1.With only 9% of organizations currently implementing real-time personalisation, what challenges do you foresee in adopting these new technologies within your team?

The true customer value of real-time personalization is all about the context. Today, we live in two parallel worlds:

A: Expectation economy: Customers now expect to be treated as individuals and it does not matter which company or organization that interacts with them. The bar has been raised.

B: The Attention economy: We are now being exposed to more digital messages across various channels than ever before. As humans, we can´t process all of them in which leaves our brain to focus on whatever information that is most relevant and timely important.

The best chance of interacting with your customers would be when they are open for a dialogue, and that usually means that you need to be able to gather information from one channel, and instantly in real-time use that intelligence to continue the dialogue in another channel. 

For this to happen, we need to better understand what part of the customer journey we can impact, for what reason and in what channel. The challenges we see implementing real-time personalization is both to understand what data you should collect from various channels (customer support, in-store, online) and how you could swiftly use that information to continue the dialogue in another channel, leading to the following main challenges:

•        Data silos
•        Legacy tech not integrated between channels
•         Misalignment in different teams, and how they are being measured
•         Privacy compliance

2.What role will machine learning models play in helping you optimize real-time user interactions based on behavioral data?

Machine learning has already played an instrumental part in increasing both efficiency and effectiveness of personalized marketing, far most in the shape of recommendation engines. Looking back, it has provided companies the tools to draw better conclusions from previous transactions and interactions and by that scaling personalization to provide better recommendations to customers.

Looking ahead, machine learning will take the next step by not only providing recommendations based on what has happened in the past, but also predicting what will happen next. By gathering more data points, predictions will provide more accurate recommendations down to an individualized level in context leading to personalization is not just delivered to the right person in the right channel, but also at the right time with more granular precision. 

3. How will the collaboration tool streamline the management of personalisation efforts, especially for large-scale marketing programs across multiple teams?

When speaking about personalization, most companies refer to data. The ability to collect, connect and segment data from different sources. But to deliver a great personalized experience, you also need content that speaks to the different segments or groups of people (or even individuals) you want to engage with. Personalization sits in the intersection between content and data.

For personalization not to become fragmented, you need an overview of the customer journey and the different personalization and you also need a content engine to deliver personalized content and messaging at scale.

A collaboration provides the different capabilities you need to:

•        Unified workspace with project overview, task assignments and cross-channel coordination.
•     Content development and audience segmentation collaboration
•     Insights of results and analytics for better planning
  
  

4. As personalisation continues to grow in importance, how do you intend to collaborate with data and marketing teams to maintain a customer-centric approach at all times?

Personalization is a topic that is moving up the C-Suite. It needs to become a management topic, since personalization rely on different dependencies like data, content, analytics and omnichannel execution. Removing silos is a first step, but also focusing on customer-centric KPIs to move towards  relationship building dialogues in opposite of mass communication and a shorter term campaign focused mindset. 

5.Given the advancements in personalisation technology, how do you plan to balance automating personal experiences with maintaining human oversight and creativity?

Successful personalization is complex, and you would need to run multiple experiments to not only deliver a personalized experience, but a meaningful personalized experience. Automation and scale can only be the results of personalization that matters, that is delivering value for the customer while also having a positive impact of the business KPIs.

What led to Instreamatic’s focus on AI-driven contextual video and audio ads, and how does this approach differ from more traditional advertising methods?

Our focus on AI-driven contextual advertising (which we began offering before the industry’s broader AI boom of the past couple of years) came from recognizing 1) a fundamental shift in consumer expectations for more contextualized campaigns, and 2) a disconnect in how brands and agencies were able to deliver on those expectations.

Back when we first started Instreamatic as a programmatic audio ad platform in 2015, we saw that brands were struggling with the traditional ‘one-size-fits-all’ approach to campaigns. But marketers that tried to create more individualized ad variations—manually—were spending enormous resources doing so. And even then, they were never going to achieve true personalization at scale. It would take weeks of production time and significant budget just to create a few variations of an ad. 

Our AI-driven approach fundamentally transforms this process by enabling brands to generate hundreds of personalized video, audio, or CTV ad variations from a single creative within minutes. What really sets our approach apart is that we’re not just creating random variations—we’re using AI to analyze context and data to ensure each variation resonates with its intended audience. The numbers support the strategy: our work this year with one of the largest technology companies in the world showed that even simple contextual relevance in ads drove an 18 percentage point increase in purchase intent compared to standard ads. We’re essentially enabling brands to have meaningful conversations with their audiences at scale, rather than broadcasting the same message to everyone. This shift from mass messaging to personalized communication represents the future of advertising, and we’re proud to be at the forefront of this transformation.

How can marketers use AI to enhance storytelling in video and audio ads, making them more impactful and relevant?

AI is revolutionizing storytelling by enabling dynamic narrative adaptation in real-time. Rather than creating one linear story, we can now craft flexible narratives with multiple elements that can be personalized while maintaining the core brand message. Our platform uses AI to analyze what storytelling elements resonate most with different audience segments—whether it’s adjusting the pacing, music, voice-over style, or even visual sequences in video ads. For instance, we might find that morning commuters respond better to energetic, quick-paced narratives, while evening audiences engage more with relaxed, thoughtful approaches.

The key is that we’re not just making superficial changes or changes for the sake of changes; rather, AI helps us identify deeper patterns in how different audiences connect with storytelling elements. This allows brands to maintain their authentic voice while adapting how they tell their story based on context. What’s particularly exciting is how AI can help brands extend the lifespan of their creative assets by continuously finding new ways to make existing content relevant to different audiences.

What challenges do brands typically face in ad personalization, and how does Instreamatic address these?

The biggest challenge brands face with personalization is scale. Most brands understand the value of personalization. But traditional methods make it prohibitively expensive and time-consuming to create enough variations to truly personalize at scale. A typical video ad campaign might need hundreds of variations to account for different locations, times of day, audience segments, and other contextual factors. Creating these manually could take months and cost hundreds of thousands of dollars. Our platform addresses this by automating the personalization process, allowing brands to generate these variations in minutes rather than months.

We’re also solving the quality control challenge—our solution ensures that each variation maintains brand consistency while still optimizing for relevance. It’s not just about making more versions; it’s about making the right versions for each context.

What key metrics are used to measure the success of personalized ad campaigns for the industry?

We look beyond traditional metrics to understand the full impact of personalization. As we detailed in our recent report “2025: AI in Creative Production,” the industry needs to expand its measurement framework to capture the nuanced effects of contextual relevance. Our platform analyzes the performance delta between personalized and non-personalized versions, measuring not just if an ad worked, but why it worked. For example, in this campaign case study, we saw a 22 percentage point increase in brand favorability with personalized ads. But more importantly, we could attribute which personalization elements drove that improvement. Even seemingly minimal personalization can deliver impressive results, prompting customers to lean into a more tailored experience. As additional contextual elements are then layered into ads—like time of day, location, or audience behavior—the outcomes only become more impactful. 

We also measure adaptation efficiency—how quickly our AI can optimize ad variations based on real-time performance data. This helps brands understand not just the end result, but the ongoing optimization process that got them there. The findings in that new report linked to above reinforce what we’ve seen across campaigns: successful personalization requires looking at both immediate performance metrics and longer-term brand impact indicators.

Can you explain how your platform optimizes the delivery and performance of contextual ads at scale?

Our platform’s optimization process works on three levels, simultaneously. First, we use AI to analyze the initial creative assets and identify elements that can be modified without compromising brand integrity. This could include everything from background music to visual transitions in video ads. Second, our system creates variations based on contextual parameters—time, location, user behavior, and platform-specific requirements.

But what makes our approach especially unique is the third level: real-time performance optimization. As these variations are deployed, our AI continuously monitors performance data to refine the personalization rules. For example, if we notice certain elements performing better in specific contexts, the solution automatically adjusts the distribution to favor those combinations. This creates a virtuous cycle where each ad served helps improve the performance of future ads. We’re essentially building a self-improving system that gets smarter with every interaction, ensuring brands can maintain peak performance at scale. Our platform also integrates with existing DSPs and SSPs, so marketers can incorporate our enhanced personalization into current workflows without additional costs or infrastructure changes.

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1.Why should brands reconsider their advertising strategies in light of recent industry trials and disruptions? 

The recent antitrust trial against Google has revealed significant scrutiny over their practices in search and programmatic advertising. The trial's findings have prompted many brands to reconsider their dependency on Google’s ecosystem, and the risks at hand should encourage advertisers to use a platform that enable them to navigate all these channels on a level footing. This presents an opportunity for brands to diversify their ad tech strategies, adopting a more omnichannel approach that reduces reliance on a single ecosystem. By leveraging alternative channels like Connected TV (CTV), Digital Out of Home (DOOH), and audio, brands can ensure that they are not overly exposed to changes in any one platform, thereby fostering a more balanced and resilient advertising strategy.

2.What are some alternative data sources that brands can explore to replace third-party data? 

With third-party data deprecation already impacted the majority of browsers and new channels, brands are looking to bolster their first-party data strategies and exploring other privacy-compliant data sources. Some effective alternatives include:

• First-party data from direct interactions with consumers, such as website behaviour, purchase history, and customer feedback.
• Second-party data through strategic partnerships with other brands that share a similar audience but do not compete directly.
• Contextual data that allows advertisers to place ads based on the content of a webpage rather than specific user data.
• Cookieless ad tech providers with a transparent privacy first approach.   

3.What are the benefits of audience curation and more focused targeting for brands in the current ad tech environment? 

Audience curation allows brands to refine their target groups based on well-defined characteristics and behaviours, leading to higher engagement and improved ROI. In the current landscape, where data privacy is paramount, focusing on specific audience segments rather than broad-based targeting helps brands achieve more relevance and reduce wasted ad spend. Additionally, curating audiences based on first-party and contextual data allows brands to retain more control over their messaging while aligning with consumer privacy expectations. This curated targeting approach mitigates against a reliance on third-party cookies and aligns well with omnichannel strategies that emphasise cohesive customer experiences across multiple platforms.

4.How has the deprecation of third-party cookies impacted the digital advertising ecosystem? 

The phase-out of third-party cookies has fundamentally shifted how advertisers approach data-driven marketing. Without cookies, advertisers face challenges in tracking user behaviour across sites and personalising ads at scale. This shift has led to a greater emphasis on building first-party data infrastructures and adopting privacy-first advertising models. Many brands and platforms are exploring cookieless tracking methods, like browser APIs (e.g., Google’s Privacy Sandbox) contextual advertising, not to mention probabilistic and deterministic cookieless soloutions including ID5, RampID and Ftrack. The cookie deprecation is also accelerating innovation in data privacy technologies, including AI-powered audience segmentation, which offer advertisers new ways to achieve effective targeting without compromising user privacy.

5.What are the long-term implications of current ad tech trials for the broader digital advertising ecosystem? 

The ad tech trials, particularly those focusing on Google, are likely to reshape the entire digital advertising landscape by pushing the industry towards greater transparency and reduced customer lock-in. Regulatory actions, like the Digital Markets Act (DMA), underscore a move toward more equitable competition, which could amplify opportunities for independent ad tech companies and smaller players to thrive. In the long term, this will likely result in a more diverse ecosystem with multiple viable options for advertisers, reducing reliance on a few dominant platforms. For brands, this means a shift toward more diversified, omnichannel strategies that emphasise flexibility and independence from any single ecosystem. Additionally, the rise of AI in advertising could bring new considerations for data privacy and ethical ad targeting, shaping the future of consumer engagement across channels.

These trials indicate an industry-wide push toward a fairer marketplace that values transparency, accountability, and consumer trust —fundamentals that will define the next era of digital advertising.

What is Multilocal’s mission in the programmatic advertising space? 

Multilocal was born out of a deep frustration with digital advertising inefficiencies. We saw an industry fragmented, where advertisers and media owners were losing control, and consumers were bombarded with irrelevant ads. We experienced the flaws first-hand: reduced targeting signals, privacy concerns, inefficient supply chains, and a rising demand for sustainability. This inspired us to develop a new approach.  

From day one, our mission has been to transform programmatic advertising by providing advertisers and publishers with the control, transparency, and efficiency they need. We believe curation empowers both sides - driving incremental outcomes from the open web in a controlled and transparent way. 

What is the role of audience curation in addressing issues related to inefficiency in digital advertising? 

To understand this inefficiency, we need to look back at past tactics. Historically, DSPs were expected to handle all decisioning, logic, and targeting, while SSPs were viewed as simple pipelines or clearinghouses for purchased media. This setup created a problem: DSPs would only process a small sample of billions of available opportunities, leading to missed chances to reach the right audiences and content in real-time. 

Curation offers a smarter approach to this. It recognises that SSPs, with their deep knowledge of domains and apps, can highlight high-performing inventory and audience opportunities for marketers. This doesn’t replace the role of DSPs, but enhances the programmatic strategy by tapping into the SSP’s potential beyond just being a data pipeline. 

How is curation solving for a new era of addressability?

Addressability on the open web has declined over the years, with limitations in data-driven targeting and reduced transparency often pushing advertisers towards walled gardens with richer first-party data. Curation offers an alternative by enabling the use of sell-side data. By matching data directly on the sell side and linking it with various first- and second-party assets, curation strengthens open-web addressability.

The growth of retail media highlights curation's effectiveness in improving targeting precision and enabling closed-loop measurement for advertisers. Similarly, CTV leverages curation by allowing broadcasters to securely use their first-party audiences, reflecting the data control seen in walled gardens.

What are the key benefits of Multilocal's Active Curation™ technology for advertisers and agencies? 

Multilocal’s Active Curation™ continuously refines audience segments to optimize campaign performance across both the supply and demand sides, offering a unique dual-pronged approach unmatched by any other curation service. This enables us to deliver highly relevant audiences for advertisers, resulting in a 75% increase in CTR over projections and a 38% rise in video completion rates.

Active Curation™ also boosts efficiency by reducing the number of bid transactions needed to meet impression goals, tackling the issue of unnecessary waste common in programmatic advertising.

Performance data from our CarbonSmart™ solution shows a 55-80% reduction in carbon emissions per campaign. Advertisers, agencies, and DSPs can further leverage Active Curation™ to filter for low-carbon marketplaces - such as those committed to a low-carbon pledge - when selecting placements.

How is Multilocal doing curation differently?  

We’re an independent, platform-agnostic service company powered by proprietary curation technology, with five years of experience and a global team of experts. Unlike others, we take a horizontal approach to curation, collaborating across the entire programmatic ecosystem for a comprehensive view. 

Our partnerships with major SSPs, DSPs, and DMPs give us a 360-degree view of the supply and data landscape, allowing us to leverage first-party data effectively for both advertisers and media owners. Our custom-built supply taxonomy ensures quality by categorizing sites based on type of content, campaign performance, and volume of traffic.

With each brief we receive, we create audience packages aligned to KPIs, then use our Active Curation™ platform to curate audience segments and optimize in real-time, ensuring precise targeting and impactful results. 

Over the past 12 months, we've seen agencies and brands increasingly embrace curation as it demonstrates its value - resulting in a 197% increase in brief requests from clients.

1. Given the saturation of Super Apps in Asia, how can new entrants differentiate themselves from established players like WeChat or Gojek?

“Even in saturated markets, new apps can (if perfectly executed) disrupt established Super Apps by offering a solid Unique Selling Proposition (USP) that sets them apart. This differentiation doesn’t have to be a radical change; it can be an improvement in functionality, cost, or user experience.

To differentiate effectively, new Super Apps should:

• Identify a clear USP: Highlight how your app improves upon existing solutions, whether through innovative features or better value.
• Combine services creatively: Offer a unique mix of services that aren’t currently bundled together by competitors.
• Invest in marketing: Launch a well-resourced and optimized marketing campaign to gain visibility and enter the conversation.
• Target untapped markets: Consider focusing on regions outside of Asia where Super Apps are gaining popularity but competition is less intense. Building a sizable user base in these markets can provide a strong foundation before entering the more competitive Asian landscape.

By focusing on these strategies, new apps can narrow down and master their niche and compete effectively against established players with a wider and lesser focussed product offering.”

2. How can brands leverage the data provided by Super Apps, such as user preferences across various services, to create more targeted and effective advertising campaigns?

“One of the reasons why Super Apps are so successful is because they keep all their user data primarily for themselves. This increases the value of the Super App due to the better understanding of their users (in various all-day life situations). This data is made accessible to marketers by tapping into the native advertising possibilities offered by these Super Apps. With all that data, you can set up very targeted and specific ads, targeting various user types as well as user segments and use-cases for which the Super App is used in the first place.”

3. In a market where users are turning to specialized apps for media consumption, how should media companies balance diversification with focused user experiences to stay competitive?

“The app industry is among the most competitive and dynamic sectors around. Consumer preferences are in a constant state of flux and there are literally thousands of new apps launched every single day. This means that even very successful apps can’t stay still - they need to be constantly reevaluating both their offering and their marketing initiatives. What is important to remember is that the average consumer does not think about using a Super App or a specialized app as a binary choice. Most wouldn’t even recognise an app like Uber as a Super App. The critical factor is that every aspect of the app works as well as it can because if it doesn’t there are plenty of competitors that a user can switch to. Key to ensuring the best user experience is responding to change. This means knowing what new innovations can enhance your app or how it is marketed, what your competitors are doing and how market conditions and consumer demands are changing. If you stay ahead of the curve, you will be able to stay competitive.” 

4. What strategies can developers adopt to tap into the growing appetite for Super Apps in Western markets, particularly with the rise of Uber and Revolut?

“Although we often talk about Asia or Western markets as a homogeneous block, the reality is that there are huge variations between each country. What may work well in France, might not appeal to an audience in Australia. Knowing the market and tailoring the app offering and how it is marketed to the demands of consumers in each country is fundamental. App developers will naturally know their home market the best, so that’s the most obvious place to start. From there, it is about identifying the next most similar market and modifying the app and how you promote it to that audience. It may seem appealing to go after the most lucrative markets first, for example, the US. However, not only will they be the most competitive, they will also likely be the most expensive places to do business. It is better to take an incremental, pragmatic approach to growth - learning lessons on the way - and build up your audience and capabilities on this journey.”  

5. With growth slowing, how should investors evaluate the future of Super Apps in Asia and emerging markets? Are there specific sectors within Super Apps that still hold high-growth potential?   

“Although growth has slowed, the number of new users being acquired is still considerable. User growth is also not the only metric of success, it also really matters how engaged an existing user base is as that will have the most important impact on the bottom line. In addition to this, Asian Super Apps also have the capacity to break out of the region and look to acquire substantial growth in other markets that are largely untapped. One of the virtues of Super Apps is that there are an endless configuration of services that can be created. Fintech, ecommerce and mobility services are all growing at a considerable pace in Asia, so too are AI tools - everything from companion apps to art generation. There is therefore plenty of scope for these Super Apps to add a new service from a fast growing industry and maintain their market position. I think investors will look at all of this potential and keep up the level of investment.”   

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• The Consumer
• Less data is used to deliver streamed creative assets; firstly the whole ad file is not downloaded to the consumers divide; once the creative is in-view we begin streaming 2-4 seconds of video at a time, if the consumer watches half the video only around half of the total creative file is transferred to the consumer’s device. If the consumer scrolls straight past the ad then little data is transferred.
• The Website
• Publishers websites have to load a substantial amount of data when loading a page, this is exacerbated by the advertising they carry - streamed ads from SeenThis only transfer data when the creative is in-view therefore our ads do not disproportionately impact page-load times. Oftentimes a streamed video creative can be less impactful on page load than a static creative due to file sizes; SeenThis uses adaptive bitrate and as such we deliver smaller data packets sequentially so many small data packets rather than one large data packet, this lessens the overall data that needs to laid for a publisher page to be rendered fully for a consumer.
• The Brand
• Brands are able to deliver high quality video creatives into Display as never before; without compressing and lowering the quality of the creatives brands are able to match the fidelity of the ads with the origins; creative or TVC to ensure brand guidelines are never compromised to make a creative small enough to to be delivered in-App or open-web; generally every media performance KPI increases with streaming due to the speed with which streaming is able to deliver a creative to a consumer - as is widely known & reported, blank ad slots do not drive consumer engagement - streaming guarantees every video ad will start playing as soon as it is in-view. Brands also enjoy the data-saving generated by streaming versus download-tech video activations, and often reference the data they have saved in their own Case Studies, proving their own commitment to lowering the CO2e footprint for video & Outcome-based buying activations.
• The Agency
• Agencies are able to activate streamed video creatives via their programmatic teams, or directly with publishers. Our technology is tag-based so it does not impact buying methodologies leveraged by all agencies. Agencies are also able to deploy their extensive technology & experience in audience targeting with streamed videos, rather than outsource them to a video ad network where they can not control where, when, and to whom ads are shown; returning the sovereignty of video activations back to agencies is seen by many as a great use case on its own for working with SeenThis.
• The Environment
• Our technology is used by many of our clients as their own initiative to lower their overall emissions position; SeenThis measures data transfer at a very granular level, we provide dashboards for estimated savings for our clients and encourage them to use our data (exported and ingested by their measurement platforms) to enable them to define their own CO2e emissions to their own standards & measurement guidelines.