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What’s up everyone, today we have the pleasure of sitting down with Amanda Natividad, Chief Evangelist at SparkToro and co-author of Zero Click Marketing.

Summary: Zero click marketing sounds great and all, but how do you actually prove it’s working? Amanda coined the term and she’s spent over a decade building marketing that lives where the audience already is, and in this episode she takes apart the measurement crisis that it creates. We get into attribution and measurement, how HubSpot losing 80% of its organic traffic actually hid record revenue. She also shows how to turn audience research into an operational system and why winning AI visibility comes down to writing genuinely good stuff. Stick around for the launch-week playbook and the overslept-webinar story that completely reframes how she guards her time.

In this Episode…

• Why Attribution Breaks Down in a Zero Click World
• What the Alligator Graph Means for Ops Teams
• How to Run a Zero Click Launch Week You Can Actually Measure
• How Incrementality Testing Works at Enterprise Scale
• What Audience Listening Adds to the Marketing Ops Stack
• What Content Leaders Need From Marketing Ops
• How to Turn Audience Research Into an Operational System
• How AI Visibility Changes Zero Click Marketing
• How to Set Boundaries and Avoid Burnout in Marketing

About Amanda

Amanda Natividad is the Chief Evangelist at SparkToro, the audience research startup, and the founder of Zero Click Marketing, a podcast and consultancy built around the framework she co-created with Rand Fishkin in 2022. She spent 4 and a half years as SparkToro’s VP of Marketing, where she launched a newsletter that reaches more than 60,000 subscribers at a 35% open rate and built Office Hours, a webinar series that pulls as many as 1,200 registrants a show.

She’s keynoted at AdWorld, Content Marketing World, and MozCon, and guest lectured at Columbia, Cornell, and Stanford. A Le Cordon Bleu-trained chef and former journalist, she now teaches Content Marketing 201 on Maven.

Why Attribution Breaks Down in a Zero Click World

Every marketing ops team runs on a dashboard that hands out credit. This lead came from paid search. That demo came from a LinkedIn ad. This signup traces back to the nurture email. The numbers look authoritative, and leadership treats them that way. The trouble is the data underneath has been eroding for years, and most teams still read the output like scripture.

Amanda has spent more than a decade building marketing programs that don’t depend on the click. Her take on measurement starts from an uncomfortable place. Attribution was never as precise as the industry sold it, and every year it gets less precise.

“Attribution is not as rigorous as it used to be, and it’s not as rigorous as we were once promised.”

4 separate forces have chipped away at what attribution can actually see, and they stack on top of each other.

• Third-party cookies barely function. Only about 30% of users accept them, and Safari rejects them by default.
• Ad blockers hide a huge share of traffic. Somewhere between 20 and 60% of people run one, and among tech-savvy B2B audiences that number climbs toward 60%.
• The multi-device journey is untrackable pre-login. People average 3.6 devices each, so stitching a single human across all of them is mostly guesswork.
• Privacy regulation makes persistent tracking impractical. GDPR, CCPA, and LGPD mean what’s legal in the US often isn’t legal anywhere else, a real burden for any team with a global audience.

None of this means you rip attribution out of the stack. In a mature organization it’s already there, already wired into the reports leadership reads, so ignoring it would be its own kind of malpractice. The shift Amanda argues for is one of posture. Go in knowing exactly where the model goes blind, then ask the more useful question of what you can measure next to fill the gaps.

That’s where incrementality, media mix modeling, geo-testing, and holdout tests start to earn their place. The teams that keep their budgets in a down market are the ones who stopped presenting attribution as ground truth and started presenting it as one flawed witness among several. A single confident number is easy to attack. A converging set of imperfect signals is much harder to argue with.

Key takeaway: Audit where your attribution model goes blind before your next leadership review. Write down how much of your traffic Safari blocks, how many of your B2B visitors run ad blockers, and how many touchpoints happen before anyone logs in. Bring that context into the room so a drop in tracked conversions reads as a gap in measurement, not a failure in marketing.

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Why Dark Social Traffic Shows Up as Direct

Open Google Analytics on any given week and a fat slice of your traffic sits in a bucket labeled direct. The polite interpretation is that all those people typed your URL straight into the address bar. Almost none of them did. Most of that direct traffic is dark social, the shares that happen inside messaging apps and closed platforms where the referral information never makes it back to you.

SparkToro put real numbers on it. About 2 years ago the team ran an experiment, sending more than 1,100 visits across 11 social networks and then checking what Google Analytics reported. For TikTok, Slack, Discord, WhatsApp, and Mastodon, every single visit landed in direct.

“They just hide all the organic strings so that you don’t know.”

Amanda has a theory about why, and it follows the money. The platforms can see the organic referral string. They keep it invisible, because the moment you pay to join their ad network, that traffic suddenly becomes visible and measurable. Organic reach stays in the dark so paid reach looks like the only reach worth buying. And it goes well past the obvious suspects. Facebook Messenger strips the referral about 75% of the time, Instagram DMs about 30%, and even LinkedIn hides it roughly 14% of the time. A share is a share whether it happens in a feed or a private message, but only some of them ever get counted.

The practical lesson for an ops team is to stop treating the direct channel as a junk drawer. When word of mouth and private sharing drive a real share of pipeline, a measurement model that files all of it under direct is quietly erasing your best-performing channel. The brands that figure this out start asking where conversations about them actually happen, instead of waiting for a clean UTM that the platform was never going to hand over.

Key takeaway: Run your own dark social test before you trust the direct bucket. Push a known batch of clicks through TikTok, Slack, and a few DM channels, then watch how your analytics file them. Use the gap to set expectations with leadership, and lean on post-purchase survey questions like “where did you first hear about us” to recover the attribution the platforms refuse to share.

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What the Alligator Graph Means for Ops Teams

There’s a chart making the rounds that content marketers have started calling the alligator graph. Impressions climb while clicks to your website fall, and the 2 lines drift apart until they look like an open set of jaws. For a content team this reads as proof the strategy is working, because more people are seeing the brand. For a marketing ops team staring at the same chart in Google Analytics, it reads as failure, because the dashboard they own is built to count clicks and the clicks are going down. The measurement layer is undercutting the exact thing the content layer is producing.

Amanda’s first move is to reframe the problem. The jaws don’t mean fewer people care about your marketing. They mean more people are seeing it while a smaller share of them click through. So the question stops being “why are clicks down” and becomes “where is the engagement going instead.” Then you go hunt for it in the channels your click-based dashboard was never watching.

Her favorite example is HubSpot. Last year the company went viral for losing roughly 80% of its organic traffic, and half of LinkedIn’s SEO crowd lined up to declare it dead. The reality was less dramatic and a lot more instructive.

“Where they lost that traffic was to pages called things like ‘How to Type the Shrug Emoji.’ Those are things that are not tied to their business of automation software and CRM.”

The drop wasn’t an overnight collapse. It happened slowly across a couple of years of Google updates. More important, the traffic HubSpot shed was the wrong traffic. The pages that cratered were things like shrug emoji tutorials and lists of memorable quotes, content with nothing to do with selling a CRM. The pages about marketing automation and CRM strategy, the ones that actually feed the business, held their rankings. And in the same stretch the company posted record revenue. They weren’t mourning the loss of visitors who were never going to buy anything.

There’s a second layer most of the panic missed. HubSpot’s marketing mix today isn’t the blog. It’s a slate of podcasts, a YouTube presence, and the HubSpot Media Network of owned shows, all generating impressions that never touch the organic traffic line at all. Phil saw the same pattern years ago running the inbound playbook at a BI startup, where a high-ranking “what is a KPI” page pulled enormous low-intent traffic that only mattered because it funneled people toward the next step in their journey. The top-of-funnel pages worth keeping are the ones with a path to a product. The interchangeable filler that any competitor could rank for is the part AI Overviews and answer engines are quietly absorbing.

So the job for an ops team facing the alligator graph is to widen the lens before anyone walks into the executive review. Pull the revenue trend, the branded search trend, and the engagement across owned media, then put the click decline next to all of it. A traffic dip on a page that never converted is healthy pruning that deserves a calm explanation. The teams that misread the alligator graph end up defending vanity pages and cutting the channels that were actually building demand.

Key takeaway: Segment your traffic decline by business relevance before you report it. Separate the pages that drive signups and revenue from the off-topic pages that only ever drove raw sessions. Pair the click trend with revenue and branded search so leadership reads the loss of low-value traffic alongside rising impressions and revenue as the healthy outcome it is.

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Your buyers are already asking ChatGPT about your brand. Do you know what it’s saying?

Every day, ChatGPT, Claude, Gemini, Perplexity decide which brands get named, which pages get cited, and which products gets recommended—often before a buyer ever lands on your site.

Scrunch makes your brand the answer. We’ll show you how AI talks about your brand today, where the citation gaps are, and the page-level fixes that put you back in the answer.

Curious what it says about your brand?

→ See how AI reads my site

How to Run a Zero Click Launch Week You Can Actually Measure

The standard response to broken attribution is more rigor. Run an incrementality test, build a holdout group, isolate the channel. That instinct is right, but it has a failure mode. Teams design elaborate experiments that spit out numbers nobody can act on, then spend weeks debating a difference that was never going to change a single decision. A measurement experiment is only worth running if the result would actually move what you do next.

Amanda’s live example is the launch of her book, Zero Click Marketing, co-authored with Rand Fishkin. The old playbook says you line up every asset and fire them all on launch day. She doesn’t buy it.

“I don’t believe in launch days. I believe in launch weeks or launch months.”

The reasoning is honest about how people actually buy. The majority of your audience won’t convert on day one or hour one. They see the thing, they think about it, maybe they add it to a mental cart, and they come back a week later. Cramming every channel into a single day buries your own signal and exhausts your audience at the same time. So she ran the launch as a week-long sprint with a live dashboard, which in this case was a Google Sheet piping in book orders in real time. The cadence looked like this:

1. Day one opened on her Substack, which reaches about 16,000 people, and drove somewhere around 50 to 60 pre-orders.
2. A few hours later she posted on LinkedIn, which brought the next wave, helped along when Rand engaged with the post. Day one closed at 131 pre-orders.
3. The next day Rand published his own video post announcing the book, adding another 80 orders.
4. Week 2 opened past 300 pre-orders, then the email to SparkToro’s 50,000-plus subscribers went out and drove the bulk of that day’s sales. 10 days in, the total sat well over 460.

Notice what she didn’t do. She never tried to match Substack emails against order records to prove an exact count, and she never agonized over whether Rand’s comment drove 2 conversions or 5. She walked through the whole thing to make a point that cuts against most of what ops teams get asked for. You don’t need sophisticated tooling to see impact like this, especially on a 30 dollar book where the stakes per order are low. The channel each wave came from was obvious from the timing alone.

The granular version of the analysis usually fails a simple test. Suppose day one had landed at 100 orders instead of 131. Would the postmortem conclude you shouldn’t have launched on Substack? Of course not. You might decide to write the announcement better or put the link higher up, and those are fair fixes. But picking apart whether a channel drove 10 orders or 12 rarely changes the next move. The one number that would have changed everything is the broken one. If 16,000 Substack readers had produced a single order, something is badly wrong and you diagnose it immediately. Short of that signal, the precision is decoration.

This is where the opportunity cost bites. Every hour spent reconstructing which post drove which conversion is an hour not spent building the next campaign, and the next campaign is where the real upside lives. It’s the same trap Darrell sees from the ops side, the request to pull everyone who opened an email in the last 90 days, then clicked a link, then read at least 3 blog posts. The report is buildable. The honest question is what you would ever do with it. Measurement earns its keep when it changes a decision, and most of the granular requests floating around marketing change nothing at all.

Key takeaway: Before you build any report, write down the decision it would change. Stage launches across a week so each channel fires on a different day and the source of each wave is legible without complex stitching. Reserve your deep analysis for results that cross a real threshold, like a channel returning almost nothing, and skip the precision that only ever confirms what you already planned to do.

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How Incrementality Testing Works at Enterprise Scale

A scrappy book launch and a nine-figure ad budget are completely different games, and pretending they’re the same has done real damage. Darrell makes the point that the playbooks of the biggest spenders have warped expectations for everyone below them. What works when you’re pouring 10 million dollars into PPC does not transfer to a startup or a mid-market SaaS team, and the reverse is just as true. Trusting your gut and watching revenue is plenty when the budget is small. When you’re moving millions, a few percentage points of waste is a real number, and that’s exactly where incrementality earns its place.

Amanda points to a study Dropbox published in IEEE Access in March, because it shows the gap between what your dashboard reports and what your spending actually causes. The data science team ran month-long blackout experiments, going dark on mobile advertising and on search engine marketing to see what happened when the ads simply stopped. The attributed numbers looked healthy. The causal numbers told a different story.

Channel	Attributed ROAS	Causal ROAS
Mobile advertising	1.53	0.70
Search engine marketing	~2.0	0.92

Read across the rows and the problem jumps out. Mobile ads that appeared to return 1.53 were actually returning 70 cents on the dollar once you measured the sales that wouldn’t have happened anyway. Search looked even stronger on paper and still came in underwater. The ads were losing Dropbox money while the attribution report applauded them.

“They took $25 million away from this low incrementality spend, and their portfolio lifetime value to CAC improved by 53%.”

The payoff wasn’t the discovery on its own. It was the reallocation. Once Dropbox could see the causal truth, it pulled 25 million dollars out of spend that wasn’t pulling its weight and watched its portfolio efficiency jump. This is the scale where the math flips. If you’re running a couple 100 dollars of mobile ads for a book, a 1.53 ROAS feels like a win and a small loss barely registers. Multiply the same percentages across a nine-figure budget and they decide whether a quarter works. Phil sees both ends of this every week, the tight-knit teams who just chase revenue and the data science orgs at companies like Uber and Canva triangulating every signal they can get. The right answer depends entirely on which game you’re playing.

The through line is that incrementality is a budget tool before it’s a measurement tool. Its whole purpose is to tell you what to stop funding. Attribution can rank your channels all day, but only a holdout or blackout test tells you which of them would keep producing if you turned them off, and that is the only question a CFO staring at a media budget actually cares about.

Key takeaway: Run a blackout test on your largest paid channel before you renew its budget. Pause spend for a defined window, measure the conversions that still come in, and compare that causal return against the attributed return your dashboard shows. Use the gap to reallocate budget toward the channels that hold up when the ads go dark.

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What Audience Listening Adds to the Marketing Ops Stack

Almost every signal in a marketing ops stack is backward-looking. Web events, email engagement, CRM activity, intent data from a vendor, all of it describes what someone did after they were already in your world or already shopping. None of it tells you where your audience spends attention between purchase cycles, or before they’ve ever heard your name. That blind spot is exactly what audience listening exists to fill, and it has implications well past the content calendar.

Amanda treats audience listening and audience research as the same discipline. You’re watching where your audience actually spends time, what they engage with, how they describe their problems, and how they phrase those problems when they search in Google or in an AI tool. SparkToro lets you describe an audience in plain language and see which platforms they over-index on. The obvious result for a B2B crowd is LinkedIn. The useful result is the unexpected one.

“Maybe you’ll find they also over-index on GitHub and WhatsApp and Slack. You can’t just do marketing in GitHub, but it tells you the kind of audience they are.”

That last point is where ops should perk up. Knowing your buyers cluster on GitHub and live in Slack isn’t a publishing instruction, it’s a description of who they are. It tells you what to ask about in enrichment, how to weight a lead that shows developer-adjacent behavior, and which affinities are worth piping into your scoring model. Audience research can feed the same pipelines you already run, it just feeds them a layer of context that behavioral data never captures. No single tool covers the whole job, so Amanda works across a small stack:

• SparkToro profiles where an audience spends attention, which social networks they favor, branded search lift, and even what they tend to prompt in ChatGPT.
• BuzzSumo handles social listening, showing the content your audience is actually consuming and sharing.
• Exploding Topics surfaces the macro trends rising over time, useful for spotting a shift before it’s obvious.
• AlertMouse is the Google Alerts replacement that finally works, sending a daily digest of brand and topic mentions weighted by source, so a Wall Street Journal hit ranks above a random blog.

Her habit with AlertMouse is worth copying. Instead of only tracking her own brand, she sets alerts on the topic or category she serves, which turns the digest into a running map of where the conversation is happening and, by proxy, where her audience is paying attention. Feed that back into the SparkToro picture and the planning gets concrete. You can do the SEO keyword work too, and you should. But keywords tell you what people type, while audience research tells you who they are, what they care about, and how to talk to them. Hand a marketer a brief that just says “write a post about RevOps” and the first questions are what to say, how to say it, and who it’s for. Audience research is what answers those questions.

Key takeaway: Add an audience affinity layer to your segmentation, not just your content brief. Profile your best customers in SparkToro to find the platforms and topics they over-index on, then use those affinities to weight lead scores and shape enrichment fields. Set topic-level alerts in a tool like AlertMouse so the team sees where the category conversation is happening in close to real time.

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What Content Leaders Need From Marketing Ops

Most of this podcast’s audience is the people behind the dashboard, the ops and martech pros pulling the reports rather than writing the posts. So Darrell flips the question. If you’re running content for a brand, what do you want from the ops team that would actually change your strategy? Amanda’s answer doubles as a model for how the 2 functions should work together.

She starts by admitting she’s not the one pulling the reports, so her job is to name what she wants and then partner to uncover it. The first ask is sharper segmentation of the core customer. Who are the most engaged people, and what do they have in common? In a SaaS example, she’d want to find the overlap between the people consuming the content, reading the blog, listening to the podcast, showing up to webinars, and the people actually logging in and using the product. Then profile that group hard. What company size, what industry, what role. Are they content marketers, performance marketers, or PR people? The shape of that overlap tells you who your work is really for.

The second ask is the one ops teams rarely get asked for, and it’s the more interesting half.

“Who are the people who consume all our free content, come to all the webinars, read all the blog posts, open all our emails, but they aren’t customers yet?”

Those 2 groups call for completely different plays. For the loyal customers who already use and love the product, the question is what else they need and how to enable them. That might mean standing up a customer advisory board, the kind with alpha access and a quarterly Zoom where you work through marketing problems together. This group is small by definition. It’s never 10,000 people, and it doesn’t need to be. For the heavy content consumers who haven’t converted, the move is to test bottom-of-funnel material that’s tied more directly to a buying decision, then watch whether they start to convert.

The part worth holding onto is her defense of that bottom-of-funnel content even before the data comes back. You want those assets anyway. A piece built to move someone toward a decision isn’t trapped on the blog. It becomes sales talking points, slides in a customer success deck, material for employee onboarding. Calling it a blog post just describes where it happens to live first. When a single asset can serve 4 teams, the question of whether it drove a measurable conversion this quarter stops being the only thing that justifies making it.

The relationship she describes isn’t content handing ops a ticket. It’s content arriving with a hypothesis about its audience and ops shaping the segments that test it. The teams that operate this way stop arguing over who owns the number and start using the data to decide what to build next.

Key takeaway: Build 2 segments your content team can act on. The first is the overlap of engaged content consumers and active product users, profiled by company size, industry, and role. The second is the high-engagement audience that consumes everything but hasn’t bought, which is your live test group for bottom-of-funnel content that doubles as sales and onboarding material.

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How to Turn Audience Research Into an Operational System

Most audience research dies as a slide deck. Someone runs a project, builds a beautiful presentation, hands it to the content team, and 6 months later nobody remembers the findings. Phil poses the harder version of the problem with a thought experiment. Suppose you joined a company at Canva’s scale and owned its zero click strategy. How would you turn research from an occasional artifact into an ongoing system that keeps producing? Amanda lights up at the question, partly because she actually uses Canva, and her answer maps the loop.

She’d start with the highest performing pages, where a page could mean a blog post or a free tool, and at Canva it’s probably a free tool. But raw traffic isn’t enough, so she’d pin down what best performing even means. The honest definition is a blend of traffic, conversions, real usage, and people coming back. Next she’d look at the popular pages that pull plenty of visits but convert nobody, which are often posts orbiting the product without a clear action to take. People are enjoying the content for its own sake, and that’s fine as long as you know that’s what it is. Then she’d dig into churn, because that’s where the money leaks. Is it people who get close to upgrading from free to paid and stall, or people who pay, hit dissatisfaction, and cancel almost immediately? At Canva’s scale, understanding that well would take a while, but it’s the foundation for everything downstream.

The richest signal hides in how people fight with the product, especially the AI assistant.

“What are the queries people type into the AI assistant where they keep prompting like, ‘No, I said remove the background, but you removed part of my arm. Put my arm back.'”

That re-prompting loop is a content brief in disguise. Canva is the kind of tool you open with one job in mind, usually something like stripping the background out of a photo, and the moment you’re stuck is the moment you’d search for help. So the operational system reads those failure points and turns them into targeted enablement, a written or video tutorial that solves the exact thing people keep getting wrong. Her example writes itself: a guide on how to remove a background without accidentally erasing your arm. That’s research, product analytics, and content production wired into one continuous feedback loop instead of 3 teams lobbing decks at each other.

The shift here is treating usage data as a content engine. Every place a user stalls, re-prompts, or churns is a topic the market is actively searching for, sourced from inside your own product rather than a keyword tool. The companies that build this loop stop guessing what to publish and start mining the friction their users generate every day.

Key takeaway: Wire your product analytics into your content pipeline as a standing input. Track where users stall between free and paid, where they churn, and which AI or search queries trigger repeated, frustrated attempts. Turn each recurring friction point into a specific tutorial, then measure whether the people who hit that wall convert after they find it.

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How AI Visibility Changes Zero Click Marketing

zero click marketing started as a response to social platforms that trap audiences inside their walls and reward content that never links out. AI search is the newest wall. ChatGPT and the rest answer the question directly, cite a source without sending the click, and the user moves on having never visited your site. Darrell’s question is where that leaves the whole zero click playbook. Amanda’s answer is that the playbook was built for this all along.

For more than a decade her approach has been to meet the audience where they already are and earn influence by publishing things genuinely worth coming back for. Leaders pushed back for years with the same complaint, that you can’t measure it.

“The way I’ve done marketing has always been zero-click marketing before that was a thing.”

AI visibility, she argues, validates that whole thesis. What makes content show up in an LLM’s answer comes down to 3 things working together:

• Crawlable. The bots can reach it, which is the plumbing problem of enabling your robots.txt and clearing the technical path.
• Extractable. The content is well-written, structured, and focused enough that a model can understand and lift it. That description is just good writing by another name.
• Credible. You publish original research with the methodology and limitations attached, which signals to the model that the source can be trusted.

Stack those over time and the second-order effect kicks in. Good work gets people talking about you in public, linking to you, mentioning your reports, and those mentions are what move your visibility inside the AI tools. That’s also why it feels so slippery to measure, and why the whole industry is anxious about it. Amanda recently sat through a run of GEO and AIO webinars to learn the technical side, and what struck her was how badly people want a cheat code. Someone asked, in all seriousness, what the optimal length of an H2 header is. There’s no magic character count. The right length is however long it takes to be clear, concise, and easy to understand, which is the same standard good writing has always had.

The reassuring part is that the mechanical side is small. Retrievability is a solvable problem, the kind of thing you can largely fix in 30 to 60 minutes rather than with a 20-person team. Tools like Lily Ray’s checker at algorithmic.co let you drop in a domain and see whether the bots can read it and where the gaps are. Phil’s seen the same gold rush from the partnership side, with AEO companies like Air Ops, Ahrefs Brand Radar, and Scrunch AI all racing to score and improve brand visibility against competitors. It rhymes with what AI did for marketing ops, where “garbage in, garbage out” finally made everyone care about data quality. AI didn’t invent the need for credible, well-structured, genuinely useful content. It just made that content the thing that wins.

Key takeaway: Treat AI visibility as 2 jobs and do the cheap one this week. Run your domain through a checker like the one at algorithmic.co, confirm your robots.txt lets the bots in, and clear any technical blocks in an afternoon. Then invest in the slow job that actually compounds, publishing original, well-structured research that earns the public mentions and links AI tools use to decide who to cite.

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How to Set Boundaries and Avoid Burnout in Marketing

We close every episode with the same question. How do you decide what deserves your energy, and what keeps you aligned with what actually makes you happy? Amanda, who somehow runs a career as an author, podcaster, and speaker alongside being a mom, a home chef, and a newly minted sourdough baker, had a fresh and slightly painful answer ready.

A couple of weeks earlier she’d flown in late, landing home from the airport around 2 in the morning. She had a webinar booked for first thing the next day, roughly 400 people signed up. She slept straight through it. Her husband, taking pity on how late she’d gotten in, didn’t wake her. Her Eight Sleep mattress was warming up on schedule, so her body was quietly cooking while her phone alarm and her backup alarm both went off into the void. She surfaced 30 minutes into the webinar to 10 messages from the organizers asking if she was okay. There was no good spin, so she didn’t try one. She told them she’d overslept, that it was on her, and that agreeing to present at that hour after that travel was the real mistake.

“You need to know your limits in advance. You cannot do this just one more thing, just push through.”

The lesson wasn’t that the webinar didn’t deserve her energy. It was that willpower has a hard ceiling. Your brain and your heart can be fully committed and your body will still overrule both of them and put you to sleep. So she set an actual rule: 2 webinars or podcast interviews a week, maximum. Anything past that and there’s no room left to do the actual job. The boundary isn’t aspirational, it’s a number she can enforce.

The second half of her answer is about presence. She and her husband both work from home and drop the kids off together every morning. Neither of them strictly needs to, but all 4 of them in the car is genuinely fun, and getting to see her kids off makes her happy in a way she’s stopped apologizing for. Her real point is that the specific ritual matters less than the act of choosing it on purpose. Plenty of people can’t do the school run because an office is waiting, and that’s fine, no guilt required.

The question she’d put to anyone is simpler. What are you choosing instead, and do you actually like that choice? If the honest answer is no, find the thing in your day you can shape, whether that’s making pickup less of a scramble by leaving 15 minutes early or deciding that email gets triaged after dinner with a glass of wine and not a second before. Outside a real launch or a genuinely big project, nobody needs a reply inside 90 minutes. The whole career holds together fine when you stop pretending otherwise.

It’s the same philosophy she brings to a product launch, just pointed at her own life. Build in the slack, refuse the false urgency, and trust that the important things still land. As Phil put it, it’s a launch week, not a launch day. Amanda agreed. She doesn’t believe in launch days anymore, at work or at home.

Key takeaway: Set a hard weekly cap on the commitments that drain you, like a fixed limit of 2 webinars or interviews, and enforce it before your calendar fills. Pick one daily ritual that genuinely makes you happy and defend it on the calendar. Drop the false urgency on everything else, because almost nothing actually needs a response within the hour.

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Episode Recap

Amanda makes one central argument across this whole conversation. Attribution is structurally broken for any team doing zero click marketing well, and the honest response is to measure differently rather than to measure less. The click was always a lagging indicator of trust that gets built somewhere upstream, and the tooling most ops teams own can only see the part of the journey that happens after someone is already in your world. Pretending that partial view is the whole picture is how good marketing gets defunded.

The tactical thread runs from diagnosis to action. It starts with naming exactly where attribution goes blind, from third-party cookies and ad blockers to dark social traffic that lands in the direct bucket. It moves through the alligator graph, where rising impressions and falling clicks look like failure until you separate the traffic that mattered from the traffic that never converted. From there it gets constructive: stage launches across a week so each channel’s contribution is legible, run blackout tests at scale the way Dropbox did to find which spend actually causes sales, and feed audience research into segmentation and scoring instead of leaving it stranded in a slide deck.

The bigger picture is a shift in what counts as proof. A single confident attribution number is fragile and easy to attack. A converging set of imperfect signals, branded search lift, engagement across owned media, incrementality results, audience affinity, holds up far better in a budget conversation. AI search raises the stakes again, because visibility inside an LLM is earned through crawlable, credible, genuinely useful content and the public mentions it generates, which is the zero click thesis pushed to its logical end.

Amanda is refreshingly honest about the limits. She says outright she’s not the data person, that granular attribution really does matter once you’re spending hundreds of millions, and that measuring AI visibility is a genuinely unsolved problem the whole industry is anxious about. She closes on the same principle she applies to a book launch, build in slack and refuse false urgency, only this time pointed at protecting her own time and focus.

Follow Amanda on LinkedIn, learn more about audience research at sparktoro.com, and pre-order her book at zeroclickmarketing.co/book.

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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