What’s up everyone, today we have the pleasure of sitting down with Phyllis Fang, Head of Marketing at Transcend.

• (00:00) - Intro

About Phyllis

Phyllis Fang leads marketing at Transcend, where enterprise growth depends on clear choices about data, consent, and accountability. Her work shapes how privacy becomes part of how companies operate, communicate, and earn confidence at scale.

Earlier in her career, she spent several years at Uber, working on global product marketing for safety during periods of intense public scrutiny. She helped bring new safety features to market at moments when user behavior, policy decisions, and brand credibility were tightly linked. The work required precision, restraint, and an understanding of how people respond when stakes feel personal.

Across roles in e-commerce, lifecycle marketing, and platform strategy, a pattern holds. Fang gravitates toward systems that must work under pressure and messages that must hold up in practice. Her career reflects a belief that marketing earns its place when it reduces uncertainty and helps people move forward with confidence.

Uber Safety Marketing Shaped A Trust First Marketing Playbook

Trust-focused marketing depends on people who can move between systems work and narrative work without losing credibility in either space. Phyllis built that fluency by operating inside lifecycle programs while also leading product marketing initiatives at Uber. One side of that work lived in tools, triggers, and delivery logic. The other side lived in rooms where progress depended on persuasion, alignment, and patience. That dual exposure trained her to see how fragile big ideas become when they cannot survive real execution.

Lifecycle and marketing operations reward control and repeatability. Product marketing inside a global organization rewards influence and restraint. Phyllis describes moments where moving a single initiative forward required negotiation across regions, channels, and internal politics. Every message faced review from people who owned distribution and reputation in their markets. Messaging tightened quickly because weak logic did not survive long. Campaigns became sharper because every assumption had to hold up under pressure.

“We were all in the same company, but I still had to convince people to resource things differently or prioritize a message.”

Safety marketing pushed that pressure even further. The work focused on features designed for rare, high-stakes moments, including emergency assistance and large-scale verification during COVID. Measurement shifted away from habitual usage and toward confidence and credibility. The audience expanded well beyond active users. Phyllis had to speak clearly to riders, drivers, regulators, and the general public at the same time. Each group carried different fears, incentives, and consequences. Messaging succeeded only when it respected those differences without creating confusion.

That mindset carries directly into her work at Transcend. Privacy and consent buyers often sit in legal or compliance roles where personal and professional risk overlap. These buyers read closely and remember details. Phyllis explains that proof needs to operate on two levels at once. It must withstand careful review, and it must connect to human motivation. Career safety, internal credibility, and long-term reputation shape decisions more than feature depth ever will.

“You have to understand the human behind the role, because their motivation usually has very little to do with your product.”

Many martech teams still lean on urgency and fear to move deals forward. That habit collapses quickly in trust-driven categories. Buyers trained to manage risk respond to clarity, evidence, and empathy. Marketing teams that understand systems and human cost create messages people can defend internally, even when scrutiny rises.

Key takeaway: Trust product marketing works best when teams pair operational rigor with persuasive clarity. Build messages that survive legal review, internal debate, and public scrutiny, then ground those messages in the real career risks your buyer carries. When proof holds at the detail level and the story respects human motivation, credibility compounds instead of eroding under pressure.

How Permissioned Data Systems Power Personalization

Permissioned data systems sit quietly underneath every durable personalization program. Phyllis describes them as the machinery that keeps experiences coherent when traffic spikes, regulations tighten, and teams ship faster than documentation can keep up. When privacy and data infrastructure receive the same attention as creative and lifecycle planning, personalization gains endurance. It stops wobbling every time a new channel, region, or regulation enters the picture.

When asked about what a system of permission actually contains, Phyllis anchors the idea in everyday user choice. Preferences, opt-ins, unsubscribes, and topic interests form the marketing layer most teams recognize. Consent records, deletion rights, and data sharing controls form the privacy layer that usually lives elsewhere. Together, these signals decide what data you collect, where it flows, how long it lives, and which systems get to act on it. That layer governs every downstream decision you make about segmentation, targeting, and automation.

“We are talking about a layer of user controls that determine what personal data a company collects, how it is collected, how it is stored, how long it is stored, and what gets shared across systems.”

Phyllis points out that teams often rush toward tooling before understanding their own surface area. She pushes marketers to start with an audit that feels closer to whiteboarding than compliance. That work cuts across marketing, product, privacy, and partnerships, and it usually exposes uncomfortable overlaps and blind spots. Most organizations already run this exercise for campaigns and funnels, and they rarely include consent in the room. When permission signals stay disconnected from journey design, personalization feels impressive in demos and brittle in production.

Operationalizing consent requires discipline across systems. Preference signals need to flow cleanly into the CDP, CRM, messaging platforms, and analytics tools. That way campaigns, audiences, and triggers operate on live, permissioned data ins...

What’s up everyone, today we have the pleasure of sitting down with Jordan Resnick, Senior Director, Marketing Operations at CHEQ.

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About Jordan

Jordan Resnick is Senior Director of Marketing Operations at CHEQ, where he leads the systems, data, and workflows that support go-to-market security across a global customer base. His work sits at the intersection of marketing operations, revenue operations, attribution, automation, and analytics, with a clear focus on building infrastructure that holds up under scale and scrutiny.

Before CHEQ, Jordan led marketing operations at Atlassian, where he supported complex GTM motions across multiple business units and global markets. Earlier roles at Perkuto and MERGE combined hands-on execution with customer-facing leadership, integration design, and process ownership. His career also includes more than a decade as an independent operator, delivering marketing operations, automation, content, and technical solutions across a wide range of organizations. Jordan brings a deeply practical, execution-driven perspective shaped by years of building, fixing, and maintaining real systems in production environments.

Demystifying Go-to-Market Security

Go-to-market security shows up when growth metrics look strong and revenue outcomes feel weak. Marketing operations teams live in that gap every day. Jordan describes GTM security as a business-facing discipline that protects the integrity of acquisition, funnel data, and downstream decisions that depend on clean signals. The work sits inside marketing operations because that is where traffic quality, lead flow, and revenue attribution converge.

When asked about how GTM security differs from traditional fraud prevention, Jordan frames the difference through decision-making pressure. Security teams historically focus on defending infrastructure and minimizing exposure. Marketing ops teams focus on maintaining momentum while spending real budget. GTM security evaluates risk in context, with an eye toward revenue impact, forecasting accuracy, and operational trust across teams that rely on shared data.

Jordan grounds the concept in specific failure points that operators recognize immediately. GTM security examines where bad inputs quietly enter systems and multiply.

Paid traffic that inflates sessions without creating buyers.
Analytics skewed by automated interactions that look legitimate.
Form submissions that pass validation and still never convert.
Sales pipelines filled with activity that drains time and morale.

Each issue compounds because systems assume the data is real. Teams keep optimizing against numbers that feel precise and still point in the wrong direction.

“You are putting money into driving people to your website, and the first question should be how many of those people are real and able to buy.”

Invalid traffic behaves like a contaminant. It flows from acquisition into attribution models, forecasting tools, CRMs, and revenue dashboards. Marketing celebrates growth, sales chases shadows, and finance questions confidence in the entire funnel. The problem rarely announces itself as a security incident. It surfaces as confusion, missed targets, and internal friction.

GTM security matters because it gives marketing ops teams a framework to protect the inputs that shape every downstream decision. The work runs alongside traditional security while staying anchored in go-to-market outcomes. That way you can spend with confidence, trust your reporting, and hand sales teams signals grounded in real buying behavior.

Key takeaway: Treat go-to-market security as part of your core marketing operations workflow. Validate traffic quality, filter lead integrity, and block funnel contamination before data enters analytics and sales systems. That way you can protect budget efficiency, restore confidence in reporting, and align growth decisions with real customer behavior.

The Fake Traffic Surge

AI-powered automation now sits at the center of the fake traffic surge, and the data from CHEQ makes that pattern hard to dismiss. The jump from 11.3 percent to 17.9 percent happened because automation became accessible to almost anyone with intent. Writing scripts once required time, skill, and trial and error. AI removes that friction and replaces it with speed and scale, which changes who can participate and how quickly abuse spreads.

Jordan ties that accessibility directly to incentives that marketing teams quietly tolerate. Fraud still generates money. Inflated traffic still props up dashboards. Higher visit counts still circulate in board decks without hard questions attached. AI accelerates activity that already existed and widens the group capable of producing it. That combination turns fake traffic into background noise instead of a visible threat, especially when volume metrics continue to earn praise.

“You don’t need to be a hardcore coder to write a script anymore. You can get AI to do it for you.”

Automation also introduces a layer of ambiguity that most teams are not prepared to handle. Bots now perform legitimate tasks that look suspicious inside analytics tools. Some scan pricing pages. Some analyze product specs. Some gather research for downstream purchasing decisions. Jordan points out that people already configure agents to place orders, and that behavior blends seamlessly into traffic logs. Marketing systems treat those visits the same way they treat fraud, which creates confusion across attribution and forecasting.

That confusion pushes teams toward blunt fixes that create new problems. Blanket blocking removes useful signals. Loose filtering leaves waste untouched. Jordan frames the real work as classification rather than suppression. Teams now need to separate intent categories instead of chasing a single definition of fake traffic. That work forces uncomfortable conversations about which metrics deserve trust and which exist only because nobody benefits from challenging them.

Fake traffic keeps growing because systems reward volume and rarely penalize distortion. AI makes production easier, incentives keep demand high, and measurement practices lag behind reality. Marketing ops teams that continue to treat traffic as a vanity me...

What’s up everyone, today we have the honor of sitting down with Aleyda Solís, SEO and AI search consultant.

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About Aleyda

Aleyda Solís is an international SEO and AI search optimization consultant, speaker, and author who leads Orainti, the boutique consultancy known for solving complex, multi-market SEO challenges. She’s worked with brands across ecommerce, SaaS, and global marketplaces, helping teams rebuild search foundations and scale sustainable organic growth.

She also runs three of the industry’s most trusted newsletters; SEOFOMO, MarketingFOMO, and AI Marketers, where she filters the noise into the updates that genuinely matter. Her free roadmaps, LearningSEO.io and LearningAIsearch.com, give marketers a clear, reliable path to building real skills in both SEO and AI search.

Crawlability Requirements for AI Search Engines

Crawlability shapes everything that follows in AI search. Aleyda talks about it with the tone of someone who has seen far too many sites fail the basics. AI crawlers behave differently from traditional search engines, and they hit roadblocks that most teams never think about. Hosting rules, CDN settings, and robots files often permit Googlebot but quietly block newer user agents. You can hear the frustration in her voice when she describes audit after audit where AI crawlers never reach critical sections of a site.

"You need to allow AI crawlers to access your content. The rules you set might need to be different depending on your context."

AI crawlers also refuse to process JavaScript. They ingest raw markup and move on. Sites that lean heavily on client-side rendering lose entire menus, product details, pricing tables, and conversion paths. Aleyda describes this as a structural issue that forces marketers to confront their technical debt. Many teams have spent years building front-ends with layers of JavaScript because Google eventually figured out how to handle it. AI crawlers skip that entire pipeline. Simpler pages load faster, reveal hierarchy immediately, and give AI models a complete picture without extra processing.

Search behavior adds new pressure. Aleyda points to OpenAI’s published research showing a rise in task-oriented queries. Users ask models to complete goals directly and skip the page-by-page exploration we grew up optimizing for. You need clarity about which tasks intersect with your offerings. You need to build content that satisfies those tasks without guessing blindly. Aleyda urges teams to validate this with real user understanding because generic keyword tools cannot describe these new behaviors accurately.

Authority signals shift too. Mentions across credible communities carry weight inside AI summaries. Aleyda explains it as a natural extension of digital PR. Forums, newsletters, podcasts, social communities, and industry roundups form a reputation map that AI crawlers use as context. Backlinks still matter, but mentions create presence in a wider set of conversations. Strong SEO programs already invest in this work, but many teams still chase link volume while ignoring the broader network of references that shape brand perception.

Measurement evolves alongside all of this. Aleyda encourages operators to treat AI search as both a performance channel and a visibility channel. You track presence inside responses. You track sentiment and frequency. You monitor competitors that appear beside you or ahead of you. You map how often your brand appears in summaries that influence purchase decisions. Rankings and click curves do not capture the full picture. A broader measurement model captures what these new systems actually distribute.

Key takeaway: Build crawlability for AI search with intention. Confirm that AI crawlers can access your content, and remove JavaScript barriers that hide essential information. Map the task-driven behaviors that align with your products so you invest in content that meets real user goals. Strengthen your reputation footprint by earning mentions in communities that influence AI summaries. Expand your measurement model so you can track visibility, sentiment, and placement inside AI-generated results. That way you can compete in a search environment shaped by new rules and new signals.

LLMs As A New Search Channel In A Multi Platform Discovery System

SEO keeps getting declared dead every time Google ships a new interface, yet actual search behavior keeps spreading across more surfaces. Aleyda reacted to the “LLMs as a new channel” framing with immediate agreement because she sees teams wrestling with a bigger shift. They still treat Google as the only gatekeeper, even though users now ask questions, compare products, and verify credibility across several platforms at once. LLMs, TikTok, Instagram, and traditional search engines all function as parallel discovery layers, and the companies that hesitate to accept this trend end up confused about where SEO fits.

Aleyda pointed to the industry’s long dependence on Google and described how that dependence shaped expectations. Many teams built an entire worldview around a single SERP format, a single set of ranking factors, and a single customer entry point. Interface changes feel existential because the discipline was defined too narrowly for too long. She sees this tension inside consulting projects when stakeholders ask whether SEO is dying instead of asking where their audience now searches for answers.

Retail clients provided her clearest examples. They already treat TikTok and Instagram as core search environments. They ask for guidance on how to structure content so it gets discovered through platform specific signals. They ask for clarity on how product intent gets inferred through tags, comments, watch time, and creator interactions. Their questions treat search as a distributed system, and their behavior hints at what the wider market will adopt. Aleyda considers this a preview, because younger customers rarely begin their journey inside a traditional search engine.

Her story from a conference in China made the point even sharper. She explained how Baidu no longer carries the gravitational pull many Western marketers assume. People gather information through Red Note, Douyin, and several specialized platforms, and they assemble answers through a blend of formats. That experience changed Aleyda’s expectations for Western markets. She believes...

What’s up everyone, today we have the honor of sitting down with the legendary Scott Brinker, a rare repeat guest, the Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself.

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About Scott

Scott has spent his career merging the world of marketing and technology and somehow making it look effortless. He co-founded ion interactive back when “interactive content” felt like a daring experiment, then opened the Chief Marketing Technologist blog in 2008 to spark a conversation the industry didn’t know it needed. He sketched the very first Martech Landscape when the ecosystem fit on a single page with about 150 vendors, and later brought the MarTech conference to life in 2014, where he still shapes the program. Most recently, he guided HubSpot’s platform ecosystem, helping the company stay connected to a martech universe that’s grown to more than 15,000 tools. Today, Scott continues to helm chiefmartec.com, the well the entire industry keeps returning to for clarity, curiosity, and direction.

Scott Brinker’s Guidance For Marketers Rethinking Their Career Path

Mid career marketers keep asking themselves whether they should stick with the field or throw everything out and start fresh. Scott relates to that feeling, and he talks about it with a kind of grounded humor. He describes his own wandering thoughts about running a vineyard, feeling the soil under his shoes and imagining the quiet. Then he remembers the old saying about wineries, which is that the only guaranteed outcome is a smaller bank account. His story captures the emotional drift that comes with burnout. People are not always craving a new field. They are often craving a new relationship with their work.

Scott moves quickly to the part that matters. He directs his attention to AI because it is reshaping the field faster than many teams can absorb. He explains that someone could spend every hour of the week experimenting and still only catch a fraction of what is happening. He sees that chaos as a signal. Overload creates opportunity, and the people who step toward it gain an advantage. He urges mid career operators to lean into the friction and build new muscle. He even calls out how many people will resist change and cling to familiar workflows. He views that resistance as a gift for the ones willing to explore.

“People who lean into the change really have the opportunity to differentiate themselves and discover things.”

Scott brings back a story from a napkin sketch. He drew two curves, one for the explosive pace of technological advancement and one for the slower rhythm of organizational change. The curves explain the tension everyone feels. Teams operate on slower timelines. Tools operate on faster ones. The gap between those curves is wide, and professionals who learn to navigate that space turn themselves into catalysts inside their companies. He sees mid career marketers as prime candidates for this role because they have enough lived experience to understand where teams stall and enough hunger to explore new territory.

Scott encourages people to channel their curiosity into specific work. He suggests treating AI exploration like a practice and not like a trend. A steady rhythm of experiments helps someone grow their internal influence. Better experiments produce useful artifacts. These artifacts often become internal proof points that accelerate change. He believes the next wave of opportunity belongs to people who document what they try, translate what they learn, and help their companies adapt at a pace that competitors cannot easily match.

Scott’s message carries emotional weight. He does not downplay the exhaustion in the field, but he reinforces that reinvention often happens inside the work, not outside of it. People who move toward new capabilities build careers that feel less fragile and more future proof.

Key takeaway: Mid career marketers build real leverage by running small AI experiments inside their current roles, documenting the results, and using those learnings to influence how their companies adapt. Start with narrow tests that affect your daily work, share clear outcomes with your team, and repeat the cycle. That way you can build rare credibility and position yourself as the person who accelerates organizational change.

If You Started Over in Martech, What Would You Learn First

Cross functional fluency shapes careers in a way that shiny frameworks never will, and Scott calls this out with blunt honesty. He shares how his early career lived in two worlds, writing brittle code on one side and trying to understand marketers on the other. He laughs about being a “very mediocre software engineer” who built things that probably should not have survived contact with production. That imperfect background still gave him an edge, because technical fluency mixed with genuine curiosity about marketing created a role no one else was filling. He could explain system behavior in a language marketers understood, and he could explain marketer behavior in a language engineers tolerated. That unusual pairing delivered force inside teams that usually worked in isolation.

Scott makes the case that readers can build similar momentum by leaning into roles where disciplines collide. He argues that the most useful skills often come from pairing two domains and learning how they influence each other. He highlights combinations like:

Marketing and IT for people who enjoy systems.
Marketing and finance for people drawn to modeling and forecasting.
Marketing and sales for people who want to connect customer signals with revenue conversations.

He believes these intersections are crowded with opportunity because organizations rarely invest enough in communication across teams. You can create real leverage when you speak multiple operational languages with confidence.

“The ability to serve as a bridge of cross pollinating between multiple disciplines has a lot of opportunity.”

Scott also shares the part he would invest in first if he were twenty two again. He spent years focusing almost entirely on what systems could do. He cared deeply about architecture diagrams and technical possibility, and he assumed people would adopt anything that worked. He later realized that adoption follows trust,...

What’s up everyone, today we have the pleasure of sitting down with Matthew Castino, Marketing Measurement Science Lead @ Canva.

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About Matthew

Matthew Castino blends psychology, statistics, and marketing intuition in a way that feels almost unfair. With a PhD in Psychology and a career spent building measurement systems that actually work, he’s now the Marketing Measurement Science Lead at Canva, where he turns sprawling datasets and ambitious growth questions into evidence that teams can trust.

His path winds through academia, health research, and the high-tempo world of sports trading. At UNSW, Matt taught psychology and statistics while contributing to research at CHETRE. At Tabcorp, he moved through roles in customer profiling, risk systems, and US/domestic sports trading; spaces where every model, every assumption, and every decision meets real consequences fast. Those years sharpened his sense for what signal looks like in a messy environment.

Matt lives in Australia and remains endlessly curious about how people think, how markets behave, and why measurement keeps getting harder, and more fun.

Canva’s Prioritization System for Marketing Experiments

Canva’s marketing experiments run in conditions that rarely resemble the clean, product controlled environment that most tech companies love to romanticize. Matthew works in markets filled with messy signals, country level quirks, channel specific behaviors, and creative that behaves differently depending on the audience. Canva built a world class experimentation platform for product, but none of that machinery helps when teams need to run geo tests or channel experiments across markets that function on completely different rhythms. Marketing had to build its own tooling, and Matthew treats that reality with a mix of respect and practicality.

His team relies on a prioritization system grounded in two concrete variables.
Spend
Uncertainty

Large budgets demand measurement rigor because wasted dollars compound across millions of impressions. Matthew cares about placing the most reliable experiments behind the markets and channels with the biggest financial commitments. He pairs that with a very sober evaluation of uncertainty. His team pulls signals from MMM models, platform lift tests, creative engagement, and confidence intervals. They pay special attention to MMM intervals that expand beyond comfortable ranges, especially when historical spend has not varied enough for the model to learn. He reads weak creative engagement as a warning sign because poor engagement usually drags efficiency down even before the attribution questions show up.

“We try to figure out where the most money is spent in the most uncertain way.”

The next challenge sits in the structure of the team. Matthew ran experimentation globally from a centralized group for years, and that model made sense when the company footprint was narrower. Canva now operates in regions where creative norms differ sharply, and local teams want more authority to respond to market dynamics in real time. Matthew sees that centralization slows everything once the company reaches global scale. He pushes for embedded data scientists who sit inside each region, work directly with marketers, and build market specific experimentation roadmaps that reflect local context. That way experimentation becomes a partner to strategy instead of a bottleneck.

Matthew avoids building a tower of approvals because heavy process often suffocates marketing momentum. He prefers a model where teams follow shared principles, run experiments responsibly, and adjust budgets quickly. He wants measurement to operate in the background while marketers focus on creative and channel strategies with confidence that the numbers can keep up with the pace of execution.

Key takeaway: Run experiments where they matter most by combining the biggest budgets with the widest uncertainty. Use triangulated signals like MMM bounds, lift tests, and creative engagement to identify channels that deserve deeper testing. Give regional teams embedded data scientists so they can respond to real conditions without waiting for central approval queues. Build light guardrails, not heavy process, so experimentation strengthens day to day marketing decisions with speed and confidence.

What Happened When Canva Turned Off Branded Search

Geographic holdout tests gave Matt a practical way to challenge long-standing spend patterns at Canva without turning measurement into a philosophical debate. He described how many new team members arrived from environments shaped by attribution dashboards, and he needed something concrete that demonstrated why experiments belong in the measurement toolkit. Experiments produced clearer decisions because they created evidence that anyone could understand, which helped the organization expand its comfort with more advanced measurement methods.

The turning point started with a direct question from Canva’s CEO. She wanted to understand why the company kept investing heavily in bidding on the keyword “Canva,” even though the brand was already dominant in organic search. The company had global awareness, strong default rankings, and a product that people searched for by name. Attribution platforms treated branded search as a powerhouse channel because those clicks converted at extremely high rates. Matt knew attribution would reinforce the spend by design, so he recommended a controlled experiment that tested actual incrementality.

"We just turned it off or down in a couple of regions and watched what happened."

The team created several regional holdouts across the United States. They reduced bids in those regions, monitored downstream behavior, and let natural demand play out. The performance barely moved. Growth held steady and revenue held steady. The spend did not create additional value at the level the dashboards suggested. High intent users continued converting, which showed how easily attribution can exaggerate impact when a channel serves people who already made their decision.

The outcome saved Canva millions of dollars, and the savings were immediately reallocated to areas with better leverage. The win carried emotional weight inside the company because it replaced speculati...

What’s up everyone, today we have the pleasure of sitting down with Anna Aubuchon, VP of Operations at Civic Technologies.

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About Anna

Anna Aubuchon is an operations executive with 15+ years building and scaling teams across fintech, blockchain, and AI. As VP of Operations at Civic Technologies, she oversees support, sales, business operations, product operations, and analytics, anchoring the company’s growth and performance systems.

She has led blockchain operations since 2014 and built cross-functional programs that moved companies from early-stage complexity into stable, scalable execution. Her earlier roles at Gyft and Thomson Reuters focused on commercial operations, enterprise migrations, and global team leadership, supporting revenue retention and major process modernization efforts.

How AI Flipped the Build Versus Buy Decision

AI tooling has shifted so quickly that many teams are still making decisions with a playbook written for a different era. Anna explains that the build versus buy framework people lean on carries assumptions that no longer match the tool landscape. She sees operators buying AI products out of habit, even when internal builds have become faster, cheaper, and easier to maintain. She connects that hesitation to outdated mental models rather than actual technical blockers.

AI platforms keep rolling out features that shrink the amount of engineering needed to assemble sophisticated workflows. Anna names the layers that changed this dynamic. System integrations through MCP act as glue for data movement. Tools like n8n and Lindy give ops teams workflow automation without needing to file tickets. Then ChatGPT Agents and Cloud Skills launched with prebuilt capabilities that behave like Lego pieces for internal systems. Direct LLM access removed the fear around infrastructure that used to intimidate nontechnical teams. She describes the overall effect as a compression of technical overhead that once justified buying expensive tools.

She uses Civic’s analytics stack to illustrate how she thinks about the decision. Analytics drives the company’s ability to answer questions quickly, and modern integrations kept the build path light. Her team built the system because it reinforced a core competency. She compares that with an AI support bot that would need to handle very different audiences with changing expectations across multiple channels. She describes that work as high domain complexity that demands constant tuning, and the build cost would outweigh the value. Her team bought that piece. She grounds everything in two filters that guide her decisions: core competency and domain complexity.

Anna also calls out a cultural pattern that slows AI adoption. Teams buy AI tools individually and create isolated pockets of automation. She wants teams to treat AI workflows as shared assets. She sees momentum building when one group experiments with a workflow and others borrow, extend, or remix it. She believes this turns AI adoption into a group habit rather than scattered personal experiments. She highlights the value of shared patterns because they create a repeatable way for teams to test ideas without rebuilding from scratch.

She closes by urging operators to update their decision cycle. Tooling is evolving at a pace that makes six month old assumptions feel stale. She wants teams to revisit build versus buy questions frequently and to treat modern tools as a prompt to redraw boundaries rather than defend old ones. She frames it as an ongoing practice rather than a one time decision.

Key takeaway: Reassess your build versus buy decisions every quarter by measuring two factors. First, identify whether the workflow strengthens a core competency that deserves internal ownership. Second, gauge the domain complexity and decide whether the function needs constant tuning or specialized expertise. Use modern integration layers, workflow builders, and direct LLM access to assemble internal systems quickly. Build the pieces that reinforce your strengths, buy the pieces that demand specialized depth, and share internal workflows so other teams can expand your progress.

Why In House AI Provides Better Economics And Control

AI tooling has grown into a marketplace crowded with vendors who promise intelligence, automation, and instant transformation. Anna watches teams fall into these patterns with surprising ease. Many of the tools on the market run the same public models under new branding, yet buyers often assume they are purchasing deeply specialized systems trained on inaccessible data. She laughs about driving down the 101 and seeing AI billboards every few minutes, each one selling a glossy shortcut to operational excellence. The overcrowding makes teams feel like they should buy something simply because everyone else is buying something, and that instinct shifts AI procurement from a strategic decision into a reflex.

"A one year agreement might as well be a decade in AI right now."

Anna has seen how annual vendor contracts slow companies down. The moment a team commits to a year long agreement, the urgency to evaluate alternatives vanishes. They adopt a “set it and forget it” mindset because the tool is already purchased, the budget is already allocated, and the contract already sits in legal. AI development moves fast. Contract cycles do not. That mismatch creates friction that becomes expensive, especially when new models launch every few weeks and outperform the ones you purchased only months earlier. Teams do not always notice the cost of stagnation because it creeps in quietly.

Anna lays out a practical build versus buy framework. Teams should inspect whether the capability touches their core competency, their customer experience, or their strategic distinctiveness. If it does, then in house AI provides more long term value. It lets the company shape the model around real customer patterns. It keeps experimentation in motion instead...

What’s up everyone, today we have the pleasure of sitting down with Pam Boiros, Fractional CMO and Marketing advisor, and Co-Founder Women Applying AI.

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About Pam

Pam Boiros is a consultant who helps marketing teams find direction and build plans that feel doable. She leads Marketing AI Jump Start and works as a fractional CMO for clients like Reclaim Health, giving teams practical ways to bring AI into their day-to-day work. She’s also a founding member of Women Applying AI, a new community that launched in Sep 2025 that creates a supportive space for women to learn AI together and grow their confidence in the field.

Earlier in her career, Pam spent 12 years at a fast-growing startup that Skillsoft later acquired, then stepped into senior marketing and product leadership there for another three and a half years. That blend of startup pace and enterprise structure shapes how she guides her clients today.

How To Audit Data Fingerprints For AI Bias In Marketing

AI bias spreads quietly in marketing systems, and Pam treats it as a pattern problem rather than a mistake problem. She explains that models repeat whatever they have inherited from the data, and that repetition creates signals that look normal on the surface. Many teams read those signals as truth because the outputs feel familiar. Pam has watched marketing groups make confident decisions on top of datasets they never examined, and she believes this is how invisible bias gains momentum long before anyone sees the consequences.

Pam describes every dataset as carrying a fingerprint. She studies that fingerprint by zooming into the structure, the gaps, and the repetition. She looks for missing groups, inflated representation, and subtle distortions baked into the source. She builds this into her workflow because she has seen how quickly a model amplifies the same dominant voices that shaped the data. She brings up real scenarios from her own career where women were labeled as edge cases in models even though they represented half the customer base. These patterns shape everything from product recommendations to retention scores, and she believes many teams never notice because the numbers look clean and objective.

"Every dataset has a fingerprint. You cannot see it at first glance, but it becomes obvious once you look for who is overrepresented, who is underrepresented, or who is misrepresented."

Pam organizes her process into three cycles that marketers can use immediately.
The habit works because it forces scrutiny at every stage, not just at kickoff.

Before building, trace the data source, the people represented, and the people missing.
While building, stress test the system across groups that usually sit at the margins.
After launch, monitor outputs with the same rhythm you use for performance analysis.

She treats these cycles as an operational discipline. She compares the scale of bias to a compounding effect, since one flawed assumption can multiply into hundreds of outputs within hours. She has seen pressure to ship faster push teams into trusting defaults, which creates the illusion of objectivity even when the system leans heavily toward one group’s behavior. She wants marketers to recognize that AI audits function like quality control, and she encourages them to build review rituals that continue as the model learns. She believes this daily maintenance protects teams from subtle drift where the model gradually leans toward the patterns it already prefers.

Pam views long term monitoring as the part that matters most. She knows how fast AI systems evolve once real customers interact with them. Bias shifts as new data enters the mix. Entire segments disappear because the model interprets their silence as disengagement. Other segments dominate because they participate more often, which reinforces the skew. Pam advocates for ongoing alerts, periodic evaluations, and cross-functional reviews that bring different perspectives into the monitoring loop. She believes that consistent visibility keeps the model grounded in the full customer base.

Key takeaway: You can reduce AI bias by treating audits as part of your standard workflow. Trace the origin of every dataset so you understand who shapes the patterns. Stress test during development so you catch distortions early. Monitor outcomes after launch so you can identify drift before it influences targeting, scoring, and personalization. This rhythm gives you a reliable way to detect biased fingerprints, keep systems accountable, and protect real customers from skewed automation.

Why Emotional Intelligence Improves AI Prompting Quality

Emotional intelligence shapes how people brief AI, and Pam focuses on the practical details behind that pattern. She sees prompting as a form of direction setting, similar to guiding a creative partner who follows every instruction literally. Women often add richer context because they instinctively think through tone, audience, and subtle cues before giving direction. That depth produces output that carries more human texture and brand alignment, and it reduces the amount of rewriting teams usually do when prompts feel thin.

Pam also talks about synthetic empathy and how easily teams misread it. AI can generate warm language, but users often sense a hollow quality once they reread the output. She has seen teams trust the first fluent result because it looks polished on the surface. People with stronger emotional intelligence detect when the writing lacks genuine feeling or when it leans on clichés instead of real understanding. Pam notices this most in content meant for sensitive moments, such as apology emails or customer care messages, where the emotional miss becomes obvious.

"Prompting is basically briefing the AI, and women are natural context givers. We think about tone and audience and nuance, and that is what makes AI output more human and more aligned with the brand."

Pam brings even sharper clarity when she moves into analytics. She observes that many marketers chase the top performer without questioning who drove the behavior. She describes moments where curiosity leads someone to discover that a small, highly engaged audience segment pulled the numbers upward. She sees women interrogating patterns by asking:

Who showed up
Why they behaved the way they did
What made the pattern appear more universal than it is

Those questions shift analytics from scoreboar...

What’s up everyone, today we have the pleasure of sitting down with Anna Leary, Director of Marketing Operations at Alma.

• (00:00) - Intro

About Alma

Anna Leary is the Director of Marketing Operations at Alma, where she builds scalable systems that help marketing teams work smarter. With a focus on lead flow, data architecture, and enablement, she’s known for creating centers of excellence that turn fragmented operations into cohesive, measurable programs. As a Marketo Certified Solutions Architect and Marketo Measure (Bizible) specialist, Anna brings a rare balance of technical depth and strategic clarity to every initiative she leads.

Before joining Alma, Anna spent more than a decade shaping marketing operations strategies for brands like Uber, Teamwork, Sauce Labs, and Bitly. Whether optimizing attribution models or training teams to adopt new workflows, Anna’s work always centers on efficiency, empowerment, and driving impact across the full marketing ecosystem.

Burnout and Balance

Marketing ops work demands constant precision. Teams juggle system integrations, data cleanups, and new tech rollouts, often all before lunch. The job requires mental endurance and a tolerance for chaos. Anna understands this well. “Everyone’s trying to be the person who knows the newest tech,” she said. “It’s hard to keep up, and that adds to the mental load.” The competition to stay relevant has turned into a quiet stress test that too many operators fail without noticing.

The strange part is that ops teams often create systems designed to protect their organizations but rarely use those same systems to protect themselves. Anna explained how Service Level Agreements (SLAs) can lose their meaning when teams treat them as flexible. Urgent requests push through, exceptions pile up, and structure dissolves. Each “quick favor” chips away at the purpose of having defined processes. She put it plainly:

“If we’re making an exception for everything, then we’re not respecting the process.”

When teams stop respecting their own boundaries, burnout follows quickly. SLAs exist to create stability, and stability is what keeps people sane. Following process is not bureaucracy; it is protection. It gives operators time to think clearly, plan ahead, and make fewer reactive decisions. That way you can build predictability into your week instead of letting other people’s emergencies define it.

Anna also shared how her team reworked its entire planning system to reduce stress. “We used to do quarterly capacity planning,” she said, “but half the projects fell apart by week four.” She scrapped the process and replaced it with smaller, rolling cycles that fit the unpredictable nature of marketing requests. For someone who identifies as Type A, letting go of that much structure felt risky, but the tradeoff was worth it. Her team now works with more energy, less anxiety, and a better sense of control.

“Giving up some of that control is actually good in the end because it’s less stressful.”

Her story shows how burnout prevention depends on structure that adapts. Ops professionals do their best work when their systems reflect real life, not an idealized version of it. Boundaries, planning, and discipline should support humans, not box them in.

Key takeaway: Protect your team’s mental health by enforcing the systems you build. Treat SLAs as promises, not preferences. Review your planning cycles regularly and adjust them to match the actual pace of work. Stability in ops comes from building rules that people respect and structures that evolve as the business changes.

The Power of No

Saying no is one of the hardest and most necessary skills in marketing operations. Every week brings a new request, a “quick fix,” or a last-minute idea that someone swears will only take five minutes. Anna treats these moments as boundary checks. They test whether her team can protect their focus without losing trust or influence across the company.

“Boundaries in your personal life mirror boundaries in your professional life. You can’t sustain either without learning to say no.”

Anna connects this discipline to mental health. After years of therapy, she learned that setting boundaries preserves energy and prevents resentment from creeping into work. In marketing ops, that means understanding when to say no and why. A no can be temporary, like “no for now,” or conditional, like “come back once X, Y, and Z are ready.” That clarity gives teams space to plan properly instead of reacting in chaos.

Too many ops teams still act like order-takers. They manage tickets, fix errors, and scramble to meet every demand, even when requests come without context. Anna believes teams must reposition themselves as strategic partners. That means asking sharper questions such as, “How does this connect to our business goals?” or “Which KPI does this move?” Every yes should come with evidence, not obligation. When ops speaks in the language of impact, their boundaries start to hold.

To back that up, Anna recommends showing the work already in motion. Pull up your team’s Notion or Asana board, point to the commitments everyone approved, and remind stakeholders that priorities are already locked for this sprint. That way you can shift the conversation from emotion to logic. Plans exist for a reason. If the company wants to keep changing direction, it must accept the cost of constant interruption.

Anna’s approach creates psychological safety for her team. She recently told a contractor to stop overthinking a request that was technically impossible. Her words were simple: “It’s okay to tell them we can’t do this.” Those six words carried permission to rest, to stop chasing unrealistic expectations, and to respect the limits of their tools and time. Teams that learn this kind of confidence avoid burnout and deliver better results with less noise.

Key takeaway: Boundaries are an operational discipline, not an act of defiance. Use clear priorities, visible sprint boards, and company KPIs as your guardrails. Frame every no around impact and alignment. That way you can protect focus, maintain trust with stakeholders, and keep your team mentally healthy while still driving the business forward.

Hiring Experts Only to Tell Them What to Do

Every marketing ops professional eventually faces a request that makes their skin crawl. For Anna, it was the “no-reply” email debate. A stakeholder wanted to send a campaign from a no-reply address in Marketo. She had explained countless times why that idea goes against every principle of customer experience. It blocks responses, damages trust, and kills engagemen...

What’s up everyone today we have the pleasure of chatting with Blair Bendel, Senior Vice President of Marketing at Foxwoods Resort Casino.

• (00:00) - Intro

About Blair

Blair Bendel has spent nearly two decades shaping brands that make casinos feel alive. As SVP of Marketing at Foxwoods Resort Casino, one of the world’s largest gaming and entertainment destinations, he leads strategy across brand, digital, loyalty, and guest experience for a property owned by the Mashantucket Pequot Tribal Nation.

Before Foxwoods, Blair drove marketing for Boyd Gaming and Pinnacle Entertainment, guiding multi-property teams through high-stakes launches and rebrands. Known for blending data and instinct, he’s built campaigns that turn foot traffic into fandom and moments into measurable growth.

The Evolution of Casino Martech

Casinos thrive on the energy of real people in real spaces. Blair has spent his career in that environment, where the hum of slot machines and the rhythm of foot traffic define success. He points out that while other industries rushed to digitize, gaming and hospitality focused on the on-property experience that drives most of their revenue. Technology in this world serves the guest standing in front of you, not a distant audience online.

“There’s a lot of innovation, but it’s all centered around that customer and that on-property experience,” Blair said.

Walk across a modern casino floor and you see how far that innovation has gone. Slot machines now reach twelve feet high, lit by curved screens that feel more like immersive art installations than games. Even bingo, once a paper-and-pen ritual, lives on tablets. These changes reflect more than aesthetic upgrades. They mark the blending of digital personalization with in-person entertainment. Each new machine and experience collects data, interprets patterns, and helps casinos understand what keeps players coming back.

Blair sees the next phase of progress in the pairing of martech systems and artificial intelligence. Casinos have long collected data on player habits, but much of it stayed locked in isolated databases. AI now connects those dots, linking preferences, visit frequency, and loyalty activity into one living profile. That way you can predict what a guest wants before they ask for it. Personalized dining offers, targeted game promotions, or well-timed follow-up messages all become part of a continuous loop that strengthens engagement.

Still, Blair focuses on the human side of this transformation. “People assume tech makes everything easier, and it doesn’t,” he said. Each new platform requires training, integration, and trust. Martech without people who know how to use it becomes clutter. Blair spends much of his time ensuring his team understands the technology deeply enough to keep the guest experience effortless. The strategy depends on teams who can think like data analysts and act like hosts.

Key takeaway: Martech and AI can elevate on-property hospitality when used to deepen human connection instead of replacing it. Integrate systems that unify guest data, but prioritize training and comfort among your team. When your people trust the tools and your guests feel known, technology quietly fades into the background while loyalty takes center stage.

Customer Loyalty and Personalization in Casino Marketing

Casino marketing has operated on autopilot for too long. Guests still get dropped into massive segmentation buckets, treated as if their weekend habits, entertainment tastes, and spending patterns are interchangeable. Blair describes it bluntly: “We still send show offers to guests who’ve never been to a concert in their life.” That single sentence captures the outdated logic behind much of hospitality marketing. The data is there, but the systems fail to translate it into actual relevance.

Blair’s vision for Foxwoods looks very different. He wants every guest communication to reflect an individual’s real-world behavior across the property. The system should recognize the guest who booked a John Legend concert last year, scheduled a spa visit before dinner at the steakhouse, and played slots into the night. That pattern should generate communications that align with their habits instead of contradicting them. The goal is not another loyalty campaign; it is a personalized experience that extends far beyond the walls of the casino.

“Pre-booking, post-booking, everything in between should feel connected and meaningful,” Blair says. “It should never just be noise.”

The complexity behind that ambition is immense. Each behavioral variable—favorite artist, time of year, dining preference, game type—multiplies the possible outcomes. A small addition in logic can create thousands of potential message combinations. Casinos also face stricter rules on data sensitivity than most industries, so scaling personalization demands precision. The technical lift is enormous, but the payoff is real: when every offer feels relevant, engagement increases without resorting to gimmicks or discounts.

The most important shift is cultural, not technological. Marketing teams need to stop thinking of messages as promotions and start thinking of them as part of the guest experience. When personalization is treated as hospitality, not marketing automation, it starts to feel natural. That mindset transforms every text, push notification, and offer into something that extends the stay rather than interrupts it.

Key takeaway: One-to-one personalization in casino marketing depends on operational discipline, unified data, and a mindset shift. Start by mapping how guests actually experience your property, then use that data to inform relevant communication across every channel. That way you can replace noise with value, and marketing becomes an extension of the hospitality experience itself.

Using the Right Marketing Channel for the Right Goal in Hospitality

Coordinating multiple marketing systems inside a casino is like running a live concert with half the band still tuning. Each channel (email, mobile, social, in-property signage) operates on a separate timeline, using different data and often speaking a different language. Blair knows this chaos well. His goal is to make those systems play in harmony, producing a s...

What’s up folks, today we have the pleasure of sitting down with Megan Kwon, Director, Digital Customer Communications at Loblaw Digital.

• (00:00) - Intro

About Megan

Megan Kwon runs digital customer communications at Loblaw Digital, the team behind how millions of Canadians hear from brands like Loblaws, Shoppers Drug Mart, and President’s Choice. She’s part strategist, part systems thinker, and fully obsessed with how data can make marketing feel more human, not less.

Before returning to Loblaw, Megan helped reshape how people discover and trust local marketplaces at Kijiji, and before that, she built growth engines in the fintech world at NorthOne. Her career has been a study in scale; from scrappy e-commerce tests to national lifecycle programs that touch nearly every Canadian household. What sets her apart is the way she leads: with deep curiosity, radical ownership, and a bias for collaboration. She believes numbers tell stories, and that the best marketing teams build movements around insight, empathy, and accountability.

Building a Career Around Conversations That Scale

Running digital messaging at Loblaw means coordinating communication at a scale that few marketers ever experience. Megan oversees the systems that deliver millions of emails and texts across brands Canadians interact with daily, including Loblaws, Shoppers Drug Mart, and President’s Choice. Her team manages both marketing and transactional messages, making sure each one aligns with a specific stage in the customer journey. The workload is immense. Each division has its own priorities, and every campaign needs to fit within a shared infrastructure that still feels personal to the customer.

“We work with a lot of different business divisions across the entire organization. Our job is to make sure their strategies and programs come to life through the customer lifecycle.”

Megan’s team operates more like a connective tissue than a broadcast engine. They bridge the gaps between marketing, product, and data teams, translating disconnected strategies into a unified experience. That work involves building systems capable of:

Managing multiple brand voices while keeping messaging consistent
Triggering real-time communications that respond to customer behavior
Integrating old and new technologies without breaking operational flow

Every campaign becomes part of a continuous conversation with the customer. Each message is one step in a long dialogue, not a one-off announcement.

Megan’s perspective comes from experience earned in very different industries. She began her career at Loblaw during the early days of online grocery, a time when digital operations were experimental and resourceful. She later worked across fintech, marketplaces, and paid media before returning to Loblaw. That journey helped her understand every layer of the customer funnel, from acquisition through retention. It also taught her how to combine growth marketing tactics with enterprise-level communication systems, that way she can scale personalization without losing humanity.

Most large organizations still treat messaging as a collection of isolated programs. Megan treats it as an ecosystem. Her work shows that when lifecycle and acquisition efforts operate within a shared framework, communication becomes more coherent and far more effective. Alignment between data, channels, and teams reduces noise and builds trust with customers who engage across multiple brands.

Key takeaway: Building a unified messaging ecosystem starts with structure, not volume. Create systems that connect channels, data, and brand voices into one coordinated experience. Treat messaging as a relationship that continues long after the first conversion. That way you can make enterprise-scale communication feel personal, intentional, and consistent across every touchpoint.

Customer Journey Pods and Martech Team Structures

Running digital communications at Loblaw means managing one of the largest customer ecosystems in the country. The team sends millions of messages across grocery, pharmacy, and e-commerce brands every week. Each interaction has to feel personal, relevant, and timely, even when it comes from a massive organization. Megan explains that the only way to handle that kind of scale is to treat data as the operating system and collaboration as the backbone.

Her team relies on analytics to shape every message. Real-time signals from dozens of digital properties guide what customers see, when they see it, and how those experiences evolve. It is a constant feedback loop between behavior and communication. “We lean a lot into the data that we gather,” Megan says. “That pretty much drives almost everything that we do.” The systems are only half the story, though. The other half is how her team stays connected across offices, divisions, and projects. They share knowledge in Coda, manage progress in Jira, and rely on Slack to keep conversations fluid. Even their emojis have purpose, creating a shared language that makes collaboration faster and more human.

“Everything that we do, we share that knowledge back and forth so that we can continue to learn off each other,” Megan said.

The team structure used to follow the company’s business units. Each division had its own specialists who acted like small internal agencies. It worked for speed, but it made collaboration harder. Megan reorganized everything around the customer journey instead. Her teams now work in “pods” that align with stages such as onboarding, discovery, shopping, and post-purchase. Each pod has both data and creative ownership over its domain. That way, a single team can experiment, learn, and apply what works across multiple brands.

Megan also built intentional overlap between pods to keep ideas moving. For example, the loyalty and early engagement pod owns both new-member activation and retention. That connection helps them understand the full customer arc, from first purchase to repeat visits. The result is a flexible structure that shares expertise fluidly without losing focus. Large enterprises tend to slow down under their own weight, but this model keeps Loblaw’s marketing engine fast, synchronized, and grounded in customer behavior.

The work Megan’s team does might look complex from the out...