Eric Collins

Amazon doesn’t want to advertise through Google Shopping in the US, but it does want to grow its pharmacy business. Looks like that desire for growth is winning out. Amazon[.]com appeared very sparingly in US Google Shopping auction insights reports in Q4, mostly for brands in the health and beauty sector, seemingly signaling a shift from its pause in July. However, the only examples of these listings we can produce in search results are tied specifically to Amazon Pharmacy, a segment of Amazon’s business that it's very keen to grow. The screenshot below is an example of these listings, with the links heading to pharmacy[.]amazon[.]com. It appears that Amazon has decided to make an exception to its Shopping hiatus in the US when it comes to Amazon Pharmacy, which the ecommerce giant expected to provide same-day delivery to more than half of the US by the end of 2025. This would explain why Amazon has primarily appeared in US Shopping Auction Insights reports for health and beauty advertisers over the last few months and remains inactive across the vast majority of US Shopping auctions (leaving a massive void for other US advertisers, with Tinuiti US Shopping click growth hitting 17% Y/Y in Q4). If this is the extent of Amazon’s Shopping advertising in the US, it’s possible that it will continue to ramp spend as it expands same-day delivery to new cities. It would also mean that US advertisers in other categories shouldn’t necessarily expect the 900 lb Shopping gorilla to come back. h/t Mike Ryan for inspiring us to dig in further

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If I were to start a brand today, I wouldn't look at Google Analytics, platform ROAS, or really ANY granular "performance" metrics. Including any kind of "incrementality" metrics or other complex performance (MMM, MTA, inc testing, etc). I'd simply look at: • Daily trending P&L metrics (CM) • Daily trending new customers (NTF) • Daily trending efficiency metrics (CAC) • Monthly cohorts of customer value (LTV) • Eventually get to a P&L view that has cash flow Every day I'd be able to see the last 7, 30, 90, and 365 trends of the above four metrics. I'd actually do a rolling average of each of those time periods because I don't believe in day-by-day unless I'm super tight on cash. How do I know what's working and not working? • List of hypotheses and tests • List of actions taken + tests done • Change of BUSINESS kpis (above) after each • Investigative exploratory data analysis of unexpected changes For example, I'd have something like: August • Problem: CAC is too high • Hypothesis: spend is too high • Test: reduce overall ad spend by 20% • Results: monitor CAC trend over next 30 days In September, we saw that CAC was reduced by 10% vs the trending 30 day average. This got our CAC under control and was deemed "successful" as a test. To get more granular, I might have a hunch that a particular channel or tactic is less effective at adding incremental customers. Rather than a blanket 20%, maybe we cut 50% out of Google Ads and keep everything else flat, etc. You could have tests surrounding other areas of the business beyond just ad spend: • New product drops • Pricing strategy • Promos • PDPs • New ICP • New category • New overall channel Etc. And then you'd have to keep your tests fairly clean, testing only 1 directly integrated activity<>KPI relationship at a time. For example, a lot of things affect new customers. So you need one "chief" test at a time that takes a big swing. Keep in mind -- I'm talking small brands here. If you're working on things and overall your brand is growing on those primary KPIs every day or month (e.g. contribution margin, new customers, etc) then you're in a good state. If you're not, then you need to be more prescriptive with those tests. Once you're at like $50-$100m a year, then you can start getting cute with measurement. MAYBE even $25m a year. But if you're sub $20m, quit wasting time. (For the record, there are clients at Power and fusepoint that are multiple hundreds of millions of dollars just focused purely on CAC and iCAC, at least for their marketing teams). How would you run a startup? #attribution #cac #startup

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8 things healthcare marketers should have their eye on in May: 1) Google has CANCELED their repeatedly-delayed third-party cookie deprecation plans as of last week. Interesting timing of the announcement given their current monopoly hearings started the same week. I'm still bullish on privacy-first healthcare marketing but I think a lot of marketers will be watching these developments very carefully and wondering if there's a tide shift. 2) Tariffs are going to have a significant effect on the healthcare sector. Majority of healthcare experts agree the tariffs will directly hit costs in a way that's no bueno. Likely to bump healthcare costs at least 15% by summer. May lead to supply shortages too> don't forget to factor anything that affects capacity into marketing. 3) Meta recently announced Threads ads are open to everyone. Still not as broad-reaching as Google PMAX but an interesting channel expansion - and one that's turned on by default. So check your ad placement settings and while you're at it check your inventory filter> might not want to throw your brand out to appear next to everything with expanded inventory especially with Meta rolling back a lot of content moderation recently. 4) Staffing issues are likely to become a larger problem for healthcare providers nationwide this year. We're seeing lower employee engagement that often signals higher turnover. Add that to the drop in staff not born in the US and the rising costs and tightening budgets and you've got a perfect storm for staffing issues. Don't forget that marketing isn't just for patients> it's also essential to keep your staffing levels steady. 5) BeReal launched their ad platform in April. Insta/YouTube/TikTok remain the Top 3 platforms for GenZ but always pays to be aware of other potential channels and this is a potential opportunity to be a first mover. 6) Consumer Healthcare Experience Survey showed 2 out of 3 patients delayed or skipped health care in the past year due to cost. And a full 80% of patients worry about affording health care in the future. Every single healthcare org should be highlighting accepted insurances on your digital front door. 7) Good patient digital experience can DOUBLE patient loyalty rates - 2025 U.S. Healthcare Digital Experience Study shows it's true for health insurers and providers alike. 85% of members who rated their digital experience satisfaction at 8/10 or higher say they "definitely will" renew their plan, more than double the rate of those who rated 5/10 or lower. 8) Big Performant Healthcare Survey showed 56% of healthcare tech leaders view enhancing operational efficiency as a top priority - second only to patient outcomes. Asked what's gonna have the biggest impact, 77% said AI and 35% said EHR and interoperability. Very interesting second-place responses. Stay thirsty for knowledge my friends. #healthcaremarketing             

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This Q4 the funnel has flipped. For the first time, millions of shoppers will disappear just before they’re about to buy. Traditionally, the funnel narrowed towards the bottom: the closer someone got to purchase, the easier they were to find with paid ads. But this Q4 is different.  A meaningful minority will disappear, by going where no paid ads exist: * AI assistants 18% of UK now use AI assistants to make purchase decisions (YouGov). * AI overviews 9% decline in search-referred traffic to ecommerce sites (Similarweb). 👉 For millions of potential customers, the bottom of the funnel is no longer the tight, predictable place it used to be. It’s wide and hard to reach. Meanwhile, the top has become tighter, as recommendation engines get better at predicting what people want before they start searching. 💡 Which makes this Q4 unique: Harder to shape decisions at the end. Easier to inspire earlier. So the playbook this Q4 should flip too: - Build memory so your brand is recalled before people ask AI. - Focus recommendation engines on reaching them first. Inspire earlier, because by the time they’re in-market, it may be too late. 💬 Are you planning for a Flipped Funnel this Q4?

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CMO’s and agencies are so enamored with bottom-funnel targeting tactics that they totally lost the ability to move people effectively—their judgement is out of balance with real needs today. The targeting down funnel ‘disease’ gives marketers a feeling that they aren’t being wasteful but so much is showing they aren’t being effective either. “This ….reveals …a fundamental mismatch. Respondents are applying bottom-of-funnel tactics to what they've identified as top-of-funnel brand building objectives.”

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Tough truth: Channels with smaller ad budgets often get overlooked in MMMs. When a channel represents a small portion of the mix, brands don’t always prioritize collecting detailed data on it. Instead, it gets lumped into a broad “everything else” category with minimal granularity. When the data is aggregated, inconsistent, or incomplete, it creates real problems for MMM. Without granular, well-structured data, it’s tough to get a robust measurement. And when we can’t measure impact properly, it’s hard to make the case for optimizing or increasing investment in that channel. So how do we fix this? By improving MMM data feeds. If we look at how social media platforms evolved their measurement capabilities, it’s clear that better data leads to better outcomes. When Meta, TikTok, and Google started providing structured, MMM-ready data, their impact became much easier to measure. As a result, brands felt more confident investing in those platforms. The same needs to happen across all media channels. To ensure a media channel can be effectively measured in MMM, there are three key data standards that should be met: - Periodicity: Data should match the periodicity of MMM models, which typically require weekly or daily breakdowns rather than aggregated monthly figures. - Metrics: Spend alone isn’t enough. To truly understand a channel’s impact, we need exposure and engagement data - impressions, reach, frequency, and actions taken by users. - Dimensionality: The more information we have on targeting, formats, and execution strategy, the more accurate and meaningful the measurement will be. If media owners want to see their share of ad spend grow, they need to follow the same path: providing clean, structured data. Without this, they risk being deprioritized, no matter how valuable they may be in reality. I believe this shift is not just necessary but inevitable. As the industry moves toward more sophisticated, data-driven decision-making, media platforms will need to adapt to ensure they remain a measurable and strategic part of the marketing mix. What challenges have you faced in measuring different media channels? And what steps do you think need to happen next?

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How We're Tracking on Leveling Up at Digitas North America This Week Every Friday, I take stock—where are we pushing boundaries with DigitasAI. Here’s the scoreboard, mapped to what matters most: 1. Accuracy: We’re doubling down here—tapping into unique data pipes that give our teams a sharper, more truthful view of the landscape. Those unseen signals are pure gold. The advantage? More context, less guesswork. 2. Efficiency: This week, AI agents got formally mapped into core workflows across Brand Strategy, Connections Strategy, Social Strategy & Experience Strategy. It’s already freeing up our best minds for lateral leaps instead of just linear lifts. 3. Impact: Biggest impact right now? 2026 planning. DigitasAI is helping us juggle more variables and scenarios without drowning in data—teams can focus on both today’s work and tomorrow’s vision (without losing sleep). 4. Sparkle: Highlight of the week: watching Caitlin Looney of our funniest strategists—turn her personal magic into a coded assistant. She merged her signature style with a brand’s DNA to build a Social Strategy super tool for that brand. That’s what “sparkle” looks like when tech meets talent. Daily usage numbers have jumped since July 1st—and as my boss Amy Worcester Lanzi always reminds me, change is HARD. But that’s why we tackle it from every angle. #NetworkedExperiences #DigitasAI #EarnYourWayIn

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Agencies often talk crap on other agencies and it's a pointless exercise but here's why I find it amusing Hiring is hard Running a company of 10 is hard Running a company of 40 is harder Most agency owners can never scale above either 40 heads or 40 clients because they don't replace themselves The reason I am at 450 clients and 500 employees is because processes (sops etc) simply outperform talented people who ignore checklists My interns beat anyone Thrasio hired for example, they with ultimate sophistication ignored commandment 1 of selling on Amazon. They let their top sellers go out of stock. Boutique agencies claim better talent allows them better results, often posting on social media like a solo fashion show Not only is this misleading but it's also their only claim they can make because they can't or haven't yet scaled their own company to merit a higher level argument But with that said, it's not me against you (other agencies), it's all of US vs Amazon There is simply no need to attack other agencies to gain ground, the ground is infinite and I get more client referrals when I help my competitors anyway Business is not a zero sum game, on the contrary it's an infinite value giving game This is why I give away everything Some of you will hate on me just because I am successful But most of you who learn something and apply it to your own business will grow And to the few who come back and say thank you for all the content? You're the reward that keeps me going

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How do you measure incrementality? 🧩 Every brand asks: did my media actually drive sales beyond what would have happened anyway? Here are three of the most effective approaches in play today: 📊 Randomized Controlled Trials (RCTs / A/B Tests) The cleanest measure of causal lift by splitting audiences into exposed vs. control. Still the gold standard, but scaling across hundreds of supplier campaigns is tough. 🗺️ Matched Market / Geo Experiments Turn media on in one region or store set, and off in another. Highly effective for brick-and-mortar, but sensitive to local dynamics like competitor promos or seasonality. 🧪 Synthetic Controls When true RCTs are not possible, synthetic controls create a statistical twin of the exposed group by weighting historical sku, sales, loyalty, and category data. This lets retailers simulate what performance would have been without media. 📈 Effective for enterprise networks running many campaigns at once 👥 Useful for comparing audience tiers such as new-to-brand, loyal, or lapsed 📊 More reliable than simple pre/post because it adjusts for seasonality and baseline shifts ⚖️ The right approach depends on balancing rigor, cost, and scalability. Without incrementality, retail media risks losing credibility and budget share. https://lnkd.in/egquzpJ8

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🎯 Smarter HCP Targeting Is Transforming B2B Healthcare Campaigns Healthcare brands are raising the bar by moving beyond surface-level targeting. It’s no longer just about an HCP’s specialty or NPI — it’s about understanding their professional journey and tailoring outreach accordingly. Here’s what’s working now: 🔹 Prescribing Behavior Insights – Shape messaging based on actual treatment trends 🔹 Clinical Trial Involvement – Connect with HCPs contributing to innovation 🔹 CME Participation & Speaker Programs – Align with educational and leadership roles 🔹 Patient Population & Device Usage Segmentation – Reflect the realities of their practice These strategies are driving higher engagement, better response rates, and stronger partnerships across pharma, medtech, and healthcare services. 📈 We help B2B healthcare marketers turn enriched data into meaningful HCP connections — at scale. Let’s build campaigns that speak their language, not just their job title. #LakeB2B #HCPMarketing #HealthcareB2B #PharmaOutreach #MedtechMarketing #HCPEngagement #DataDrivenMarketing #CME #ClinicalTrials #PrescriberInsights #HealthcareStrategy

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Are You Targeting the Right Personas? Probably Not. Most brands launch ads with a single customer profile in mind. 🚨 Big mistake. 🚨 If you want to scale past $10K/day, you need to think bigger—and that starts with understanding your Total Addressable Market (TAM). Here’s How to Find Hidden Buyer Personas That Unlock Scale: 🔍 Step 1: Define Your TAM Your market isn’t just one type of buyer. It’s a collection of micro-segments with different needs. ✔ Who could buy your product? (Broad industry) ✔ Who should buy your product? (Ideal customers) ✔ Who’s being ignored in your market? (Untapped segments) Example: A premium hiking supplement brand. 🚶‍♂️ The obvious buyer? Hardcore hikers. 🎯 The untapped audience? Runners, gym-goers, busy professionals who want sustained energy. ⸻ 🧑‍💻 Step 2: Persona Mapping For each potential buyer, map out: ✔ Their biggest pain points (What problem does your product solve?) ✔ Their decision triggers (What makes them actually buy?) ✔ Their preferred messaging style (Emotional? Logical? Social proof-driven?) ⸻ 🎥 Step 3: Build Ads That Speak to Each Persona Once you’ve identified hidden buyer segments, create ads that feel made just for them. 🔥 Outdoor Adventurer → “Stay fueled on the trail—without the crash.” 🔥 Busy Professional → “More energy, more focus, no jitters.” 🔥 Athlete → “Outperform. Outlast. Recover faster.” Different personas → Different messaging → Bigger scaling potential. ⸻ 💡 Takeaway: If you’re stuck in a single persona mindset, you’re leaving money on the table. Your audience isn’t just who you think they are—it’s who the data says they are. Who else is testing new personas right now? Drop a comment. ⬇️

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