, data providers, and audience segments. When new opportunities arise, teams can evaluate them without going through lengthy legal contract negotiations or spending time learning an entirely new system just to try a different strategy. This lowers the barrier to entry for testing outside of primary partners. From a cost perspective, ensuring that media runs in the right places with the best targeting and audience data maximizes ROAS while minimizing waste. Ultimately, this leads to more efficient marketing spend and better overall performance.

2. What impact does a DSP-agnostic platform have on data consolidation and audience targeting capabilities ? 

Anytime you lower the barrier to testing and scaling new solutions, the best strategies get adopted faster. In a DSP-agnostic environment, the size of existing players matters less while the quality and value of data take the lead. Advertisers can consolidate data more efficiently, gaining better audience insights and more precise targeting. A great thing for advertisers and the rest of the industry!

3. How might the adoption of DSP-agnostic solutions affect relationships between advertisers, agencies, and technology providers ? 

We don’t expect major shifts in core relationships between advertisers, agencies, and technology providers. The real change will be in how easily all three can work with new partners. Agencies can better serve clients they previously couldn’t, advertisers can test new mediums or data providers without the usual hurdles, and tech providers can expand their reach in ways that weren’t possible before.

4. How is the shift towards DSP-agnostic platforms influence the future landscape of programmatic media buying ? 

A DSP-agnostic approach creates a more efficient marketplace by giving marketers (whether agencies or brands) expanded access to inventory, data providers, and audiences. This ensures that the best value wins more often, and media plans can be more complex without the extra work.

5. What are the potential implications of DSP-agnostic platforms on the evolution of media buying strategies in the coming years ? 

It means more channels, more strategies, and more testing. When marketers spend less time dealing with platform limitations and have easier access to inventory and data, they can focus more on strategy. This leads to more diverse, data-driven media plans that continuously evolve.

6. What role do DSP-agnostic platforms play in addressing challenges related to transparency and brand safety in programmatic advertising ?

Taking advantage of solutions and strategies to better understand your ad spend requires time, money, and the ability to interpret different measurement models - MMM, third party attribution beyond the platform buying in, attention metrics, among others. The easier we make it for marketers to integrate and analyze additional solutions to measure the effectiveness of their spend, the better we can shape the ecosystem around what our clients consider transparent and brand safe.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How DataOS enables AI in enterprises with minimal engineering effort:

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


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

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

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


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

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

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

1. How does this partnership help publishers recover lost ad revenue while maintaining a high-quality user experience ?

Our partnership with Ad-Shield helps publishers reclaim lost revenue by identifying and restoring high-value impressions that would otherwise be blocked or go unmonetized. Ad-Shield’s detection ensures ads are delivered only when they meet quality and user experience standards, while our exclusive demand sources and optimized ad placements maximize revenue without sacrificing user experience. With this partnership, publishers have already seen a daily increase in ad revenue of 8-15% due to Ad-Shield’s innovative adblock recovery solution.

2. What metrics should advertisers and publishers track to measure the effectiveness of ad recovery strategies ?

Publishers should focus on incremental revenue recovered to assess the impact of ad recovery. Engagement metrics like session duration and bounce rates are also critical to ensuring ad recovery doesn’t negatively affect user experience. Advertisers, on the other hand, should track conversion rates, CTR, and ROAS to ensure recovered impressions are driving real business results.

3. What are the main causes of ad revenue loss, and how can technology address these challenges ?

Publishers lose revenue due to ad blockers, low viewability, poor auction performance, and restrictive browser policies. Our partnership with Ad-Shield directly tackles these challenges by using ad recovery to detect and reintroduce lost impressions in a way that aligns with user expectations and brand safety. Combined with Next Millennium’s premium demand and advanced optimization, publishers can recover lost revenue without disrupting their audience’s experience.

4. What are the key considerations for publishers when implementing ad recovery solutions without disrupting content consumption ?

The key is ensuring ad recovery works in harmony with user experience. Publishers should prioritize solutions that respect ad quality standards, avoid intrusive formats, and maintain fast page loads. Technology that selectively recovers impressions while optimizing placement and frequency ensures monetization and user experience go hand in hand. For example, one of our publisher partners Minute Media had a main concern when partnering with us, and that was its page speed. Next Millennium supplied unique demand to Minute Media that they were not getting from their existing Open Exchange partners. This resulted in more intensified competition, and stronger bid density - all without compromising the user experience. Overall, Minute Media saw $2 Million in new revenue after adding Next Millennium as a bidder.

5. How can publishers strike a balance between maximizing ad revenue and ensuring a seamless user experience ?

Smart monetization beats ad overload. Publishers should focus on high-quality ad demand, non-intrusive formats, and intelligent ad refresh strategies rather than increasing ad volume. Leveraging data-driven optimization, frequency capping, and contextual relevance helps maximize revenue while keeping the user experience intact. One of the key aspects of Next Millennium’s success is our direct approach to working with advertisers. We triple ad revenue for our website publisher partners by selling their ad inventory to global brands who value quality over quantity.

6. How do you see the future of ad monetization evolving with advancements in AI-driven ad recovery technologies ?

AI is transforming ad monetization by making it more adaptive, efficient, and personalized. Expect to see real-time optimization, improved fraud detection, and greater contextual relevance driving better performance for both advertisers and publishers. As AI advances, the industry will shift toward fewer but more impactful ads, ensuring revenue growth aligns with user experience expectations.

1. What’s the single most game-changing innovation in commerce or fulfilment that decision-makers should embrace today? Why is it critical for their success?

There are many exciting innovations in commerce, however, what is more important for retail success is having the ability to critically evaluate your tech strategy, and understand which tools can truly help maximise business effectiveness. 

For example, something that at face value may be ‘boring’  is unified commerce. A unified commerce infrastructure makes it possible for merchants to bring together sales channels, inventory management, order fulfilment, and other retail activities under one platform. This not only lowers costs significantly but gives retailers access to a holistic view of their operations. 

Additionally, with today’s consumers often toggling between online and offline channels as part of a single transaction, having the tools to facilitate orders seamlessly across online, offline and social is vital.  For example, Aussie brand  Bared Footwear found that by adopting solutions like Shopify POS and Ship-to-customer, they were able to consolidate and unify their commerce stack to save time, optimise resources, and streamline their customer service. JB Hi-FI took a similar approach, establishing a 90-minute delivery service with Uber. In just over 12 weeks, the retailer integrated its inventory management system with Shopify via API, including the checkout page, enabling stores to pack and ship online orders to customers in less than two hours from the time of purchase. 

While a unified commerce infrastructure might not sound like the most exciting innovation, it is one of the most game-changing when it comes to business effectiveness. And with so many tools and innovations at retailers’ doorsteps, focusing on the tech that streamlines the foundations of a customer’s journey is crucial. 

2. What emerging trend in commerce and fulfilment should businesses prepare for in 2025, and how can they best position themselves to stay ahead of the curve?

Last year’s Black Friday Cyber Monday weekend saw  Shopify POS sales in Australia grow by 29%, proving that the future of commerce is offline as much as online.  With brick-and-mortar sales likely to continue playing an important role in 2025, merchants need to equip their floor staff with POS systems that support efficient workflows. For example, maintaining cart visibility on the home screen while scanning barcodes or using the search function helps reduce unnecessary steps. 

The checkout also remains one of the best areas for merchants to collect and leverage valuable first-party data. Shopify recently made metafields available through POS, aiming to improve clientelling and make data capture a natural part of the checkout flow. Teams can now record specific details directly in the customer’s profile, such as a pet store noting a customer’s pet type for future interactions. This enriched data not only enables a more personalised checkout experience but can be used to deliver highly segmented marketing communications that feel authentic and relevant to each customer.

3. What is the biggest challenge decision-makers face today in optimizing fulfilment or commerce experiences, and how should they tackle it with the help of technology?

The 2024 Australian Retail Report found that 61% of businesses struggle with efficiency-related challenges, from complex systems and manual processes to inefficient supply chains. Staffing pressures have added another layer of strain as cost-of-living increases and a tight labour pool drive up wage demands and impact retention. 

In light of these challenges, it’s no surprise that many retail leaders are prioritising operational improvements that help them do more with less. Automation can be particularly helpful in this regard, with tools like Shopify Flow allowing merchants to set up custom workflows to automate repetitive tasks related to inventory management, loyalty programs and discounts. We’ve also made it possible to automate more of the returns process, cancel returns if products aren’t sent back, and automate communication of abandoned cart or welcome emails using ready-made templates. 

Technology can also improve fulfilment efficiency. Today’s consumers want products delivered quickly with frequent updates on their order status and at little or no additional cost,  which puts pressure on merchants to fulfil orders swiftly and cheaply. Tools like Shopify Shipping make this process much simpler. With our built-in shipping software, businesses can access pre-negotiated low rates with global carriers, ship from up to 1,000 fulfilment locations, easily display different shipping options at checkout, and track the status of all orders in one place. We’ve also added time-savers like bulk printing of labels and packing slips, automations, and address validation to help retailers save precious time and money.

4. In what ways does technology directly improve the experience for end users and customers? What should decision-makers prioritise to enhance customer satisfaction?

Technology has a vital role to play in creating a frictionless shopping journey. By harnessing the right tools and unified software, retailers can adapt to the changing needs and habits of today’s shoppers, engaging with them seamlessly when and where they want to shop. 

However, while it can be tempting to keep adding flashy features to your website, or invest in another piece of software in the name of customer satisfaction, merchants should first prioritise refining the basic touchpoints in the customer journey. These core aspects – customer relationships, inventory and stock management, and operations – are crucial for building a consistently positive customer experience.

For example, retailers should make sure customers can effortlessly log into their accounts, easily track and return orders, and view their progress on any loyalty programs. Providing a smooth checkout experience is also key. In-store, this might involve offering multiple payment options and instant refunds, while online shoppers benefit from fast load speeds and features like draft orders, guest checkout, chatbots for last-minute questions, and estimated delivery dates.

By getting the fundamentals right, retailers can strengthen customer loyalty and be prepared to tackle today’s thrilling retail storm head-on.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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