Humans of Martech

What’s up everyone, today we have the pleasure of sitting down with Ankur Kothari, Adtech and Martech Consultant who’s worked with big tech names and finance/consulting firms like Salesforce, JPMorgan and McKinsey.The views and opinions expressed by Ankur in this episode are his own and do not necessarily reflect the official position of his employer.Summary: Ankur explains how most AI personalization flops cause teams ignore the basics. He helped a brand recover millions just by making the customer journey actually make sense, not by faking it with names in emails. It’s all about fixing broken flows first, using real behavior, and keeping things human even when it’s automated. Ankur is super sharp, he shares a practical maturity framework for AI personalization so you can assess where you currently fit and how you get to the next stage. AI Personalization That Actually Increases Retention - Practical ExampleMost AI personalization in marketing is either smoke, mirrors, or spam. People plug in a tool, slap a customer’s first name on a subject line, then act surprised when the retention numbers keep tanking. The tech isn't broken. The execution is lazy. That’s the part people don’t want to admit.Ankur worked with a mid-sized e-commerce brand in the home goods space that was bleeding revenue; $2.3 million a year lost to customers who made one purchase and never returned. Their churn rate sat at 68 percent. Think about that. For every 10 new customers, almost 7 never came back. And they weren’t leaving because the product was bad or overpriced. They were leaving because the whole experience felt like a one-size-fits-all broadcast. No signal, no care, no relevance.So he rewired their personalization from the ground up. No gimmicks. No guesswork. Just structured, behavior-based segmentation using first-party data. They looked at:Website interactionsPurchase historyEmail engagementCustomer service logsThen they fed that data into machine learning models to predict what each customer might actually want to do next. From there, they built 27 personalized customer journeys. Not slides in a strategy deck. Actual, functioning sequences that shaped content delivery across the website, emails, and mobile app.> “Effective AI personalization is only partly about the tech but more about creating genuinely helpful customer experiences that deliver value rather than just pushing products.”The results were wild. Customer retention rose 42 percent. Lifetime value jumped from $127 to $203. Repeat purchase rate grew by 38 percent. Revenue climbed by $3.7 million. ROI hit 7 to 1. One customer who previously spent $45 on a single sustainable item went on to spend more than $600 in the following year after getting dropped into a relevant, well-timed, and non-annoying flow.None of this happened because someone clicked "optimize" in a tool. It happened because someone actually gave a damn about what the customer experience felt like on the other side of the screen. The lesson isn’t that AI personalization works. The lesson is that it only works if you use it to solve real customer problems.Key takeaway: AI personalization moves the needle when you stop using it as a buzzword and start using it to deliver context-aware, behavior-driven customer experiences. Focus on first-party data that shows how customers interact. Then build distinct journeys that respond to actual behavior, not imagined personas. That way you can increase retention, grow customer lifetime value, and stop lighting your acquisition budget on fire.Why AI Personalization Fails Without Fixing Basic Automation FirstSigning up for YouTube ads should have been a clean experience. A quick onboarding, maybe a personalized email congratulating you for launching your first campaign, a relevant tip about optimizing CPV. Instead, the email that landed was generic and mismatched—“Here’s how to get started”—despite the fact the account had already launched its first ad. This kind of sloppiness doesn’t just kill momentum. It exposes a bigger problem: teams chasing personalization before fixing basic logic.Ankur saw this exact issue on a much more expensive stage. A retail bank had sunk $2.3 million into an AI-driven loan recommendation engine. Sophisticated architecture, tons of fanfare. Meanwhile, their onboarding emails were showing up late and recommending products users already had. That oversight translated to $3.7 million in missed annual cross-sell revenue. Not because the AI was bad, but because the foundational workflows were broken.The failure came from three predictable sources:Teams operated in silos. Innovation was off in its own corner, disconnected from marketing ops and customer experience.The tech stack was split in two. Legacy systems handled core functions, but were too brittle to change. AI was layered on top, using modern platforms that didn’t integrate cleanly.Leaders focused on innovation metrics, while no one owned the state of basic automation or email logic.To fix it, Ankur froze the AI rollout for 120 days and focused on repair work. The team rebuilt the essential customer journeys, cleaned up logic gaps, and restructured automation to actually respond to user behavior. This work lifted product adoption by 28 percent and generated an additional $4.2 million in revenue. Once the base was strong, they reintroduced the AI engine. Its impact increased by 41 percent, not because the algorithm improved, but because the environment finally supported it.> “The institutions that win with AI are the ones that execute flawlessly across all technology levels, from simple automation to cutting-edge applications.”That lesson applies everywhere, including in companies far smaller than Google or JPMorgan. When you skip foundational work, every AI project becomes a band-aid over a broken funnel. It might look exciting, but it can’t hold.Key takeaway: Stop using AI to compensate for broken customer journeys. Fix your onboarding logic, clean up your automation triggers, and connect your systems across teams. Once the fundamentals are working, you can layer AI on top of a system that supports it. That way you can generate measurable returns, instead of just spinning up another dashboard that looks good in a QBR.Step by Step Approach to AI Personalization With a Maturity Framework - The First Steps You Can Take on The Path To AI PersonalizationMost AI personalization projects start with a 50-slide vision deck, three vendors, and zero working use cases. Then teams wonder why things stall. What actually works is starting small and surgical. One product. One journey. Clear data. Clear upside.Ankur advised a regional bank that had plenty of customer data but zero AI in play. No need for new tooling or a six-month roadmap. They focused on one friction-heavy opportunity with direct payoff: mortgage pre-approvals. Forget trying to personalize every touchpoint. They picked the one that mattered and did it well.They built a clustering algorithm using transaction patterns, savings trends, and credit utilization to detect home-buying intent. From there, they pushed pre-approvals with tailored rates and terms. The bank already had the raw data in its core systems. No scraping, no extra collection, no “data enrichment” vendor needed.That decision paid off fast:The data already existed, so implementation moved quicklyThe scope was limited to a single high-stakes journeyThe impact landed hard: mortgage application rates jumped 31 percent and approval-to-close conversions climbed 24 percent within 60 days> “Start with a high-value product journey where pers...

What’s up everyone, today we have the pleasure of sitting down with Kim Hacker, Head of Business Ops at Arrows. Summary: Tool audits miss the mess. If you’re trying to consolidate without talking to your team, you’re probably breaking workflows that were barely holding together. The best ops folks already know this: they’re in the room early, protecting momentum, not patching broken rollouts. Real adoption spreads through peer trust, not playbooks. And the people thriving right now are the generalists automating small tasks, spotting hidden friction, and connecting dots across sales, CX, and product. If that’s you (or you want it to be) keep reading or hit play.About KimKim started her career in various roles like Design intern and Exhibit designer/consultantShe later became an Account exec at a Marketing AgencyShe then moved over to Sawyer in a Partnerships role and later Customer OnboardingToday Kim is Head of Business Operations at Arrows Most AI Note Takers Just Parrot Back JunkKim didn’t set out to torch 19 AI vendors. She just wanted clarity.Her team at Arrows was shipping new AI features for their digital sales room, which plugs into HubSpot. Before she went all in on messaging, she decided to sanity check the market. What were other sales teams in the HubSpot ecosystem actually *doing* with AI? Over a dozen calls later, the pattern was obvious: everyone was relying on AI note takers to summarize sales calls and push those summaries into the CRM.But no one was talking about the quality. Kim realized if every downstream sales insight starts with the meeting notes, then those notes better be reliable. So she ran her own side-by-side teardown of 22 AI note takers. No configuration. No prompt tuning. Just raw, out-of-the-box usage to simulate what real teams would experience.> “If the notes are garbage, everything you build on top of them is garbage too.”She was looking for three things: accuracy, actionability, and structure. The kind of summaries that help reps do follow-ups, populate deal intelligence, or even just remember the damn call. Out of 22 tools, only *three* passed that bar. The rest ranged from shallow summaries to complete misinterpretations. Some even skipped entire sections of conversations or hallucinated action items that never came up.It’s easy to assume an AI-generated summary is “good enough,” especially if it sounds coherent. But sounding clean is not the same as being useful. Most note takers aren't designed for actual sales workflows. They're just scraping audio for keywords and spitting out templated blurbs. That’s fine for keeping up appearances, but not for decision-making or pipeline accuracy.Key takeaway: Before layering AI on top of your sales stack, audit your core meeting notes. Run a side-by-side test on your current tool, and look for three things: accurate recall, structured formatting, and clear next steps. If your AI notes aren’t helping reps follow up faster or making your CRM smarter, they’re just noise in a different font.Why Most Teams Will Miss the AI Agent Wave EntirelyThe vision is seductive. Sales reps won't write emails. Marketers won’t build workflows. Customer success won’t chase follow-ups. Everyone will just supervise agents that do the work for them. That future sounds polished, automated, and eerily quiet. But most teams are nowhere close. They’re stuck in duplicate records, tool bloat, and a queue of Jira tickets no one’s touching. AI agents might be on the roadmap, but the actual work is still being done by humans fighting chaos with spreadsheets.Kim sees the disconnect every day. AI fatigue isn’t coming from overuse. It’s coming from bad framing. “A lot of people talking about AI are just showing the most complex or viral workflows,” she explains. “That stuff makes regular folks feel behind.” People see demos built for likes, not for legacy systems, and it creates a false sense that they’re supposed to be automating their entire job by next quarter.> “You can’t rely on your ops team to AI-ify the company on their own. Everyone needs a baseline.”Most reps haven’t written a good prompt, let alone tried chaining tools together. You can’t go from zero to agent management without a middle step. That middle step is building a culture of experimentation. Start with small, daily use cases. Help people understand how to prompt, what clean AI output looks like, and how to tell when the tool is lying. Get the entire org to that baseline, then layer on tools like Zapier Agents or Relay App to handle the next tier of automation.Skipping the basics guarantees failure later. Flashy agents look great in demos, but they don’t compensate for unclear processes or teams that don’t trust automation. If the goal is to future-proof your workflows, the work starts with people, not tools.Key takeaway: If your team isn't fluent in basic AI usage, agent-powered workflows are a pipe dream. Build a shared baseline across departments by teaching prompt writing, validating outputs, and experimenting with small use cases. That way you can unlock meaningful automation later instead of chasing trends that no one has the capacity to implement.When AI Systems Meet The Chaos Of Actual Workplace ProcessesAI vendors keep shipping tools like everyone has an intern, a technical co-pilot, and five extra hours a week to configure dream workflows. The real buyers? They’re just trying to fix broken Salesforce fields, write one less follow-up email, or get through the day without copy-pasting notes into Notion. Somewhere between those extremes, the user gets lost in translation.Kim has felt that gap from both sides. She was hesitant to even start with ChatGPT. “I almost gave up on it,” she said. “I felt late and overwhelmed, and I just figured maybe I wasn’t going to be an AI person.” Fast forward to today, and it’s one of her most-used tools. She didn’t get there by wiring up agents. She started small. Simple things. Drafting ideas, summarizing content, clarifying messy thoughts. That built trust. Then momentum.“There’s a lot that has to happen before your calendar is filled with calls and nothing else. AI can help, but you have to let it earn its spot.”If you're trying to build that muscle, forget the multi-tool agent orchestration for a second. Focus on everyday wins like:Turning a messy Slack thread into a clean summaryWriting a follow-up email in your toneRewriting a calendar event title so it makes sense to your future selfCleaning up action items from a sales call without hallucinationsDrafting internal documentation from bullet pointsThe pace is accelerating. People feel it. You don’t need to watch keynote demos to know that change is coming fast. It’s easy to feel like you’re already behind. Kim doesn’t disagree. She just thinks most teams are solving the wrong problem. Vendors are focused on the sprint. Most people haven’t even laced up. “Everyone wants the big leap,” she said. “But most wins come from small, boring tools that actually do what they say they’ll do.”That’s the root issue. A lot of AI features today are solving theoretical problems. They assume workflows are tidy, perfectly tagged, and documented in Notion. Real work is messier. It happens in Slack threads, half-filled records, and follow-ups that never got logged. If your tool can’t handle that, then it doesn’t matter how shiny your roadmap is.Key takeaway: Stop evaluating AI features based on potential. Evaluate them based on current chaos. Ask whether the tool handles your worst-case scenario, not your ideal one. Prioritize small, boring use cases that save time immediately. That way yo...

Keith Jones, Head of GTM Systems at OpenAI, has a rich background in sales operations and tech. He reveals that the best way to buy martech isn't through spreadsheets, but through cognitive extraction, combining stakeholder input with AI. Keith shares insights on his career journey from sales to operations, exploring how empathy shapes decision-making. He discusses the future of SaaS with fewer tools and stronger data infrastructure, and emphasizes the importance of a hands-on approach to integrating AI in marketing strategies for better outcomes.

Summary: Elena has done what most startup marketers only guess at; made it through multiple acquisitions and now leads global integrated marketing at Visa. In this episode, she breaks down what actually changes when you go from scrappy lead gen to enterprise brand building, why most martech tools don’t survive security reviews, and how leadership without authority is the skill that really matters. We get into messy tech migrations, broken attribution dreams, and why picking up the phone still beats Slack. If you’ve ever wondered why your startup playbook stops working at scale, this conversation spells it out.What Startup Marketers Learn the Hard Way When They Land at a Big CorporationElena does not call herself an “acquisition master,” even though her resume might suggest otherwise. Three startups she worked at were acquired, Sivan by Refinitiv, WorkMarket by ADP, and Currencycloud by Visa, where she works today. Some might spin that track record as a strategic playbook for career navigation. Elena sees it differently. She credits great teams and good companies, not some personal Midas touch.The truth is, you cannot force an acquisition. What you can do is get really good at reading the room. Elena’s career started deep in the weeds of lead generation and demand marketing, chasing performance metrics and measuring everything that moved. Early on, she dipped into other areas, event planning, employee engagement, but demand gen was where she built muscle. That was her lane at WorkMarket, where the first big learning curve hit.It turns out the skills that build the lead gen engine are not the same ones you need when a company shifts from hypergrowth to prepping for acquisition. Elena experienced firsthand the moment when leadership stops asking about lead volume and starts asking about brand perception. Suddenly the focus pivots from how many MQLs you can squeeze out of a campaign to how the company is positioned in the market, what the media is saying, and whether the brand looks credible at scale. She admitted she did not fully appreciate that switch at first.> "I came there with a mindset of if I can't track it, I'm not gonna do it," Elena said. "Every performance marketer would probably relate."That perspective doesn’t fly for long in environments where brand and reputation start to outweigh click-through rates. Elena’s time at Visa has only reinforced that lesson. Today, much of her work revolves around brand building and awareness, the same areas she once side-eyed for being soft and unmeasurable. It is one thing to believe in brand. It is another thing entirely to understand how hard it is to build one well.The scale jump from startup life to a company with over 30,000 employees does not just change the headcount. It rewires the entire pace and process of how work gets done. Elena described the gut-check moment that made it clear she was not at a scrappy startup anymore. It was not a high-level strategy meeting or a sweeping corporate memo. It was the moment she tried to get a simple social graphic approved.In a startup, that kind of thing takes a few minutes on Canva and the green light from whoever’s closest to the Slack channel. At Visa, especially as a regulated financial institution, it involves legal reviews, vendor contracts, approval workflows, and enough compliance checks to make your head spin. Campaigns that once rolled out in days now take months. Not because anyone is slow, but because the stakes are high and the rules are different.That culture shock is where many startup marketers either adapt or tap out. What Elena figured out is that the skills that work at one stage of company life are not the ones that get you through the next. If you want to survive the jump from lean team to enterprise machine, you have to stop resenting the process and start respecting what it protects.Key takeaway: If you're coming from startup life, expect a painful adjustment when you move into a large, regulated company. The speed, autonomy, and scrappiness you are used to will collide hard with approval chains and compliance processes. The faster you stop fighting it and start learning why those systems exist, the faster you'll find your footing. Metrics-driven marketing only gets you so far. To thrive at scale, you need to understand the power and patience required to build brand trust.What Nobody Tells You About Merging Tech Stacks After an AcquisitionThe fantasy version of an acquisition is clean and celebratory. Two companies come together, the deal closes, the press release goes out, and life moves on. The reality, especially for marketing teams, is a long, often frustrating grind of systems audits, security reviews, and endless conversations about whether your beloved tools will survive the merger.Elena has lived through that grind more than once. When Visa acquired Currencycloud, she was not navigating that shift alone. Many of her teammates made the journey with her, which helped. But solidarity does not make the process move faster. It just means you have people to vent to while you wait for approvals.One of the first and hardest parts of that transition was not a debate between marketers. It was the clash between marketing teams and security teams. Every single piece of tech Currencycloud used, whether it was their website hosting, HubSpot marketing automation, or even individual add-ons, had to go under the microscope. Security teams needed to assess, vet, and approve each tool, often asking questions that made sense from a cybersecurity perspective but sounded completely out of touch to anyone in marketing.The back-and-forth was not casual. It escalated all the way up to the chief technology officer and the cybersecurity team at HubSpot sitting down with Elena's group to explain, in detail, what the platform could and could not do. None of this was about malice or incompetence. It was about two fundamentally different mindsets trying to find common ground.> "These are security people. They’re not marketers. They don’t always know why we need a particular tool or what it does," Elena explained.That learning curve is brutal if you're not prepared for it. The deeper into operations you sit, the more of these conversations you end up having. Elena found herself in rooms with people from multiple marketing ops teams across Visa, comparing tech stacks, workflows, and priorities. There was no easy answer to which system would win out. Sometimes the decision was clear. Other times it came down to questions like, is it really worth fighting for this tool, or is now the time to adapt to what already exists?She describes it as less like transferring from one job to another and more like moving from a Montessori school to a traditional classroom. Both systems can deliver a good education. They just teach in wildly different ways. One thrives on flexibility and autonomy. The other runs on structure and process. Neither is wrong. They are simply different environments, and surviving the switch requires a willingness to adjust.The biggest mistake marketers make in these situations is believing the process is about what *they* want. Elena was quick to point out that the companies she has worked for, especially Visa, keep customer experience at the center of these decisions. It is not about which tool is most familiar to the internal team. It is about which systems create the least friction for the end user. That mindset helps keep the process grounded, even when the day-to-day feels like a slow march through bureaucracy.Patience is not optional in these transitions. You will hit walls. You will repeat yourself. You will explain the same use case to five different people across three different teams. And eventually, you will e...

What’s up folks, today we’re diving into the AI talent crunch and exploring which marketing roles have the strongest staying power and which are most likely to be replaced by AI.Summary: Shit is changing fast. Don’t wait for someone to guide you. Navigate this transition by focusing on judgment tasks while letting AI handle predictions. At risk are campaign operators, generic content creators, and report-pulling analysts. Set to thrive are resident AI implementation experts who select worthy tools, data orchestrators connecting proprietary data to AI, product/customer marketers with genuine empathy, ethics guardians preventing bias issues, and localization specialists understanding cultural nuances.Marketing Jobs AI Will Kill (And What Skills Actually Matter Now)AI tools have cut strange new patterns into the marketing job market. Pay attention and you'll spot which roles face extinction risk, which command premium salaries, and which hang precariously in the balance. We've watched marketing teams across dozens of companies scramble to realign their talent strategies around this new reality. Some roles vanish while entirely new job titles materialize almost weekly.One of the good things is that AI impacts marketing jobs based on task predictability and context, not seniority or experience. A CMO who mostly approves creative and manages schedules faces more displacement risk than a junior analyst who excels at extracting bizarre but valuable insights from data chaos. You probably feel this tension already. Half your marketing tasks could disappear next quarter, but the other half suddenly requires superpowers you're frantically trying to develop before your next performance review.This episode is meant to give you something to think about in terms of your particular role in marketing. We’ll explore roles we think are at risk of vanishing and roles that are well positioned to become even more valuable. Shit is changing fast, no one is going to take your hand through this transition. You need to own it and take action.Marketing Roles Most at Risk to be Replaced by AIAI's Coming for Your Campaign Ops Job (Unless You Evolve Now)Phil and Darrell explored which campaign operations roles will vanish first and which might actually strengthen in the algorithmic storm ahead. Darrell struck first with brutal honesty about traditional campaign operations: "The role of configuring marketing automation tools to spec will be definitely at risk." He's talking about those roles where marketers simply implement predefined elements - predetermined images, pre-written text, established CTAs, and mapped-out lead routing. AI already handles this configuration work. Darrell has witnessed actual demos from startups building tools where marketers type requirements and - poof - the system builds it automatically. What seemed like science fiction months ago now exists in alpha versions across the industry.Phil slightly pushed back by referencing one of Darrell's recent posts, fracturing campaign ops into distinct categories rather than treating it as one vulnerable block. "Campaign ops encompasses way more than pressing buttons in Marketo," he insisted. He sorted these functions into two buckets:* **Highly vulnerable to AI replacement**:   * Reporting execution  * Campaign analysis and performance tracking  * Paid media bid adjustments  * Email automation and nurture flows  * Landing page and form creation* **Likely to survive the AI wave**:  * Setting strategic objectives and KPIs  * Creative decision-making requiring business understanding  * Budget planning involving cross-functional negotiation  * QA processes demanding human judgment  * Development of truly innovative best practices> "I had it in the unclear bucket because there's a box of some things under there that I feel like are still pretty likely to survive," Phil explained. "Coming up with campaign goals requires so much business understanding, strategic alignment, and political navigation."The conversation crystallized around evolution rather than extinction. Darrell sees campaign ops professionals transforming from button-pushers to strategic partners: "What it's going to evolve into is actually looking at objectives and KPIs, changing requirements, and modifying briefs." He advocated for campaign ops to shift toward continuous "always-on programs" requiring constant optimization rather than churning out repetitive one-off campaigns - a far more AI-resistant position.Key takeaway: To keep your campaign operations job when AI comes knocking, immediately shift your focus from tactical execution to strategic functions. Master business alignment skills, develop creative decision-making capabilities, and build continuous optimization programs. The marketers who survive will be those who stop configuring systems to spec and start reshaping campaign requirements based on deep business understanding and cross-functional collaboration.AI Will Eat Generic Content Creation (But Experts Will Thrive)Phil explored a pretty obvious category of marketing roles: "I think a lot of folks are really excited about Generative AI and using it to create basic posts and pages without editing any of the text." The bloodbath has already begun. Copywriters and content marketers producing unremarkable work find themselves outpaced by algorithms that can churn out mediocre content at scale, for pennies. The particularly exposed are those creating "routine content without a distinctive voice or cultural nuance," especially when working across global markets where nuance matters deeply.Darrell pulled no punches on what's coming: "Bad content is going to become obsolete." AI tools supercharge this dynamic, flooding channels with generated material that looks competent but lacks soul. The truly valuable is content that actually connects with people. Content that makes them feel something. Content that solves real problems in ways that show genuine understanding.What struck me as particularly insightful was Darrell's observation about subject matter experts potentially winning big in this new reality. These experts:* Often possess deep knowledge but lack time or writing skills* Can now leverage AI to amplify their expertise with minimal effort* Only need to provide "the spark of an idea and a few bullet points" * Create output that vastly outperforms generic content from disconnected marketers> "All it takes is like the spark of an idea and a few bullet points. And you have a full post and it's gonna be way better than someone, like a marketer for example, that doesn't really care about the product or about the industry and is writing like crappy content."This represents a fundamental power shift in content creation. The value no longer sits with those who can string sentences together but with those who bring authentic expertise, perspective, and lived experience. AI struggles with these human elements, the exact qualities that make readers stop scrolling and actually pay attention.Key takeaway: Your content survival strategy requires becoming either irreplaceably human or strategically AI-augmented. Build genuine subject matter expertise, develop a distinctive voice that reflects your unique perspective, and learn to use AI as an amplifier rather than a replacement for any kind of original thought. The future belongs to the specialized expert who can provide the strategic direction that AI can't generate on its own.Which Data Analyst Jobs Will Survive the AI Revolution?Marketing data analysts who build dashboards for a li...

Moni Oloyede, founder of MO Martech, is a veteran in marketing operations with a passion for teaching. She discusses the flawed nature of attribution systems, emphasizing that buyers often forget why they purchased. Moni advocates for understanding content performance over tracking random touchpoints and argues that marketing efforts need not always be tied to revenue. Additionally, she critiques job title inflation in GTM engineering and explores the balance between digital strategies and in-person events, all while sharing her love for teaching.

What’s up everyone, today we have the pleasure of sitting down with Constantine Yurevich, CEO and Co-Founder at SegmentStream. Summary: Multi-touch attribution is a beautifully crafted illusion we all pretend to believe in while knowing deep down it's flawed. The work is mysterious, but is it important? The big ad platforms sell us sophisticated solutions they don't even trust for their own internal decisions. Is it time we accept marketing causation is a thing we can’t measure? Visitor behavior scoring is a really interesting alternative or extra ingredient to consider. Often thought of as a tool for lead management to help prioritize your SDR’s time, the team at SegmentStream started using the same scoring methodology, but with an attribution application. Enter synthetic conversions. Instead of just tracking conversions, track meaningful visits like  time spent, pages explored, comparisons made. This allows you to connect upper-funnel campaigns to real behavior patterns rather than just looking at who converted in a single session. About Constantine/SegmentStreamSegmentStream was founded in 2018 in LondonFeb 2022 raised a first funding round of 2.7MSegmentStream is now trusted by more than 100 leading customers across the globe including L’Oreal, KitchenAid, Synthesia, Carshop, InstaHeadShots, and many othersThe Messy Truth About B2B vs B2C Attribution ModelsPrice tags and decision timeframes obliterate the B2B/B2C attribution divide faster than most marketers realize. Constantine shatters conventional wisdom by showing how his team leverages their own attribution tools to measure website engagement because enterprise software purchases rarely follow predictable patterns. "Trusting last click is impossible," he explains, "because it takes too much time before conversion happens."You've likely noticed this pattern in your own marketing stack. A $2,000 direct-to-consumer exercise bike creates the same multi-touch, 60-day consideration journey as many supposedly "straightforward" B2B software purchases. Meanwhile, those $30/month SaaS tools targeting small businesses convert with the immediacy of consumer products. Constantine points out how this pricing reality creates measurement challenges that transcend business categories:High-ticket B2C products demand extended 30-60 day consideration windows SMB-focused B2B subscriptions ($20-30/month) behave like impulse purchasesEnterprise B2B sales cycles stretch beyond a year with critical offline componentsThe offline measurement void plagues marketers everywhere. Constantine admits many of his most valuable marketing activities resist quantification. "I write a lot of LinkedIn posts, newsletters, we do podcasts. Some of these activities are very hard to measure unless you explicitly ask someone, 'How did you hear about us?'" Your gut tightens reading this because you've felt this same tension between attribution models and marketing reality.Scale transforms your attribution approach more dramatically than business classification ever could. Small operations handling 100 monthly leads can simply ask each prospect about their discovery journey. Large enterprises processing thousands of conversions require sophisticated multi-touch models regardless of whether they sell to businesses or consumers. Constantine explains this convergence clearly: "When we talk about larger B2B businesses with thousands of leads and purchases, it becomes more similar to B2C with a long sales cycle plus an offline component."The unmeasurable brand-building activities require a leap of faith that makes data-driven marketers squirm. Constantine embraces this uncertainty with refreshing honesty: "When you post on LinkedIn, build your personal brand, share content—that's really hard to measure and I don't even want to go there." His team focuses on delivering value through content, trusting that results will materialize. "You just share your content and eventually you see how it plays off." This pragmatic acceptance of attribution limitations feels like cool water in the desert of measurement obsession.Key takeaway: Match your attribution model to purchase complexity rather than business category. Implement multi-touch attribution with lead scoring for high-consideration purchases across both B2B and B2C, while accepting that valuable brand-building work often exists beyond the reach of your measurement tools.Why Marketing Attribution Still Matters Despite Its FlawsAttribution chaos continues to haunt marketers drowning in competing methodologies and high-priced solutions. Constantine blasts through the measurement fog with brutal practicality when tackling the Multi-Touch Attribution (MTA) debate. While many have written MTA's obituary due to its diminishing visibility into customer journeys, his take might surprise you.The attribution landscape brims with alternatives that look impressive in PowerPoint presentations but crumble under real business conditions:Geo holdout testing sounds brilliant: Turn off ads in half your markets, keep them running in others, measure the difference. Simple! Except it'll cost you millions in lost revenue during testing. Constantine points out the brutal math: "For some businesses, this is like losing 1 million, $2 million during the test. Would you be willing to run a test that's gonna cost you $1 million?" These tests require a minimum 5% revenue contribution from the channel to even register effects, making them impractical for anything but your biggest channels.MMM promises statistical rigor: But demands absurd amounts of data covering everything from your competitors' moves to presidential elections and global conflicts. Good luck collecting that comprehensive dataset spanning 2-3 years, then validating whether the TV attribution your fancy model spits out actually reflects reality.> "Mathematically, everything works fine, but when you apply it in reality, there is no way to test it. You just see some numbers and there is no way to test it."For scrappy D2C brands, SaaS startups, and lead gen businesses, Constantine argues MTA still delivers more practical value than its supposedly superior alternatives. You won't achieve perfect attribution, but you can compare campaigns at the same funnel stage against each other. Your lower-funnel campaigns can be measured against other lower-funnel efforts. Mid-funnel initiatives can compete with similar tactics.Constantine drops a bombshell observation that should make you question the industry's MMM evangelism: "If Google and Facebook so willingly open-source different MMM technologies and they really believe in this technology, why wouldn't they implement it into their own product?" These data behemoths with unparalleled user visibility still rely on variations of touch-based attribution internally. Something doesn't add up.Key takeaway: Stop chasing perfect attribution unicorns. MTA delivers practical campaign comparisons within funnel stages despite its flaws. For most businesses, sophisticated alternatives cost more than they're worth in lost revenue during testing or impossible data requirements. Compare apples to apples (lower-funnel to lower-funnel campaigns) with MTA, test different creatives, and focus on relative performance improvement. The big platforms themselves don't fully trust their publicly promoted alternatives - why should you bet your marketing budget on them?Simplified MMM is a Measurement Fantasy You're Being SoldMarketing Mix Modeling has roared back into fashion as third-party cookies crumble and marketers scramble for measurement alternatives. Constantine cuts through the hype with brutal clarity. Traditional MMM demands...

What’s up everyone, today we have the pleasure of sitting down with Ashley Faus, Head of Lifecycle Marketing at Atlassian. Summary: Marketing frameworks often fail because they ignore how humans actually behave. People don't follow neat, linear paths; they explore, double back, and leap ahead based on genuine interests. Drawing from her diverse experience across corporate communications and lifecycle leadership, Ashley exposes how artificial walls between marketing functions create dysfunction while offering a solution: an integrated ecosystem where audience insights, compelling content, and strategic distribution flow continuously between teams. Her approach identifies truly predictive behaviors and measures success through bold experiments rather than smaller tweaks. By respecting how people naturally learn and make decisions, Ashley's content structure creates pathways that connect conceptual, strategic, and tactical pieces, making your content genuinely valuable to visitors and dramatically more effective at converting those ready to purchase.About AshleyAshely started her career with generalist marketing roles at a bunch of different small companies before settling into a role in the commercial aviation industryShe took on a generalist Marketing role at a training firm where she got a taste of marketing operations including a Marketo integration and lots of email campaignsShe later had 2 content strategy and product marketing roles at network security companiesToday Ashley is Head of Lifecycle Marketing, Portfolio at Atlassian where she’s been for over 7 yearsShe’s been interviewed on more than 50 podcasts, her writing has been published on TIME, Forbes, MarketingProfs, she’s a well traveled speaker and she has an upcoming book coming out in May called ‘Human-Centered Marketing: How to Connect with Audiences in the Age of AI’Why You Should Look for a New Job Every 18 MonthsAshley has spent over seven years at Atlassian, navigating through four distinct roles while the company itself transformed dramatically around her. This longevity stands out in an industry where most professionals change employers every 2-3 years. Through corporate communications, integrated media, product marketing, and now lifecycle marketing, she's crafted multiple careers without changing her email address.> "I look for a new job every 18 months, so that I am prepared to make a move and solve for any gaps at that roughly two to two and a half year mark.""I look for a new job every 18 months," Ashley explains, "so that I am prepared to make a move and solve for any gaps at that roughly two to two and a half year mark." This calculated strategy creates perpetual career momentum. You begin exploring opportunities six months before the typical stagnation point, positioning yourself to evolve professionally right when most people start feeling restless. The genius lies in the timing: plan your next move while you still love your current role, not after burnout or boredom sets in.The company Ashley joined barely resembles today's Atlassian. "We actually have grown like five or six times, both from an employee standpoint and from a revenue standpoint as well," she notes. This parallel evolution of both person and organization created a unique synergy, allowing her to ride waves of company growth while pursuing her own skill development.Her initial role came with an unexpected twist. Despite being hired for corporate communications, PR represented one of her weaker skill areas. During interviews, the hiring manager focused more on her versatility across content strategy, email marketing, and social media. Genuine curiosity opened doors that formal applications never could. "Because I was nosy and stuck my nose in other people's business," she admits candidly, "they were like, 'should you come sit with us?'" These informal interactions led to her integrated media role, which connected previously siloed functions:Press relationsOwned channels like email and socialThought leadership contentBrand marketing campaignsAshley applies this proactive mindset when managing her team. She challenges them with pointed questions about their future: "Who do you want to be when you grow up? Are you growing up in the next year? In the next five years?" This framing transforms vague aspirations into concrete timelines. "That breakdown of how to get to where you want to be in 10 years, 15 years, 20 years starts with the next 12 months or 24 months," she explains.The social media team placement at Atlassian illustrates how organizations evolve their understanding of marketing channels. "At the time, our social media person sat on the email team because the mindset was that this is a broadcast channel," Ashley recalls. Both she and her interviewer recognized the flawed logic in treating social platforms as one-way communication tools, creating immediate rapport around a shared marketing philosophy.Key takeaway: Schedule dedicated job hunting time every 18 months, even when fully satisfied with your current position. This practice maintains your market value, expands your professional network, and positions you to make strategic moves at the two-year mark when growth typically plateaus. The next perfect role might exist within your current company if you actively seek it out.The Overlap Between Lifecycle, Content and Product MarketingMarketing departments love creating artificial walls between functions. Product marketing owns messaging. Content creates assets. Lifecycle handles channels. We've all seen the org charts with their neat little boxes. Ashley brings refreshing clarity to this organizational fallacy, particularly for companies using product-led growth strategies where traditional marketing borders simply cannot hold.The organizational divide shifts dramatically depending on your go-to-market motion. "In larger companies using product-led growth versus a sales-led motion, there's a lot more blurring of the lines," Ashley explains. SEO strategy, trial signups, and in-product upgrade experiences often migrate to product marketing in PLG companies, even at enterprise scale. This reveals a fascinating truth many marketers miss: your core GTM motion fundamentally reshapes role boundaries more than company size does.> “I don't understand how you're gonna write content with no insights from the market, the competition, and the audience. I don't understand how you're gonna distribute content with no understanding of the channel mix and the quirks of the different channel."Ashley's decade of experience across multiple marketing functions gives her rare perspective on their interdependence. Ten years ago, she led marketing strategy at Duarte when marketing automation platforms were just becoming table stakes. "I actually had to do the RFP, choose between Marketo, Pardot, or Silverpop," she recalls. This hands-on experience taught her how lifecycle marketing (channels, nurture campaigns, cross-sell strategies) and content marketing (creating assets for those channels) form an inseparable partnership:Content marketing typically focuses on creating assetsLifecycle marketing typically focuses on channel strategyBoth become meaningless without the other's expertiseAt large companies like Atlassian, specialization creates absurd scenarios where a single email might involve five different people: one writing copy, another creating visuals, someone handling lead scoring, another doing audience segmentation, and finally someone building and testing the actual email. While this level of specialization brings depth, it risks bre...

What’s up everyone, today we have the pleasure of sitting down with Ruari Baker, Co-Founder and CEO of Allegrow. Summary: Your fancy AI personalization messaging strategy doesn’t mean anything if you don’t also have a strategy for email deliverability. Ruari busts long-standing myths about HTML vs plain text, why open rates died with Apple's 2021 privacy changes, and why the spam complaints visible in your marketing platform represent a fraction of reality. You'll walk away with 3 deliverability tactics that will help you reach the inbox and stay there: implement multi-subdomains to isolate high-risk traffic, adopt contact risk scoring that transcends basic validation and start using Google Postmaster to see your actual reputation metrics. Escape the promo tab without sacrificing design, resurrect damaged domains, and find out why traditional seed testing is worthless. If you depend on email, this might be the best 40 minutes you’ll spend this month. About RuariRuari started entrepreneurship early (at 18 years old) and joined a startup accelerator where he received mentorship from top tech founders in the UK as well as his first investmentHe co-founded Direct Software, a GDPR-first marketing automation solution where he gained a deep understanding of email deliverabilityToday, Ruari is Co-Founder and CEO of Allegrow, an email deliverability platform to help emails reach the primary inbox, not the spam folderPlain Text Emails Will Always Outperform HTML Emails When it Comes to Inbox PlacementThe HTML vs text email question hangs over every marketer's campaign planning session like a dark cloud. Ruari slices through this persistent debate with razor-sharp clarity. Context dictates winners here, not blanket rules. For outbound sales, plain text creates an authenticity that HTML instantly kills. Think about it—you craft personal emails without fancy formatting. The second your recipient spots that polished design, their brain categorizes your message as "marketing material," and your personalization efforts crumble.> “You can't just simply say 'always plain text, that's better.' The reality is there are still good business reasons for using Rich HTML, and that's why it is such a popular way to send emails from a marketing perspective.”Email providers have learned to associate complex formatting with promotional content that users often ignore. Your deliverability suffers accordingly. Yet HTML emails persist for good reason:Large subscriber lists benefit from HTML's clickthrough tracking capabilitiesE-commerce companies generate higher engagement when customers see products directlyVisual brands communicate their identity more effectively through designed templatesData-heavy messages become more scannable with proper formatting and hierarchyThe winning strategy lives somewhere in the messy middle. "If you're using HTML for legitimate marketing emails to an opted-in list, implement these practices to maintain deliverability," Ruari advises. Clean your entire list monthly—remove invalid contacts, keep bounce rates low, and eliminate potential spam trap subscriptions. This simple 30-day hygiene ritual dramatically improves your sender reputation with both ESPs and inbox systems.HTML devotees should strategically incorporate plain text messages at key points in the subscriber journey. These unadorned communications slip past promotion folder algorithms, landing you in the primary inbox. This placement success creates a virtuous cycle, improving future message placement—even for your HTML campaigns. You must also implement a sunset policy for engagement maintenance. When subscribers show zero activity over your predetermined period, place them into a final-attempt workflow. No response? Remove them proactively. This keeps your engagement metrics healthy, the exact data points email providers scrutinize when judging your sending quality."I once worked with an e-commerce client who switched half their abandoned cart emails to plain text," Ruari shares. "Their revenue per email jumped 22% because more messages reached the primary inbox." The results speak volumes about matching format to objective rather than defaulting to what looks prettiest in your marketing dashboard.Key takeaway: Match email format to specific objectives. Use plain text for sales outreach and relationship-building. Deploy HTML strategically for e-commerce and visual campaigns. Maintain ruthless list hygiene by removing invalid contacts monthly, sending occasional plain text messages regardless of your primary format, and cutting unengaged subscribers after final reactivation attempts. Your deliverability—and ultimately your results—depend on this discipline.Create a Sunset Policy Based on Your Specific Industry Engagement PatternsYour email list contains a ticking time bomb of disengaged contacts that silently damage your sender reputation with every campaign. When asked about the right timeframe for removing inactive subscribers, Ruari offers a refreshingly nuanced take that shatters the "six-month rule" most marketers blindly follow. The optimal sunset policy timing depends entirely on your industry and baseline engagement metrics. Smart marketers look to identify and remove the bottom quartile or decile of subscribers based on engagement patterns specific to their audience.High-engagement industries demand different standards than low-engagement sectors. Imagine running email marketing for a compliance software company—a field few people find "sexy." Your engagement metrics naturally run lower than consumer brands, but those rare engagement spikes matter tremendously. When someone suddenly engages with your SOC 2 audit content after months of silence, that signals a critical buying window. Cutting them off after six months of inactivity would sacrifice valuable revenue opportunities unique to your industry cycle.You must establish internal benchmarks that reflect your specific business reality. Study your engagement patterns over 12-18 months. Look for natural dropoff points. Analyze which inactive subscribers eventually reactivate and what triggers that behavior. Create segments based on these findings, then craft sunset workflows that reflect the actual customer journey in your space. For some businesses, 90 days makes sense. For others, 12 months barely captures their sales cycle.> “At least having some sunset policy in place would already put you leaps and bounds ahead of the majority of your peers.”The mere existence of a sunset policy puts you "leaps and bounds ahead of the majority of your peers," Ruari points out. Most marketers obsessively protect their list size, treating subscriber counts as a vanity metric rather than focusing on engagement quality. They hoard inactive emails like digital dragons, destroying deliverability in the process. Your sunset policy doesn't need to be perfect—it simply needs to exist and run consistently. Start by removing obvious dead weight: bounced addresses, spam complaints, and truly inactive accounts. Then refine your approach as you gather more data about your specific audience patterns.Key takeaway: Create a sunset policy based on your specific industry engagement patterns rather than arbitrary timeframes. Identify your bottom-performing subscriber segment (by quartile or decile) and implement an automated workflow to either re-engage or remove them. Even an imperfect sunset policy executed consistently will dramatically improve your deliverability metrics and campaign effectiveness compared to never removing inactive subscribers.How to Escape Google Promotions Tab Prison Without Sacrificing DesignYou send a gorgeous HTML email campai...

Danielle Balestra, Director of Marketing Technology and Operations at Goodwin, is an expert in martech within regulated enterprises. She discusses transforming marketing operations into strategic drivers for business. Danielle highlights the unique challenges of implementing AI and martech in regulated fields like law and healthcare. She also delves into how budget management and organizational dynamics impact marketing success. Additionally, the role of emotional connections in storytelling and its relevance to engaging with martech content is briefly touched upon.