1. How is your marketing team managing manual processes in terms of influencer relationships, and how are you addressing scalability challenges?

A lot of brands still rely on spreadsheets, manual outreach, and disconnected tools to manage influencer programs which makes it nearly impossible to scale efficiently. Once a brand grows from 10 to 50+ influencer partnerships, the wheels start to fall off. Teams get bogged down in manual follow-ups, managing approvals, handling gifting logistics, and compiling performance reports. It ends up consuming their entire bandwidth. That’s where automation changes everything. Influencer marketing platforms, like Endlss, that combine outreach, gifting, commission tracking, and communication in one place have become essential to keeping programs scalable. With the right tools, marketing teams can manage 3x the creator volume without needing to grow their headcount, freeing up time for the work that actually drives results.

2. How is your organization evolving its influencer marketing strategy to shift from brand awareness to measurable revenue generation?

Influencer marketing used to be all about reach and impressions, but the most forward-thinking brands today are treating it like a true performance channel. Instead of chasing vanity metrics, they’re focused on driving measurable, attributable growth. To meet that demand, more teams are adopting attribution tools that link creator content to conversions—whether through custom landing pages, affiliate links, or dynamic tracking infrastructure. On our end, we’ve built SmartLinks into the core workflow, so each creator’s impact is measured in real-time, and with partners of ours like Creator Commerce, together we provide co-branded shopping sites to elevate the consumer experience with a trusted shopping experience that increases conversions. Weekly performance reports make it easy to see which partnerships are generating returns and which need to be re-evaluated. That kind of visibility helps transform influencer marketing from a brand play into a predictable revenue stream.

3. How are you approaching influencer selection and outreach to ensure alignment with your brand values and audience segments at scale?

Alignment is everything in influencer marketing and not just in terms of values. The right creator should reflect the brand’s tone, speak to the right audience segment, and have a track record of driving action. Brands are getting more precise with how they vet creators, looking at engagement quality, audience breakdowns, content style, and past performance before making a move. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content.

But finding the right fit at scale is a different challenge. That’s where AI and smart filters are transforming outreach. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content. Combined with branded application forms and full creator analytics, brands are scaling high-quality outreach without losing that human touch. Inviting existing customers to apply is low hanging fruit when you want to scale effectively, and authentically—people who already know and love the brand often make the best partners.

4. What limitations have you encountered with traditional tracking methods (e.g., promo codes, UTM links), and how are you planning to evolve your attribution strategy?

Traditional tracking methods come with real friction. Promo codes can get leaked or shared in unintended ways, making attribution muddy. UTM links often break in-app or get stripped entirely, especially on mobile. This creates a gap between creator activity and the sales data that marketers rely on to optimize spend. To move past these limitations, we’re focusing on more robust attribution tools that work reliably across platforms and devices. SmartLinks, for example, generates unique tracking for each creator and integrates directly into conversion and payout workflows. Clean attribution is foundational to scaling today’s influencer programs responsibly. Whether it's to manage budgets or reward high performers, teams need to trust the data.

5. How are you evaluating new MarTech platforms to determine their potential impact on operational agility and cross-functional collaboration?

When evaluating MarTech tools today, agility is at the top of the list. Marketing teams need tools that are fast to implement, intuitive to use, and flexible enough to support cross-functional workflows. If a platform takes weeks to implement or requires engineering support to operate, it’s already a blocker. The best tools today integrate seamlessly with existing systems, whether that’s ecommerce platforms like Shopify, payment processors like Stripe, or internal communication tools. Endlss replaces four different tools in one, so brand, finance, and CX teams can all work from a single system. At the end of the day, the best platforms don’t just do more; they reduce friction across every team.

6. What competitive advantages do you see in adopting lean, AI-powered influencer marketing platforms compared to legacy tools with heavier infrastructure and higher costs?

Legacy influencer marketing platforms were often built with large enterprises in mind. They’re powerful, but also complex, expensive, and heavy to manage. For fast-moving teams, that’s become a real disadvantage—especially when speed and efficiency are critical. Lean, AI-powered platforms are flipping the script. By automating outreach, tracking, and gifting workflows, brands can move from idea to execution in 20 minutes, not weeks. And because these tools are often modular and self-serve, they’re far more cost-effective. What we’ve seen is most brands using Endlss are cutting their software spend by 50% or more while getting campaigns live that day, not weeks. That kind of agility has become a major competitive edge, especially for brands trying to maximize output with lean teams.

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1. Given that traditional demographic and transactional data explain only a small fraction of buying behavior, how are you reassessing your current data frameworks to account for deeper emotional and situational drivers? 

Q.1) What frameworks do you follow to ensure your AI initiatives scale across global teams while remaining aligned with your core business strategy? 

Murf AI helps global enterprises create high-quality voiceovers and dubs effortlessly. At Murf AI, we follow three key principles to ensure our AI initiatives scale globally across diverse industries. 

1. Consent-first creation - Every Murf voice begins with the active involvement and agreement of the artist.
2. Full control - Artists have the freedom to withdraw consent at any time.
3. Royalties that grow - Artists earn royalties each time their voice avatar is used, and earnings grow with the avatar’s popularity.

1. How can businesses balance user experience (UX), and conversion rate optimization (CRO) for maximum impact?

User experience and conversion rate optimisation (CRO) go hand-in-hand. One is the discipline user research, understanding user requirements and best practices around things like accessibility and design. The other is the mechanism, or process, to allow you to test different experiences against another. By utilising both together you should be able to drive better experiences on the website, with confidence in only deploying new features, components or functionality if you know they are going to positively impact your website. At the very least, you should expect the changes won’t harm them!

2. What are the most common website optimization mistakes, and how can businesses avoid them? 

The most common mistake is not testing changes to a website. We’ve seen multiple pieces of research that has converged around a similar figure – the likes of Optimizely (80%) and Google (70%) have both found that the majority of changes don’t do a thing to improve engagement and conversion rates. And some of those will even make those metrics worse. It’s really important that you’re using data to understand challenges, but then also using data to validate that the changes you’ve made to address those challenges is a positive one.

3. How has the role of website optimization evolved with the rise of mobile-first and omnichannel experiences? 

We will always be guided by data and as you would expect the majority of B2C sites now have more traffic on mobile sites. As a result, there’s a much heavier weighting towards website optimisation on mobile. For some B2B brands, we still see the majority of traffic on desktop, and as such, our research and efforts would pivot that way too. More broadly, we see an increase in, and advocate for, connecting experiences together from media or CRM to touchpoint to website. This “symmetrical messaging” has generated incredible results when we’ve deployed it and it’s as simple as targeting experiences based on the presence of a certain value in the landing page URL. For one client, we saw a 46% increase in conversion by tying the PPC ad to landing page experience more closely together.

4. What emerging technologies are set to redefine website optimization in the coming years? 

AI is disrupting all areas of marketing and business, and website optimisation is no different. Most of our technology partners are embedding agents within their platforms, so you can either ask them to support with insight generation, suggestions for new web page layouts, or even to build out those experiences so that they can be tested. Likewise, those platforms are leaning into the analysis and categorisation of users into different buckets depending on “digital body language” to allow you to personalise experiences. For example, some users might be researching or just more conscientious in wanting to review more information. You should give those users a different experience to those you can infer are in buying mode or are more impulsive and want to simply get through the purchase process as quickly as possible.

5. How can businesses prepare for the future of privacy-first digital marketing while optimizing their web presence? 

This is a two-fold answer. The first is ensuring that you maximise the data you’re able to collect whilst respecting user privacy and the relevant legislation in your country. The second is ensuring you’re using your collected data to fill in the gaps on those users and sessions where you couldn’t collect their data. On the first point, there’s a multitude of steps you can take to ensure you’re best able to measure and optimise your marketing and website. From looking at server-side set-ups to very specific solutions like Google Tag Gateway, they help to mitigate some of the solutions that block tracking as a by-product of blocking ads. Likewise, collecting first-party data and sharing that back to the media platforms to allow for ad to conversion matching (amongst other things) helps increase the amount of data these platforms have to use in their algorithm. On the second point, modelling is a critical component in helping to optimise in a privacy-first way. Whether you’re using Google’s own Advanced Consent Mode – which tracks users who reject cookies in a cookieless way and utilises modelling off the users who accepted cookies to fill in the gaps – or you’re doing your own modelling, it’s a natural step to take to ensure we’re working with as much as we can.

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SurveyMonkey, the world’s most popular platform for surveys and forms, recently launched its new Trust Center, a new transparency hub that helps businesses evaluate the company as a trusted partner, strengthen internal accountability, and build lasting customer trust. We chatted with Sally-Anne Hinfey, VP, Legal, to learn more.

1. From your perspective, what are the primary disconnects between claimed GDPR comprehension and actual real-world compliance within organizations?

The disconnect often lies between policy and practice. Many organizations believe they’re compliant because they’ve ticked the right boxes on paper. In reality, true compliance requires strong leadership and strategy, effective program management, comprehensive training and education, continuous monitoring, internal accountability, and diligent vendor oversight. In fact, our research affirms that while 95% of UK businesses say they understand and meet all GDPR requirements, over half have still experienced data-related issues—proof that confidence doesn’t always equal control.

2. Budget constraints and legacy technology are identified as significant barriers to cybersecurity investment. How does your organization navigate these financial and infrastructural challenges to ensure robust data protection in a threat-prone environment?

We take a focused, risk-based approach—prioritizing security investments that deliver the greatest impact given our business’s risk profile and leaning into our existing tools and assets. Rather than trying to do everything at once, we identified the highest-risk areas for our business and layered protections accordingly. It is an iterative approach, not a one-and-done project. It requires a layered and multi-faceted threat prevention and detection program that you are continually reviewing and updating. Steps we took included appointing a strong leadership team for security, strengthening our cloud and zero-trust architecture, implementing rigorous monitoring and incident response processes, and designing access controls that made sense for our business and our customers. Finally, we keep our teams trained and informed. By embedding security and privacy-by-design into our workflows, we avoid costly retrofits later on.

3. How is your organization addressing the unique data privacy and security implications introduced by AI technologies, particularly generative AI? 

We’re actively building guardrails to manage AI responsibly. This includes establishing internal governance policies that are mapped to industry standards as well as regulatory requirements, a working group with responsibility for defining and managing risk, restricting certain high-risk use cases, and providing AI-specific privacy training to employees. Our research states that 70% of UK businesses are already developing or implementing policies to manage AI-related privacy concerns. Thoughtful governance is becoming a baseline. For us, it’s not just about compliance—it’s about using AI in a way that builds trust and creates value.

4. How do you quantify or assess the ROI of robust data protection practices in terms of customer loyalty and market differentiation?

Trust has become a key differentiator, with three-fourths of respondents (75%) from Cisco’s 2024 Consumer Privacy Study admitting they will not purchase from organizations they don't trust with their data. When customers see that we handle their data with care—and can back it up with transparency and credentials—they’re more likely to stick with us and refer others. That kind of loyalty doesn’t just protect revenue, it fuels growth. Our new Trust Center is a perfect example: it makes our commitment visible, helping procurement teams choose us with confidence.

5. What are the key criteria for your organization to verify a vendor's data security and privacy posture?

We look for a clear, documented commitment to privacy—ideally backed by third-party audits, recognized certifications, and transparent practices. But beyond paperwork, we assess how embedded data protection is within a vendor’s culture and operations. Do they train their teams? Can they answer detailed questions about data handling, data retention, and deletion practices? Can they show—not just tell—that they’re trustworthy? Those are the markers that give us confidence.

6. Looking forward, what are your organization's top priorities for future data privacy investments to maintain a competitive edge and ensure long-term compliance?

Looking ahead, our focus is on scalability and resilience. As privacy regulations evolve and AI adoption accelerates, we’re investing in technology that helps us stay ahead, like automated privacy management tools, advanced encryption, zero trust architecture, and stronger vendor risk assessment frameworks. We’re also doubling down on transparency, because as SurveyMonkey research cites, nearly 90% of businesses now insist on clear proof of compliance before partnering. Making that information accessible isn’t just good practice—it’s becoming table stakes.

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1. To what extent is your AI strategy informed by input from interdisciplinary fields such as psychology, neuroscience, and ethics?

Our AI strategy is deeply informed by interdisciplinary input from psychology, neuroscience, and ethics. The XSTEREOTYPE platform is grounded in a science-driven methodology that integrates over 40 unique psychometric measurements. These include personality psychology through the HEXACO model, emotional and sentiment analysis across 26 emotional states, and diversity experience research informed by social science. We analyze how lived experiences influence content perception and validate our findings through extensive focus groups and empirical data from over 50 million data points.

Our ethical commitment is reflected in tools like Bias IQ™, Inclusion IQ™, and Emotional EQ™, which collectively measure unconscious bias, representation authenticity, and emotional impact—ensuring our AI not only performs accurately (99% model accuracy) but also promotes fairness and inclusivity. This interdisciplinary approach allows us to generate human-centric insights that go beyond stereotypes, supporting ethical content creation and responsible AI use.

2. What steps are taken to ensure AI content not only informs but emotionally connects with your target audiences?

We ensure emotional connection by embedding psychometric intelligence directly into our AI-powered platform. XSTEREOTYPE™ goes beyond surface-level data by leveraging:

• Emotional EQ™:  Analyzes 26 distinct emotional expressions to predict how content will emotionally resonate with different audiences. We calculate the probability of specific emotions being evoked, grouped into positive, negative, neutral, or ambiguous responses.
• Diversity Experience Modeling:  Our AI incorporates social science-backed models to reflect how lived experiences shape content perception, especially in terms of inclusion and internalized bias. This helps tailor messages that are emotionally relevant and authentic.
• Bias IQ™ and Inclusion IQ™:  These indices evaluate how inclusive and unbiased content is, combining emotionality with measures like authenticity, language, image portrayal, and equality. This ensures our messaging resonates across diverse audiences while avoiding alienation or stereotype reinforcement.
• Focus Group Validation: Consumers from various backgrounds participate in validating our models, ensuring that content is not only emotionally intelligent but also culturally and contextually aware.

By grounding our AI in psychology, sentiment analysis, and lived experience research, we help brands create content that fosters trust, empathy, and emotional engagement, not just information delivery.

3. How do you measure the impact of emotionally intelligent content on customer trust, loyalty, and brand perception?

We measure the impact of emotionally intelligent content through a combination of advanced psychometric scoring and real-world validation:

• Emotional EQ™: Tracks how likely content is to evoke specific emotions. By identifying which emotions drive positive sentiment, we can align content with emotions that correlate with trust and connection.
• Bias IQ™: Helps us identify and remove content that could undermine trust due to unconscious bias or stereotype reinforcement. Reducing this risk strengthens brand credibility.
• Inclusion IQ™: Serves as a proxy for brand authenticity and fairness. Higher Inclusion IQ scores indicate that audiences perceive the brand as respectful and representative—key drivers of brand trust and loyalty.
• Conversion Score: Integrates emotional response, brand likability, and purchase intent—providing a quantifiable view into how emotionally resonant content directly affects consumer behavior and perception.
• We also conduct focus group testing and ongoing sentiment tracking, allowing us to validate that emotionally intelligent content translates to improved perception and sustained engagement.
• Together, these tools form a closed feedback loop ensuring that emotionally intelligent content doesn’t just feel right, but delivers measurable impact on trust, loyalty, and brand affinity.

4. How is contextual intelligence integrated into your AI systems to better tailor messaging based on user behavior and intent? 

At XSTEREOTYPE™, contextual intelligence is embedded through the dynamic integration of psychographic and emotional data. Here’s how we tailor messaging with precision:

• Personality Mapping via the HEXACO Model: Allows us to understand core traits that shape user preferences and reactions. This insight helps adapt tone, language, and narrative style to better align with behavioral tendencies.
• Emotional EQ™: Measures 26 emotional states and their likelihood of being evoked by specific content. This lets us dynamically adjust messaging based on the emotional context of the audience, enabling more personalized and relevant engagement.
• Diversity Experience Modeling provides nuanced insight into how lived experiences influence perception allowing us to align messaging with cultural context, belief systems, and identity markers.
• Sentiment & Purchase Intent Scoring allows our system to interpret both what users are doing and why, helping shift content from static delivery to behavior-responsive storytelling.
• Bias and Inclusion Intelligence further refine messaging to avoid emotional misfires or alienating language, ensuring each message honors the user's lived experience and emotional state.

In short, contextual intelligence in our system means that AI doesn’t just react to clicks or views it interprets why users engage and delivers content that resonates on a psychological and emotional level.

5. How are emerging AI platforms (e.g., ChatGPT, Gemini, Claude) evaluated for contextual accuracy and cultural sensitivity before being deployed in your ecosystem? 

At XSTEREOTYPE™, we apply a rigorous 4-step process before integrating any external AI tool into our ecosystem:

• Bias & Inclusion Scoring

We run all AI outputs through our proprietary Bias IQ™, Inclusion IQ™, and Emotional EQ™ models to detect stereotypes, emotional tone, and cultural fit.

• Diverse Scenario Testing

We test content across varied personas to ensure relevance and respect across race, gender, identity, and emotional experience.

• Expert Review

Social scientists andI experts review outputs to ensure alignment with our values of authenticity, fairness, and emotional intelligence.

• Continuous Monitoring

After deployment, we monitor content performance and audience response, continuously updating to reflect evolving cultural norms and expectations.

6. What role does leadership play in championing a culture of responsible AI adoption across departments and functions?

Leadership plays a foundational role in embedding a culture of responsible AI at XSTEREOTYPE™. We approach this from three key angles:

• Modeling Ethical Standards from the Top Down

Our leadership prioritizes interdisciplinary collaboration—drawing from psychology, ethics, research, and behavioral science—to ensure our AI not only performs technically but acts responsibly. This commitment is embedded into every product, metric, and partnership we build.

• Cross-Functional Integration of Responsible AI Principles

Leaders actively champion AI literacy and accountability across teams—from data science to marketing. By ensuring every function understands the ethical implications of AI, we promote shared ownership of outcomes, not just technical delivery.

• Transparency, Validation, and Inclusion

Our leadership ensures that bias detection, emotional impact, and inclusion scoring are not optional add-ons, but core KPIs. Through focus group validation and psychometric alignment, leadership enforces standards that hold our teams accountable to human-centric, culturally sensitive AI outputs.

In short, leadership doesn’t just approve our AI roadmap—they shape a vision of AI that’s inclusive, trustworthy, and deeply responsible across all customer touchpoints.

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