1. What role does real-time data play in optimizing digital marketing performance across web and mobile?

Many businesses rely on first-party data, typically available in real time or with minimal delay, in order to optimize their performance]. However, businesses don’t operate in isolation—you need market context, competitor insights, and consumer behavior trends to understand why your metrics are shifting and how to respond. This is where market intelligence comes in.

Traditionally, this data was siloed, delayed, and lacked actionability. Now, real-time insights enable brands to react instantly to shifting demand and competitor moves. For example, during the 2024 U.S. elections, Similarweb observed a surge in crypto-related searches, followed by increased downloads and engagement in crypto trading apps like Robinhood. The app even rebranded itself in the App Store to "Now with Election Market" on November 3rd—an agile response to market needs. That’s one reason Robinhood was able to outperform competitors on important measures of engagement such as daily stickiness (the ratio of daily to monthly usage) and sessions per user.

2. How does unifying web and app analytics help businesses create a more comprehensive digital strategy?

Consumers interact with brands across multiple touchpoints - web, mobile apps, and offline channels. A customer might see an ad on TV, search for the brand on Google or ChatGPT, download the app from the store, and then make a purchase. For example, email marketing may drive users to a website, where a promo code encourages app activation. And of course these days we are observing referrals from AI-driven platforms like ChatGPT influence user journeys as well.

Without a holistic view, businesses miss critical insights and unique opportunities to acquire customers and generate revenue. It can be easy to misinterpret market trends or business performance – for example, a decline in website traffic might not indicate a market downturn but rather a shift toward mobile app adoption. Similarly, tracking loyalty program sign-ups in an app alongside segment-level website traffic provides a fuller picture of customer behavior. Understanding both web and app data is key to an effective digital strategy.

3. What challenges do companies face when integrating cross-platform data insights, and how does your solution address them?

The biggest challenge to integrating cross platform insights is data normalization - ensuring fair comparisons between web and app metrics. Web visits and app sessions aren’t identical, as app sessions often reflect deeper engagement. To bridge this gap, we provide frameworks and dashboards that align engaged web visits with app interactions, making cross-platform analysis more accurate and actionable.

4. How does AI improve the accuracy of app performance benchmarking and competitive analysis?

AI is a game-changer in market intelligence. By processing petabytes of data, Similarweb enhances estimation models for competitor benchmarking, delivering more precise insights than ever before. We leverage AI to analyze and categorize customer reviews, automatically clustering feedback into key themes providing a fast and detailed understanding of user sentiment. We’re also continuously integrating AI-powered insights to accelerate decision-making and improve competitive intelligence products, working on AI agents right now, so stay tuned!

5. What key KPIs should brands track to optimize mobile and web experiences?

The right KPIs depend on your business goals. If you’re looking to increase engagement, consider focusing on metrics like daily stickiness (daily active users comapred with monthly active users for apps, and the ratio of daily visitors to monthly visitors for the web), time spent per user, exclusive and returning visitors on website, app ratings, sentiment trends, and retention on apps. For customer acquisition efficiency, we would consider a different set of KPIs such as  paid traffic versus bounce rate on web, and store downloads versus 30-day retention for apps.

When integrating market intelligence with your first-party data, it's important to put absolute numbers into context. Calculate your share within the market and compare it to competitor averages. It’s also valuable to analyze the performance of top players in your category. By aligning KPIs with your business objectives, you can build a more effective optimization strategy. Enriching your data with full market context not only shows how you're performing - it also helps explain why it’s happening and what actions you can take.

6. How can businesses use predictive analytics to anticipate trends in user engagement and behavior?

Similarweb provides daily behavioral insights and can even get down to the hourly level for keyword trends data. On the other hand, historical trends have great predictive value—seasonal patterns, advertising spend from market leaders, consumer demand shifts, and broader economic sentiment, all of which contribute to better forecasting. By layering these insights, businesses can anticipate trends and proactively adjust their strategies.

1. How can brands craft authentic narratives that cut through digital noise and resonate with their target audience? 

In a world where information spreads in seconds, one wrong move can turn into a full blown crisis. Your brand is your blueprint. It should be real and clear. Today, people are drowning in digital noise, and that will not buy you an audience. Ditch the marketing fluff, your brand needs to bring something that actually strikes a chord. 

If you ask me how we can do that, I can give you three rules I share with my clients. Number one, drop the jargon and talk like a real human. Cut the corporate language and polished messaging. Just do the real talking. You don't want to sound like a robot, right ?

Secondly, own your voice. That very element is your brand’s face. If the consistency feels like a mismatched collage, people won't know what to trust. Lastly, and my personal favourite - make it about them. People are frivolously searching for a brand that gets them like their best friend. And just like that, share stories they can put themselves in. Remember, they're not looking for just ‘another’ one in the market. 

To answer this question, the solution is simple. When your brand sounds like a corporate memo, it tends to get ignored. Spill the real story, and people will listen. That’s how you stand out. 

2. What role do emerging platforms (such as metaverse, Web3, and AR/VR) play in shaping the next wave of branding? 

Honestly, Web3 and the metaverse aren't just futuristic concepts anymore. If you think about it, they're already shifting how we live, work and connect. When there’s a hype, there exists skepticism too. From what I infer, brands that sit back and wait will miss the moment. 

Leaders need to do a deep scan of their businesses and customers. They need to ask questions like Where Web3 can actually add value, or how businesses can thrive using VR, AR and collaborate seamlessly. This is an interesting fact - people are already socialising in virtual spaces, buying digital fashion and attending concerts in game worlds. 

If brands want to stay ahead and cut through, they need to experiment. They need to challenge Web2-era models. Maybe it’s productising virtual goods, expanding brand presence or offering enterprise services in the metaverse. Others might need to scale up infrastructure, from cloud to compute, and bring in fresh talent to make it happen. If you think the next wave of digital is coming, you’re wrong. It’s already here. We need to ask ourselves this question - are we ready for it ?

The shift is not about business or tech specs. It’s about the people. The next foundation for progress is being built, but brands cannot just go behind trends for the sake of it. Big changes need bigger perspectives. Think something beyond profit making, it should consider the real impact on society. 

So if you ask me - they need to build. Nobody waits for permission. 

3. How has consumer behavior evolved, and what role does personalization play in modern branding strategies? 

Gone are the days of the one-size-fits-all marketing strategy. People are not into just purchasing anymore, they love to engage. The way I see it - consumer behaviour has fundamentally changed. People expect seamless experiences, authentic interactions and expect brands to understand them on a personal level. 

If you thought personalisation is extra, you’ve got the equation wrong. It is essential. Customers want brands to anticipate their needs and not simply track them. The brands that master this trick aren't just selling their products/ services, they’re crafting a relationship that’ll last. 

I love how Netflix and Spotify do this. Their recommendations feel intuitive and personal. They feel right, and that’s personalisation done well. 

In a world where customer attention is scarce, the brands that go an extra mile to personalise will stand out. The rest will struggle to keep up, probably paying for more ads. People look into the relevance, and not into who’s making the most noise. Brands need to get this right- attention spans are shorter, expectations are higher. 

The bottom line? Generic won’t cut it. Brands that personalize will stay ahead. The early bird gets the worm – and in this case, the worm is customer loyalty.

4. How does branding differ in B2B vs. B2C environments, and what lessons can each sector learn from the other? 

In my opinion, branding in B2B and B2C is at the core about building trust and delivering value. While B2B marketing focuses on building personal relationships, B2C is all about transactional focus. 

Just to get the points right here- B2B is built on credibility, expertise and long term relationships. Buyers think and purchase. They’re focused on strategic decisions. The spotlight is on leadership, reliability and solving real business problems. 

Whereas in B2C branding, things come down to emotion. It’s about storytelling, relatability and making customers feel something. For example, I love how Nike makes me feel. ‘Just do it’ isn't just a slogan, it speaks to me. It pushes me to do a little extra. 

Interestingly, I think B2B brands can learn from B2C by becoming more human. Give it a strong brand voice, put in engaging content and use customer centric storytelling to set them apart in a sea of sameness.  

On the flip side, B2C brands should take notes from B2B for focusing on trust and long-term loyalty. While most B2C brands focus on chasing trends, the best ones build lasting relationships- just like B2B brands. 

At the end of the day, what matters most is about understanding how your audience makes decisions- and meeting them there. The takeaway? B2B can borrow emotions from B2C and B2B can borrow depth from B2C. It’s a great give and take lesson for branding. 

The goal should be the same. They should connect. They should add value, and stay relevant. 

5. How does the balance between organic and paid branding efforts affect a brand’s long-term success? 

Paid gets you seen. Organic gets you remembered. It’s as simple as that. Imagine you’re throwing a great party. You can rent the space, send out invites and fill the room. It’ll get you more attention. 

Fast forward to the next bit. Will people remember the conversations at the party? Will they come back next time? Will they tell their friends about it? That’s organic branding – the reputation you build after the whole party is over. 

The challenge is that you need to have both. Paid gets people to come for your party. Organic makes them stay, and come back. Brands that understand this concept play the long game. 

Paid branding helps you scale fast and is great for reach. But the moment you stop paying, the spotlight fades. Organic branding on the hand is built through content, consistency and trust. It’ll take time, but it compounds. 

Some brands blend both- which is a smart move. They pay to amplify, and go organic to sustain. The secret is to make sure that you’re paying to promote authenticity. 

In the long run, paid will win you clicks, but organic wins you hearts. That keeps the brands relevant. 

6. What are the biggest shifts in branding that businesses need to embrace to stay competitive in 2025 and beyond?

This is quite interesting. We’re all in 2025, and we no longer need the fluff. The game has changed. Branding is beyond visibility- it’s about relevance, speed and depth. 

Embrace co-creation if you need to stay competitive. Brands shouldn't just speak to their audience anymore. They need to build with them. User generated content, collaborative storytelling and communities are the new foundation. 

Winning brands respond fast, read culture in real time and pivot without losing their identity. Agility lies at the heart of consistency. 

People care about what you stand for. If your brand doesn't have a clear purpose, you’ll struggle to earn loyalty from customers who are younger audiences. They expect more. Set the bar high- one size fits are a thing of the past. Customers expect brands to read them like a book. AI , data and technology now enable real-time , tailored experiences. 

Brands need to think beyond today’s platforms and start building for where people are eyeing next. Branding is alive. If you’re not evolving. You’re falling behind. 

The relevant ones aren't afraid to question old books, they write entirely new ones. 

1. What challenges have you faced in integrating AI tools into your existing marketing infrastructure?

Great question – and I think the honest answer is many!

When we’re looking at integrating new AI tools into our tech stack, we’re ultimately considering three factors when evaluating potential value and chances for success: data quality and integrity, scalability and adoption potential, and privacy and compliance.

What we have found is that if one of these factors is missing, the rate of success of integrating a new AI tool into the business declines significantly.

And this isn’t just a technical consideration, organizational readiness is just as important. Ensuring the workforce has had the training and has the skills required to adopt new AI tools is critical, and more important than simply introducing new tools.

This is something that we are hyper-focused on, and we’ve set clear goals to upskill our workforce to be AI ready and enabled globally by the middle of 2026.

2. What factors influence your decision to adopt AI-powered tools for search discovery optimization?

Firstly, we know how people research has already changed and that this change is only going to continue at a rapid pace.

This is being driven of course by user adoption and changes in user preferences for search (it’s said that up to 40% of more complex queries are now being done in AI answer engines) – but also in the way AI answer engines work.

We are moving from a world of traditional search engines which are based on lexical search methodologies (exact matches of keywords and phrases) to the AI answer engines which use semantic search to present answers back to users (return results based on the meaning and context behind a query).

This change means that the types of content that are being used to pull information from is changing, as are the sources that are being cited when presenting information back to users. And this would be a big shift for our clients in how their target audiences would discover information about them, their competitors and their industry.

Therefore, we developed our own proprietary agentic AI solution, Hotwire Spark, that is designed to reflect user and target audience behavior in AI answer engines and help us identify the key sources of information and content that is being recommended. The insights and recommendations we provide to our clients from the Hotwire Spark data enables them to optimize content for improved discoverability in AI search but also ensure clear and consistent messaging across channels to get in front of target audiences who are searching for information on solutions and topics related to their brand.

3. What investments are planned to further integrate AI into your marketing operations over the next 1-3 years?

We have recently just announced the launch of our AI Lab, which is a team focused on ensuring Hotwire is AI-enabled and continuously innovating.

This means developing new AI-enabled tools and solutions for our team such as Hotwire Spark but also looking at how we can integrate new third-party tools into our infrastructure to help us continue to deliver high-quality work for our clients. It’s not just one new tool, but many—for instance, along with Hotwire Spark we also launched Hotwire Ignite, an AI-enabled solution that analyses multiple streams of first and third-party data for ongoing account prioritization and optimized go-to market strategies-as well as ensuring our teams have a tech and AI forward mindset to take advantage of these tools.

Our main objective will be to deliver sharper insights faster for clients across the whole lifecycle of campaigns from strategy and planning, creative concepting, through to delivering measurement and reporting solutions to help demonstrate the value of marketing and communications work.

4. How does your organization ensure data privacy and compliance when utilizing AI-driven marketing tools?

Everything that we do regarding tools, whether it be AI-driven or other tools that we are looking to bring into our tech stack, we do so in partnership with our IT and legal team to ensure we are compliant and privacy-first in our decision making.

All our AI practices and tool usage is in adherence to our AI governance policy that has been developed by our parent organization, Enero Group, and which we have adopted across our agency.

5. What role do you foresee AI playing in the future of your organization's marketing and communications strategy?

It is already integral, and that influence is only set to continue to grow over the next few years.

One of our key goals as a business is to be a tech-forward consultancy, and central to the success in achieving this goal is that we invest in the best technologies – whether that be the development of proprietary solutions or third-party tools - and in upskilling our workforce to confidently utilize new tools and solutions for our clients and to enhance our own business.

We have encouraged all our workforce to embrace AI and look for ways to responsibly integrate this into daily workflows and processes. As a leadership team, we know that if we don’t integrate and utilize AI we will be at a competitive disadvantage, and therefore it is central to our current and future planning.

6. How does your leadership team stay informed about emerging AI technologies relevant to marketing?

With how quickly things are developing and evolving, it can be so easy to become overwhelmed!

Internally, would say it’s a mix of reading news, forums, newsletters, LinkedIn posts, listening to podcasts, having daily digests curated by AI … it’s certainly a challenge! But we have a global group of AI champions who are active in consuming this information and testing new technologies, and alongside our Analytics and AI leadership team (Anol Bhattacharya, Laura Macdonald and I) are sharing updates across internal Slack channels to help us stay as up-to date as possible. We also run our own research, for example we have an upcoming Frontier Tech report which examines the disconnect between how business leaders perceive impact of AI and how employees and the wider public feel about it.

And then we also ensure we’re engaging with other experts. For example, we work with think tank House of Beautiful Business to engage with their community, as well as running a series of reports and salon events (including at Davos) to explore what other leaders are addressing. We don’t just participate but are taking active leadership roles – for example, I’m on the board of AMEC where how AI is impacting how companies can more effectively measure comms programs is unsurprisingly a hot topic.

1. What role does predictive AI play in anticipating and resolving customer pain points before they escalate?

Predictive AI plays a crucial role in identifying potential customer pain points by analyzing behavioral clues and feedback data in real-time. For example, Qualtrics Digital Experience Analytics uses indicators like rage-clicking to spot issues before they escalate, enabling organizations to intervene proactively and enhance the customer experience. 

Another great example is ServiceNow, a Qualtrics customer, which uses real-time insights from over 24 survey programs to inform their customer journeys. By proactively recommending easy-to-consume content that matters most to our customers and meets them where they are, ServiceNow is creating a unified, personalized, and guided digital experience to help our customers get to value fast. Additionally, ServiceNow is continually reimagining its customer journey to ensure that customers are connected to the right resources and partners at every stage, ultimately driving success on their platform.

2. What are the biggest challenges businesses face in turning customer feedback into actionable insights, and how does AI address them?

Businesses face several challenges in turning customer feedback into actionable insights, primarily due to the volume and variety of data they receive from multiple channels. This can be overwhelming to process manually. AI helps address these challenges by automating the analysis, categorizing, and summarizing the data to highlight key themes and sentiments efficiently. Another significant challenge is dealing with unstructured feedback, which can be complex, as it often includes text, voice, and other forms of data. AI-powered text analytics and natural language processing (NLP) can convert this unstructured feedback into structured insights, revealing the emotions and intentions behind customer comments.

Timeliness is crucial as well, with businesses needing to respond quickly to feedback in today's fast-paced environment. AI facilitates real-time processing and analysis, allowing companies to swiftly gain insights and make informed decisions to enhance customer experiences.

Qualtrics addresses these challenges head-on with its suite of advanced AI capabilities, including newly launched features like conversational feedback and established strengths in conversation analytics. With conversational feedback, businesses can seamlessly engage with customers across various channels, capturing rich insights through natural interactions. This real-time engagement allows companies to respond more dynamically to customer needs.

More than 50 brands are already using Conversational Feedback and are doubling the feedback collected, with 90% of survey respondents opting to answer follow-up questions when prompted. 

3. How can AI-driven CX solutions help businesses measure and optimize customer loyalty and retention?

AI-driven customer experience (CX) solutions, such as Qualtrics' Location Experience Hub, offer businesses unparalleled real-time insights into customer interactions and experiences, analyzed down to the individual store level. These solutions empower businesses to swiftly identify and address trends, enabling rapid responses that enhance customer loyalty and retention. By delivering granular insights, businesses can make informed decisions that significantly improve the overall customer experience and solidify customer relationships.

A prime example of leveraging AI-driven CX solutions is KFC's global omnichannel experience management program. By collecting both structured and unstructured feedback from sources such as in-store transactions, online surveys, and delivery platforms, KFC has seen a 300% increase in customer feedback. This influx of valuable insights equips team members with the information needed to refine and improve the customer experience continuously.

Qualtrics enhances this process through customized dashboards that deliver insights tailored to employees based on roles and locations. Feedback from various channels is aggregated and instantly analyzed, highlighting issues that require immediate attention. For example, restaurant managers receive specific feedback pertinent to their location, allowing them to make meaningful changes, while market managers and executives access broader insights related to larger business units. This comprehensive approach not only boosts customer retention but also cultivates a more engaged and proactive workforce dedicated to delivering exceptional experiences.

4. How does Qualtrics’ AI improve sentiment analysis and voice-of-customer (VoC) programs?

Qualtrics’ AI-enhanced tools, such as Insights Explorer and Assist for CX, significantly improve sentiment analysis and VoC programs by analyzing both structured and unstructured feedback to provide a comprehensive view of customer sentiment. Users can easily access insights without needing a background in data analytics; they can pose straightforward questions like, “What are the top three customer complaints affecting loyalty?” or “What themes are emerging from recent feedback?”

For example, Qualtrics Assist quickly surfaces relevant insights and offers informed recommendations based on expert methodologies and industry benchmarks. This accessibility allows employees at all levels to understand customer sentiments and act on them effectively.

5. What are the best practices for organizations to integrate AI-powered CX tools without overwhelming existing teams?

Organizations need omnichannel listening and comprehensive customer journey data to ensure a holistic understanding of customer behaviors and preferences. By capturing insights across multiple touchpoints, companies can better inform their AI strategies, allowing for more personalized interactions and proactive responses to customer needs. This cohesive data foundation not only enhances AI effectiveness but also enables organizations to create seamless and engaging experiences that drive customer satisfaction and loyalty.

To successfully integrate AI-powered CX tools, organizations should adopt a centralized strategy rather than running disparate programs. This ensures a cohesive approach to AI that aligns with overall business objectives. While 89% of executives report having at least one AI initiative, only 12% have a comprehensive strategy in place.

Market leaders are notably more successful, being 2.3 times more likely to take a strategic approach. Key actions to realize the value of AI in customer experience include setting clear AI ambitions, establishing guidelines for responsible use, creating a strong technology and data foundation, and designing a governance team to oversee implementation. Companies should also focus on launching high-impact use cases to build momentum, developing employee training strategies, and fostering a culture that embraces AI as a core driver of customer experience.

6. How can businesses measure the ROI of AI-powered CX enhancements and ensure they are driving real value?

According to new research, almost half of executives (42%) anticipate seeing a significant measurable impact from using AI to improve experiences within two years, with another 42% expecting results within three to five years. There is huge business incentive to do this – Organizations stand to gain an estimated $1.3 trillion by using AI to improve the experiences they deliver to customers.

To measure ROI effectively, businesses need to establish a clear AI ambition and value strategy that outlines where to invest in AI initiatives.

Key performance indicators should be defined upfront, along with risk and ethics guidelines for responsible AI use. By creating a solid data foundation and implementing AI-related governance, companies can track their progress and outcomes more effectively. This organized approach not only helps ensure that AI-driven improvements translate into real, measurable value but also facilitates ongoing evaluation and refinement of AI initiatives to align with strategic goals.

1. What role does conversational AI play in transforming how users interact with digital platforms?

The way customers discover your brand or your products and services is very different when it's done through conversational AI. The results are very specific.

For the last twenty years or so, customers typed a search query in Google, and that required them to chase down various links.

But now with conversational AI, you ask a question, and you get a pretty direct answer. Or you can refine your request in far more natural ways. It isn’t natural to have to ask for things like [jaguar speed -animal] , but it’s really easy to conversationally look for the exact information you are seeking just like you would with a person.

Even better, if you use these tools frequently, it starts to learn about you. It starts to know your preferences and provide even better answers tailored for you.

2. What are the key technical advancements behind Yext Scout’s AI-powered search?

As a marketer, AI-powered search is a black box. There is no dashboard to update your information. There are no metrics to measure your performance. So how does a Marketer survive in this new world?

AI Search and their LLM’s still need the web. The web and its data are the fuel that they run on. All of the information the LLM is sharing with your customers comes from the web.

With Yext, we are connected to more web publishers than any other solution in the market. We help brands manage their reviews and social presence across the web. We help brands firmly establish their 1st party websites as a source of truth. We do this really well.

And so what we can see now, and map out with Scout, is that your AI strategy is your Digital Presence strategy. We can show a marketer where their information is being picked up from and give them real actionable recommendations on how to improve their visibility.

3. What are the key benefits of integrating AI-driven search solutions into digital platforms?

AI search is still somewhat in its infancy. You know, there is the Mom test. Does my Mom use SearchGPT, Perplexity, Gemini, or Copilot? No, not yet. But do I think she is far off? No, not at all.

This technology is getting baked into our everyday technology. My Mom has an iPhone. She uses Siri. Apple is baking OpenAI into their devices through Apple Intelligence - they are still honing it - but we are really close to crossing the chasm in my opinion. Taking advantage of agentic AI technology like Scout is how brands can put their best foot forward — or they risk getting left behind.

4. How does Yext Scout enhance user experience by understanding search intent?

Scout is really meant to be your AI search and competitive intelligence agent. It's for anyone looking to measure and optimize brand visibility and sentiment across both traditional and AI-driven search. Scout delivers a unified platform that delivers deep search insights, competitive benchmarking, and actionable recommendations, and all with a seamless execution in one place.

Unlike legacy SEO tools that only focus on Google and other traditional digital networks, Scout uniquely tracks AI-driven search presence, sentiments, and share of voice, and provides prioritized recommendations that can be deployed seamlessly from within the Yext platform.

5. What are the security and compliance considerations when adopting AI-driven search technologies?

LLMs and AI search agents are trained on everything that's found across the web. They crawl listings, reviews, first-party web content, social profiles, and more. Scout is helping you to know how you are already appearing in the public domain.

Which comes to the point: more than ever, it's important to have accurate, clear, and consistent data across your entire digital presence.

To illustrate further: with SearchGPT, there could be a small listings website that your brand is mentioned on — but that you're not active on, and the information there might be out of date. In AI search, this information could still get pulled up as a citation and thus give outdated or incorrect information to your customers.

It's incredibly important to have a tool like Scout and Yext with a knowledge graph that can take all of your data, manage it in a clean, consistent way, and push that information out in an accurate, consistent, and manageable way across the entire web. That gives you the best chance to show up in AI search.

6. What types of organizations can benefit most from Yext Scout’s capabilities?

Right now we have honed Scout to really help out multi-location businesses. Think of large franchises across verticals like Food & Beverage, Retail, Financial Services or Healthcare.

In Scout, we provide a map-like overview so these brands can really quickly see at a glance which locations are performing well, and where they can benefit from our recommendations. We provide all of the data to help them identify the problem spots, but with our deep data science expertise and massive depth of data, we can find the trends and opportunities that would be impossible for any one brand to find on their own.

1. What differentiates AI-driven ad technology from existing solutions in the market?

Unlike black-box platforms that automate isolated parts of the process, Yieldmo uses AI across media, audience, and creative to ensure every ad not only performs, but belongs. We give advertisers full visibility and control over where their ads run, who sees them, and how the creative adapts in real time. It’s intelligence with intention, not automation without insight.

2. What key behavioral insights has Yieldmo uncovered through AI-driven ad engagement?

Our AI has shown that there’s no one-size-fits-all creative experience or message. What performs on one page, device, or environment can fail in another. That’s why Yieldmo tailors creative variations to each ad slot—using real-time context like page sentiment, attention, device, and layout—to unlock stronger engagement impression by impression.

3. How does AI help in optimizing ad creatives for different audiences and platforms?

Most platforms rely on shallow signals like viewability or sparse CTRs. Yieldmo’s proprietary dataset captures dozens of attention and contextual signals—up to five times per second—to predict which creative will perform best in any given environment. That depth of data requires a foundational, end-to-end AI layer and is essential for real-time optimization across audiences, platforms, and impressions.

4. What challenges do brands face when adopting AI-driven ad solutions, and how do you address them?

Many AI tools feel like a black box, and brands worry about losing control. Yieldmo solves this by offering complete visibility into supply, targeting, creative variations, and performance signals while keeping brand teams fully in the loop.

5. How do the newly patented AI innovations enhance ad engagement and performance?

While most AI tools optimize one element—like bids or audiences—Yieldmo’s patented AI enhances performance by combining high-quality, impression-level signals with predictive creative testing. We use deep behavioral and contextual data to generate and test creative variations before launch, so advertisers run only what’s likely to perform, tailored to each environment from the start.

6. What trends in AI-powered ad engagement do you predict will define the industry in the next five years?

In the next five years, marketers will demand more accountability from AI. They’ll expect explainable models, pre-launch predictions, and direct links between creative choices and outcomes. At the same time, AI-powered contextual intelligence will become the new targeting standard, not as a fallback to cookie loss, but as a smarter, privacy-resilient way to connect with audiences in meaningful moments. Yieldmo is already building for that shift.

1. With influencer marketing becoming more mainstream, how can brands navigate the balance between authenticity and commercial success? 

The key to striking a balance between authenticity and commercial success is for brands to be true to their roots and to make sure everyone involved in the influencer marketing campaign is aligned with their brand values; and is a believer of the brand. Influencers who are already loyal and passionate customers will have no problem creating authentic content because they genuinely love the products. 

The last thing a brand would want is an influencer-endorser promoting a brand on various platforms, but gets caught using a competitor brand. 

Alignment of values is important and a campaign that has successfully done this is Dove’s long-running Real Beauty campaigns with diverse creators. Other great examples are Glossier and Gymshark who have built communities by turning customers into micro-influencers.

On the performance side of things, metrics should balance commercial KPIs with engagement quality. Brands must resist over-editing creator content and allow their authentic voice to shine through while maintaining brand guidelines.

2. What emerging trends in content creation and social media storytelling should marketers be paying attention to? 

Collaborative storytelling is gaining momentum, with brands creating narrative universes where multiple creators contribute different perspectives. This approach, pioneered by companies like Netflix with multi-creator campaigns, drives deeper engagement through interconnected content.

Also, AI-assisted creation tools are democratizing production quality, with creators using tools to enhance their content while maintaining personal style. This allows smaller creators to produce professional-quality content. 

Another trend I’ve seen increasingly becoming popular is values-based storytelling, with audiences connecting with creators who take clear stances on social issues. 

Others include interactive content formats, such as livestream shopping, AR experiences, and gamified content which are becoming mainstream. Creators who master these formats are seeing higher engagement and conversion rates.

It is also worth noting that community-centered approaches rather than broadcast models are proving more effective, with creators building dedicated communities across multiple platforms rather than chasing viral moments on a single platform. We see some of the more successful influencers inviting audience participation through polls, challenges, and user-generated content initiatives.

3. What are the biggest challenges in reporting on influencer trends, and how can media platforms ensure credibility? 

Increasingly fragmented platforms are creating significant reporting challenges. With creators spread across TikTok, Instagram, YouTube, Twitch, and emerging platforms, this will require comprehensive trend analysis which in turn will need multi-platform expertise. Of course, this can already be partly overcome with the help of AI, but data verification is still going to be a challenge, with the rise of fake engagement. Media platforms must invest in tools to distinguish authentic metrics from artificial inflation through bots or engagement pods.

Brands should also consider that the speed of innovation is now outpacing reporting frameworks. The rapid evolution of features like TikTok's "Series" or Instagram's Broadcast Channels requires constant education or skills upgrade for meaningful analysis.

Moreover, meaningful measurement has remained elusive with inconsistent metrics across platforms. While some prioritize view count, others emphasize watch time or engagement rates, making cross-platform comparisons difficult.

For media platforms covering the industry, publishers, editors and journalists must balance timeliness with sufficient validation to ensure they're reporting on substantive shifts rather than fleeting changes.

To ensure credibility, media platforms should:

• Maintain editorial independence from the brands and agencies they cover

• Develop relationships with diverse sources across the ecosystem

• Combine quantitative data with qualitative insights from practitioners

• Contextualize metrics rather than reporting numbers in isolation

• Acknowledge limitations in available data

• Follow up on previous trend predictions to assess accuracy

Also. the most trusted industry publications transparently disclose their methodologies and sources while maintaining healthy skepticism toward hyperbolic claims about influencer marketing effectiveness.

4. What editorial strategies will be key in educating brands and influencers on industry best practices? 

I think a case study-based education will provide the most actionable insights. Detailed analysis of both successful and failed campaigns helps brands and creators understand practical applications rather than theoretical best practices. It helps make informed decisions, as long as the analysis is obviously spot on.

Editorial content should also recognize platform peculiarities as these differences will help provide relevant "influencer marketing" guidance.

And as with campaign performance, so it is with editorial and/or content: data-driven content balanced with qualitative insights provides the most comprehensive education. We look at various data points such as how much time is spent on a particular  story or why a specific content is shared more  than the others. 

For me personally the most effective educational strategies facilitate peer learning, creating opportunities for brands and creators to share insights directly rather than positioning the media platform as the sole authority.

5. How do influencer marketing trends vary regionally and globally, and how can brands adapt their approach? 

Platform dominance varies significantly by region. While Instagram remains strong globally, TikTok dominates in Asia, YouTube leads in many African markets, and regional platforms like RED (Xiaohongshu) in China require completely different approaches.

Content preferences also show distinct regional patterns. Highly produced aesthetic content performs well, for example, in South Korea and Japan, while raw authenticity resonates more in Western markets.

There are also regulatory environments to consider. For instance, the EU's strict disclosure requirements, China's content restrictions, and the FTC's guidance in the US require brands to adapt their strategies by region.

Another obvious difference will be because of cultural context. Though some content succeed globally, there are cultural nuances to be mindful of when it comes to content creation and distribution. 

In Asia, particularly China and South Korea, live shopping and social commerce are deeply integrated with influencer activities. The minimalist aesthetic popular in Scandinavian markets contrasts sharply with the more vibrant, energetic approach resonating in Latin America and Southeast Asia.

For global brands, successful adaptation requires:

• Local talent partnerships rather than simply translating campaigns

• Sensitivity to cultural contexts and regional events

• Platform strategies tailored to regional usage patterns

• Adjusted expectations for metrics based on market maturity

• Consideration of internet infrastructure and accessibility

• Localized compliance with varying disclosure regulations

6. What impact does short-form vs. long-form content have on audience engagement in today’s creator economy?

Short-form excels at discovery and awareness, with platforms like TikTok and Instagram Reels effectively introducing audiences to creators and brands. These formats drive initial interest through algorithm-powered distribution.

Long-form builds deeper connection and loyalty, with podcasts, YouTube videos, and newsletters fostering stronger audience relationships through sustained attention.

The most effective strategies combine both approaches in coordinated content ecosystems. Short-form content drives discovery while linking to long-form content that converts interested viewers into committed community members.

What is important to remember is that content length increasingly correlates with funnel position rather than platform. Short-form serves top-of-funnel awareness while long-form supports middle and bottom-funnel consideration and conversion.

Also another point worth mentioning is that engagement quality differs significantly between formats. While short-form may generate higher engagement rates, long-form typically produces more meaningful audience actions and stronger brand recall.

The creator economy increasingly rewards those who master both formats, with the most successful creators developing platform-specific content strategies that leverage the strengths of each format while maintaining a consistent brand identity.

1. How do you balance fraud prevention with customer experience to reduce false declines?

False declines are a costly problem for merchants. They not only lose the order value, merchants incur sunk acquisition costs and risk damaging their reputation with each false decline. It's also a negative experience for customers: 40% do not return to a merchant after a false decline, resulting in lost future business. The challenge lies in identifying orders that are statistically risky but don't fit clear fraud patterns. Merchants want to prevent fraud, but some of these orders are legitimate but flagged as false positives.

The key is balancing a seamless checkout experience for legitimate customers with robust fraud prevention. Requesting additional verification for every order might prevent fraud but increase cart abandonment. A balanced approach uses verification only when necessary and employs sophisticated fraud detection.

Adaptive Checkout addresses this challenge by tailoring each checkout flow to the order's risk profile. This minimizes friction for low-risk orders and requests additional verification only when needed.

The process begins by filtering out blatant fraud before authorization and enriching orders with additional data, making it easier for banks to identify and approve legitimate customers. By surgically analyzing each order's risk, even riskier orders have a better chance of approval, turning potential false declines into approved transactions. This selective verification approach is crucial for improving conversion rates without sacrificing fraud protection or positive checkout experiences for legitimate customers.

2. How do you integrate machine learning and behavioral analytics to identify fraud patterns? 

The power of machine learning is the ability for it to ‘learn’ independently as it runs. There are various ways to implement this, but we believe the best approach is a layered one. 

It starts with having vast amounts of quality data. Riskified trains its machine learning models on hundreds of millions of data touch points from across our global merchant network. Our algorithms learn to distinguish safe behavior from suspicious patterns and stop existing fraudulent behaviors. But to truly stay ahead of emerging fraud tactics, we also apply real-time anomaly detection using unsupervised machine learning, which flags unusual behavior based on combinations of various order characteristics — such as many orders suddenly exhibiting copy-pasting of credit card details alongside proxy use. Our engine can detect it and stop fraud  MOs before they spread. 

Machine learning also adapts the checkout flow for every transaction, surgically applying additional security measures for select higher-risk orders and enabling merchants to confidently approve more genuine orders while blocking fraud at various stages of the process.

3. What impact does AI-powered fraud prevention have on cart abandonment rates and conversion optimization? 

Cart abandonment has many causes, but a long or overly complicated checkout process is a major one. Consider an order that initially appears suspicious, such as one placed from a new device or with a recently issued credit card. Using AI and Riskified’s merchant network data, we can compare an order against millions of data touchpoints to draw a clear picture of a shopper’s true identity. This allows us to identify legitimate customers and expedite their checkout. Conversely, imagine a returning customer with a stored credit card and no unusual activity in their order data.Why ask them for a CVV if there is no need? Some customers may not have this information readily available, potentially leading to cart abandonment. AI enables us to precisely request verification, such as a CVV or a one-time passcode, only when needed. This reduces checkout friction and increases successful conversions of legitimate orders.

4. What best practices should businesses follow when implementing AI-driven payment security solutions? 

It’s important to think of fraud prevention and conversion as two sides of the same coin. Make sure that whatever solution you’re assessing for payment security also helps to optimize conversions and prevent the false declines.

Make sure to connect with other stakeholders to understand the full scope of security issues related to payments. For example, does the payment security solution address post-purchase concerns like returns and policy abuse? Is there a need to protect against abusive behaviors like serial returns or false claims of INR? To empower merchant fraud and customer service teams with real-time ability to address these challenges, ideally, the fewer integrations you have to deal with, the better. 

Lastly, look for more than a solution - look for a partner that future-proofs your business. AI and ML solutions should never be a black box - merchant teams need technology that provide them with the visibility, flexibility, and control they need to tailor solutions aligned with their business strategy and success.  

For example, when you have declines, do you understand why they were declined? Can you add your own logic into the decisioning? How can you get a clearer image of the customer’s identity? Mapping this out can help you choose the right solution and make decisions that will improve revenue and security in the long run. 

5. How does AI-powered checkout impact payment authorization rates across different industries? 

Machine learning-based solutions can detect far more patterns than a rules-based solution – and unlike static rules, they can adapt and learn in real time. 

Using a sophisticated AI-powered solution that detects and screens out fraud prior to issuer authorization ensures that fewer fraudulent orders reach the issuer. Elite fraud prevention solutions are able to analyze high- quality data and share, that data with issuers at scale. This helps them filter out fraud while understanding the context behind orders better. For example, there can be a significant difference between the risk of buying a $1000 fridge online versus buying a $100 gift card at the same merchant. AI solutions provide the context that distinguishes between safe and risky orders. In the long run, this will lead to more trust with the issuer, higher authorization rates, and less risk of falling into a monitoring program.

6. How can AI-powered fraud prevention solutions be customized to meet specific business needs? 

The ideal AI-powered solution should be very customizable. No two businesses are alike, no one can decide your risk tolerance, and no one knows exactly how your business operates – so generic products won't cut it. 

Start by making sure fraud is viewed properly. Alongside the standard ML models, Riskified has developedgeo-specific, vertical-specific and sometimes even merchant-specific models, all of which is crucial for accuracy. 

Also, when you review transactions for fraud, do you want to review them after authorization, where you can manually overturn declines and leverage additional data like AVS match in the US? Do you prefer to review orders pre-authorizatio? Or maybe leverage both pre- and post-auth review?

For example, policy abuse is extremely specific to the merchant and their defined policies, so having ML to determine risk is an elemental part of the equation. And each merchant will want to deal with policy abusers differently – warning them, blocking them at checkout, denying claims, etc. 

Even in CNP fraud, how strict do you want to be? If you want to send a one-time password to some risky orders with verifiable phone numbers, how long would you give your customers to verify the code? High-end fashion offering limited stock would have a different view from fast fashion. And some forms of verification, like 3DS, will work better with consumers in one region, and less well in others. 

There is no one-size-fits-all solution, and it will look different from merchant to merchant, as it should.