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.
We'll cover:
- (00:00) - Intro
- (01:14) - In This Episode
- (06:37) - Why Attribution Breaks Down in a Zero Click World
- (16:09) - What the Alligator Graph Means for Ops Teams
- (22:46) - How to Run a Zero Click Launch Week You Can Actually Measure
- (32:35) - How Incrementality Testing Works at Enterprise Scale
- (38:09) - What Audience Listening Adds to the Marketing Ops Stack
- (42:50) - What Content Leaders Need From Marketing Ops
- (47:59) - How to Turn Audience Research Into an Operational System
- (52:00) - How AI Visibility Changes Zero Click Marketing
- (58:36) - Setting Boundaries to Protect Your Energy and Focus
Summary: zero click marketing won the strategy war, and now nobody can prove it's working. Amanda has spent over a decade building marketing that lives where the audience already is, and in this episode she takes apart the measurement crisis that creates. We get into why your attribution dashboard is quietly lying to you, how a viral story about HubSpot losing 80% of its organic traffic actually hid record revenue, and what a 25 million dollar ad blackout taught Dropbox about the gap between credit and cause. 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.
About Amanda Natividad
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.
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.
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.
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.
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 j...
