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. 

1. How can AI-powered apps balance automation with organic discovery in travel and outdoor recreation?

That balance is something we’re really focused on with Globe Chaser. AI handles the behind-the-scenes work, like smart route suggestions and location-based activity planning, but we leave space for real-world spontaneity. The goal isn’t to over-automate. It’s to empower users to make the most of their time outdoors. So while the app helps guide and personalize the experience, it still feels like authentic exploration.

2. How does the app leverage real-time data to optimize route planning and adventure recommendations?

We use real-time GPS data to detect the user's current position. If location sharing is enabled, the app automatically checks for nearby routes or adventures that match the surroundings. When no preloaded routes are available, our AI engine, called AVA, steps in to generate a personalized adventure based on the user's location and preferences. Route suggestions are powered through Google’s API, ensuring the paths are walkable and engaging. Our database of routes and experiences is growing every day, and we’re also working on integrating real-time weather data to make adventures even more dynamic and safe. The goal is to offer a smooth, location-aware experience that requires minimal planning on the

3. How do you integrate gamification and interactive features to boost user engagement?

Gamification plays a key role in how users experience Globe Chaser. Players can earn points while exploring, compete in team-based battles, and even purchase in-game coins to unlock extra features and enhance their adventures. Whether it’s families, groups of friends, or corporate teams, the competitive element keeps people engaged and coming back. We're also working on a badge and level-up system that’s already part of our product roadmap. The goal is to make every adventure feel rewarding, dynamic, and a little addictive — in the best way possible.

4. What privacy and data security measures are crucial for AI-powered outdoor exploration platforms?

Data privacy is a top priority. With Globe Chaser, we use strict internal access policies, and anonymized analytics. Location data is only used when necessary and never kept longer than needed. We also give users full control over their personal data, including the option to request to delete it completely. Outdoor exploration should feel safe, both physically and digitally.

AI can actually be a powerful tool for bringing people together. It helps us recommend local group adventures and routes, highlight community-created content, and connect users with similar interests and activity levels. With Globe Chaser, users can create their own scavenger hunts, upload photos, and compete in teams, both locally and online (players can also simulate real world adventures without leaving their home). It’s not just about solo exploration. It’s about being part of something bigger.

We’ve also started working with tourism companies, hotels, and even city councils to use Globe Chaser as a tool to make their regions more attractive. Whether it’s guided city tours, themed adventure trails, or interactive explorations for visitors, we’re helping local communities offer new, tech-powered experiences that bring people together in the real world.

5. In what ways can AR (Augmented Reality) or VR (Virtual Reality) enhance outdoor adventure apps in the future?

AR has a lot of exciting potential. Imagine pointing your phone at a landmark and instantly seeing facts, hidden clues, or educational content layered onto the real world. It makes the experience more immersive and fun. VR, on the other hand, could help users preview adventures or explore places they might not be able to visit in person. Used the right way, these technologies can make the outdoors even more engaging without taking away the real-life magic. Plans to integrate augmented reality and even VR are already underway, opening the door to even more immersive and interactive outdoor experiences.

1. How can businesses leverage applied security data to enhance threat detection and incident response? 

The book is a great reference guide for measuring maturity and leveraging what you have effectively.  It provides several easily adoptable methodologies to help holistically manage and utilize your security data.  From discovery, to ingestion, to analysis and reporting, these methodologies provide sustainable frameworks upon which to improve and build.  Learning how to measure your detection hypotheses and the required data to signal effectively will lead threat detection teams down a much shorter path. Real world examples of streamlining ingestion, processing and analysis will quickly enable your teams. 

2. What best practices should companies follow to ensure secure data collection, storage, and analysis? 

Know your requirements!  Governance is critical, not just to maintaining compliance, but to developing an effective program which can quickly evolve to counter threat actors with new hypotheses.  

By ensuring governance, engineering, and operations teams are all embedded in your security data strategy you enable both rapid response and innovation safely. 

We want all teams to be able to evolve quickly, run with scissors safely, and affect change within your wider organization to achieve desired outcomes. 

3. What are the critical metrics and KPIs for evaluating the effectiveness of a security data strategy? 

Seek to understand your own organization, your risks, exposures, and adversaries. Building processes, procedures, and adopting methodologies to measure this repeatably is paramount.  

 Start with basic health and observability: 

- Feed fidelity & health (up/down time) 

- Feed usage (number of detections per feed) 

- Feed efficacy (number of true positives per feed) 

 What can be done with what you have: 

 - What can I effectively signal on? What can’t I effectively signal on?  Why not?  

- Where do these detection blind spots exist on the risk register? What should be prioritized? 

- The number of secondary investigations initiated by signal. 

- The number of secondary signals for N-level triage (forensic images, DFIR-as-code) 

- Detection & countermeasures blind spots mapped to a common framework (ATT&CK, etc.) 

Finally understand how well you are performing: 

- How effective are the signals? What about signals per feed? Have they ever triggered? How often have you tested or tuned them? 

- Are the tests fully automated? Do they always fire as intended?  

- Do you test for false negative scenarios? 

This isn’t an exhaustive list, but I would start by answering those questions, and ensuring you have supportable frameworks in place to facilitate effective changes. 

4. How can organizations transition from reactive security measures to proactive threat intelligence? 

Organizations need to be able to evolve their countermeasures more quickly than their adversaries, in a safe, effective manner. Hypotheses need to be able to prove, or disprove, a theory so that lessons can be learned and applied more quickly. That starts with ensuring you have some ability to flexibly ingest and process your data. When incidents occur, sustainable mechanisms to detect the needles in the haystacks need to be quickly developed and implemented.  Ensuring easy, governed, detection development and quick iterations are critical to building an adaptable security operations and intelligence program. 

5. How is cloud adoption influencing security data strategies?

Organizations need to have a game plan to effectively navigate and balance the risks and rewards associated with cloud adoption. Most organizations have some form of hybrid environment which requires a more holistic approach towards collecting, managing, and analyzing data. Understanding what the requirements are from a business, governance, and operations standpoint will better enable your overall execution. 

6. How can businesses integrate security data strategies into their overall digital transformation efforts? 

Adopting methodologies for each stage of your security data program will enable your organization to measure and improve your internal processes and their effectiveness.  By implementing these frameworks, solid foundations can be built to capture the full value of your data.

1. In your experience, how do ad formats that mimic social media interactions (e.g., swipeable, tap-to-reveal) perform compared to traditional banner or static ads? 

Our approach emphasizes thoughtful implementation of social-inspired elements across platforms, recognizing that each environment, whether mobile, desktop, or CTV, requires nuanced creative adaptation to maximize consumer connection. By carefully tailoring social interaction patterns to match platform-specific consumer behaviors and expectations, we create experiences that feel native and intuitive regardless of where they appear.

When familiar social features are used in the right context, they have been proven to drive a 5.81% engagement rate and 11X more exposure time than standard banner ads. This effectiveness is further validated by PadSquad's Social Skin creative, which is proven to outperform standard ad formats, driving 39% higher lift in brand favorability and 21% higher lift in purchase intent compared to standard ad formats. 

With a strategic application of familiar social mechanics, we’ve created a comfortable, engaging experience that resonates with audiences and drives measurable performance across diverse media environments. 

2. Which features of social platforms (polls, UGC, reactions, etc.) have you successfully adapted for your digital advertising strategy? 

We’ve successfully adapted a variety of social features into our display, video, and emerging formats—what we refer to as Social Replicas. These are ad experiences designed to emulate the familiar UI/UX of popular social platforms, creating a seamless and intuitive environment for the viewer. From social stories to feed-inspired layouts, these formats mirror the native content experience consumers are accustomed to, driving stronger engagement.

We’ve layered interactivity such as likes, shares, reactions, and swipe-ups, to invite consumers to engage with the ad the way they would on social platforms. We also incorporate both UGC-inspired and authentic UGC elements to build a sense of relatability and authenticity throughout our campaigns. Social-inspired overlays, familiar navigation cues, and contextual visual treatments reinforce the look and feel of the platforms where consumers spend the most time.

As we design these experiences, we’re mindful of how they translate across screens. What works well on mobile might need to be adapted for larger formats, including CTV, where viewing behaviors and interaction patterns differ. Social Replicas are designed with the environment, audience, and campaign goals in mind. This ensures each execution feels native and effective, no matter the screen.

3. How does your team ensure brand authenticity when designing ads to resemble user-generated or influencer-style content? 

Because we're not simply using the same exact asset in a new box, we're crafting unique ad experiences for each campaign to match its objective. Our belief is that the assets used in social are an incredible resource for brands, but our view is that you can't simply place it in another box outside of the social platforms.

4. What metrics do you use to evaluate the success of socially inspired ad formats (e.g., engagement rate, watch time, CTR)? 

Each campaign has unique objectives and outcomes, but many times we are designing the experience to hit on that key objective: video views, reach, engagement, site traffic, or even lifts in brand health metrics. The value of using the assets in new and different ways is what makes custom creative so valuable.

• Engagement metrics: interaction rate, completion rate

• Attention indicators: scroll depth, scroll speed, video completion

• Creative-specific actions: reactions, shares

• Traditional performance metrics: CTR, conversion rate, ROAS

• Brand impact: brand recall, brand preference, purchase intent

5. Is there any industry-specific compliance that limit your use of more informal, social-inspired ad styles? 

This is an area where it's not a compliance issue but one of technology limitations and lagging consumer behaviors/adoption: Interactive CTV. Unfortunately, the industry is asking for something that consumers don't innately grab the remote to do, more than that, the tech stacks for TV aren't in a place where there is uniformity in the ad serving standards, with new walled gardens in this ecosystem making the challenge even more prominent.

6. How frequently do you A/B test traditional versus social-style ad creatives?

Repetitive content risks ad fatigue and disengaged audiences. That’s why we experiment with a variety of creative formats and features, refreshing assets and messaging throughout the campaign to drive performance. Our testing goes beyond simply comparing traditional versus social-style ads. We continuously evaluate how different formats and features perform throughout the campaign. 

We also strategically adapt messaging to speak to different audiences and campaign phases. For example, the same ad format can be customized to highlight back-to-school essentials for younger students while showcasing back-to-college gear for older audiences. Similarly, an entertainment brand might evolve its messaging from “Coming Soon” to “Watch the New Trailer” to “See It This Weekend,” using unique creative assets and tailored CTAs at each stage.

This agile, audience-first approach not only helps prevent ad fatigue but ensures sustained engagement over time. By emulating the feel of social media interactions, we create a more seamless and familiar ad experience—one that drives deeper, more meaningful audience engagement.