All posts by Mukund Mohan

My discipline will beat your intellect
investing, Marketing

AdTech and AI with Vibhor Kapoor: Navigating the Future of Marketing Technology

 In the rapidly evolving world of advertising technology, few leaders stand out with as much insight and forward-thinking vision as Vibhor Kapoor, President at AdRoll. With a rich career spanning Microsoft, Adobe, Twilio Segment, and now AdRoll, Vibhor brings a unique perspective on how AI, data, and personalization are reshaping the marketing landscape. In this article, I’ll share an in-depth conversation that delves into how AI is transforming adtech, the complexities of the digital advertising ecosystem, and the exciting challenges and opportunities ahead for marketers and brands alike.

From Engineering to Marketing: A Journey Fueled by Customer-Centric Innovation

My journey into technology and marketing wasn’t a straight path. It began with a passion for engineering and a desire to communicate the value of technology to customers. Early on, I realized that to truly serve customers, I needed to understand their challenges firsthand. So I transitioned from sales to building technology products—spending over a decade driving change and helping teams adopt new ways of working. Technology change isn’t just about software; it’s about evolving processes and mindsets.

Eventually, I gravitated towards marketing, which itself was undergoing a transformation. Marketing became a blend of left-brain analytics and right-brain creativity—an exciting space where data, experimentation, and rapid iteration met storytelling and brand building. For the last 15 years, I have stayed in this intersection, helping organizations harness data-driven marketing to grow and innovate.

AdRoll and the Role of AI in Programmatic Advertising

Today, I lead marketing at AdRoll, a programmatic advertising platform that partners with brands and agencies to drive full-funnel, multi-channel marketing. We help businesses reach new customers and nurture prospects across various digital channels—from connected TV to premium publishers like The New York Times and CNN. At our core, we’re an adtech company powered by data and machine learning.

AI is deeply embedded in what we do. For over 15 years, we have leveraged machine learning to analyze bidstream and ad engagement data, helping us predict which ads will resonate best with specific audiences. Our proprietary large language model adds another layer of sophistication, enabling smarter campaign creation and optimization.

But AI’s impact goes beyond just our product. Internally, we use AI to enhance marketing workflows, customer success, and content production. For example, we’ve developed conversational AI assistants that allow marketers to create campaigns, track performance, and optimize budgets using natural language interfaces instead of complex dashboards. This approach simplifies campaign management and improves efficiency.

Prioritizing AI Applications: Where to Focus for Maximum Impact

One of the biggest challenges with AI is deciding where to apply it. The temptation to “AI everything” can lead to paralysis or endless experiments that never reach production. At AdRoll, we prioritize AI initiatives based on deep insights from our product, customer conversations, and data analysis.

The key theme is synthesis—helping marketers make sense of vast, complex data sets that are impossible for humans to process manually. Our customers often run tens or hundreds of campaigns across geographies, industries, and objectives. Optimizing budgets, messaging, and channels at this scale is daunting.

AI helps by providing intelligent recommendations—such as when and how to shift budgets between campaigns within guardrails set by the marketer. This not only improves performance but also reduces manual workload. While many platforms offer AI-powered campaign creation for efficiency, the real game-changer is AI that acts as a co-pilot, synthesizing data and guiding decision-making.

The Complex AdTech Ecosystem: AI’s Role Across the Value Chain

The advertising technology ecosystem is notoriously complex. It involves multiple players—from brands and agencies to content publishers, demand-side platforms (DSPs), supply-side platforms (SSPs), and a host of intermediaries. Navigating this trillion-dollar industry requires deep expertise and coordination.

AI’s potential to improve productivity spans the entire value chain, but I see the most interesting dynamics at the two ends of the spectrum:

  • Brands and Agencies: AI enables smarter audience identification, campaign planning, and performance forecasting. With so many signals and personalization opportunities, AI enhances return on ad spend (ROAS), cost per click (CPC), and cost per mille (CPM) metrics.
  • Content Publishers: Publishers face headwinds from the rise of walled gardens like Instagram and Reddit, which keep users locked into their ecosystems. These publishers must rethink their revenue models, balancing advertising with subscription and paid content strategies.

Ad-supported publishers are innovating in the face of these challenges, exploring hybrid models to sustain quality journalism and content creation. AI will play a crucial role in optimizing ad placements and personalizing experiences, but the broader business model evolution is equally important.

AI-Generated Content and the Future of Creative Marketing

With AI increasingly capable of generating content and optimizing ad placements, one might wonder if human creativity and brand authenticity will be sidelined. I believe the opposite is true. AI-generated content can break the inertia of creativity by providing scalable tools, but it heightens the need for brands to focus on authenticity, messaging, and identity.

Creative tools like Adobe Firefly, Runway, and Sora offer unprecedented content velocity, but the fundamental question remains: who are you as a brand, and what problem are you solving for your audience? Data and AI give marketers the power to hyper-personalize and scale, but the responsibility to maintain authenticity and emotional connection is greater than ever.

Reimagining Marketing Workflows with AI

AI’s impact goes beyond content creation and campaign management. Marketing organizations have many internal workflows—content production, social media posting, performance analysis, demo bookings—that are ripe for automation. Many of these processes involve repetitive, mundane tasks that AI can handle efficiently.

However, simply layering AI onto existing workflows yields marginal benefits. The real value comes from reimagining workflows from an AI-first perspective, redesigning processes to leverage AI’s strengths fully.

For example, tools like relay.app provide extensive libraries to automate sales and marketing workflows. We are actively exploring such solutions internally to free our teams from routine tasks, allowing them to focus on strategic and creative work.

Embracing Full Automation: Real-World Applications

Personally, I’ve automated many aspects of my content production and outreach. Until recently, I had a team of four people editing videos, transcribing content, and repurposing it for blogs and social media. Today, this entire process is automated using AI tools like NA10, saving time and resources.

I’ve also built custom GPT models that analyze conversation transcripts, extract insights, and provide contextual prompts. This enhances recall and helps integrate knowledge seamlessly into workflows.

Similarly, at AdRoll, we have automated LinkedIn and email outreach, routing incoming demo requests to AI-powered systems that generate personalized product demos based on customer data—all without human intervention. This kind of automation improves scalability and customer experience.

Looking Ahead: The AI-Driven Future of Marketing

As we move forward, AI will become even more integral to marketing technology. The next frontier involves deeper integration of AI into both products and internal business processes. Marketers must balance embracing automation with preserving creativity and authenticity.

The challenges are complex, but so are the opportunities. By harnessing AI to synthesize data, optimize campaigns, automate workflows, and generate content, marketers can unlock new levels of efficiency and impact. But success requires thoughtful prioritization, continuous learning, and a commitment to understanding and serving customers better.

In this ever-changing landscape, the key to thriving is not just adopting AI but reimagining how we work and connect with audiences. The future of adtech and marketing is bright, and I’m excited to be part of the journey.

Marketing

Marketing Is Not a Vending Machine — And That’s a Good Thing with Kathleen Schaub

So I just listened to this fascinating conversation with Kathleen Schaub — longtime tech marketer, ex-IDC CMO advisor, and now author of “The Really Big, Messy, Real World: Rewire Your Marketing Organization to Navigate Anything.”First off, yes, that title is a mouthful — but once she explained it, I got it. Marketing is messy, unpredictable, and human. It’s not some vending machine where you put in budget and pipeline pops out. And if you’ve ever tried to forecast your lead flow by campaign type and then had it all go sideways? You know exactly what she means.

Kathleen’s been in the game a long time. From retail marketing to product marketing to working with hundreds of CMOs across startups and tech giants alike during her decade at IDC. What’s cool is she’s seen the patterns — the hype cycles, the disconnects, the exec team misalignments — and now she’s putting that into a framework we can actually use. Not another “do these five tactics and win marketing” book, but a legit mindset shift.

The whole thesis of her book is that we need to rewire how we think about marketing. Because the world has changed — and keeps changing faster than we can keep up. AI, customer behavior shifts, macroeconomic swings… it’s chaos. And we’re trying to navigate it with models built for a more linear, controlled world.

One thing Kathleen says really stuck with me: “Marketing is more like the stock market or the weather.” And if you’ve ever run a multi-touch attribution model or tried to get sales to align with brand campaigns, you know exactly how unpredictable that system is. It’s not about control. It’s about adaptation.

She spent a bunch of time studying other complex, turbulent environments — like emergency response teams, the military, even healthcare — to see how they operate under chaos. What can marketers learn from them? Turns out, a lot.

Her approach boils down to two foundational capabilities: agility and what she calls “market system health.” Think of agility as your ability to respond and adapt quickly — pretty straightforward. But market system health? That’s more like building up your immune system. You can’t avoid every downturn or misfire, but if your org is resilient, you’ll bounce back faster.

Now, the mindsets part is where it gets practical.

Kathleen lays out four marketing mindsets in the book. My favorite? “Think like an investor.” Instead of seeing marketing spend as a cost center, she wants CMOs and founders to think like they’re making bets — strategic, long-term investments aimed at creating future value. You wouldn’t yank all your money out of a 401(k) because one quarter underperformed, right?

She also talks about shifting from linear planning to navigation. Picture plotting a boat journey: you’ve got a destination, but you need to adjust course constantly due to currents, storms, or obstacles. That’s how modern marketing should operate — iteratively, flexibly. There’s even a concept she mentions called wayfinding (from the world of design), which is about finding your path even in unfamiliar territory.

Naturally, the topic of AI came up. Kathleen was refreshingly grounded about it. Yes, CMOs and marketers are under pressure to “do more with less” thanks to AI hype. Yes, AI tools like ChatGPT, Claude, Gemini, etc., are now everywhere. But she made a critical point — most people are still using AI for surface-level stuff like writing emails faster or tweaking ad copy. The real opportunity is in the analytical and strategic side — using AI to augment decision-making, model scenarios, and understand the why, not just the what.

She’s especially intrigued by causal AI, which can help answer those deeper “what-if” questions and identify triggers and indicators that can drive smarter strategies. Way more valuable than basic predictive analytics — which, let’s face it, most companies aren’t even doing well yet.

Another important topic they covered was the evolving role of the CMO. Is marketing gaining more influence or losing it to CROs? Kathleen had a nuanced take: it’s less about titles and more about integration. The future isn’t sales vs. marketing, but blending both at the edge. She’s not against CROs per se, but she warns against making them just glorified sales leaders with a sprinkle of marketing.

In fact, she flipped the script and asked: What if marketing owned revenue? Could marketers actually lead the revenue charge instead of reporting to it? Her answer: yes — if they can shift their mindset from running a department to influencing the entire business. That’s the level of strategic thinking the next-gen marketing leader needs.

And here’s what I appreciated most — she’s not pretending to have all the answers. The book isn’t a checklist; it’s a map for developing your org, your team, and your thinking. Some parts are easy. Others take real work. But the underlying truth is simple: the world is uncertain, messy, and unpredictable — and marketing should stop pretending it isn’t.

Like she said at the end, quoting from a book on resilience: “If we cannot control the volatile tides of change, we can learn to build better boats.” Kathleen’s book? It’s a blueprint for building that better boat.

Management, Marketing

Winning by design with Dave Boyce

Alright, so I just finished listening to this fantastic podcast episode with Dave Boyce, and let me tell you—it was like a masterclass in modern go-to-market strategy, systems thinking, and how AI is flipping the game. Imagine sitting with a smart, laid-back friend who’s figured out how to scale SaaS, sell a company to Oracle, and now spends his time thinking deeply about how to make GTM systems work smarter, not just harder. That’s this convo in a nutshell.

So, here’s the vibe: Dave’s hanging out in Provo, Utah, with the mountains behind him and a fresh breeze coming through his office window. And yeah, you can’t help but feel a little jealous. But the real sunshine is in the insights he drops.

We start off with his journey—Dave’s been a founder a few times over, sold his last company, and instead of jumping into another build-from-scratch grind, he wanted to pause. Reflect. You know, zoom out and figure out what worked, what didn’t, and what patterns kept showing up. That led him to Winning by Design (WbD), where he didn’t just join a consulting firm—he joined a team of systems thinkers who speak his language.

Now, if you’ve heard about the “bow tie” model and wondered why everyone’s obsessed with it—Dave breaks it down beautifully. Basically, most companies obsess over the left side of the funnel: acquisition. Pipeline. Bookings. Then they hit the gong and call it a day. But in SaaS, that’s not the win—it’s just the start. The bow tie model maps out the full customer lifecycle—acquisition on one side, but then post-sale? You’ve got onboarding, renewal, expansion, and if you do it right, compound growth. That’s the right side of the bow tie. That’s where the money and retention magic happens.

One thing I loved—Dave draws a clear line between “systems thinking” and “framework thinking.” They’re not that different, really. It’s about poking one part of your GTM system and knowing how it’ll ripple downstream. Lowering price might get you more deals… but what happens to profitability? Widening the top of your funnel might sound sexy, but what happens when your sales team spends their time chasing unqualified leads? Systems thinking helps you see those second- and third-order effects.

Now enter AI.

Dave talks about how AI is just jet fuel for the self-service motion. At WbD, they’re embracing what he calls Assistive, Agentic, and Orchestrative AI. Think of Assistive as your AI-powered intern—summarizing documents, writing drafts, doing research. Agentic AI takes that up a notch—it’s trained to do a job. And Orchestrative? That’s the C-suite bot. It’s making calls, triggering workflows, handling real-deal ops.

They’ve even named their agents—Celeste (internal GTM intelligence) and Jack (inbound SDR)—because we still think in human terms. Celeste attends every call, reads every email, taps into CRM data, and can brief anyone on the full context of an account. Jack qualifies inbound leads right on their site and directs them toward the right path—self-serve or human.

And Dave makes this point that really stuck with me: AI-native companies are just wired differently than SaaS-native ones. In SaaS-native, you sign 12-month contracts, and you get your renewal chat once a year. In AI-native, like with tools we all use now—Replit, Cursor, ChatGPT—you upgrade or downgrade based on value, not contract cycles. If you’re not delivering impact every day, your users are gone. That means the mindset needs to shift from “sell once” to “deliver value continuously.” Renewals are monthly, even daily.

Even the way we think about a “customer” is changing. Dave says: think of a customer as a collection of users, each with their own trust networks. If you deliver impact to one, they’ll recruit others. The virality becomes product-led and AI-assisted.

For marketing agencies—and this part is gold—Dave suggests they’ll need to evolve into GTM agencies. Why? Because recurring revenue depends on recurring impact, and the only way to sustain that is to bake systems thinking into your DNA. Tips and tricks won’t cut it anymore. The agency of the future is empathic, metrics-driven, and deeply attuned to the customer’s business goals.

Oh, and about the naming of AI agents? Dave laughs, but he’s clear—it’s partly because humans need to personify things to build trust. Plus, when you’re onboarding or managing AI agents like team members, giving them a name helps you slot them into your org just like a human hire.

The whole convo wraps with Dave and Mukund reflecting on how the ground has shifted under our feet. Higher cost of capital, AI reshaping user behavior, the rise of DIY SaaS… you can’t just do what worked in 2018 and expect magic. If you’re not re-earning your customers’ trust and business every month, you’re toast.

Anyway, this one was a ride—part therapy session for GTM leaders, part AI strategy briefing, and all heart. Highly recommend.

Marketing, SaaS

The Future of Marketing Agencies? Anne from Sierra Ventures Says “It’s Their Moment”

So I just listened to this super insightful conversation with Anne, the CMO at Sierra Ventures, and let me tell you — if you’re in marketing, freelancing, or running an agency right now, this is your moment. Seriously. It’s not doom-and-gloom like some people say AI is coming for all the jobs. Nope. Anne’s take? This is probably the best time ever to be running a small agency or be a consultant. Here’s why.

Anne’s journey is already wild — she started in Hollywood at Paramount Pictures, went up to the Bay Area, worked at Sony PlayStation, then jumped into the startup world, and eventually built her own agency before landing in venture capital. She’s now helping portfolio companies scale their go-to-market (GTM) strategies and digging into deal flow as part of Sierra’s investment team.

But the real heart of the convo was about what she’s seeing right now in the world of GTM and marketing, especially with AI taking over.

Let’s rewind for a second. Remember how we used to think agencies were on their way out because of AI? That’s what a lot of VCs were buzzing about. But Anne flipped the script.

Her LinkedIn post that kicked off this whole discussion made a big, bold point: agencies are not dying — they’re evolving, and fast.

Why? Because the tech is finally working for the agencies.

AI tools have leveled the playing field. A small shop can now spin up content, videos, and even podcasts at lightning speed, and often better than a bloated team. And here’s the kicker — many agencies are still charging the same prices they did pre-AI, while doing the work in a fraction of the time.

Anne said it best: “These tools only work as well as you know how to make them work.” That’s the sweet spot where agencies and freelancers are thriving — becoming true experts in tools like video editing, podcast production, AI writing assistants, and platforms like Clay (which is powerful but not exactly beginner-friendly).

One of the cooler metaphors that came up was the idea of the “full-stack agency.” Back in the day, a marketing agency might have had one SEO person, one video person, one designer, and so on. Today? You’ve got folks who are full-stack in their specialty. Like: “I do video, and I do it all — record, edit, optimize, publish.” Same with podcasts, same with content. Fewer handoffs. Less friction. More speed. And much higher margins.

The conversation also veered into why so many AI platforms are now offering “Do It For Me” or “Concierge” services, even though their tools are supposed to be self-serve. Isn’t that kind of ironic?

Anne explained this perfectly: Even if the tools are powerful, people — especially in startups and enterprises — don’t have the time to learn 10 new things while putting out a million fires. Startups are already running on overdrive quarter after quarter. So they need help. Not just software — implementation, guidance, and actual execution.

Enter the freelancers and micro-agencies. These folks are not just service providers anymore — they’re becoming educators and implementers. Some consultants are making a killing just teaching teams how to use tools like Clay effectively. That’s not going away anytime soon.

Another interesting thread: impact. The days of “I’ll make content and check the MQL box” are over. Brands want to see revenue. If your agency or tool isn’t tied to business outcomes — not just leads, but dollars — you’re going to get passed over. AI may automate execution, but strategy, impact, and real performance still need smart humans in the loop.

Anne even mentioned some cost-saving hacks on the CRM side — one of the podcast hosts built his own internal CRM using a $20 tool instead of paying $400 a month to HubSpot. That’s wild — and again, speaks to how agile and efficient marketing ops have become if you’re tech-savvy and AI-literate.

And honestly? This is just the beginning.

We’re still early. The models and tools we’re using now? They’re the worst we’ll ever use, Anne said — everything’s only going to get faster, smarter, and more intuitive. But right now, there’s this golden window of opportunity for agencies and freelancers to learn the tools, stack their skills, and lead the charge before everything becomes “click-and-done.”

So if you’re in the agency game, thinking of starting a consultancy, or even just freelancing on the side — this episode was your pep talk. You’re not being replaced by AI. You’re being amplified by it.

But only if you lean in.

Management, Marketing

From Corporate to Full-Funnel: How Tiffany’s Marketing Agency Is Redefining AI-Powered Growth

Okay, so let me tell you about this podcast episode I just listened to—it was with Tiffany, who’s had this amazing 17-year ride through sales and marketing. She’s done the whole big corporate thing, climbed all the way to VP of Marketing, and now she’s running her own agency. But not just any agency—this one’s built for the chaos of modern marketing, where AI is everywhere and everyone wants results yesterday.

She’s based in South Florida (right between Miami and Fort Lauderdale—jealous), and the conversation kicks off super casual. She’s the kind of person who’s clearly walked the walk. At one point, she jokes about being jealous of people posting org charts with “98 AI agents” running their entire marketing department. You know, the ones where someone brags about replacing their entire team with ChatGPT prompts? Yeah, she’s not buying it.

So here’s the deal: Tiffany started her agency originally as a side hustle—just her, trying to help other business owners get to that magical “freedom and flexibility” we all chase. But what kept bugging her, even when she was a corporate exec, was how hard it was to find a good agency partner. Like, even when she was hiring agencies as a client, she was still leading the strategy. And she’d be like, “Why am I paying you if I have to tell you what to do?”

That frustration turned into fuel. She eventually rebranded her firm as Third & Tailor, an end-to-end revenue marketing agency. Not “full-service” (she avoids that term—too “jack-of-all-trades”), but she helps B2B companies and well-funded startups figure out what’s holding them back. Sometimes it’s messaging, sometimes it’s funnel friction, and sometimes it’s paid media that needs a total revamp.

What’s wild is how the work has evolved. Five years ago, people would just say, “Can you run SEO or Google Ads for us?” Now, they’re like, “Something’s off—our funnel feels disjointed, we don’t know what’s working, and we need to hit aggressive targets but don’t have the right team.” That’s where Tiffany comes in with audits, CRO, messaging optimization, and whatever else moves the needle.

And then, of course, the AI convo. It wouldn’t be 2025 if we weren’t all talking about AI agents, right?

So Tiffany’s take on the whole “everyone needs 90 AI agents” trend is refreshingly grounded. Yes, she uses AI every single day. No, she doesn’t trust it to run wild. In fact, she still reviews everything herself before it goes out. She’s got AI helping her synthesize discovery calls, SEO results, brand messaging—you name it—but she’s not handing over full control. She said it best: “Speed to value. Not speed for the sake of it. Not to cheat. But to deliver faster.”

And for outreach? She’s built some pretty slick prospecting systems using AI. Her example: traditional sales reps used to skip LinkedIn touchpoints because it wasn’t automated. So she’s using tools that hit voice, LinkedIn, email—multi-touch, more personal (but not creepy). She called out how even “Hey, I saw you raised funding” messages are still generic if you’re not training your AI properly.

Oh! And this part was so cool: she’s using a tool called TopVoice.com.club (a client of hers, actually) that custom-curates LinkedIn content based on her voice, past writing, and favorite topics. It scours the internet for posts that are actuallyrelevant for her to engage with or respond to. None of that same-old AI-written post garbage. This one keeps her voice consistent and saves her time.

She’s also seeing a huge trend: clients asking for outcome-based or performance-based pricing. Like, 5 out of 10 discovery calls now involve someone saying, “Can we tie this to results?” She’s flexible—some projects get a rev share, others stick with retainers. But she’s very clear: not everything can be performance-based. AI can help, sure, but it doesn’t control the internet’s mood that day.

The future? By 2030, Tiffany thinks the entire agency model will shift. Not necessarily cheaper—because you’re still delivering value, just faster—but definitely different. Expectations will be way higher, timelines way tighter. Agencies that don’t adapt will get replaced—maybe not by AI itself, but by teams who know how to wield it. She’s already thinking ahead, even developing tech to support that shift.

Her advice? Marketers and agency owners need to get off the sidelines and into the AI game. But don’t go full autopilot. Use it to enhance your work. Keep your human edge.

So yeah, this episode was a gem. Tiffany is real, strategic, and future-facing without drinking the full AI Kool-Aid. She’s building a business that’s flexible, fast, and outcome-focused—and not afraid to rethink what it means to be a modern marketing partner in the AI age.

Highly recommend giving this one a listen if you’re in marketing, run an agency, or just wondering how the heck you’re supposed to keep up. Tiffany’s got answers—and probably an audit waiting for you.

Ai, Angel investing, Marketing

AI and GTM for startups with ex-A16Z partner Michele Griffin

The GTM Playbook Is Dead — Here’s What Comes Next

In a recent podcast conversation with Michele Griffin — former GTM lead at Andreessen Horowitz and now founder of Premier GTM — we explored how go-to-market (GTM) strategy is evolving in the age of AI. One thing was clear: the traditional B2B sales and marketing playbook is being rewritten in real time.

“Startups used to run on playbooks. Now, the winners will run on feedback loops.”

Michele’s insight reflects a broader shift we’re seeing across SaaS and enterprise: GTM is no longer about executing a static sequence of steps. It’s about learning fast, adapting faster, and building AI-native systems that continuously evolve.

Here are four takeaways from our discussion:

  1. AI-native is the new default.Adding AI to an old process isn’t enough. Founders building today are designing their GTM from the ground up to be AI-native — automating research, personalization, prospecting, and even follow-ups.
  2. Lean teams are a feature, not a bug.Michele made the case that founders can — and should — delay building large sales teams. If you can reach Series B with two humans and a well-orchestrated AI stack, do it.
  3. Enterprise buyers are moving faster than you think.The old model of slow-moving procurement cycles is shifting. Boards are pressuring leadership to modernize data and infrastructure for AI-readiness — and they’re doing it now, not in five years.
  4. Feedback beats playbooks.Instead of relying on one-size-fits-all messaging and personas, winning GTM orgs are instrumenting their processes for continuous learning: real-time ICP refinement, live persona mapping, and signal-driven campaign adjustment.

As Michele put it: “We’re entering the era of intelligent GTM.”

Premier GTM is at the forefront of this transformation — helping AI-first startups, investors, and enterprise teams design go-to-market strategies that are built for speed, feedback, and scale.

If you’re rethinking how to reach your customers in an AI-dominated market, this episode is worth your time.

Marketing

Why the Old-School Analyst Model is Broken—and What to Know About Composable CDPs with Jacqueline from Monarch

Okay, so let me tell you about this fascinating convo I just listened to with Jacqueline—she’s the founder of Monarch and has this amazing background in marketing ops, martech, and GTM strategy. She’s done time at big names like WeWork and Grammarly, built teams from the ground up, and now runs her own consulting firm. But the real juice? The deep dive she took us on about industry analysts like Gartner, Forrester, and IDC—and why their model might be totally outdated for today’s fast-moving tech world.

First off, Jacqueline kicked things off from sunny Dallas (on what she called “the best day of the year,” according to Miss Congeniality—yes, April 25th). And then we dove right into this big industry gripe that a lot of GTM folks quietly have: analyst firms aren’t actually helping as much as we pretend they are.

Back in the day—like 2005 through 2015—analyst firms were the gatekeepers. You were either on the “Magic Quadrant” or you weren’t even in the conversation. Big companies would lean on Gartner or IDC to tell them what tools to buy, what trends to watch, and what best practices to follow. On the other side, vendors would spend crazy amounts of money and headcount trying to get on these reports. Entire analyst relations teams were (and still are) a thing.

But here’s the kicker: Jacqueline points out that the velocity of change in tech, especially since OpenAI and ChatGPT entered the picture, has completely broken that model. Analyst firms are just too slow. She said it bluntly: by the time they publish a report, the world has already shifted. And the idea that these reports are gospel? Not even close. There’s solid research, yes—but it’s rarely enough to make strategic, forward-thinking decisions.

She even made this great point about how many of the companies that make it onto a Gartner quadrant have already invested huge internal resources—think six-figure salaries—just to engage and keep up with the analysts. It’s not officially “pay to play,” but it kinda feels like it, right?

Now, the most eye-opening part of the conversation? When she started talking about CDPs—Customer Data Platforms.

CDPs are supposed to be this holy grail for marketers, helping unify all your fragmented data (CRM, ad platforms, website analytics, transaction history, the works) into one place so you can take smarter actions. But Jacqueline’s take? Most CDPs are basically repackaging your own data and selling it back to you at a markup. It’s like taking Belgian chocolate, melting it down, putting it in a box, and selling it back to you at 150% of the price. She actually compared it to Nestle—super relatable.

The traditional CDP approach, she explained, involves a lot of data duplication and reverse ETL. You’re pulling data from your warehouse, transforming it, loading it into a CDP, then turning around and pushing it right back into tools like Salesforce or HubSpot. It’s inefficient and expensive. Especially when you already have that data sitting in Snowflake or Databricks.

Enter: Composable CDPs.

This is the part that had me really leaning in. Composable CDPs flip the whole thing on its head. Instead of duplicating and warehousing the same data in a new system, you’re building on top of your existing stack. You connect directly to your data warehouse and give marketers the power to build audiences and segments without needing SQL or engineering help.

She gave a real-world example from her time at Grammarly. They weren’t pulling marketing data into their warehouse at all, and she realized they were burning budget without activating on that data. So she brought in Hightouch (a reverse ETL tool), worked with the data team, and boom—they were able to feed insights directly into ad platforms, personalize campaigns better, and cut waste. Later, Grammarly even published a case study on it.

And here’s what’s wild: Composable CDPs don’t just help marketing. Jacqueline broke down how they make life easier for engineering and product teams too. With unified data definitions and access, everyone—marketing, sales, product, analysts—can actually agree on who the customer is, what they did, and how to act on it.

She also made a great point about segmentation. Take Grammarly again. They’ve got free users, premium users, and business accounts. Each of those has subsegments, and keeping that up to date across Google Ads, Facebook, LinkedIn, email, etc. is a huge lift. But with composable CDPs and tools like Hightouch, Census, or GrowthLoop, you can sync those segments every few hours, automatically. If you’re running big paid budgets, that pays for itself within weeks.

So what’s the takeaway?

Jacqueline was clear: analyst firms like Gartner aren’t useless—but they’re just not moving at the pace the market needs. And when it comes to martech, CDPs, and activation strategies, you’re better off doing the hard work internally and trusting your team to test, learn, and optimize.

And if you haven’t looked into composable CDPs yet, it’s time. They’re not just buzzwords. They’re a more agile, efficient, and actually understandable way to get value from your data—without burning budget or depending on clunky legacy platforms.

This episode was jam-packed and flew by—definitely worth a relisten. And Jacqueline? Total rockstar. She’s sharp, funny, and super practical. I hope she comes back on the show because we only scratched the surface.

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Unlocking LinkedIn X-Ray Search: A Secret Weapon for Modern Sales Pros

If you’re in sales and you haven’t heard of LinkedIn X-Ray search, buckle up—because you’re about to add a high-precision tool to your prospecting arsenal.

LinkedIn X-Ray search is the practice of using Google (or any search engine) to bypass LinkedIn’s limitations and uncover publicly available profiles—without needing Sales Navigator, InMail credits, or a 1st-degree connection. It works by combining advanced search operators with LinkedIn’s public profile structure to zero in on your ideal prospects.

Why Sales Teams Should Care

Let’s be honest: LinkedIn’s native search is limited, especially if you’re on the free plan. Even with Sales Navigator, filters can be clunky, and LinkedIn isn’t always generous with visibility outside your network. That’s where X-Ray search shines.

With a few smart keywords and operators, you can:

  • Find decision-makers at target accounts
  • Filter profiles by job title, geography, or tech stack
  • Discover hidden talent or past roles that align with your ICP
  • Avoid recruiter and job listing noise

A Working Example

Say you’re looking for Content Marketing Strategists in the US at Fortune 1000 companies. Here’s a sample search string you can drop into Google:

site:linkedin.com/in ("content marketing strategist" OR "content lead") ("United States" OR USA) ("Fortune 500" OR "Fortune 1000") -intitle:job -intitle:hiring

Boom. You now have access to dozens (if not hundreds) of profile pages with the right role, right market, and a filter to exclude job listings and recruiter spam.

Pro Tips for X-Ray Mastery

  • Use Boolean logic: Combine keywords using AND, OR, - (exclude) and parentheses.
  • Stick to site:linkedin.com/in: This narrows results to actual LinkedIn profiles, not groups or jobs.
  • Add filters: Use "location", "past company", or "current company" in quotes to refine further.
  • Exclude the fluff: Add -intitle:job -intitle:hiring -site:linkedin.com/jobs to avoid noise.
  • Use intitle and intext for any mentions on the profile

When to Use It

  • Pre-call research
  • Target account list building
  • Persona validation for ABM
  • Prospecting for outbound email or LinkedIn DMs

One Warning

LinkedIn can throttle access or obfuscate some data due to privacy settings. So don’t expect 100% coverage. But for scrappy sellers or SDRs looking for an edge—it’s a goldmine.

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The future of Pricing and Packaging with Adam Hauff of Sentinel One

The conversation centers around Adam, a marketing professional deeply experienced in pricing, packaging, and monetization strategies, particularly in the cybersecurity and SaaS sectors. Adam shares insights into his career trajectory, the significance of pricing and packaging in go-to-market strategies, and how companies evolve to adopt specialized pricing roles, especially as they prepare for IPOs or scaling phases. He highlights the complexities of pricing new products, exemplified by OpenAI’s pricing missteps, and explains evolving pricing methodologies, including cost-plus, value-based, and the emerging outcome-based pricing.

The discussion further explores how SentinelOne, a well-known cybersecurity company, approaches revenue growth beyond sales scaling, emphasizing reducing friction, improving customer experience, and expanding into new markets or products through self-service and low-barrier strategies.

Adam articulates how AI, specifically large language models like ChatGPT, are changing individual productivity and organizational workflows. He illustrates practical AI uses from summarizing notes, generating project ideas, conducting research, to iterative content creation. However, he also notes AI’s limitations such as hallucinations and the challenge of integrating AI tools company-wide due to approval processes.

The conversation ends on an engaging note with Adam’s humorous anecdote about using AI to explore the improbable question of how long it would take to “eat” a cast iron skillet by cooking with it, which reinforces how AI can be a powerful “second teammate” for brainstorming and problem-solving when paired with human logic and oversight.

Highlights

  • 🔥 Adam’s career uniquely blends pricing, marketing, and go-to-market strategy expertise in cybersecurity SaaS.
  • 💰 Pricing and packaging become critical roles for scaling tech startups, especially around IPO readiness.
  • 🤖 AI tools like ChatGPT amplify productivity by assisting with research, communication, and iterative content creation.
  • ⚖️ Emergence of outcome-based pricing as an evolution beyond traditional value and cost-plus pricing models.
  • 🌍 SentinelOne’s growth strategy prioritizes reducing friction and enabling self-service to expand revenue channels sustainably.
  • 🚀 Companies are still adapting to AI integration at scale; individual use far outpaces organizational deployment.
  • 😂 Fun anecdote showcases AI’s ability to engage in complex, absurd queries and collaborate interactively with humans.

Key Insights

  • 📊 The importance of specialized pricing roles during scale-up phases: Adam stressed that hiring dedicated pricing professionals often happens during late-stage startup phases or IPO preparation. This is when pricing can no longer be “winged” by founders but requires systematic strategy to optimize revenue and market fit. Many companies could benefit from introducing pricing roles earlier to avoid “skeletons in the closet” and structural inefficiencies that stifle growth. This insight emphasizes maturity in pricing as a key factor in business scaling.
  • 💡 Pricing complexity in AI products reflects the unpredictability of user behavior: Adam’s commentary on OpenAI’s $200/month pricing mistake illustrates a larger problem—when launching novel AI-driven offerings, predicting user consumption and cloud costs is challenging. This unpredictability makes pricing pilots, iterative learning, and flexible price adjustments crucial. It also highlights that cost-plus pricing, while less favored, remains an important reference point to ensure product sustainability amid scaling user demand.
  • ⚙️ Outcome-based pricing represents the next evolutionary step beyond value-based pricing: Outcome-based pricing monetizes actual results achieved rather than anticipated value or simply usage. This model aligns incentives more closely with customer success but requires clearer metrics and sophisticated tracking. Adam’s view that this approach is an evolution rather than a replacement suggests organizations will adopt hybrid pricing models before fully transitioning, signifying a gradual shift in monetization philosophies.
  • 🔄 Expanding revenue beyond traditional sales requires reducing friction and enabling self-service models: Adam highlighted ways to grow revenue beyond just increasing sales headcount, such as enabling easier product access, trials, and bundled offerings that empower customers to explore solutions independently. This strategy allows companies like SentinelOne to scale revenue while controlling costs, entering new markets or verticals with lower barriers, and creating diversified monetization “engines.” This insight is essential for organizations aiming for hypergrowth without proportional increases in sales investment.
  • 🤝 Cross-functional collaboration is key to successful pricing and monetization initiatives: Adam’s role involves coordinating pilots and rolling out pricing changes with sales, marketing, operations, and internal teams to ensure organizational alignment. Pricing is not just a finance or marketing function but requires enterprise-wide buy-in, reinforcing that monetization strategy is foundational to broader go-to-market execution.
  • 🤖 AI significantly enhances individual productivity while presenting challenges for organizational adoption: Adam uses AI tools daily for note summarization, idea development, and project research, improving efficiency and creativity. However, organizational adoption lags due to concerns over AI hallucinations, content quality, and governance. This gap highlights the need for better frameworks, training, and AI governance in enterprises to fully leverage AI’s capabilities.
  • 🎭 Success with AI tools depends on curiosity and persistence rather than immediate perfection:Adam emphasized that users who gain the most from AI experimentation usually display curiosity and iterative usage, including trial and error. This mindset applies universally; AI’s limitations mean early abandonment forfeits its potential benefits. His approach of personal experimentation, including creative projects like writing children’s stories, demonstrates how familiarity with AI’s quirks fosters meaningful adoption.
  • 💬 The analogy of AI as a “second teammate” underscores its complementary role in decision-making: Adam described AI as both highly knowledgeable yet occasionally “stupid,” echoing the idea that AI needs human collaboration to check and guide its outputs. This interplay is crucial for realizing AI’s benefits while managing risks related to accuracy and context. It suggests the future workplace will emphasize symbiotic human-AI partnerships rather than replacement.
  • 😂 Humor and creative use cases reveal AI’s potential beyond standard business applications: The cast iron skillet experiment humorously highlights how AI can engage with intricate logical puzzles and help humans think through unconventional problems. Such playful interactions foster better understanding of AI’s capabilities and limitations, encouraging innovative use while serving as a reminder that AI requires human judgment to avoid absurd conclusions.
  • 📈 AI’s role at SentinelOne spans both product innovation and internal productivity: The company integrates AI and machine learning directly into cybersecurity offerings, enhancing customer SOC analyst efficiency. Internally, employees experiment with AI tools to improve research, data synthesis, and business operations. This dual application signals AI as a strategic asset not only in product development but also as a productivity multiplier, essential for tech companies competing on innovation.
  • 🧩 The evolving role of marketing now includes monetization strategies and revenue enablement:Adam’s broader remit beyond pricing packages involves creating new revenue channels that do not rely solely on increasing sales personnel, reflecting the shifting marketing landscape. Marketing intersects deeply with product packaging, pricing, partner enablement, and customer experience, positioning marketers as pivotal in holistic revenue growth strategies.
Overall, this conversation paints a comprehensive picture of how modern pricing and packaging intersect with marketing, AI adoption, and strategic revenue expansion in a high-growth, tech-driven environment. Adam’s practical and candid insights offer a roadmap for organizations navigating pricing complexities, AI integration, and sustainable growth initiatives in today’s dynamic market.
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Why Bordy Might Be the Most Charming AI in Your Network (and What It Means for the Future of Marketing)

So I just got off this incredible conversation with Clark, who’s one of the driving forces behind Bordy – yes, that Bordy, the AI voice connector that everyone in the tech scene seems to be whispering about on LinkedIn and beyond. And let me tell you… this isn’t just another chatbot or networking gimmick. This is something else entirely.

Picture this: you DM an AI on LinkedIn.

It calls you. Not a form, not an email, not a survey—a literal voice call from an Australian-accented AI who somehow makes small talk feel less awkward than your last Bumble date. And after a short, easy chat, it says, “Hey, should I introduce you to someone?” Then boom—real intros to real people with actual context and mutual value. It’s like a super-connector friend who knows everyone, never forgets, and is always available.

Clark gave us the lowdown on how he landed at Bordie. His path wasn’t the traditional ladder-climbing story either—he went from building his own business, dipping into sales, doing a stint in VC, learning startup growth at a company called Reveal in Paris, and even doing B2B work at an e-commerce agency. Eventually, a cold DM to Bordie’s Head of Ops led him into the heart of this fast-moving AI startup. Total serendipity.

Now, here’s the juicy part: what actually makes Bordy stand out?

Clark calls Bordy an “AI superconnector”—but don’t think of it like LinkedIn with voice. There’s no interface. No app to download. No endless forms to fill out. It’s just a DM, a phone number, and a surprisingly human-feeling voice call.

Then comes the magic. Bordy doesn’t just listen. He remembers. He suggests. He connects. And it’s opt-in, both ways. That means the intros are intentional, curated, and not some spammy blast. He sends follow-ups, intros both parties with a thoughtful message, and makes it feel like a friend-of-a-friend intro.

From a marketing perspective, though, Clark admits the positioning is tricky. It’s not a classic B2B tool. It’s not quite B2C either. It lives in this weird (and fascinating) B2P world—Business to Professional. And because there’s no interface, the challenge becomes even bigger: how do you scale something that’s entirely experience-based?

That’s where the genius of Bordie’s go-to-market strategy comes in. Clark and team aren’t just building a product. They’re building a character.

Yep, they actually have a screenwriter on the team helping develop Bordy’s “personality.” Think about that for a second. This is next-level AI marketing. Instead of sterile automation, they’re creating a digital persona—someone you’d actually want to talk to. The Australian accent? Intentional. The humor and empathy? Crafted. And when Bordie “raised” his own $8 million seed round, the headlines weren’t just about funding—they were about an AI raising money on its own. It hit differently. It felt different.

Clark emphasized something important: in an age of AI-driven everything, the winners won’t be the ones with the most APIs or data—they’ll be the ones with personality. AI with charisma. Agents you remember, want to use, and trust. And that’s what they’re leaning into with Bordie.

But how do they keep the content human in this overwhelming ocean of AI junk? According to Clark, it’s all about iterations and intentionality. It’s about having the right principles, good training data, and most of all—context. AI can sound magical if it understands who it’s talking to. That’s why they do deep research on the people Bordie engages with, which helps him tailor conversations and introductions with nuance. Clark believes the future of AI will feel like having your own personal, customized agent—one that really “gets” you.

Clark himself uses tools like Clay, Nanonets, and Lovelable in his own marketing stack. One of the coolest things he shared? He uses Clay to auto-reply to tweets as Bordie. It captures the tweet, does deep research on the person, runs the info through custom prompts, and then replies in Bordie’s voice. Talk about scalable authenticity.

That blend of no-code automation and creative spark is what defines the new marketer, according to Clark. It’s no longer just about writing copy or designing pretty visuals. It’s about being able to build automations, ship MVPs, and tell stories fast—without needing a dev, designer, or an entire agency. AI is reducing the friction between idea and execution to almost nothing.

And if you’re a young marketer reading this? Clark has a message for you: Get curious. Learn tools like Clay, Nanonets, and GPTs. Screenshot your problems, drop them into GPT, and ask better questions. That’s the real superpower now. Knowing how to solve with speed, not just strategy.

Before we wrapped, he told me about one of his favorite campaigns—sending out 200 physical Bordie box-heads with handwritten notes from each AI call. People literally built little cardboard Bordie heads, put them on their desks, and felt a tangible connection to the brand. Old-school meets new-school, and it totally worked.

So yeah, Bordie’s not just another AI product. He’s a whole vibe. A voice. A friend with a few megabytes of empathy. And thanks to folks like Clark, he’s slowly turning AI from something cold and robotic into something personal and unforgettable.

If that’s the future of marketing, sign me up.