Humans of Martech

What’s up everyone, today we have the pleasure of sitting down with Sandy Mangat, Head of Marketing at Pocus. Summary: AI and outbound prospecting has flooded our inboxes with poorly personalized, irrelevant, and frankly lame template attempts at human connection. But some teams are seeing the light… the purple light. Sandy takes us inside the dimly lit fortune telling parlor of Pocus where we gaze into the swirling galaxies of the crystal ball of modern sales. We travel through visions of product-led sales, network referrals, signal correlation and AI agents all swirling together to fill pipelines. About SandySandy is based in beautiful Vancouver BC, she got her start at GE Digital in Product MarketingShe later moved on to ThoughtWire, a tech company specializing in smart buildingShe then joined Charli AI, a multidimensional AI company specializing in the finance sectorToday Sandy is Head of Marketing at Pocus, an AI-native prospecting platform trusted by high growth companies like Asana, Monday, Canva, and MiroOutbound Needs a Cold Hard ResetThe blunt reality about outbound sales is that automation obsession and meeting quotas have created a wasteland of deleted emails and blocked LinkedIn profiles. Sales teams continue spraying prospects with templated messages, while response rates plummet to new lows. Yet leadership keeps pushing for higher volumes, creating a self-destructive cycle that poisons potential customer relationships before they begin.This mess stems from sales organizations fundamentally misunderstanding what drives genuine business relationships. Sales leaders chase efficiency through automation, treating prospects like data points rather than future partners. The result? Inboxes overflow with desperate attempts at "personalization" that read like they were written by a caffeinated robot trying to sound human. Meanwhile, genuinely interested prospects have built fortress-like defenses against the daily barrage of cookie-cutter outreach.Consider how actual business relationships form: through authentic interactions, shared understanding, and carefully built trust. Successful outbound motions mirror this natural process, whether through thoughtful event networking, well-researched phone conversations, or precisely targeted digital outreach. Even companies swimming in inbound leads eventually require strategic outbound capabilities, especially when expanding into new markets or launching products that demand fresh customer conversations.The path forward demands embracing what experienced sales professionals already know: shortcuts and automation cannot replace genuine human connection. Sales organizations must rebuild their outbound approach from the ground up, focusing on quality interactions over vanity metrics. This means investing serious time in prospect research, crafting genuinely personalized messages, and showing patience as relationships develop organically.Key takeaway: Sales teams have to abandon the lame industrial approach to outbound prospecting and return to building relationships and human-centered selling. Ditch your batch and blast automation addiction, focus on qual over quant, and giving sales professionals the time and tools to build authentic relationships rather than chasing arbitrary activity and volume metrics.Building Sales Teams for Product Led Growth CompaniesProduct-led growth companies harbor a poorly kept secret: they all run sales teams. The idealistic vision of products that "sell themselves" crashes into market realities faster than venture capitalists can say "negative churn." Companies like Miro, Asana, and Canva discovered that relying solely on product-driven acquisition limits their growth potential, especially when expanding into new markets or use cases.The evolution of PLG sales teams reflects a sophisticated marriage between product usage data and human-driven outreach. These teams capitalize on product signals that indicate expansion potential, creating what Sandy calls "warm outbound" opportunities. When users demonstrate specific engagement patterns or hit usage thresholds, sales professionals step in to guide them toward broader adoption or premium offerings. This approach transforms traditional cold outreach into data-informed conversations with already-engaged users.Yet even these PLG darlings recognize the strategic value of traditional outbound sales. They approach their go-to-market strategy like a diversified investment portfolio, using cold outreach to hedge against the limitations of product-led acquisition. This hybrid model proves particularly valuable when testing new markets, launching products, or exploring different use cases. The rapid feedback loop from direct sales conversations provides invaluable insights that pure product analytics might miss.The WordPress.com experience illustrates this evolution perfectly. Despite massive organic traffic and brand recognition, they eventually built a sales team to capture enterprise opportunities and service-based revenue. This mirrors the broader industry pattern where even the most product-centric companies discover that sustainable growth requires a balanced approach combining automated product experiences with strategic human intervention.Key takeaway: Successful PLG companies build sales teams that leverage both product usage signals and traditional outbound tactics. Rather than choosing between product-led or sales-led growth, organizations should create a balanced strategy that uses product data to inform outreach while maintaining direct sales capabilities for market expansion and enterprise opportunities.How Product Led Sales Teams Time Their Customer OutreachThese days every SaaS company wants the magic of product-led growth: minimal sales headcount, viral expansion, and revenue that scales without an army of account executives. Yet behind the glossy investor decks and growth charts lurks an uncomfortable reality about human intervention in the sales process. Even the most automated, product-led companies scramble to hire sales teams the moment enterprise deals enter the picture.The data tells a ruthlessly practical story: throwing sales resources at every free trial wastes everyone's time while ignoring high-value accounts costs serious money. Smart companies obsess over usage patterns, tracking signals that indicate when a prospect needs human guidance versus automated nurturing. They build sophisticated scoring models to spot accounts teetering between self-service success and quiet abandonment, timing their outreach to tip the scales toward expansion.Sandy points out how divergent growth patterns demand radically different playbooks. Some products drive natural expansion through viral team adoption but struggle with initial activation. Others convert early users easily yet hit a wall when trying to expand across departments. These distinct patterns create clear intervention points where human touch generates outsized returns, whether that means helping a complex enterprise implementation succeed or guiding teams toward advanced features that unlock real value.The reality of product-led sales revolves around mapping your market's actual behavior, not following someone else's playbook. Enterprise deals often demand early sales involvement due to security requirements and complex buying processes. Other segments thrive on automated expansion until they hit specific technical or organizational barriers. Companies who understand these patterns build flexible systems that deploy sales resources at precise moments of maximum leverage rather than burning cycles on low-value outreach.Key takeaway: Map your actual user b...

What’s up everyone, today we have the pleasure of sitting down with Chris Golec, Founder & CEO at Channel99. Summary: The Godfather of ABM takes us through his humble beginnings in Detroit's industrial trenches to category creation and entrepreneurial expeditions. His journey spans building magnetic company cultures, cracking the code on remote work, sharing candid hiring wisdom, and transforming marketing failures into fuel for growth. Now building Channel99, he's rewriting attribution with a touch of AI engineering, predicting marketing ROI, using a white box approach. About ChrisChris started his career in the manufacturing world, working at DuPont and then GE where he moved from Engineering, Sales and Marketing rolesThe first startup he co-founded was a supply chain enterprise software where he also had the role of VP of Marketing, He grew the company to 75 people and raised $10M in VC. After only 6 years he sold to i2 Technologies for $380M A few years after his exit, Chris started his next company, Demandbase, the well known ABM platform. Along a 13 year journey as CEO he would create and lead the category of ABM software, hiring more than 1,000 people and crossing the elusive 200M in revenueToday Chris is on his 3rd company, Channel99, an AI powered attribution platform for B2B marketersFrom Industrial Paint Lines to Silicon ValleyChemical engineering graduates in Detroit followed a well-worn path: automotive paint lines, waste treatment facilities, and methodical career progression through established industry giants. The conventional trajectory promised stability but offered minimal room for pioneering new ground. This reality sparked Chris's pivotal decision to pursue innovation beyond Motor City's industrial confines.DuPont's Delaware operations presented an intriguing opportunity to spearhead European manufacturing technology adoption in the US market. The role demanded technical expertise while cultivating strategic thinking, setting the stage for an unorthodox career evolution. Engineering polymer sales, though seemingly mundane, opened doors to Boston's dynamic business landscape, where GE recognized potential in this chemical engineer turned sales strategist.The 1990s tech boom transformed the West Coast into a crucible of innovation. As GE's industry marketing lead for high-tech materials, Chris orchestrated global deals with Apple and HP, bridging the gap between traditional manufacturing and Silicon Valley's emerging titans. The experience revealed a stark reality: technical expertise alone created opportunities, but market understanding determined success. In 1995, this insight drove Chris and fellow GE engineers to launch Supply Base, despite their complete unfamiliarity with software development.Supply Base embodied Silicon Valley's audacious spirit. A team of engineers, armed with industrial experience but zero software knowledge, secured funding through sheer determination. The venture grew into a profitable enterprise, culminating in an exit that coincided precisely with the market peak on March 13, 2000. Yet amid this success, frustration brewed. B2B marketing remained technologically underserved, a gap that became increasingly apparent as Supply Base scaled. This observation planted seeds for future innovations in marketing technology, proving that sometimes the most valuable insights emerge from professional pain points.Key takeaway: Career evolution thrives on identifying market gaps and embracing unconventional paths. Chris's journey demonstrates how technical expertise combined with market understanding creates opportunities for innovation, especially when traditional industry boundaries blur in the face of technological advancement.Why Top Talent Gravitates to Companies with Purpose-Led CultureCreating genuine company culture runs deeper than the usual corporate playbook suggests. Demandbase's remarkable journey illuminates how sustained, intentional investment in organizational DNA attracts and retains exceptional talent. Chris discovered through years of leadership that authenticity, transparency, and meaningful impact serve as the bedrock of thriving workplace environments, transcending typical office perks or superficial initiatives.Demandbase's cultural investment materialized into tangible recognition, propelling them to the tenth spot among 500,000 companies on Glassdoor by 2016. The achievement reflected genuine employee satisfaction measured through independent surveys rather than manufactured accolades. This momentum persisted as the company consistently earned "Best Places to Work" distinctions throughout the Bay Area, validating their approach to fostering genuine workplace connections.The company's distinctive approach integrated philanthropy seamlessly into their organizational fabric. A partnership with Stop Hunger Now transformed from an office-wide meal-packaging initiative into a stadium-scale operation at their annual customer conference. This resonated profoundly with their marketing-focused clientele, spawning similar programs across multiple organizations. Additional initiatives supporting women's education and the Challenge Athlete Foundation enabled employees to contribute meaningfully beyond their B2B software focus, creating ripple effects throughout the industry.Cultural development demands attention from inception, though its manifestation evolves with company growth. While Series A funding often marks the formal introduction of HR functions and recruitment strategies, companies under 20 employees thrive when leadership directly shapes and nurtures cultural foundations. The rise of remote work introduces new challenges, requiring deliberate effort to maintain community through strategic in-person gatherings and shared experiences that transcend virtual boundaries.Key takeaway: Purpose-driven culture requires deliberate cultivation from day one. Organizations that prioritize authentic connections, maintain radical transparency, and create opportunities for meaningful impact naturally attract and retain exceptional talent. This foundation enables sustainable growth while fostering genuine employee satisfaction and engagement.Why Remote Work Fails Junior Employees (And Soars for Veterans)Remote work demands a brutally honest examination beyond the standard flexibility narrative. The stark reality reveals a complex equation where career stage, personality type, and organizational DNA collide to determine distributed success. During a pre-pandemic executive assessment at Demandbase, the remote work preference split tracked perfectly along introvert-extrovert lines, foreshadowing the fundamental role of personality in distributed work effectiveness.Career stage emerges as the make-or-break factor in remote work dynamics. Fresh graduates and early-career professionals require an apprenticeship period that Zoom simply cannot replicate. The professional polish developed through observing seasoned colleagues handle meetings, presentations, and workplace politics creates an invisible foundation for later career success. Close CRM's remote-first model crystallizes this reality; they exclusively hire veteran professionals with 10+ years of experience, acknowledging that virtual environments demand battle-tested practitioners who learned their craft in the trenches of traditional offices.The SDR experience at Demandbase's New York office illustrates this principle in vivid detail. Post-pandemic, a group of SDRs met face-to-face for the first time, creating an impromptu laboratory for examining remote work's limitations. Physical proximity unlocked a treasure trove of professional development opportuniti...

What’s up everyone, today we have the pleasure of sitting down with Meg Gowell, Director of Growth Marketing at Typeform. Summary: Marketing leadership in 2025 is a wild time. After years of learning martech and technical concepts to become a full stack marketer, you finally land that dream director gig... only to watch your hard-earned tech skills collect dust while you drown in meetings. Megan helps us see the way forward. She takes us on a ride that covers marketing measurement, experimentation and building brand momentum, all while having tons of fun. We get into how data warehouses are not so quietly changing the martech universe while most teams are still stuffing everything they can in their CRM. Welcome to the wild world of modern marketing leadership – where you somehow need to be both a tech wizard and a strategy genius just to keep up. And we’re here to guide ya. About MegMeg started her career in wedding planning while she was in college, she also started a luxury branding business for high-end weddingsShe then worked at a marketing agency for 4 years where she focused on social media, paid media and budget managementShe switched over to a boutique agency where she got a breath of experience across all facets of marketing including web design, conversion rate optimization, project management and also got to lead a team of marketersShe then moved over to a real estate startup – which was one of her former clients – as VP of Marketing and automation where she helped grow the company from $9MM to $22MM in less than a yearShe then moved over to B2B SaaS at Appcues as Director of Growth marketing where she led funnel optimization, experimentation strategy and execution, event sponsorships, biz dev and moreToday Meg is Director of Growth Marketing at Typeform where she oversees paid, web/site, lifecycle, partner marketing and campaigns How Full Stack Marketers Drive Marketing ExcellenceFull stack marketing capabilities command premium compensation in today's market, mirroring the pattern seen with full stack engineers who rank among the highest-paid technical professionals. The comparison raises interesting questions about the relationship between full stack and T-shaped marketing skill sets, particularly regarding depth versus breadth of expertise.The distinction between full stack and T-shaped marketers centers on the distribution of knowledge and capabilities across different marketing disciplines. While T-shaped marketers typically possess deep expertise in one area complemented by broader surface-level knowledge, full stack marketers maintain substantial working knowledge across multiple marketing domains. This broader distribution of skills enables them to engage meaningfully with specialists and make informed decisions across the marketing spectrum.A critical advantage of the full stack marketing approach lies in its impact on team building and hiring decisions. When marketing leaders possess comprehensive knowledge across various disciplines, they can better evaluate potential hires and identify genuine experts in specialized roles. This knowledge framework helps prevent the common pitfall of making poor hiring decisions due to limited understanding of specific marketing functions or technologies.The full stack marketer's broad knowledge base serves as a foundation for effective collaboration and decision-making. Rather than requiring mastery in every area, the key is maintaining sufficient expertise to ask incisive questions, recognize genuine talent, and understand the interconnections between different marketing functions. This comprehensive perspective enables better strategic planning and more efficient resource allocation.Key takeaway: Full stack marketers need sufficient knowledge across marketing disciplines to recognize expertise, make informed hiring decisions, and drive strategic initiatives. Success in this role doesn't require mastery of every area but rather the ability to understand key concepts, ask relevant questions, and identify genuine expertise when building and managing teams.Balancing Technical Proficiency and Leadership in Marketing TeamsRemember getting that dream marketing leadership role? Corner office, eager team, the works. But then reality hits - you're spending more time in strategy meetings than actually doing the hands-on work you love.It's a weird spot to be in. The higher you climb, the further you get from the technical skills that got you there. Take Megan's story - she was crushing it at AppCues, deep in the technical weeds while leading cross-functional teams. Now at Typeform, she's managing 10 people and her calendar is packed with meetings while her technical skills collect dust.Here's the thing - you can't fake technical knowledge. Real understanding comes from getting your hands dirty - tweaking platforms, figuring out complex filters, and really getting how things work under the hood. The best marketing leaders are like chefs who still know their way around the kitchen, not just writing menus. Your team can smell it a mile away if you've lost touch with the technical side. The real magic happens when you can switch between big-picture thinking and nuts-and-bolts knowledge. It's like being bilingual in both strategy and technical speak.Some leaders live in the strategy clouds, others get lost in the details. The sweet spot? Knowing when to zoom in and when to step back. When you ask about campaign metrics or question technical decisions, your team knows if you're genuinely curious or just micromanaging. Feedback is a delicate art, you have to ask yourself if your input makes something better versus just different. Sometimes we suggest changes based on personal preference rather than what actually works. The key is knowing when to speak up and when to let your team run with it.Takeaways: Your technical skills got you the leadership role. Now they need to evolve, not evaporate. The future belongs to marketing leaders who keep one foot in the code and one in the boardroom – masters of both the how and the why.Trusting Your Gut vs Measuring All Of The ThingsThe marketing metrics obsession has gone too far. While CFOs salivate over spreadsheets demanding ROI calculations for every LinkedIn post and email blast, they're missing a crucial reality check: humans are gloriously unpredictable creatures who refuse to follow our carefully crafted attribution models. The digital advertising revolution sold us a compelling fantasy of perfect measurement, but reality stubbornly refuses to play along. After the "growth at all costs" party ended with a nasty hangover, companies sobered up and started demanding receipts for every marketing dollar spent. Logical? Sure. Realistic? Not even close. This myopic fixation on measurable channels creates a dangerous illusion of control. Paid search might give you beautiful conversion tracking, but try building a billion-dollar brand on Google Ads alone. Spoiler alert: it won't work. Real growth demands embracing the uncomfortable truth that some of your most powerful marketing moves will resist neat ROI calculations.Modern marketing success requires omnipresence, not just optimization. Your target audience bounces between platforms like a caffeinated pinball, interacting with your brand across countless touchpoints. Social media, influencer collaborations, and content marketing often defy precise attribution, yet they create the vital ambient awareness that drives long-term growth. The magic happens in the messy middle, where multiple channels work together in ways that no attribution model can fully capture.Getting leadership buy-in for this reality requi...

What’s up everyone, today we have the pleasure of sitting down with the lads from We're not marketers. Summary: When did everyone on LinkedIn suddenly become a GTM expert? The misfits from ‘We're Not Marketers’ dive into this chaos, explaining why Go-to-Market strategy has become the most misused term in marketing. They share product marketing stories about rigid product launches, cross-functional chaos, and small test groups. They open up about their love and admiration for marketing operations folks, similar cross functional translators between tech and marketing and how martech can support message testing. We explore the debate of who should have final word on messaging, PMMs or the channel SMEs. Join us for the laughs, stick around for the love between PMMs and martech. About the 3 MisfitsAll 3 of these gentlemen work for themselves as fractional PMMsGab Bujold (Bu-jo) is based in Quebec city, Canada. He’s a messaging expert and also a marketing advisor for early-stage startups, he’s a former product marketer and 4-time solo marketer at various different brands and sports an incredible mustacheAlso joining us today is Zach Roberts is based in California, he worked in B2B SaaS sales for half a decade before pivoting to product marketing with a focus on enablement, he’s worked at big names like Dropbox, LinkedIn and Google. He’s a 2x recognized Product Marketing Influencer by PMALast but certainly not least, we’re also joined by Eric Holland who’s based in Pennsylvania, he’s a product-led content pro also runs a retail apparel startup and is a recovering in-house product marketer. He’s the mastermind behind the creative AI skullies artwork of their podcastWhy Go to Market Strategy Has Become a BuzzwordThe concept of go-to-market (GTM) strategy has entered peak buzzword territory in recent years. What was once a product marketing-specific term focused on launching new products or features has been hijacked by nearly every department under the sun. These days, everyone from sales and marketing ops to customer success is suddenly a "GTM expert" on LinkedIn. The term has become so diluted that it's starting to lose its meaning entirely.The transformation of GTM into a catch-all phrase stems largely from corporate politics and self-preservation. Teams across organizations are scrambling to attach themselves to GTM initiatives, fearing that being left out might signal their irrelevance. As Zach points out, there's an underlying anxiety that not being involved in GTM somehow makes a team dispensable, leading to a kind of organizational FOMO that has stretched the term beyond recognition.The reality is that successful GTM execution has always required coordinated effort across multiple teams. Product marketing traditionally orchestrates these initiatives, but they can't execute alone. It takes sales for implementation, product teams for development, and marketing for awareness. The problem isn't collaboration; it's the current trend of every team claiming to be the primary GTM driver, creating confusion about who actually owns the strategy.Eric makes a crucial distinction between "going to market" and "go-to-market strategy" that cuts through some of the noise. While the strategy might come from product marketing or revenue leadership, the execution involves multiple teams working together. The challenge is maintaining clear ownership of the strategy while preventing it from becoming another meaningless corporate buzzword that everyone claims expertise in.Key takeaway: Organizations need to stop the free-for-all claiming of GTM expertise and return to clearly defined roles within the GTM process. Success depends on having centralized strategic ownership while enabling individual teams to excel in their specific GTM responsibilities, not turning every department into self-proclaimed GTM experts.Who is Responsible for Operationalizing GTMPicture a chill Broadway production: everyone from lighting to sound plays a crucial role, but someone needs to direct the show. Product Marketing's role in GTM execution presents a fascinating operational challenge. While multiple teams claim ownership over GTM initiatives, the real question isn't about territorial control but about orchestrating complex product launches effectively.The operational reality of GTM involves intricate coordination across specialized teams. Marketing and sales ops teams manage the technical infrastructure, configuring everything from CRM workflows to marketing automation. Lifecycle marketing teams often gatekeep new feature and product notification announcements and balance that with existing messages. Product marketing develops the strategy and messaging, while sales teams handle direct customer engagement. Each group brings essential expertise to the table, making territorial claims over "GTM Ops" not just unnecessary but counterproductive.Gab's makes a really good point that Product Marketing Managers excel at running small-scale experiments, gathering feedback, and iteratively refining go-to-market approaches. This methodology allows teams to validate strategies before full-scale deployment, reducing risk and improving outcomes. It's not about owning GTM ops; it's about facilitating successful product launches through methodical testing and collaboration.You should view GTM operations as a collaborative framework rather than a power structure. PMMs serve as strategic conductors, coordinating efforts across teams while respecting each group's expertise. When campaigns underperform, the root cause typically traces back to poor coordination or unclear direction, not technical execution. Success requires letting each team excel in their domain while maintaining a unified strategic vision.Key takeaway: Focus on establishing clear operational frameworks where Product Marketing Managers guide strategy and testing, while specialized ops teams manage technical implementation. Success comes from collaboration and respect for expertise, not from claiming ownership over the entire GTM process.Prioritizing Product Marketing Requests vs Martech RoadmapsThere’s often a natural tension between PMMs who think every feature deserves a big email to everyone in the database and the martech or marketing ops team who has an existing roadmap and existing comms in place. New GTM initiatives don’t get to market on certain channels without the SME team converting words into code and automation. This creates a complex decision making process that often requires somewhat lame but important evaluation of business impact and strategic alignment.Strategic prioritization requires product marketers to approach each situation with an analytical mindset focused on identifying the most pressing business needs. As Eric explains, the process resembles assessing multiple issues requiring attention but having limited resources to address them all simultaneously. The key becomes determining which initiative will deliver the most significant impact toward established organizational goals and objectives.The reality of product marketing involves making difficult trade-offs between seemingly equally important initiatives. While new product launches naturally generate excitement and momentum, they must be weighed against the potential impact of operational improvements that are already on the martech roadmap like enhanced product analytics or refined lead scoring mechanisms. These behind the scenes projects often create foundational improvements that enable better execution of future go to market activities.At the end of the day, most product launches have flexible timing - what's critical is identifying the few relea...

What’s up everyone, today we have the pleasure of sitting down with Sundar Swaminathan, author of the experiMENTAL newsletter and part time Marketing and Data science advisor?Summary: After leading Uber's Marketing Data Science teams, Sundar shares insights that work for both tech giants and startups. Beyond uncovering that Meta ads generated zero incremental value (saving $30 million annually), they mastered measuring brand impact through geo testing and predicting LTV through first-week behaviors. Small companies can adapt these methods through strategic A/B testing and simplified attribution models, even with limited sample sizes. Building data science teams that embrace business impact over technical complexity, and maintaining curiosity, like when direct driver engagement revealed that recommending Saturday afternoon starts over Friday peak hours improved retention. About SundarSundar started his career as a software developer at Bloomberg before managing $19 Trillion at the US Treasury as a Debt ManagerHe pivoted to growth marketing and data science consulting where he worked with DirectTV and an ed-tech AI startupHe then made the mega move to Uber where he spent 5 years building Brand, Performance, and Lifecycle Marketing Data Science teamsHe moved over to a travel tech startup and helped them go from $0 to $100K MRRToday, Sundar is a marketing and data science advisor, he helps B2C founders and marketers He’s also working on an upcoming podcast and has a newsletter where he shares frameworks, how-to guides to help B2C marketersMarketing Incrementality Testing Reveals Meta Ads Ineffective at UberPerformance marketing often reveals surprising truths about channel effectiveness, as demonstrated by a fascinating case study from Uber's marketing operations. When confronted with unstable customer acquisition costs (CAC) that fluctuated 10-20% week over week despite consistent ad spend on Meta platforms, Uber's performance marketing team, led by Sundar, decided to investigate the underlying causes.The investigation began when the team noticed significant volatility in signup rates despite maintaining steady advertising investments. This inconsistency prompted a deeper analysis of Meta's effectiveness as a primary performance marketing channel. The timing of this analysis was particularly relevant, as Uber had already achieved substantial market penetration eight years after its launch, especially in major urban markets where awareness wasn't the primary barrier to adoption.Through rigorous data analysis, the team implemented a three-month incrementality test to measure Meta's true impact on user acquisition. The test utilized a classic A/B testing methodology, comparing a control group receiving no paid ads against a treatment group exposed to Meta advertising. The results were striking: Meta advertising showed virtually no incremental value in driving new user acquisition, a finding that was validated by Meta's own data science team.The outcome of this experiment led to a significant strategic shift, resulting in annual savings of approximately $30 million in the U.S. market alone. While this figure might seem modest for a company of Uber's scale, its implications were far-reaching when considered across global markets. The success of this experiment also highlighted the importance of data-driven decision-making and the willingness to challenge assumptions about established marketing channels.Key takeaway: Established marketing channels should never be exempt from rigorous effectiveness testing. Regular incrementality testing can reveal unexpected insights about channel performance and lead to substantial cost savings. Marketing teams should prioritize data-driven decision-making over assumptions about channel effectiveness, even for seemingly essential platforms.How to Run Marketing Experiments With Limited DataMost companies don’t have the volume of signups or users that an Uber does. Marketing experiments require a mindset shift when working with small data samples. While A/B testing remains the gold standard for measuring marketing effectiveness, Sunday thinks that companies with limited data can still validate their marketing efforts through strategic pre-post testing approaches.Pre-post testing, when properly implemented, serves as a valuable tool for measuring marketing impact. The key lies in isolation: controlling variables and measuring the impact of a single change. For instance, a marketplace company successfully conducted a pre-post test on branded search keywords in France by isolating specific terms in a defined region. This focused approach provided reliable insights despite not having the massive data volumes typically associated with incrementality testing.That being said, Sundar adds that early-stage companies should prioritize high-impact experiments capable of delivering substantial results vs testing tiny changes that will barely have detectable effects. With small sample sizes, tests should target minimum detectable effects (MDE) of 30-40%. These larger effect sizes become measurable even with limited data, making them ideal for fundamental changes such as exploring new ideal customer profiles (ICPs) or revamping core value propositions, rather than pursuing minor optimizations.An example that Sundar recalls while working at a travel tech startup demonstrated the value of running A/B tests even with limited data. Despite having only 100-200 weekly signups, they detected a 40% conversion drop after modifying their onboarding flow. While the test might have been considered "poorly powered" by strict statistical standards, it successfully prevented a significant negative impact on the business. This illustrates how even small-scale testing can provide crucial insights; it's better to have 60% confidence in a positive change than to miss a catastrophic drop with 95% confidence.The confidence level in marketing experiments operates on a spectrum, with A/B tests providing the highest confidence and pre-post tests offering valuable but less definitive insights. Success depends on maintaining experimental discipline, carefully controlling variables, and understanding the tradeoffs between confidence levels and the humbling reality of practical constraints. Marketing teams must balance their confidence requirements against their risk tolerance when designing and interpreting tests.Key takeaway: Companies with limited data should focus on measuring high-impact marketing changes through carefully controlled pre-post tests. Success comes from isolating variables, targeting substantial effect sizes, and maintaining experimental discipline. This approach enables meaningful measurement while acknowledging the practical constraints of smaller data sets.The Difference Between AB Testing and Incrementality TestingMarketing experimentation terminology often creates unnecessary complexity in what should be straightforward concepts. The fundamental structure of both A/B testing and incrementality testing follows the same principle: comparing outcomes between groups that receive different treatments.Statistical analysis remains consistent across both testing approaches. Whether using Bayesian or frequentist methods, the underlying comparison examines differences between groups, regardless of what those groups receive. The statistical calculations remain indifferent to whether one group receives no treatment (as in incrementality tests) or a variation of the treatment (as in traditional A/B tests).Incrementality testing extends beyond simple presence versus absence comparisons. For example, marketers can test spending increm...

What’s up everyone, today we have the pleasure of sitting down with Sarah Krasnik Bedell, Director, Growth Marketing at Prefect. Summary: What happens when a data engineer with an obsession for truth-testing crashes into marketing's sacred cows? Sarah's journey from code to campaigns unfolds like a detective story, where she picks apart marketing myths and rebuilds them with an engineer's precision. Her fresh take transforms marketing tools from black boxes into purposeful instruments, while her approach to AI echoes "Limitless" - it's not about letting machines take the wheel, but supercharging human creativity. Whether you're wrestling with developer outreach or trying to get sales and marketing teams to actually talk to each other, Sarah's technical-meets-tactical perspective offers a compelling roadmap for modern marketing that actually works.About SarahSarah studied math and cognitive science before completing a masters in data scienceShe started her career at Amsted working on data aggregation and machine learning models and eventually moved to a customer-centric role where she helped engineer data architecture for supply chain optimizationsShe had short stints in financial forecasting and company-wide data architectureShe then joined Perpay as a data engineer focused on product analytics as well as reverse-ETL for their marketing team. She was eventually promoted to Lead data eng, managing the full team of data engineers She’s an Analytics and GTM Advisor for devtoolsToday she’s Director of Growth Marketing at Prefect, a workflow orchestration tool for data and ML engineersUnconventional Paths From Data Engineering to Marketing LeadershipThe traditional career trajectory rarely follows a straight line, particularly in Sarah's fascinating pivot from data engineering to marketing. While leading the data engineering team at Perpay, she found herself knee-deep in an Iterable implementation project that would unknowingly alter her professional DNA. This wasn't just another technical integration; it was a complex orchestration of customer data streams, product catalogs, and audience segmentation capabilities that secretly doubled as an apprenticeship in modern marketing mechanics.Marketing technology projects have a peculiar way of revealing their true nature over time. What begins as lines of code and data pipelines often transforms into something far more intriguing: a window into the soul of marketing operations. Sarah discovered that while her peers remained captivated by the elegance of their code, she found herself increasingly magnetized by the downstream impact of these technical solutions. This subtle shift in perspective proved transformative, compelling her to venture beyond the comfortable confines of engineering meetings and into the dynamic world of marketing strategy sessions.The pandemic's isolation birthed unexpected opportunities, as Sarah's technical writing began attracting attention in the data community. What started as casual documentation of her engineering adventures morphed into paid writing engagements, creating a surprising bridge between technical expertise and marketing communications. This organic evolution suggested something more profound lurking beneath the surface, a hidden pathway connecting the precision of data engineering with the artistry of marketing strategy.The final pieces of her transition fell into place through a combination of hands-on consulting work, mentorship from industry veterans, and immersion in marketing literature. Her participation in the Reforge community added structured learning to her toolkit, while her unique perspective as a former technical buyer provided invaluable insights into marketing dynamics. This multifaceted approach to learning, mixing practical experience with theoretical knowledge, transformed what might have seemed like an improbable leap into a natural progression.Key takeaway: Career transitions in technology rarely require formal education; they thrive on practical experience and curiosity. The most valuable skills often develop through side projects, technical writing, and a willingness to understand the business impact of your work. For those considering a similar path, start by documenting your technical experiences, engaging with cross-functional teams, and focusing on how your current role impacts business outcomes rather than just technical implementations.First Principles Marketing Against Best PracticesMarketing orthodoxy often goes unchallenged, with practitioners blindly following conventional wisdom without questioning its validity. Sarah brings a refreshing perspective to this dilemma, approaching marketing strategies with an engineer's skepticism and a commitment to first principles thinking. This natural inclination to question established norms stems from her background in data engineering, where decisions require rigorous validation rather than mere acceptance of industry standards.The notion that Tuesday morning at 8 AM represents the optimal time for email sends exemplifies the kind of unexamined marketing wisdom that pervades the industry. Rather than accepting such practices at face value, Sarah advocates for a two-pronged approach: first envisioning the ideal outcome, then assessing what's practically achievable within existing constraints. This methodology creates space for innovation while maintaining pragmatic boundaries, allowing marketers to challenge assumptions without losing sight of business objectives.The parallel between architectural decisions in software engineering and strategic choices in marketing reveals an interesting pattern. Just as engineers must carefully consider system architecture before writing code, marketers benefit from establishing solid strategic foundations before diving into tactical execution. This shift in focus from immediate implementation to thoughtful strategy design represents a more sophisticated approach to marketing operations, one that prioritizes intentional decision-making over reflexive adoption of industry practices.In the context of accelerating AI adoption, this first-principles approach becomes even more crucial. Rather than immediately jumping to content creation or campaign execution, successful marketing strategies begin with fundamental questions about audience selection, engagement methods, and value proposition. This methodical approach ensures that technological tools serve strategic objectives rather than dictating them, maintaining human judgment at the core of marketing decisions.Key takeaway: Transform your marketing approach by questioning established practices and applying first-principles thinking. Start by clearly defining your ideal outcome, then work backward to create practical strategies that challenge conventional wisdom. This method often reveals more effective approaches than blindly following industry "best practices." When evaluating any marketing tactic, ask yourself: "What problem are we really trying to solve, and is this truly the most effective solution?"Systems Thinking Applications For Marketing AnalyticsSystems thinking represents the essential bridge between marketing and data engineering, offering a framework for understanding how data flows through modern marketing operations. The ability to visualize and architect data pathways across platforms separates proficient marketing technologists from those merely executing tactical campaigns. This foundational skill proves invaluable when orchestrating the complex dance of customer data across marketing systems.Consider the journey of a single lead signal as it traverses through various marketing platforms. The ...

What’s up everyone, welcome to our first episode of 2025 – today we have the pleasure of sitting down with Austin Hay, Co-Founder and Co-CEO at Clarify and Martech Teacher at Reforge. Summary: Something extraordinary is brewing in the world of martech. In the near future, Austin thinks AI agents will turn into an omniscient digital butler, anticipating your needs with uncanny precision while vanishing into the background of your workday. But the real revolution unfolds in the seemingly mundane machinery of marketing operations, where innovative companies are transforming their spaghetti mess of data pipes and platforms into something approaching digital poetry. The fundamental building blocks of our systems aren't disappearing, they're gaining superpowers. Hear it from one of our industry's most thoughtful builders. About AustinAustin started his career at Accenture but he left the Fortune 500 world to join a startup called Branch where he became the 4th employeeAustin then created his own boutique mobile growth engineering consultancy. He grew the practice to 1.5M with big names like Walmart, Jet, Airbnb, Foursquare and more.His consulting practice was aqui-hired by mParticle – a leading CDP solution where he would eventually become VP of Growth He later joined Runway as VP of Business OperationsHe also started building The Marketing Technology Academy – an online learning center for martech which he would eventually sell to Reforge and become the Instructor for the new Martech courseHe was also Head of Martech at Ramp, a fintech startupLast year, Austin strapped on his jetpack and became a product founder at Clarify conquering SF and Hubspot and building the first flexible, intelligent CRM that people actually enjoy using. AI Agents and the Hidden Promise of Ambient ComputingLet's face it, manually feeding context to AI feels a bit like teaching a fish to ride a bicycle. Current AI systems, brilliant as they may be at crunching numbers and crafting responses, still stumble around our digital workspaces like a tourist without a map. Sure, they can write a decent blog post or solve complex equations, but they're essentially working with one hand tied behind their virtual back.Now, imagine your AI assistant as more of a digital detective, quietly observing and understanding everything happening on your screen. No more copying and pasting chunks of text or explaining what's in your Notion workspace for the hundredth time. Picture having a conversation with your computer while it maintains an almost supernatural awareness of your digital environment, from those buried Slack threads to that spreadsheet you've been avoiding. Recent demonstrations, like Kieran Flanagan's adventure with Gemini's screen reader, hint at this future, even if current versions move with all the grace of a sleepy sloth.The real magic kicks in when we start thinking about operating system-level integration. Platform-specific AI agents are like horses wearing blinders; they can only see what's directly in front of them. But desktop applications from companies like GPT and Anthropic are pushing toward something far more interesting: AI that can understand your entire digital world, not just a tiny slice of it. It's the difference between having a personal assistant who can only help you in the kitchen versus one who can manage your entire house.Here's where things get particularly juicy: this isn't some far-off sci-fi fantasy. We're looking at a five-year horizon where the clunky, permission-asking AI of today evolves into something far more sophisticated. The transformation won't happen overnight (sorry, instant gratification seekers), but when it does, we're talking about a 10x boost in productivity that makes current productivity hacks look like using a butter knife to cut down a forest.Key takeaway: While today's AI assistants feel like overeager interns requiring constant supervision, the next five years will usher in truly ambient AI that seamlessly integrates with our operating systems. The future isn't about teaching AI to understand us; it's about AI that already knows what we need, when we need it, across our entire digital landscape.The Limitations of AI Agent MarketplacesThe AI marketplace concept raises important questions about automation's role in our daily work. While downloading specialized AI agents for every task might sound appealing, reality suggests a different path forward. Current marketplace models mirror the Chrome extension ecosystem, where tools often remain peripheral rather than becoming essential to core workflows.Austin frames the central debate in venture capital circles clearly: will we depend on AI agents that require explicit commands, or will we embrace ambient intelligence that works proactively in the background? Looking at the CRM space, Austin points out a crucial consideration that many futurists overlook. You can't simply discard two decades of sales methodology and expect professionals to embrace a completely alien interface. Instead, sellers need familiar elements: contacts, companies, opportunities, and tasks, all presented in recognizable formats that align with established workflows.The intersection of traditional software and AI becomes particularly interesting when Austin discusses CDP platforms. Users expect certain fundamentals, like accessing persona views and tracking customer behavior. The innovation opportunity lies not in replacing these elements but in enhancing them through intelligent automation. Austin suggests that the key difference emerges in how these agents operate: will users actively assign tasks, or will agents run continuously in the background, performing expected functions without explicit direction?While some platforms champion what Austin calls the "jack of all trades" approach with Notion-style customizable workflows, he makes a compelling case for specialized, industry-specific solutions. This focused approach, where AI agents operate autonomously within well-defined parameters, might prove more valuable than a marketplace full of generic tools. Austin emphasizes that the more specialized you are in understanding user needs, the more effective your agentic experience can be, particularly when it runs seamlessly in the background without requiring constant configuration.The reality likely lies somewhere in between these two extremes. Certain straightforward tasks, like data enrichment for new records or basic categorization, seem well-suited for autonomous AI agents. However, more complex decisions involving customer lifecycle management, timing of promotional offers, or predictive modeling for next-best-action recommendations require deeper integration with historical data and sophisticated propensity models. The key to success may not be choosing between marketplace agents or integrated solutions, but rather understanding which approach best suits specific use cases and organizational needs.Key takeaway: Success in AI automation won't come from marketplace-driven point solutions but through deeply integrated, industry-specific AI that enhances existing workflows while maintaining familiar interfaces. Austin's insights suggest focusing on building AI that complements rather than replaces established business processes, creating tools that feel natural rather than revolutionary.The Core Primitives of Martech and the Path to Self-Designing APIsEver tried explaining Bitcoin to your grandparents? That's roughly how it feels watching companies try to skip straight to AI automation without understanding their data foundations. Austin breaks down the concept of primitives in martech, those fun...

What’s up everyone, today is our last episode of the year and if you paid attention to the intro, I’m excited to officially welcome Darrell Alfonso as the newest co-host of the podcast!Summary: The Humans of Martech enters an exciting new chapter with Darrell Alfonso joining as co-host, bringing fresh energy and insights to the show. As a long-time listener and new dad, Darrell offers relatable stories of juggling work, family, and community while sharing bold predictions like the shift to warehouse-native architectures in martech, which promise to streamline data operations for enterprises. With AI poised to handle executional tasks, Darrell emphasizes the evolving role of marketers as strategic thinkers guiding AI with emotional intelligence and ethical oversight. As the podcast heads into 2025, it remains committed to delivering actionable insights, thought-provoking predictions, and a fresh perspective for the martech community.Welcoming a New Co-Host and Celebrating Baby MilestonesDarrell’s journey to becoming a co-host on the podcast came full circle, blending mentorship, passion, and personal milestones. He shared how one of his mentees suggested the idea, sparking an opportunity he immediately embraced. As an early listener of the show, Darrell highlighted his admiration for its unfiltered and geeky deep dives, calling it his favorite podcast—a sentiment that fueled his excitement for the road ahead.On a personal note, Darrell and his wife recently welcomed their baby boy, just eight weeks ago. Parenthood, he admitted, has been a whirlwind of sleepless nights and steep learning curves. As ambitious and organized as he and his wife are, they’ve quickly discovered that babies don’t operate on predictable timelines. Moments of progress—like better sleep—often take a step back as developmental leaps shake up routines. While the lack of rest is taxing, Darrell’s outlook reflects a blend of exhaustion and gratitude.Balancing professional life with a newborn is no small feat. Darrell recounted a whirlwind day of delivering a keynote, driving home, and immediately diving into baby duties. He joked about the unpredictability of these moments while acknowledging the personal growth they inspire. Virtual support groups like Maven have also helped him navigate the early stages of parenthood, offering both guidance and camaraderie with other new parents.For all the challenges that come with parenthood, I always like to emphasize gratitude. Reflecting on the struggles my family faced in our journey to parenthood (and how many other couples have it much harder), we need to emphasize the importance of cherishing even the tough parts. The joy and fulfillment of finally welcoming our child outweigh the sleepless nights and ever-changing routines. Key takeaway: Parenthood is a mix of exhaustion, growth, and gratitude. Embracing the ups and downs, leaning on community support, and focusing on the meaningful moments can help navigate this transformative stage of life.Marketing Tools Without DatabasesOkay… enough baby talk haha. Darrell predicts that in 5 years, most marketing tools will no longer rely on databases. At first glance, this concept might seem shocking—after all, marketing automation platforms, CRMs, and CDPs are fundamentally built on relational databases. But Darrell suggests this assumption is rooted in tradition, not necessity, and outlines a shift toward a warehouse-native or zero-copy data architecture that could redefine how tools operate.To illustrate this point, he draws a simple analogy. Consider apps like Yelp or Google Places. When you share a restaurant with a friend, the app doesn’t create a duplicate of your contacts database; it accesses the data on-demand. Contrast this with the typical marketing stack, where almost every tool replicates contact data, creating endless updates, sync errors, and manual fixes. Darrell estimates that more than 80% of a team’s data work revolves around ensuring consistency across these copied datasets—a cumbersome and inefficient process.The inefficiency extends beyond wasted effort. Darrell shares examples of bi-directional sync loops that occur when two systems endlessly update each other, introducing a frustrating complexity to even the simplest workflows. These scenarios highlight how deeply ingrained data copying is within current systems and how much time is spent combating its limitations.Shifting to a zero-copy model, Darrell argues, could eliminate these inefficiencies. A warehouse-native approach would enable tools to work directly from a centralized data warehouse, bypassing the need for constant synchronization. This not only streamlines operations but also reduces the risk of errors. It’s a radical departure from the status quo but one he believes is inevitable as teams demand greater agility and accuracy in their tools.Key takeaway: The future of marketing tools lies in a warehouse-native approach, eliminating the inefficiencies of duplicated data. By moving beyond traditional databases, teams can reduce errors, streamline processes, and focus their energy on strategic initiatives rather than endless data synchronization.Preparing for a Warehouse Native FutureI think the shift toward a warehouse-native approach for marketing tools feels inevitable, but its timeline remains uncertain. While this approach won’t entirely replace APIs, it will change how tools interact. Instead of passing data back and forth through integrations, tools will increasingly work directly from a centralized data warehouse, eliminating inefficiencies tied to duplication and synchronization.This prediction, often misunderstood as futuristic, is already shaping current tools. Vendors like MessageGears and Castle.io are leading the charge, offering solutions that bypass traditional database structures and avoid charging based on record counts. Despite their innovations, the challenge lies in industry adoption. Many teams are accustomed to older models, making this transition as much about change management as it is about technology.A critical insight from my past research and conversations with experts on the podcast highlights the importance of internal readiness. Tools can only perform as well as the data they rely on. High-quality, structured data is the foundation for warehouse-native success. Teams must focus on improving internal processes now, rather than waiting for the perfect tool to arrive. This means investing in data hygiene, organization, and strategy to prepare for the opportunities that a warehouse-native architecture will bring.However, the path forward isn’t without challenges. Many companies are still immature in their data strategies, making widespread adoption a longer process than anticipated. Whether this shift takes five years or more, the direction is clear: vendors and teams must align their operations with the possibilities of a warehouse-first world.Key takeaway: Warehouse-native tools represent a significant step forward in reducing inefficiencies and modernizing operations. Teams can prepare for this shift by prioritizing high-quality, well-structured data. The strength of these tools lies in how they interact with clean, organized data, making internal readiness the best first step for embracing this future.Understanding When Warehouse Native Tools MatterDarrell explains that the value of warehouse-native tools becomes clear especially when dealing with large volumes of data. For small and mid-sized companies, the classic setup—a CRM, marketing automation platform, and a few connected tools—works perfectly fine. He notes that for organizations with 500 employees or fewer, traditional data architectures remain suff...

What’s up everyone, today we have the pleasure of sitting down with Kacie Jenkins, SVP Marketing at Sendoso. Summary: Marketing isn’t about cramming creativity into a spreadsheet, and Kacie’s journey proves it. She took on last-touch attribution, broke free from narrow metrics, and built a system that told the whole story, one where sales and marketing actually worked together. It wasn’t flashy; it was months of unsexy foundational work that led to record-breaking results. Kacie’s advice is to stop obsessing over proving your worth with perfect data. Focus on collaboration, long-term strategies, and building something so good it proves itself.About KacieKacie started her career as a recording artist for 6 years where she recorded and released 2 top 30’s singles on country radioShe transitioned to FANDOM as Marketing Manager where she helped build and scale entertainment and gaming communitiesShe then shifted to consumer tech and worked at Roku where she helped take their streaming stick to marketShe later joined Fastly when they were still a tiny startup and was eventually promoted to VP of Marketing while helping them scale to $200M in ARR and a massive IPOShe moved on to a few other VP of marketing stints at Ace Hotel and then SourcegraphToday Kacie is Senior Vice President of Marketing at Sendoso, the top gifting and direct mail platform for revenue teamsWhy Marketing Needs to Break Free from Last Touch AttributionKacie has strong opinions about last touch attribution and its role in marketing, calling it both misguided and overused. She recounts a memorable example where a company’s finance team mandated that every marketing touchpoint be unique, forbidding multiple efforts for a single account. The result was a fragmented strategy, with marketing forced to isolate efforts rather than integrate them—a scenario she describes as fundamentally broken. This, she says, reflects a wider misunderstanding of marketing’s role in driving success.In her experience, marketing is often held to an unrealistic standard that no other department faces. “No one questions whether a sales team should exist,” Kacie points out, yet marketers are repeatedly asked to prove their value in isolation. This obsession with single-point attribution—whether first or last touch—reduces complex buyer journeys to simplistic, unrealistic models. She likens it to sports, where success is measured by the contributions of the entire team, not just the final goal or play. In marketing, the same principle applies: campaigns succeed when brand, product, sales, and customer experience work cohesively.Kacie highlights how marketers often agree to flawed measurement practices under intense job pressure. Many leaders, she notes, demand immediate, trackable results and dismiss longer-term investments like brand building. When these short-sighted strategies fail, the blame lands on the marketing team, perpetuating a destructive cycle. This became especially apparent during the pandemic, when companies slashed budgets for brand and integrated marketing, only to see their performance suffer months later.At its core, the problem stems from a demand to quantify marketing in ways that are convenient rather than meaningful. Kacie insists that attribution models like last touch can provide insights but have been misused to force marketing into a demand capture role that undervalues its broader impact. Effective marketing, she argues, cannot succeed in a vacuum—it depends on the health and alignment of the entire organization.Key takeaway: Attribution models like last touch offer insights but become problematic when used in isolation. Marketing thrives on collaboration across teams, long-term investments, and integrated strategies. Simplistic measurement frameworks undermine this by reducing success to isolated metrics, which fail to capture the bigger picture. Focus on fostering collaboration and investing in holistic strategies rather than chasing immediate, trackable wins.What’s the Best Way to Prove What Drives Revenue in Marketing?Kacie’s candid take on the challenges of attribution didn’t stop there. She explains that board members and leadership often seek simple answers, asking, “What drove the most revenue?” This, she notes, is rarely a question with a singular answer, and it certainly doesn’t lie solely in the last touchpoint.Her approach combines every available data point, UTMs, self-reported attribution, and multi-touch models, to create a comprehensive picture. This isn’t about assigning credit to one channel or tactic but understanding the collective influence of all touchpoints. For instance, at Sendoso, Kacie leveraged this holistic perspective to reinvigorate outbound sales. By investing in trust-building, strong branding, and thoughtful partnerships, the team shifted outbound calls from cold to warm, creating an environment where sales and marketing aligned seamlessly. The results were tangible, but proving causality required a deeper story, not just a simple report.She recalls challenging her finance team’s reliance on last-touch data. When presenting a report that suggested “more direct traffic” as the solution, she asked bluntly, “What does that even mean?” This moment underscored how reductive metrics fail to capture the true impact of marketing efforts. By shifting the focus to sales-qualified opportunities and long-term patterns, she built trust with stakeholders and steered conversations toward what truly drives growth.Kacie emphasizes that this broader view isn’t fast or easy, and it requires fighting against short-term thinking. Marketers must advocate for strategies that don’t immediately show up in last-touch reports but are essential for sustainable growth. She also draws from B2C insights, where buying decisions often happen long before measurable touchpoints, reminding us that customers’ journeys rarely follow a predictable path.Key takeaway: Attribution isn’t about isolating success to one channel or tactic. By combining multiple data sources and focusing on long-term causality, marketers can tell a more accurate story. This approach builds trust with leadership, aligns teams, and justifies investments that might not show immediate ROI but are crucial for sustainable success.How to Convince Leadership to Rethink MeasurementKacie explains that driving change in marketing attribution and measurement requires aligning cross-functional teams and proving value over time. When she joined Sendoso, the disconnect between sales and marketing created distrust, and outdated metrics like MQLs dominated conversations. To address this, she set clear expectations with leadership: changes would be foundational, require significant investment, and take time to show results. This up-front agreement ensured her efforts had initial backing, though challenges arose as the process unfolded.A crucial part of the transformation was bridging the gap between marketing and finance. Kacie worked with a finance partner who embraced curiosity, seeking to understand marketing’s perspective by educating himself through webinars and discussions. This mutual respect and collaboration were essential for aligning goals and building trust. She demonstrated that her approach wasn’t about gaming numbers or securing credit but about laying a foundation for sustainable growth.Despite initial alignment, skepticism crept in as foundational work—cleaning up Salesforce fields, rethinking sales stages, and redefining metrics—took longer to yield visible results. Kacie emphasizes the importance of perseverance during this phase. Companies often lack the patience for foundational changes, cutting le...

What’s up everyone, today we have the pleasure of sitting down with Stephen Stouffer, Director of Automation Solutions at Tray.ai and the first ever returning guest. We had Stephen on earlier in the year in episode 112 where we unpacked the practical wonders of combining AI tools with iPaaS solutions. Summary: AI can transform your marketing without overwhelming you. Start with one use case. Watch the results, and go from there. You don’t need to master data science to add AI value, but you need to be willing to experiment, keep what works, and let the tech do the heavy lifting. Customer Journey Mapping EssentialsCustomer journey mapping, as Stephen puts it, is best approached as a clear, structured framework. For marketers, this often starts by examining the visitor's first few seconds on a website. Stephen’s “three, five, seven rule” is a useful guide: three seconds to capture attention, five to build engagement, and seven to prompt action. Reviewing homepage or landing page performance through this lens keeps the focus on essentials. Are calls-to-action (CTAs) clear and accessible? Does the page guide users toward the intended outcome effectively?Stephen further notes the importance of every element “above the fold.” Content here needs to be concise, visually appealing, and should naturally lead users to the next step. A well-placed CTA, such as a prominent button, encourages forward motion, while a hidden or confusing one can derail the journey. Each interaction should be straightforward and intuitive.Beyond landing pages, Stephen highlights the journey before a visitor even arrives. Campaign managers, for instance, should ensure that ad copy and visuals align with the landing page, creating a smooth transition from ad to action. Consistency here reduces friction and keeps the experience cohesive.For advanced mapping, Stephen recommends storyboarding different customer personas and their digital pathways. By tuning each stage to fit these profiles, marketers can craft a journey that feels relevant and trustworthy, engaging each segment from the very first interaction.Key takeaway: Use the "three, five, seven rule" to evaluate each customer touchpoint on your homepage or landing pages. This approach helps ensure your content captures attention, fosters engagement, and prompts action—all within a few seconds.AI’s Role in Automating Personalized EmailsStephen recently demonstrated how AI automates personalized emails with just a first name, last name, and email. AI uses data from sources like LinkedIn, company details, and job history to craft messages that feel genuinely tailored to each recipient, far beyond typical generic responses.This level of automation doesn't just boost engagement; it saves significant time. Instead of setting up complex variable fields or spending 15-20 minutes per email on manual research, AI handles it all in seconds. Stephen notes that marketing teams can skip intricate field configurations, while sales teams gain back valuable time to focus on high-impact tasks.AI also serves as a replacement for traditional enrichment tools, pulling in dynamic contact details without third-party data providers. For sales, it means delivering relevant, personalized content effortlessly. AI does the heavy lifting, creating an email that feels custom-built for the recipient—no manual assembly required.Key takeaway: AI enables efficient, data-rich personalization for customer outreach, saving marketing and sales teams time and resources while boosting the quality of each touchpoint.Automating Personalized Outreach with AI AgentsAI agents are redefining how teams approach personalized outreach, offering new ways to automate highly customized interactions. Stephen explains how Tray.ai leverages a powerful combination of APIs—OpenAI, Google, LinkedIn, and more—to build out complex automation processes directly within its platform. Each AI agent is designed to use the best tool for the task at hand. Given the right context and instructions, these agents can gather relevant data from press releases, Crunchbase, LinkedIn profiles, blog posts, and other sources to craft an email that feels genuinely tailored.Imagine a marketing email generated entirely by an AI agent. With the recipient’s email, role, and other contextual clues, the AI might produce a message like, “Hey, congratulations on your recent speaking slot at AntiCon in London. Hope you had a safe journey back!” This level of personalization would usually require about 15 minutes of research by a BDR or ISR. Now, it can be fully automated, freeing up sales and marketing teams to focus on strategy and high-priority tasks rather than time-consuming data gathering and crafting.Stephen points out that the true power of AI agents comes from implementing them in real, tangible ways. For instance, rather than abstract promises of efficiency, Tray.ai demonstrates AI’s impact with practical use cases like this automated email personalization, which resonates more directly with the people using it. By creating a functional demo that allows teams to see this technology in action, Tray.ai bridges the gap between AI's potential and its practical application.For anyone curious to test it out, Stephen offers a live demo of the personalized email automation. This hands-on approach helps users understand the realistic possibilities AI agents bring to customer engagement and outreach, transforming the process from concept to actionable, impactful workflows.Key takeaway: AI agents streamline personalized outreach, combining data sources and automation tools to generate highly customized emails without manual research. By automating these tasks, teams can focus on high-impact activities while still delivering meaningful, individualized interactions.Challenges in Implementing AI-Driven Customer Journey MappingImplementing AI-driven customer journey mapping and personalization comes with its share of challenges. Stephen highlights three primary obstacles teams face: complexity, connectivity and compliance.The technology’s complexity. Even technical professionals sometimes struggle to understand the nuts and bolts of AI integrations, making it difficult for organizations to determine how to effectively deploy these tools. The ambiguity around building and customizing AI solutions internally often becomes a barrier to adoption.The challenge of data connectivity. For AI agents to deliver relevant outputs, they need access to comprehensive data across systems. Whether it’s a CRM, sales records, or product usage data, these inputs provide the context for AI to make useful recommendations. While crafting a prompt might sound straightforward, gathering and linking all the necessary data to inform that prompt is anything but simple. Stephen explains that AI can only be as effective as the information it’s fed, making seamless data integration a top priority for effective personalization.Perhaps most daunting, the challenge is compliance. When feeding sensitive data into large language models, teams must navigate a maze of security requirements like SOC 2, HIPAA, and GDPR compliance. Many organizations hesitate to dive into AI because of the fear of regulatory risks. Legal teams often step in, concerned abo...