136: Benoit Leggieri: Livestorm’s Head of Growth on crafting revenue driving workflows with Customer.io

What’s up everyone, today we have the pleasure of sitting down with Barbara Galiza, Growth and Marketing Analytics Consultant.Summary: Attribution is a bit like navigating Amsterdam’s canals: mesmerizing but full of hidden turns that don’t always make sense. You don’t need to chart every twist—just focus on finding the direction that moves you forward. Instead of obsessing over every click, use attribution like a compass, not a GPS. Multi-touch attribution (MTA) gives you some of the story, but often misses those quiet yet powerful nudges that drive real decisions. Layering in rule-based or incrementality testing can fill the gaps, giving a clearer picture of what’s driving your wins. For startups, it’s even simpler: stick to what’s working and forget complex attribution—qualitative feedback is often the best guide in the early days. Data doesn’t need to be perfect, just practical, and sometimes trusting that a strategy is working is enough to keep pushing it.About BarbaraBarbara was an early employee at Her (YC), the biggest platform for LGBTQ women where she would eventually become Head of GrowthShe was also Head of Growth at different startups like Pariti and HomerunShe worked at an agency where she led data and analytics for Microsoft EMEABarbara then went out on her own as a GTM and Analytics consultant for various companies like Gitpod, WeTransfer, Sidekick and dbt LabsShe has a newsletter on marketing data: 021 newsletter.She produces content for data brands (dbt, Mixpanel, Amplitude) like case studies and webinarsBuilding Data Literacy Through SQLData literacy is essential for modern marketers, but it doesn't have to be intimidating. Barbara’s advice is simple: learn SQL. While marketers today are surrounded by user-friendly tools and drag-and-drop interfaces, those who want to truly grasp their data should get comfortable with SQL. It’s not about becoming a data engineer but about understanding how the numbers you rely on every day are built. SQL helps you see how data connects, how it’s organized, and how you can group it to make sense of what’s happening in your campaigns.What’s great is that you don’t need to dive into formal classes or certifications. Start where you are. Most companies are sitting on a goldmine of structured marketing data, whether it’s Google Analytics data in BigQuery or Amplitude events stored in a data warehouse. The next time you’re building a report, try using SQL for a small part of the process. It’s a skill that compounds over time. Once you get familiar with the basics, you’ll start to see data in a different way, and you’ll be able to spot insights faster.Barbara also points out a crucial, often overlooked skill: understanding why your tools give credit to certain campaigns. Why does one Facebook ad outperform others in your reports? Why does Google Analytics attribute more conversions to certain sources? Getting to the bottom of these questions puts you in a much stronger position as a marketer. If you can explain how attribution models work and why certain data points appear, you're already ahead of most.At the end of the day, it’s about making smarter decisions. Barbara believes that marketers who can confidently say, “I know why these numbers look the way they do,” are in the top 10% of data-driven marketers. It’s not just about collecting data; it’s about making sense of it and using it to steer your strategies.Key takeaway: Learning SQL gives marketers the power to truly understand their data. Starting small, even with basic queries, can unlock a deeper understanding of how marketing data is structured and why campaigns perform the way they do. The key is to build practical skills that help you make more informed decisions.Rethinking Attribution and Understanding Its Role in MeasurementBarbara brings clarity to two commonly conflated concepts: attribution and measurement. While many marketers default to thinking of attribution as purely click-based or multi-touch attribution (MTA), Barbara challenges this view. She argues that attribution goes beyond just tracking clicks and touches throughout a customer’s journey. It’s about understanding the overall impact of marketing efforts—whether through incrementality tests, media mix modeling (MMM), or holdout groups. Attribution is meant to explain how marketing drives results, but it’s not the only tool for assessing campaign success.MTA, particularly click-based models, excels at measuring bottom-funnel actions like search marketing, where high-intent users click on an ad and then convert. This method works well for campaigns that rely on clicks to move the needle. However, Barbara notes that it has its limitations, especially when it comes to non-click-based channels like video or display. MTA often over-credits search campaigns because that’s where the conversion is tracked, but it misses the broader influence of awareness-building efforts. In essence, MTA can tell you what happened after the click, but not what inspired it in the first place—be it a podcast mention or an engaging piece of content seen days before.On a broader level, Barbara explains that attribution is not the same as measurement. Attribution focuses specifically on tying marketing efforts to business results, such as leads or revenue. Measurement, on the other hand, casts a wider net. It includes performance across various metrics, not just conversions. For instance, measuring how well different messaging resonates with audiences is crucial, but it doesn’t always directly lead to immediate sales. Measurement can inform future strategies by offering insights into engagement, customer preferences, and channel effectiveness.As Barbara sees it, attribution is a subset of measurement. It’s a tool for understanding what drives business outcomes, but it shouldn’t be the only tool marketers rely on. For example, MTA has its place but should be used alongside other models like MMM to paint a fuller picture. Measurement, meanwhile, helps marketers assess the effectiveness of everything from messaging to customer touchpoints, beyond just the end goal of conversion.Key takeaway: Attribution is one piece of the measurement puzzle, focusing on business outcomes, while measurement encompasses a broader range of insights. Marketers should use a mix of attribution models to understand their campaigns and apply measurement tools to gain a holistic view of performance.Limitations of Multi-Touch Attribution in Credit DistributionMulti-touch attribution (MTA) is often seen as a way to distribute credit across different customer touchpoints, but Barbara questions its effectiveness in this role. She argues that MTA is inherently limited because it only attributes credit to interactions that involve a click. This creates a skewed view of the customer journey, where only click-driven strategies—like search ads—are recognized, leaving other key touchpoints, like connected TV (CTV) or social media, out of the equation. The result is a narrow perspective that doesn't capture the full influence of various channels.Barbara points out that for marketers to make better decisions, MTA needs more than just click data. One alternative she suggests is pairing MTA with rule-based attribution models, where data from "How did you hear about us?" surveys are integrated into the analysis. This way, marketers can capture insights from channels that don’t typically generate clicks but still play a crucial role in driving awareness or consideration. By adding this type of first-party data, businesses get a broader understanding of what’s really influencing their customers.Some data agencies are also experimenting with es...

What’s up everyone, today we have the pleasure of sitting down with Steven Aldrich, Co-CEO and Co-Founder at Ragnarok NYC. Summary: Like the aftermath of Ragnarök according to Norse mythology, the martech world is emerging stronger, more focused, and ripe with potential. Rather than being overwhelmed by the chaos, marketers should use this time to rethink how to evaluate technology choices through the lens of business value. Prioritize platforms that drive real-world impact and avoid getting lured by features that blaze brightly for a moment, only to be swallowed by the tide of irrelevance.About StevenSteven’s first job out of business school was a customs broker in Colombia, before his Visa ran out and he was forced to return to the US He started his marketing career as a Marketing and Comms Associate at a market research firm where he discovered the wonders of HTML, email development and Adobe dreamweaverWhile continuing his full time in-house career working in email and CRM roles for different industries, Steven and his co-founder Spencer launched Ragnarok, first as a side hustle where they spent their evenings moonlighting as marketing technology consultantsIn 2017, both co-founders decided to take the leap and go all in on their agencyToday Ragnarok is a 50+ person full service martech agency that’s helped well known brands like zapier, dropbox, asana, adobe and many more!The Evolution of Martech and the Impact of ConsolidationWhen asked about the future of martech, Steven immediately highlighted the ongoing consolidation in the industry. He pointed to acquisitions like Twilio snapping up Segment and Salesforce expanding its Customer Data Platform (CDP) offerings as clear signals. According to Steven, these moves indicate that we’re in the midst of a reshuffling phase—one that will shape how martech platforms are built and used over the next decade.However, it’s not just about merging and acquisitions. Steven sees the next wave of growth stemming from generative AI. This technology, while still in its infancy for many organizations, will soon be as fundamental as marketing automation tools were a decade ago. Platforms are experimenting with Gen AI features like automated content creation, but they’re still scratching the surface. “Right now, a marketer isn’t likely to sit down and have their AI tool write an entire creative brief,” Steven noted. “But once the tech reaches a level where it’s drafting briefs and campaign strategies, it’ll fundamentally change what marketers do day-to-day.”He also predicts that the next few years will separate the genuine innovators from the rest. Startups focusing on AI-powered automation and advanced integrations will emerge as key players. Those that fail to embrace this trend will struggle to maintain relevance. Steven pointed to companies like Castle.io as an example—a newer entrant that has managed to make a name for itself by rethinking traditional automation and going all-in on a warehouse-first approach.Looking ahead, Steven envisions a future where marketers become more like strategic curators rather than operators. Instead of creating every campaign element manually, marketers will outline goals and high-level structures, and let the tools figure out the rest. “Think of a platform where you set your conversion goals, outline your audience, and the tool builds the journey for you,” he explained. Some companies are testing these capabilities internally, but we’re still far from a world where it’s the norm. To reach that stage, platforms need to overcome significant technical challenges and gain marketer trust.Ultimately, Steven believes that by the ten-year mark, the martech industry will look entirely different. The focus will shift away from basic integrations and automation to more complex AI-driven orchestration. Platforms will evolve into decision-making engines, allowing marketers to focus on strategy, creativity, and innovation, leaving the grunt work to the machines.Key takeaway: The martech industry is undergoing a consolidation phase as it readies itself for the next wave of innovation: generative AI. Startups that embrace AI-driven automation will emerge stronger, while legacy platforms must integrate these new capabilities or risk becoming obsolete. In the next decade, marketers will transition from hands-on campaign execution to strategic oversight, as tools handle more of the complex work autonomously.Blending Automation with Human Spark for Smarter Martech StrategiesWhen it comes to AI and automation in martech, there’s a spectrum of opinions. On one end, some marketers insist that only a human can truly understand and engage their audience. On the other end, there’s a growing camp eager to hand over the repetitive tasks to machines and focus on strategy. Steven pointed out that the real value lies in finding a balance between the two extremes, especially for industries with strict compliance requirements like FinTech and health tech.Steven used abandoned cart programs as a foundational example of automation’s role in marketing. Not long ago, these campaigns were inconsistent and cumbersome. Companies like Klaviyo and Shopify stepped in, making abandoned cart emails table stakes for eCommerce. Now, if you abandon your cart, you can almost predict when you’ll receive that follow-up email offering a discount or reminder. “It’s just expected,” Steven explained. He believes this kind of automated functionality has become the baseline for what customers and marketers alike view as the norm.But not every industry can afford to automate at that level. With sectors like finance or healthcare, there’s a need for humans to review and validate messages for compliance. “A legal person is at the end of every review,” Steven said. “It’s frustrating and time-consuming, but the cost of sending the wrong message at the wrong time is just too high.” He sees these industries gradually adopting AI where they can—incremental optimization, message testing—but keeping a human in the loop for quality assurance.The evolution of martech, in Steven’s view, will be about advancing beyond these early stages. He predicts that the future will bring a seamless integration where humans set high-level goals, and AI takes care of execution. The role of the marketer shifts from managing individual campaigns to curating experiences and setting strategic parameters. Some platforms are already testing these capabilities, but they’re far from ready for mainstream adoption. “Imagine a future where marketers simply set their audience, goals, and content, and the tool builds the entire journey for them,” Steven envisioned. This approach would redefine what it means to be a marketing operator, giving professionals more time to think strategically rather than tactically.Ultimately, Steven sees the evolution of martech as an interplay between speed and quality. Some companies will succeed by automating faster, launching multiple initiatives, and iterating based on outcomes. Others will opt for a more deliberate approach, spending more time crafting the perfect message. “There isn’t one clear winner,” Steven concluded. “It’s about choosing the right tool for the job and understanding what’s at stake when the human element is minimized.”Key takeaway: Martech’s future lies in balancing automation with human oversight. While some industries can embrace full-scale automation, others need humans in the loop to maintain compliance and quality. Marketers must choose tools that fit their strategic goals—whether that’s rapid iteration or precision crafting.The Value of Data Science in Martech OptimizationWhen asked about the role of data science in marketing operations, Steven was quick to point out the di...

What’s up everyone, today we have the pleasure of sitting down with Danny Lambert, Director of Marketing Operations at dbt Labs. Summary: Marketers often feel like they're battling a dragon when it comes to integrating data. We’re overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams. Danny walks us through his journey of cautiously entering the data world and the role dbt can play for marketing teams. By learning just enough SQL, knowing what tools you need to get started with and leaning on dbt’s tools, you can start small and gradually build a warehouse-first martech stack. The reward is more control over your data, flexibility to deploy personalized campaigns independently, and a competitive edge that no pre-packaged solution can match.About DanielDanny started his career at an event solutions company where he wore several different marketing hats including getting his first taste of marketing automation  He then worked in marketing ops at IZEA, at marketplace that connects brands with influencers before having a short stint at McGaw.io one of the leading martech and analytics agenciesHe then moved over to healtech at CareCloud where he led Demandgen and ABMHe then transitioned to Rev.com the popular transcription company where he started in marketing ops, then demand gen before being promoted to Director of Integrated MarketingAnd today Dan is Director of Marketing Operations at dbt Labs, the creators of the most popular software for data transformation used by data engineers at more than 20k companiesNavigating the Disconnect Between Marketers and Data TeamsMany marketers struggle to engage with data teams because they feel worlds apart. Danny points out that it’s a lot like the early days of marketing’s relationship with product teams. Before product-led growth (PLG) became a buzzword, marketers and product teams operated in separate silos. It took a concerted effort to break that wall, and the same shift is needed with data. Marketers often find the mechanics of data engineering and warehousing intimidating, and for good reason—they weren’t trained for it. But it doesn’t have to be that way.Danny recounts his time at CareCloud, where he was exposed to the concept of a data warehouse. The idea was gaining traction, and he attended a Snowflake event to grasp the essentials. After an hour of slides and schemas, he walked out just as confused as when he walked in. The issue wasn’t the information; it was the delivery. Marketers need to see things in action. Theoretical talks don’t cut it—practical, straightforward tutorials that walk you through the steps are what marketers crave. Installing tools like dbt and seeing data move can make it all click. It’s the difference between hearing about a new tool and actually feeling it work in your hands.There’s also a major gap in educational resources that cater to marketers. As Danny highlights, marketing professionals who want to embrace data often get lost in the flood of courses and jargon-heavy materials. It’s a jungle out there—marketers want concise, actionable guidance, not a deep dive into tech theory. Without the right content, many opt to stay in their lane, using tools and methods they already know. It feels safer, especially when they’re under pressure to perform quickly.Danny points out that this pressure to ramp up fast can discourage experimentation with a warehouse-first approach. New roles often come with tight timelines, and there’s a tendency to lean on old habits. Shifting to something like data warehousing means slowing down, learning the ropes, and building enough belief in the new approach to back it up internally. But if you’ve spent years doing things differently, it’s hard to develop the conviction needed to push for change. Confidence comes from exposure and understanding, but without that, the warehouse-first idea feels too foreign to champion.Key takeaway: Marketers often shy away from data teams because they lack practical, accessible education and feel pressured to stick with familiar methods. Building confidence through hands-on learning and real-world examples is crucial for integrating data and marketing in a meaningful way.Overcoming Barriers to Data Literacy in MarketingMany marketers hesitate to engage deeply with data, often because they don’t see it as central to their roles. Danny explains that for most, data feels like a secondary tool—something meant to assist rather than dominate their day-to-day work. The challenge is that the pathway to becoming data-savvy isn’t straightforward. Even among those who’ve made the leap, each person’s journey looks different. Some take online courses, like those on Codecademy, learning SQL from scratch. Others find mentors who guide them through the maze of data management, or they happen to work in environments where they can lean on a data specialist nearby. But there’s no universal roadmap, which makes the process feel daunting.Danny believes that the lack of a clear, predictable path to mastering data is one of the biggest hurdles marketers face. With so many options available—some technical, others more hands-on—marketers often struggle to identify which approach will actually get them the skills they need. For those with limited time, this uncertainty can be a dealbreaker. Without knowing if the investment will pay off, it’s easier to focus on other areas of marketing that feel more familiar and essential. Danny points out that while resources like Udemy are improving the situation, marketers still need a straightforward, reliable way to become proficient in data.Another critical factor is the perceived opportunity cost. Marketers are often juggling multiple responsibilities, from staying up-to-date with industry trends to managing campaigns. For many, the idea of dedicating time to learning data—an area they may feel they have minimal expertise in—feels like too large a barrier. Why spend time learning about data warehousing when there are immediate, pressing marketing concepts to master? This fear of committing time and energy to an unfamiliar, complex area keeps many from taking the first step.Danny emphasizes that while the accessibility of learning tools is improving, there’s still a significant gap. Even for those who want to upskill, the fear of the unknown and the lack of a guided pathway can make it feel like an insurmountable challenge. Until marketers can see a clear, accessible way to develop these skills, many will remain hesitant to dive into data, choosing to stick to familiar ground instead.Key takeaway: Marketers often shy away from learning data skills due to a lack of accessible, consistent learning paths and the fear of time investment without guaranteed outcomes. Creating structured, easy-to-follow resources is crucial to making data literacy a viable option for busy professionals.Unlocking the Full Potential of Data with dbtDanny describes the transformation dbt brings to the data landscape, making it accessible not just to engineers but also to marketing ops and other non-engineering teams. In the past, accessing and manipulating data was a highly specialized skill, often requiring a marketer to rely heavily on a single engineer. As Danny puts it, you needed to build a relationship with this “one person in a closet somewhere” to get any insight or change implemented. This old approach made data access exclusive, slow, and frustrating for teams trying to move fast.With dbt, Danny explains, the dynamics shift dramatically. It creates different roles and permission levels for everyone interacting with data, enabling a self-service model for marketers and operat...

What’s up everyone, today we have the pleasure of sitting down with Lourenço Mello, Product Marketing Lead at Snowflake. Summary: Lourenço drops us straight into the gravity well of martech, where Snowflake’s latest report pulls in the tools that really matter, letting the fluff float away. It’s all about data gravity, bringing the applications to the data instead of wasting energy shuttling data around. This shift is redefining what’s possible, streamlining operations, and giving marketers a new superpower to harness the forces of AI and analytics. With composability blurring boundaries and AI breaking down silos, the takeaway is crystal clear: master data quality and you’ll have the gravitational pull to outpace the competition.About LourençoLourenço started his career at an enterprise telecom company based in Portugal where he dabbled in competitive analysis, pricing and biz devHe later completed his MBA at UCLA and then spent 5 years at Microsoft as a Senior PMM focused on Azure and their data businessToday, Lourenço is Product Marketing Lead for the Solutions team at SnowflakeUnderstanding the Marketing Data Stack Report MethodologyLourenço’s perspective on Snowflake’s Marketing Data Stack Report centers around a fundamental commitment to objective analysis. Rather than focusing on internal partnerships or pushing favored solutions, Snowflake’s report leverages comprehensive telemetry data to identify which tools are truly gaining traction among its 8,000+ customers. This approach enables them to deliver a more impartial view of the martech landscape.The methodology starts by categorizing the landscape according to current trends and customer adoption. Snowflake first identifies the relevant categories that its customers are using for marketing use cases, based on a snapshot of the industry. Lourenço emphasized that the analysis isn’t limited to tools with direct business relationships or joint ventures but looks holistically at the adoption metrics across the board. This objectivity sets the report apart, as it can spotlight tools that Snowflake hasn’t actively partnered with—yet are clearly valuable to their customers.Two primary metrics guide the analysis: breadth of adoption and depth of adoption. Breadth measures how many customers are using a particular tool or solution, offering an initial view of popularity. However, without understanding how deeply those tools are being utilized, breadth alone can be misleading. Lourenço highlighted that a platform may have thousands of users but very minimal actual engagement. Thus, the second metric—depth of adoption—assesses how sophisticated the usage is within each customer’s implementation, revealing the true stickiness and impact of the tool.By indexing both breadth and depth of adoption, Snowflake is able to create a ranked list of tools and platforms within each category. This process ensures that the final report is rooted in genuine customer behavior and preference, rather than internal biases. As Lourenço puts it, “the cool thing about this and really what's been so fun to be a part of is really the objectivity of the analysis.” The report not only highlights tools that are already well-integrated but also uncovers opportunities to build relationships with platforms that customers have independently gravitated towards.This level of transparency ultimately fosters stronger collaboration between Snowflake and its partners. By showing where their customers are seeing success, the report opens the door for potential go-to-market initiatives that were previously unexplored. In a martech landscape often clouded by promotional bias, this approach offers a rare glimpse into which technologies are truly making a difference.Key takeaway: The core strength of Snowflake’s Marketing Data Stack Report lies in its objectivity. By focusing on customer adoption metrics and removing subjective biases, the report provides a clearer view of the tools that are genuinely resonating with the market. This methodology enables Snowflake to support its customers with data-driven insights, and it paves the way for more meaningful partnerships with emerging leaders in the field.Key Shifts Defining Martech and AdTech TodayWhen asked about the notable shifts between 2023 and 2024, Lourenço from Snowflake made it clear—what were once considered trends are now fundamental changes that have reshaped marketing. Last year’s report pointed to themes like the convergence of AdTech and martech, data privacy, generative AI, and the pursuit of a single source of truth. This year, these aren’t just trends—they’re seismic shifts that have permanently altered how the industry operates.Instead of being temporary developments, Lourenço emphasized that these themes are “not going away,” likening them to the foundation of the industry itself. The report identifies three key forces driving transformation: data privacy, data gravity, and generative AI. These forces influence everything from how companies measure performance to how they monetize and manage first-party data. One of the more interesting dynamics highlighted this year is the emergence of commerce media, where industries traditionally characterized by thin margins—like retail and travel—are leveraging their vast pools of first-party data to unlock new revenue streams and drive higher profitability.Data gravity, in particular, is a crucial concept. It describes how data is increasingly becoming the central point for both martech and AdTech activities. As Lourenço points out, brands are now using the same data source for real-time bidding on the AdTech side and for personalized experiences on the martech side. This convergence is made possible by advancements in data infrastructure, such as Snowflake’s native app framework. By allowing applications to run where the data resides, brands eliminate the need to move data back and forth, reducing latency and improving privacy. An example Lourenço shared involved identity resolution, where an eight-day process to reconcile identity data is now achievable in mere hours, sometimes even minutes, thanks to this infrastructure shift.Another powerful change mentioned was how companies are transforming their roles—from being purely ad buyers to becoming ad sellers. This shift, Lourenço explains, is a direct consequence of organizations capitalizing on the value of their first-party data, looking to move up the value chain by creating new revenue channels through data monetization. Meanwhile, customers are increasingly interested in Marketing Mix Modeling (MMM) and other approaches to understand and optimize their media investment in light of these shifts.The takeaway is clear: these are not passing trends but fundamental changes in how the industry functions. Snowflake’s position at the intersection of martech and AdTech provides a unique vantage point to observe these developments, and Lourenço’s insights offer a glimpse into the future of data-driven marketing.Key takeaway: The convergence of data privacy, data gravity, and generative AI are not just fleeting trends—they’re transformative forces that are redefining the marketing landscape. Brands that align their strategy with these shifts can unlock new revenue streams, capitalize on efficiency gains, and strengthen data security, ensuring they stay ahead of the curve.The Concept of Data Gravity in Modern Data ArchitectureWhen Lourenço introduced the idea of data gravity, it wasn’t just about centralizing data; it was about rethinking how applications interact with it. The term itself evokes a sense of drawing everything—data, applications, and processes—toward a unified center. But in a broader sense, Lourenço emphasized that it’...

What’s up everyone, today we have the pleasure of sitting down with Rutger Katz, GTM Operations Consultant. Summary: Rutger helps us cut through the fluff of Lean methodology in marketing and how to spot when process gets in the way of efficiency. His advice is to cut out the waste—whether in your process, your tech stack, or how you measure success. Focus on what drives conversions, keep your systems lean, and use simple structures to maintain speed without sacrificing alignment. We also tackle tech debt and how a top-layer AI interface could simplify the case for a composable martech stack.About RutgerRutger started his career in Neuroscience as a virtual reality developer at two different public research universities to study bodily illusions in VRAs the VR industry was quite immature at the time he pivoted to martech consulting, where he would spend 12 years working with different technology consulting firms getting a breadth of experience across marketing operations, martech, customer data and go-to-market across a variety of clients including Unilever where he focused on social analyticsAnd last year Rutger decided to go out on his own as a GTM Operations Consultant and recently launched NEON Triforce, a boutique consultancy focused on optimizing GTM for B2B scale-upsHe also recently joined The Martech Weekly as Content Lead for EU & UK organizing their first event in London.Lean Marketing in PracticeLean marketing is all about eliminating waste and doubling down on what truly matters. Rutger emphasizes that no matter the size of the company, from a startup to an enterprise, inefficiencies always creep in. These processes—whether learned from someone else or ingrained as “the way things are done”—often aren’t optimal. Lean seeks to strip down these ingrained habits, perfecting the path to deliver value to customers.Rutger highlights that lean marketing goes beyond just being "efficient." It is about understanding how every action connects back to the entire organization. The real challenge is aligning marketing efforts with revenue-driving KPIs, rather than fixating on vanity metrics like page views or social media follows. For Rutger, Lean is about cutting through those superficial measures to ensure that marketing impacts the business holistically.What makes lean particularly valuable is that it doesn't stop at marketing. Rutger explains that Lean should apply to your entire go-to-market strategy. This means assessing not just how marketing operates but how it interlocks with sales, customer success, and even product development. It's about delivering maximum value to the customer while ensuring that the organization operates as efficiently as possible in providing that value.Lean marketing is not a standalone function—it’s a way to optimize the whole organization. When done right, it leads to higher customer satisfaction, longer-term retention, and ultimately, a more streamlined business. For Rutger, this is where the real impact of Lean lies—not just in marketing efficiencies but in enhancing the customer experience across every touchpoint.Key takeaway: Lean marketing is about focusing on what truly drives value. It's not just about marketing—it's about creating efficiency across your entire go-to-market approach, from sales to customer success, all while tying back to key business metrics.Solving Inefficiencies in Sales and Marketing AlignmentWhen asked about real-world applications of lean methodologies, Rutger didn’t hesitate to dig into a common yet overlooked issue: the disconnect between sales and marketing. In his experience, CMOs often claim that everything is running smoothly. But when the conversation shifts towards collaboration with sales, the cracks begin to show. One CMO even mentioned that their sales team requested fewer leads, as they were overwhelmed by the volume. Others spoke of back-and-forth frustrations trying to sync efforts between both departments.For Rutger, the root of inefficiency often comes at the handoff between marketing and sales. He explained that marketing teams frequently misinterpret sales-qualified leads (SQLs), sending what they define as SQLs but which sales deems unqualified. This misalignment creates friction, wasting time and resources on both sides. To fix this, Rutger advocates stepping back from just marketing processes and focusing on sales first. Understanding sales capacity and needs becomes essential to deliver the right leads at the right time.A critical step in this process is optimizing for sales’ actual conversion capacity. Rutger highlights that if sales needs to convert 100 leads per month, with a 5% conversion rate, marketing needs to deliver 20 times that amount—2,000 SQLs. He stressed the importance of timely response, pointing out that conversion rates jump by 40% when sales follows up with a lead within 10 minutes. Aligning on this kind of data helps both teams work more effectively toward shared goals.Rutger also urged teams to reevaluate the quality and cost-effectiveness of their campaigns. While campaigns may generate leads, some are far too costly or inefficient, with payback times stretching out to three or four years. Google paid accounts, for example, are notoriously expensive, yet still widely used, particularly in larger organizations. For Rutger, focusing on the most effective campaigns, while pruning inefficient ones, is key to driving sustainable growth.Key takeaway: Marketing and sales alignment is critical for driving efficiency. Understanding sales capacity, optimizing lead delivery, and focusing on high-converting campaigns can reduce friction, improve collaboration, and significantly increase conversion rates.Tackling Tech Debt and Building a Lean Martech StackWhen asked about navigating the complexities of consolidating a tech stack, Rutger didn’t mince words: aligning stakeholders across IT, marketing, and sales is often more political than it is technical. Large enterprises, in particular, face daunting hurdles when trying to scale back on overlapping tools. Rutger noted that the desire to build a “Frankenstack”—a collection of fragmented technologies—comes from every department wanting its own ideal solution. As a result, the journey to a leaner tech stack can seem like a never-ending project.Rutger’s approach starts with identifying the biggest redundancies. While some overlap is by design, like when one product offers a superior feature, the challenge is to minimize overlap where it's unnecessary. In some cases, up to 60% of a company’s tools perform redundant functions. His advice: focus first on those areas where feature overlap is significant, perhaps 90% or more, and tackle these redundancies gradually. Start small, prioritize high-cost inefficiencies, and avoid a complete tech overhaul in one go.Another common issue Rutger raised is "shadow IT"—the tools that departments purchase without full organizational knowledge or alignment. Marketing might opt for a quick-fix solution, or sales might buy something that works for them but doesn't integrate with other systems. These rogue tools further complicate efforts to streamline technology, making the case for better communication across departments.One of Rutger's key strategies is calculating the cost of maintaining outdated systems against the cost of migration. In legacy-heavy sectors like insurance and banking, this is critical. His pragmatic approach weighs the resources, time, and potential revenue impact of migrations. With the rise of AI, Rutger suggests that migration tools could become faster and cheaper, potentially offsetting the costs of restructuring a tech stack. His advice? Keep your options open and l...

What’s up everyone, today we have the pleasure of sitting down with Jared DeLuca, Director of Operations at Appcues.Summary: Jared takes us inside the mad but amazing world of martech at Appcues – the top product adoption SaaS on the planet. We cover his transition from demand gen to ops, how he’s integrated demo bookings within the product using RevenueHero, the difference between ops and revops. We also cover a ton of ground on AI topics for marketers like machine learning lifecycle management, how to QA AI-driven messages and how to leverage AI to uncover incremental lifts in your campaigns. About JaredJared started his career with a few internships in PR before joining a Market Research firmThat firm was later acquired by a UK based marketing data and analytics company where he worked his way up to Marketing ManagerHe then had a brief detour from SaaS at Keurig Dr Pepper in IoT Marketing Automation and Connected Panel Ops Finally Jared landed at Appcues, first in Demand Gen then Senior Martech and Ops Manager Today Jared is Director of Operations at AppcuesMoving from Demand Gen to Front-End DevelopmentJared’s shift from demand generation to front-end development was a mix of opportunity and curiosity. When his team’s operations lead left, he stepped in naturally. As the demand gen guy who relied heavily on those systems, Jared was the most logical choice. It wasn’t a calculated career move—it was about filling a gap. That’s how things go in startups, where you often find yourself doing a bit of everything.His transition into front-end development had a different spark. Budgets were tight, and they didn’t have the luxury of hiring contractors. With years of HTML and CSS experience under his belt from working on emails and landing pages, Jared figured he could handle some of the coding work. AppCue supported the idea, allowing him to stretch into JavaScript. For small teams, having someone in-house with a broad skill set is invaluable, and Jared was more than willing to step up.What made this shift special was Jared’s personal interest in coding. He enjoyed it. Coding wasn’t just a job; it was something fun to experiment with. One evening, while watching TV, he built a lead-gen magnet prototype in just an hour. It was born from a simple idea pitched by the content team, but Jared’s ability to quickly turn that into a working model showed the kind of spontaneous creativity that startups thrive on. The prototype may soon go live on their website.Jared’s experience highlights the unpredictable nature of roles in smaller companies. You often find yourself taking on responsibilities you never planned for, and those unexpected opportunities can lead to new skills and career growth. For him, it wasn’t about following a clear path—it was about being adaptable and ready to learn.Key takeaway: In a startup, being adaptable and willing to learn new skills can lead to unexpected career opportunities. It's less about having a perfect plan and more about being open to filling gaps when they appear.How AI Tools Are Shaping HTML and CSS LearningWhen asked if tools like ChatGPT make learning HTML and CSS easier today, Jared didn’t hesitate to agree. He pointed out how much simpler it is for anyone looking to pick up coding now compared to when he started. Back then, you had to figure things out manually, while now, AI tools can assist with the heavy lifting. However, there’s a caveat—knowing what to ask for is still crucial.Jared challenged the idea that AI is replacing developers. Instead, he emphasized that understanding the underlying structure of HTML and CSS is still key. Tools like ChatGPT can help speed up the process, but without knowledge of where to apply that code, the benefits are limited. AI can’t tell you how to structure a website; it can only help fill in the blanks once you know what you need.He highlighted that while AI can handle repetitive keystrokes, the real value comes when you already know what you're aiming for. It’s not about AI replacing junior developers—it’s about leveraging these tools to work more efficiently. If someone understands the basics of coding and web structure, AI can cut down the time it takes to implement those tasks significantly.For Jared, the most significant takeaway is how much time he saves. What used to take him hours can now be done in minutes with AI. The difference is in the efficiency, not the replacement of skill. If you know what you're doing, ChatGPT and similar tools become an incredible resource for improving speed and output, but they don’t replace the need for foundational knowledge.Key takeaway: AI tools can dramatically speed up coding tasks, but the real advantage comes when you already understand the basics of HTML and CSS. It’s not about replacing developers, but about working smarter with the right knowledge and tools.Why Developers Avoid Marketing in Software StartupsWhen asked why developers often seem disinterested in marketing, Jared’s perspective was insightful. In his experience, particularly in software startups, it’s not that developers are “allergic” to marketing; they simply don’t think about it. Their focus is on building and coding—creating the product itself. Marketing, and the role it plays in attracting users, often doesn’t even cross their mind.Jared pointed out that many developers operate with a clear mindset: give them the requirements, and they’ll build exactly what you need. They’re more concerned with functionality than how the product will reach customers. This differs from product teams, who tend to think more about market fit and bridging the gap between building something and getting it to the user.However, Jared has worked with engineers who do think more broadly. In some cases, especially in smaller teams, developers will ask key questions about the user experience and how people will engage with the product. But this tends to fade as companies scale. Jared mentioned his time at Keurig, where engineers were more specialized—focused on delivering exactly what was requested, with little thought to the next steps.In Jared’s view, it’s less about a lack of interest in marketing and more about developers not having the bandwidth or inclination to focus beyond the task at hand. Their job is to build, and for many, thinking about the next phase—how the product reaches customers—isn’t a priority.Key takeaway: Developers in startups aren’t necessarily disinterested in marketing; they’re simply focused on building. For those seeking to bridge the gap between engineering and marketing, fostering collaboration and highlighting the user journey can encourage developers to think beyond their immediate tasks.How Responsive Support Transforms Marketing OpsJared emphasized how crucial responsive support is in marketing ops. When discussing his shift to Revenue Hero, he highlighted the frustration many teams face when relying on traditional support teams. He described how long it can take to get a response—sometimes 24 to 48 hours—and how those responses are often unhelpful, requiring even more back-and-forth communication.What made Revenue Hero stand out to Jared was its approach to customer support. The team integrated seamlessly into his company’s Slack workspace, offering real-time access to their expertise. This level of support was a game changer. For Jared, it wasn’t just about the product performing well (which it did), but about the reassurance of knowing that if something went wrong, help was just a Slack message away.One example Jared shared was when a demo request system broke—a critical part of ...

What’s up everyone, today I have the pleasure of sitting down with Ron Jacobson, Co-founder and CEO of RockerboxSummary: Multi-touch attribution doesn’t tell you what really caused a conversion or revenue, it’s a credit distribution system. It’s still a useful guidepost in understanding where your efforts are making an impact. Incrementality testing, on the other hand, digs deeper—helping you pinpoint what’s really driving results by answering, "What would’ve happened without this campaign?" But to get there, it’s not about finding the perfect model, it’s about asking the right questions. Don’t get stuck in the basics like Google Analytics. True measurement demands first-party data and statistical modeling, especially as third-party cookies fade. For startups, the goal is momentum—nail one channel before diving into complex measurement. Build success first, then refine with tools like MTA or MMM to truly understand what drives growth.About RonRon started his career as a software engineer before transitioning to product management at AppNexus where he ran the platform analytics team and later the real time platform product teamHe then took the entrepreneurial plunge Co-founding Rockerbox, first as a programmatic advertising platform then a multi touch attribution platform And today they’ve added a suite of marketing measurement tools that also leverage marketing mix modeling. Rethinking the Role of Multi-Touch AttributionMulti-touch attribution (MTA) often sparks debate around its effectiveness in driving marketing decisions. While many recognize it as a flawed tool, few fully grasp the extent to which it misses a crucial element: causality. When asked whether MTA should be seen as a credit distribution mechanism rather than a way to measure causality, Ron agrees wholeheartedly, explaining that this is exactly how his team has framed the discussion for years.Ron emphasizes that MTA’s purpose isn’t to assign cause-and-effect between marketing touchpoints and revenue generation. Instead, it's a retrospective tool designed to distribute credit across various touchpoints in a customer’s journey. He argues that marketing teams need to shift their focus from chasing causality to understanding how customers interact with marketing efforts. This approach helps marketers assess what channels or strategies might be working, even if the exact causal impact remains elusive.A specific example Ron highlights is when clients test new channels like OTT, CTV, or linear TV. Frequently, these clients aren’t sure if the new channel is even making an impact. The issue, he notes, isn’t necessarily that the marketing is ineffective—it’s that the data simply doesn’t reflect customer engagement due to gaps in tools like Google Analytics. While causality is still out of reach, MTA can at least show that the new channel is on the customer’s path to purchase, providing some reassurance that the efforts are not entirely in vain.Ron points out that this shift in perspective helps marketing teams function more effectively. Rather than getting bogged down by the impossibility of determining exact causality, teams can use MTA to answer more immediate, practical questions: What are the touchpoints that seem to drive the most engagement? Where should we focus next? It’s not about perfectly predicting outcomes, but about gathering insights that improve day-to-day operations.Key takeaway: MTA isn’t designed to establish causality, but rather to help distribute credit among touchpoints. When marketers focus on how customers engage with their efforts rather than trying to measure cause-and-effect, MTA becomes a valuable tool in refining strategy.Understanding the Value of Path to ConversionWhen diving into the value of the path to conversion, we often struggle with the fact that it doesn’t fully address causality. Just because a customer clicks on a Google link and converts doesn’t necessarily mean that click caused the purchase. It’s possible the customer had already been influenced by a social ad or an email from days prior. Understanding the motivations behind these actions remains elusive.Ron’s take on this is refreshingly straightforward. He suggests ignoring the model entirely when pitching multi-touch attribution (MTA). Instead, focus on the question: What can you learn from understanding the customer’s path to conversion? By treating MTA as an alternative lens to last-click or first-touch attribution, Ron emphasizes that it provides more context but doesn’t necessarily give a definitive answer to causality. He argues that last-touch attribution, for example, isn’t the best method for understanding the full customer journey.The real value of analyzing the path to conversion, according to Ron, comes from the variety of questions you can answer. Questions like time to conversion, comparing paths for new versus retained customers, or how adding a new channel influences customer behavior. Retention, in particular, has gained importance as rising interest rates push companies to focus on profitability, and understanding how existing customers engage without paid media is crucial.Ron points out that the path to conversion isn’t just a credit distribution mechanism but a core dataset that allows marketers to do their jobs more effectively. By looking beyond conversions alone and examining full paths, even those that don’t lead to a sale, marketers can better assess conversion rates and session data. Still, he concedes that none of this answers the critical question of whether marketing spend was truly incremental or whether a customer would have converted without it.Key takeaway: While path to conversion analysis doesn’t solve for causality, it opens the door to deeper insights. Marketers can use it to answer key questions about customer behavior, retention, and channel effectiveness, but should remain aware of its limitations in proving incremental impact.Defining Incrementality in MarketingWhen we discuss incrementality, the core question is simple: Would the business results still have happened without marketing? It’s a shift in mindset from how we traditionally report on marketing outcomes. Instead of simply attributing revenue to specific touchpoints, incrementality forces us to ask whether that revenue would exist at all if we hadn’t spent that marketing dollar.Ron emphasizes the importance of having a baseline when assessing incrementality. Without this, everything looks like it’s driven by marketing, which isn’t always true. For him, the key is understanding the marginal return on that last dollar spent. In other words, is each dollar spent still driving profitable results? This approach helps marketers gauge if they’re spending wisely and achieving their business goals.The real challenge comes in determining the best methodologies to uncover incrementality. Ron explains that while modeling tools like multi-touch attribution (MTA) aren’t designed to measure incrementality, they provide valuable insights when combined with testing methodologies. He highlights that running a holdout test, for example, can reveal incremental results, and applying that test’s findings to MTA reporting allows marketers to optimize daily decisions while still understanding broader trends.Ultimately, Ron advises marketers to focus less on the methodologies themselves and more on the questions they need answers to. Whether you’re trying to allocate next quarter’s budget or determine the effectiveness of a new creative, the right approach depends on what you’re trying to uncover. By starting with the right questions, marketers can select the best tools or methods to answer them, rather than getting caught up in finding a one-size-fits-all solution.Key takeaway: In...

What’s up everyone, today we have the pleasure of sitting down with Erin Foxworthy, Industry Lead, Advertisers & Agencies at Snowflake. Summary: In this episode, Erin takes us on a ride through the merging worlds of martech, adtech, AI, and privacy, giving a bold glimpse into what’s next for customer data. We cover how you can use 1st party data for seed predictions, why it’s time you move on from APIs and adopt data sharing and what the unified data layer means for marketers. Oh and Erin gives us her take on the uncertainty of Google's cookie deprecation rollback.About ErinErin is former Category Development Lead at Microsoft Advertising collaborating with product, marketing and sales teamsShe later became Executive VP of Partnerships and Innovation at Horizon Media – the popular NYC-based ad agency –  focused on first-to-market creative and data opportunities for her clientsShe’s also a well traveled speaker and was awarded the Technology Leader at Cynopsis Top Women in Media in 2020Today Erin serves as the Industry Principal for media, entertainment and advertising at Snowflake, focusing on advertisers and agencies The duality of creativity and measurement in advertisingIn the early days of advertising, media was often an afterthought. Erin recalls how the majority of a CMO's focus was on perfecting commercial spots, direct mail, or magazine ads, with meticulous attention to detail. The creative side was the talk of the industry, leaving media playing a supporting role. However, as digital platforms emerged and ad units fragmented, the dynamic shifted. Creative and media teams, which were once tightly knit, began to drift apart, especially as agencies expanded to handle the complexity of new media channels.Erin notes that media became so specialized across different digital platforms that it gradually separated from the creative process. In her own career, which began at a full-service agency, she experienced this firsthand. Early on, she worked side by side with creative directors and copywriters, but as agencies scaled and media buying spread across hundreds of channels, those joint discussions became fewer. The focus shifted to simply managing the volume, leaving less time for deeper creative collaboration.What's promising, though, is the potential for artificial intelligence (AI) to bridge that gap again. Erin suggests that advancements in AI are already pushing the industry toward more integrated workflows. Platforms are increasingly using AI-driven algorithms to optimize ad performance—automating decisions and delivering results in a more turnkey fashion. This, she believes, will allow media teams to shift some of their focus back toward creative strategy.In her view, AI could also democratize creativity, empowering marketers who may not traditionally be involved in creative production to step into that space. With AI handling the data-driven optimization, there’s an opportunity for marketers and agencies to bring creative and media closer together once again, regaining the collaboration that once defined the advertising world.Key takeaway: AI advancements are reshaping the relationship between creative and media in advertising, offering a chance to reconnect these disciplines. This evolution could allow marketers to step into creative roles while freeing up time to focus on what works, both organically and through paid channels.The future of automation in creative marketingWe often wonder how far we can trust machines to handle core marketing tasks, especially in areas like email where AI-driven recommendations are common but often met with skepticism. When asked about automation in creative marketing, Erin shared a candid perspective on where the industry stands.Erin points out that automation’s impact is already visible in marketing operations, particularly in tasks like resizing creative and ad serving. These areas are primed for disruption, and automation is becoming essential in managing the growing complexity of campaign delivery. However, when it comes to more creative and brand-focused ad units, she remains unconvinced that AI is ready to replace the human touch anytime soon.For Erin, the heart of the issue lies in the nuances of brand messaging. Creative ad units are designed to build emotional connections with consumers, and this often requires a level of empathy and intuition that AI can't replicate—at least not yet. While AI can handle logistics and optimization in areas like programmatic advertising, the human element remains critical for conveying the personality and tone of a brand.She sees AI's role expanding in marketing operations, but for now, brand messaging is where human creativity holds its ground. As AI continues to evolve, marketers will need to find the right balance, leveraging automation for efficiency while maintaining the human insight necessary to craft compelling, emotionally resonant ads.Key takeaway: Automation will continue to disrupt marketing operations, particularly in optimizing workflows and ad delivery. However, for creative brand messaging, human creativity remains irreplaceable. Marketers should embrace AI for its efficiency while ensuring it complements, rather than replaces, the human touch in their messaging strategy.Understanding the convergence of Martech and AdTechWhen asked about the distinction between Martech and AdTech, Erin provides an insightful perspective. Traditionally, people often simplify the divide: Martech is for marketers and AdTech is for advertisers. However, she views this as an oversimplification that doesn’t capture the true nature of the industry’s evolution. Both are ultimately driven by technology—platforms created by companies that serve both marketers and advertisers as users. The complexity lies not in who controls the platform, but in finding the right technology to meet the needs of a specific enterprise.Erin emphasizes that this convergence is especially noticeable as personalization becomes central to marketing and advertising strategies. Where Martech was once seen as powering owned channels like email and SMS, and AdTech controlled paid channels like social ads and programmatic buys, today, the line between the two is blurring. Personalization is no longer limited to owned channels; it’s becoming essential in paid media, social platforms, and even connected TV (CTV) campaigns. This level of integration hinges on having the right data infrastructure, enabling one-to-one conversations across all customer touchpoints.What makes this especially challenging is the industry's historical lack of unified strategy across these channels. Erin notes that traditionally, marketers have operated in silos—sending emails, running social ads, and buying media independently. Now, with the growing expectation for a seamless, personalized experience, businesses must integrate these efforts to understand how various touchpoints—whether through an SMS campaign, social ad, or CTV buy—are interacting to shape the customer journey.At its core, this shift is about harnessing data across all platforms and using it to create personalized, consistent messaging. For Erin, the convergence of Martech and AdTech means unifying applications on a scalable platform that can support this kind of holistic approach. This trend is exciting and challenging, pushing companies to rethink the ways they manage customer data and interactions.Key takeaway: The traditional divide between Martech and AdTech is becoming outdated. As personalization continues to drive both marketing and advertising, the real challenge lies in unifying customer interactions across all channels on a scalable platform. Businesses must move beyond simple categorizations and focus on ...

What’s up everyone, today we have the pleasure of sitting down with Liam Moroney, Co-Founder of Storybook Marketing. Summary: Liam handed us warm tea and one of his hand-knitted beanies as we explored how marketing goes beyond just hitting pipeline numbers. It’s about building trust, shaping perceptions, and ensuring your brand is top-of-mind when it matters. Balancing short-term wins with long-term brand-building is crucial, yet often misunderstood. Clear communication and a broader approach to measuring impact are key. For startups, focusing on trust and credibility lays the foundation for success. Marketing’s true power lies in creating a lasting impact that drives real decisions.About LiamLiam started his career in various industries wearing several different marketing hatsEventually he landed at NewsCred, a content marketing agency for enterprise teams where he started leading Demand Gen before shifting to client side and advising clients on attribution and ROIHe then had Revenue Marketing leadership stints at various startups across different industries like personalization, travel, mobile and identity verificationHe then started his entrepreneurial journey by founding a consulting firm for growth-stage B2B companiesLiam is also a contributing writer at Martech.org and recently started his own podcast called The B2B BrandToday Liam is the co-founder of Storybook Marketing, a full-service demand gen agency for B2B SaaS specializing in paid media programsMarketing’s Role Beyond the PipelineMarketing, historically viewed as the "arts and crafts department," has evolved significantly. Yet, according to Liam, there’s a lingering misperception, particularly in B2B, that needs addressing. When asked about his concerns with marketing being reduced to a mere pipeline number, Liam didn’t shy away from dissecting the issue. It’s not about rejecting accountability—marketing should indeed own a number. The real problem lies in how we've overcorrected, narrowing the focus to such an extent that it undermines the broader role marketing plays.Liam points out that this shift in perception—driven by the need to demonstrate that marketing is a data-driven, outcome-producing function—has caused demand generation to become nearly synonymous with marketing. This reductionist view oversimplifies marketing’s contribution. When marketing is pigeonholed into a single metric, such as its share of the overall pipeline, it suggests that marketing is just another channel, responsible only for a fraction of the sales process. This perspective shortchanges the true purpose of marketing.Liam believes that marketing's ultimate goal is to make the sales process smoother and more efficient. When more people know about a product, believe in its value, and have confidence in its efficacy, selling becomes easier. Marketing should be responsible for influencing the entire pipeline, not just a portion of it. The role of marketing is to make deals faster, bigger, and more frequent. By restricting marketing’s scope to its contribution to the pipeline, we inadvertently diminish its impact.In B2C, marketing drives consumers directly to purchase. In B2B, it drives prospects into the sales process, partnering with salespeople to guide the purchase decision. While the dynamics differ, the overarching responsibility remains the same: marketing should facilitate the entire journey, not just the initial steps.Key takeaway: Marketing should not be reduced to a pipeline number. Its true value lies in its ability to influence and enhance the entire sales process, driving not just awareness but also belief, confidence, and ultimately, conversion.Balancing Short and Long-Term Marketing GoalsWhen asked about the perception that marketing hides behind long-term goals to avoid accountability, Liam was quick to dispel this myth. He argues that marketing isn’t unique in balancing both short and long-term objectives—many functions, like data science and financial advising, operate with a future-oriented perspective. Yet, marketing often faces undue scrutiny because it’s expected to produce immediate, tangible results each quarter.Liam acknowledges that some of this mistrust is self-inflicted. Marketing has, at times, oversold its capabilities and doubled down on being seen solely as a pipeline-generating function. This narrow focus has contributed to the misconception that marketing’s only job is to deliver immediate results. However, Liam emphasizes that marketing's true role is both long-term and short-term. The primary objective is to generate future customers by building awareness, while also activating efforts that yield results today.In B2B and B2C alike, successful marketing requires a dual approach. Brand awareness campaigns, for example, are designed to create a long-term impact by making more people aware of a product. Simultaneously, demand generation activities work to convert that awareness into action. The two functions are interdependent—effective demand gen relies on strong brand awareness, and vice versa.Liam draws an interesting parallel with B2C marketing, where the distinction between long and short-term strategies is often clearer. Brand campaigns might run over months or years to build awareness, while in-store promotions are designed to trigger immediate purchases. The same principles apply in B2B marketing, where demand gen efforts must be supported by a solid foundation of brand awareness. Without this balance, even the best demand gen strategies will falter.Key takeaway: Marketing must balance long-term brand building with short-term activation efforts. Success comes from integrating these approaches, ensuring that immediate demand generation is supported by strong brand awareness.Educating Leadership on the Value of Brand MarketingWhen marketers find themselves trapped by the constant demand for immediate pipeline results, it can be challenging to advocate for the long-term value of brand building. Liam addresses this issue head-on, acknowledging that while it’s easy to champion long-term thinking on platforms like LinkedIn, the reality for in-house marketers is different. Every marketer has targets to meet, and failure to hit those can lead to quick dismissal. However, Liam emphasizes that this doesn’t mean abandoning the long-term strategy—rather, it’s about balancing both while educating leadership on what brand marketing truly entails.Liam points out that part of the problem lies in a lack of education—both for marketers and the C-suite. Marketers need to articulate better what brand marketing is and how it contributes to the overall business objectives. However, the burden of education doesn’t end there. Liam advises against the common notion of only working for CEOs who "get" marketing, as those opportunities are rare. Instead, much of the work involves reeducating leaders on the role and impact of marketing.The key, according to Liam, is alignment with the sales team. If sales perceive that marketing isn’t contributing to their efforts, it can create friction that quickly undermines marketing’s initiatives. By engaging in conversations with sales, marketers can uncover the real challenges that hinder sales efforts. For instance, if sales teams find themselves consistently listed last in RFPs, it might indicate a brand awareness issue. Or, if there’s a widespread misconception about pricing, that points to a perception problem that marketing can address.By identifying these pain points and framing them as marketing challenges, marketers can gain the trust of their sales counterparts. This trust can, in turn, lead to greater permission to allocate resources toward long-term brand-building efforts. It’s not an overnight process, but Liam stresses that when done correctly,...

What’s up everyone, today we have the pleasure of sitting down with Benoit Leggieri, Head of Growth at Livestorm. Summary: Benoit offers a behind-the-scenes look at how Livestorm’s martech stack drives growth and personalization. At its heart is Customer.io, seamlessly integrated with tools like Amplitude, Segment, and Mutiny, creating a powerful system that delivers tailored experiences while scaling effortlessly. By leveraging data-driven workflows to address user needs with precision and automating processes like product certification, Livestorm not only boosts conversions but also deepens customer relationships. Their strategic use of gated content for complex demos further enhances engagement, showcasing a martech strategy that’s as effective as it is thoughtful.About BenoitBenoit started his career at a B2B comms agency before joining HUB Institute, Paris’ top think tank as a Growth marketerHe later joined an event software startup as a product marketing managerHe was also a part time trainer at a digital marketing school in ParisIn 2020 he joined Livestorm – the top video engagement software – as a growth manager to work CRO and after only a year he was promoted to Growth Lead and later Head of Growth where he leads a team of 3 growth managersLivestorm’s Martech Stack Built for Personalization and RetentionLivestorm’s martech stack is a powerhouse, meticulously crafted to enhance every stage of the customer journey. They use Customer.io, Hubspot, SEMRush, and Clay and Benoit unpacks a few other elements. The stack also features Amplitude, the go-to tool for analyzing website conversion rates. Amplitude also helps identify exactly where the user experience can improve, turning insights into action.Mutiny takes center stage for personalization. By tailoring website content to specific industries and personas, it ensures that each visitor feels like the site was made just for them. Whether it’s through social proofs or custom messaging, this tool helps Livestorm engage B2B clients on a deeper level, making every interaction count.Segment serves as the backbone of data management. It captures user data and seamlessly distributes it across various platforms, from email marketing to in-app messaging. This orchestration guarantees that every touchpoint with the customer is personalized and relevant, driving better engagement and retention.Benoit also gives a nod to Refiner, a key player in capturing user feedback. Whether it’s through NPS surveys or specific feedback on new features, Refiner ensures Livestorm stays in tune with its users. Integrated with Segment, it not only gathers data but triggers timely follow-ups, helping to refine the product based on real user input.Key takeaway: A well-integrated martech stack is essential for delivering personalized experiences and driving user retention. Tools like Amplitude, Mutiny, Customerio.io and Segment work together to create a seamless journey, while Refiner ensures user feedback directly informs product improvements.The Orchestrator and the Center of Your Martech StackBenoit doesn’t hesitate when asked about the centerpiece of Livestorm’s martech stack. For him, the core has always been their marketing automation tool, Customer.io. This tool has been instrumental since Livestorm's early days, especially when they were a small, self-serve business. Benoit attributes much of their growth to the strategic use of Customer.io, which not only automated specific messaging but also provided critical insights into the buyer's journey. The integration with Segment further amplified its impact, allowing Livestorm to capture and utilize data points effectively, which in turn, scaled conversions and expanded their customer base.As Livestorm grew, so did the complexity of their martech stack. Benoit mentions that as the company evolved, the data warehouse and Customer Data Platform (CDP) began playing a more significant role. Yet, despite this evolution, he remains adamant that the true orchestrator of their stack continues to be Customer.io. Its role in managing and optimizing the buyer journey is so ingrained in their operations that it remains central, even as new tools and processes are introduced.This perspective reflects a broader trend where companies initially rely heavily on marketing automation tools as their primary orchestrator. But as they scale and gather more data, the role of CDPs and data warehouses becomes increasingly important. However, for Livestorm, the foundational importance of a tool like Customer.io cannot be overstated, especially given its ability to adapt and integrate as the company’s needs grew more complex.The evolution of Livestorm’s stack from a single orchestrator to a more complex, data-driven system underscores the importance of flexibility and scalability in martech tools. While the CDP and data warehouse have become critical, the consistent thread has been Customer.io’s capacity to grow with them, demonstrating that even as the tech stack evolves, the initial building blocks remain crucial.Key takeaway: The backbone of a successful martech stack often starts with a solid marketing automation tool. As a company scales, additional tools like CDPs and data warehouses become vital, but the original orchestrator, like Customer.io in Livestorm’s case, continues to play a crucial role in managing and optimizing the customer journey.Why Customer.io Stands Out for LivestormWhen asked about what makes Customer.io a standout tool for Livestorm, Benoit highlighted three key features that have made it indispensable to their marketing efforts.1 - First on the list is data integration. Benoit praises the seamless connection between Customer.io and their Customer Data Platform (CDP), Segment. This integration enables Livestorm to map user events and attributes effortlessly, which in turn allows for highly personalized messaging. By leveraging this data, the team can trigger specific campaigns at the perfect moment for each user, ensuring that every message is relevant and timely.2 - The second feature Benoit appreciates is the user experience (UX) and user interface (UI) of the platform. Onboarding new team members has been straightforward, thanks to Customer.io’s intuitive design. The visual workflow builder simplifies the process of creating and editing campaigns and emails, making it easy for the entire team to collaborate and execute strategies without unnecessary complications.3 - Finally, Benoit highlights the segment builder as a crucial tool in their arsenal. Managing recipient lists can be a nightmare in many platforms, but Customer.io’s approach, with tags and naming conventions, makes it much easier to organize and clean up lists. This feature not only improves efficiency but also ensures that Livestorm’s campaigns reach the right audience every time.Key takeaway: Customer.io excels in data integration, intuitive UX/UI, and efficient segment management, making it a powerful tool for personalized marketing campaigns. These features have allowed Livestorm to optimize their messaging and ensure smooth operations across their team.Crafting Revenue-Driving Workflows with Customer.ioBenoit shares a fascinating look at how Livestorm has leveraged Customer.io to create sophisticated workflows, particularly those focused on driving revenue. This year, the team concentrated on revenue-centric workflows, addressing challenges like incomplete subscriptions and payment issues—common hurdles in any SaaS business.One notable experiment revolved around abandoned carts. By integrating Customer.io with their data stack, they tracked users who landed on the billing page but didn’t complete their transactions. This e-commerce-in...