Lead Scoring | Why One Number Sends Reps After the Wrong Leads

Lead Scoring | Why One Number Sends Reps After the Wrong Leads

Most lead scoring models hand sales a single 0-to-100 number that is secretly answering two different questions at once: is this the right kind of buyer, and are they ready to buy now. This episode unpacks why that single score quietly misroutes your reps — sending them after a college student poking around the pricing page instead of a perfect-fit VP who went quiet — and walks through the fit-versus-intent framework that fixes it. The whole thing runs on a worked B2B SaaS example: 1,000 marketing leads a month against a five-rep SDR team that can only seriously work about 300 of them. What we cover: * Why behavioral points (pricing-page visits, demo views, repeat sessions) compound while firmographic fit points stay capped — and how that skews the top of every single-score list * Building the fit score from two years of closed-won data, weighted for which accounts retain, not just which ones sign * Why intent is perishable — scoring behavioral signals on a decay curve so a 60-day-old pricing visit stops counting as "hot" * The fit-vs-intent 2x2 and its four quadrants: Priority, Nurture, Triage, and Hold — and the play for each * The SDR-capacity math: how a single-score model burns roughly 210 conversations a month on a pool that closes at 1% * The reallocation — working 130 routed leads instead of 300, with the deal count holding and half the rep capacity freed * Two changes to make this week: gate intent behind a real fit floor, and add decay to every behavioral signal Closing question we leave you with: if you split your current leads into this two-by-two grid today, how many of your sales team's hours are being burned chasing fake intent in the trap quadrant? Go to marketingcasebootcamp.com for hands-on case simulation practice on this exact business problem — build the routing rules and capacity math the way you would on the job. If this one helped, share with other marketer folks.

Google Marketing Live 2026 | The Year Execution Moved to Gemini

Google held Marketing Live 2026, and under the product news there was a single through-line: the parts of a marketer's job that used to be done by hand — writing the ad, picking the keyword, building the lead form, reading the report — are being handed to Gemini. This episode walks the five shifts that follow from that change, who is actually writing the words your customers read now, and where Google's own headline numbers deserve a skeptical read. What we cover: * Why AI Mode (1B+ monthly users) and AI Overviews (2.5B+) change what you optimize for — the shape of the query, not the ranking * Ads that became answers: Conversational Discovery, Highlighted Answers, AI-Powered Shopping, and a Business Agent for Leads — all composed by Gemini * Universal Commerce Protocol and the Universal Cart: agentic checkout with Nike, Sephora, Target, Walmart, and Wayfair across a 60-billion-listing Shopping Graph * Gemini in the back office: Ask Advisor across Ads/Analytics/Merchant Center, Asset Studio, Meridian moving into Analytics 360, and Qualified Future Conversions * YouTube as a performance engine: Shorts incrementality (45% not on TikTok, 65% not on Reels), Demand Gen product feeds, and Ask YouTube * Where to keep your skeptic hat on: $6 back per $1, +15% conversions, 82% of discovery journeys — Google's own marketing math * The one concrete move this week: run a real high-intent query in AI Mode and read how Gemini describes your brand Closing question we leave you with: As AI-native search and agentic commerce become the default standard, what happens to the challenger brands and the new startups that don't have massive feeds of historical data to train these AI agents? How do you break into an AI's consideration set when the machine already thinks it knows the best answer? Go to marketingcasebootcamp.com [https://www.marketingcasebootcamp.com] to get hands-on case simulation practice on the exact decisions behind these AI shifts — you learn this by making the calls, not just hearing the theory. And please share this breakdown with other marketer folks so they can build their quantitative judgment too. Read the full written blog on Marketing Case Bootcamp [https://www.marketingcasebootcamp.com]. Read the announcements on the Google Ads & Commerce blog [https://blog.google/products/ads-commerce/].

First 90 Days as a Marketing Hire | The Diagnose, Build, Ship Playbook

The marketers who get promoted within their first 18 months don't ship a flagship campaign in month one — they spend 30 days listening, 30 days building one thing, and 30 days shipping one named result. This episode unpacks the diagnose-build-ship playbook for the first 90 days as a new marketing hire, with concrete deliverables at each checkpoint: the day-30 audit memo, the day-60 experiment, and the day-90 result memo with a CFO-recognized number on it. It also doubles as an interview framework — a question you can bring to your final round to find out whether the company has actually thought about your role. What we cover: * Why the dashboard you walk into on day one is lying to you — and why your week-four campaign will land on top of that lie * Phase 1 (days 1-30): the three diagnose moves — reading four quarters of internal reports, the 30-minute adjacent-team conversations, and auditing one number end-to-end * The day-30 memo as both a deliverable AND your protection if the role doesn't work out * Phase 2 (days 31-60): how to pick the ONE thing — small enough to finish, tied to a CFO-recognized metric, testing a broken assumption from your diagnose memo * Why writing down your test design and success threshold BEFORE the data comes in protects you from goalpost-moving * Phase 3 (days 61-90): the result memo with the number front and center, the cross-functional sponsor, and the reversible-rollout tripwire * How to use this same framework in your final-round interview to evaluate whether the role is actually right for you * Three things to do this week — pick the audit number, schedule the five adjacent-team conversations, block out four Fridays for memo-writing Closing question we leave you with: if you're already in your first 90 days, what's the one number on your dashboard you'd most want to be able to defend — and have you started the audit yet? Go to marketingcasebootcamp.com for hands-on case simulation practice on this exact career scenario — work through real first-90-days decision artifacts the way you'd build them on the job. If this one helped, share your journey with other marketer folks.

Performance Marketing Incrementality | Behind Airbnb's $1B Paid Media Budget Cut

In Q2 2020, Brian Chesky walked into a room of Airbnb marketing leaders and told them to turn off paid marketing — not pause, turn off. Total marketing spend dropped from $1.62B in 2019 to $545M in 2020, and by Q4 91% of Airbnb's traffic was direct or unpaid. The performance team's worst fear never showed up. This episode unpacks what Chesky was actually testing — brand-search cannibalization, not "brand vs. performance" — and why the math on incremental paid lift gets brutal once your direct-traffic share crosses 70%. What we cover: * The real question behind the cut: how much paid-attributed traffic would have come anyway * Brand-search cannibalization — why it can absorb 60–80% of paid-search budget without anyone noticing * The three numbers from Airbnb's S-1 that made the call obvious: 90% direct/unpaid floor, $1.08B spend cut, 22% YoY Q4 2020 revenue growth * Why "incremental ROAS" on last-touch attribution is a vanity number, not an incrementality number * A three-question framework any marketer can run on their own paid budget — direct-traffic share, holdout-test discipline, six-week paid-off survivability * Three moves you can make this week: brand-vs-non-brand split, one-week branded-search holdout, taking the result to the CFO before the CMO * Why the Airbnb lesson isn't "cut performance" — it's that brand strength sets a ceiling on how much incremental work paid can ever do Closing question we leave you with: if your CFO ran the same incrementality test on your paid budget tomorrow, how much of your spend would survive the room? Go to marketingcasebootcamp.com [https://www.marketingcasebootcamp.com] for hands-on case simulation practice on this exact business problem — pull a brand-vs-non-brand split, design a holdout, and pressure-test your own paid-search budget the way Chesky pressure-tested Airbnb's. If this one helped, share with other marketer folks. Read the full written case on Marketing Case Bootcamp [https://www.marketingcasebootcamp.com].

Google AI Max Migration | Behind the September DSA Sunset

Google's AI Max moved out of beta on April 15, 2026, which starts the countdown to the September forced migration off Dynamic Search Ads. This episode unpacks what intent-based auctions actually change under the hood — DSA, Automatically Created Assets, and campaign-level broad match all replaced in one motion — why voluntary early migration is the correct call, and how to run a clean pilot before Q4 forces your paid team to learn a new auction model during the worst quarter of the year. Microsoft announced a parallel "agentic web" AI Max equivalent on April 21, so this isn't a Google-only shift; it's the leading edge of paid search moving from keyword-specified to outcome-specified across every major platform. What we cover: 1. The three campaign primitives AI Max replaces in one motion — Dynamic Search Ads, Automatically Created Assets (ACA), and campaign-level broad match• Intent-based vs. keyword-based auctions: why Google now reads query, landing page, and creative as one bundle and ranks by predicted conversion 2. Google's headline 7% conversion / conversion-value lift claim and how to actually read it 3. Why September is the wrong time to learn a new auction — the "Q4 budgets already committed" trap 4. The 5-item migration QA checklist: baseline the legacy campaign, start with a non-hero campaign, write text guidelines before flipping, rebuild reporting around query-level data, and set a 14-day no-touch window after launch 5. Text guidelines as the new control surface — how to write brand rules the AI will actually respect (e.g. banning "cheap" or "discount" in generated headlines) 6. Microsoft's parallel "agentic web" AI Max equivalent and what the platform convergence means for search marketers 7. The skill-stack rebuild: shifting from bid-manager to AI-manager — writing text guidelines, curating landing pages, and reading query-level + cross-headline reports Read the full written case on Marketing Case Bootcamp [https://www.marketingcasebootcamp.com]. Read the original Digiday coverage [https://digiday.com/marketing/google-ai-max-moves-out-of-beta-marketers-sound-off-on-the-inenvitable-migration/] of the AI Max GA announcement.