Joe Balestrino - Google Ads Expert

🚀 Key Insights from 16,000+ Paid Search Campaigns from WordStream by LocaliQ (Apr ’24 – Mar ’25): CTR: 6.66% on average - but over half of industries saw a drop CPC: $5.26 on average - 87% of industries faced higher costs CVR: 7.52% on average - 65% of industries saw improvements CPL: $70.11- up 5% year-over-year 📊 By industry: Legal continues to be the priciest for leads ($131) Auto Repair remains the most efficient ($28) Takeaway: Costs are climbing, so you need to have smarter, more relevant campaigns paired with stronger landing pages to drive better results. Reach out to the Digital Strike - Targeted Marketing team if you are not getting the results you need! 🔗 Full report: https://lnkd.in/ejvjkGxy #PPC #PaidSearch #DigitalStrike #GoogleAds #CampaignOptimizations

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The easiest way to rank in 2025? Don’t launch a site. Launch a system. 1️⃣ Aged domain w/ backlink profile 2️⃣ Parasite pages for high comp KWs 3️⃣ Programmatic AI site for ultra longtails 4️⃣ CTR & user signal manipulation 5️⃣ High ticket lead monetization where possible You don’t need a blog. You need a f*cking pipeline!

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AIO Lawsuit from a big publisher -> Penske Media sues Google, says ~20% of Google search results linking to its sites now include AI Overviews, and affiliate link revenue fell 33%+ since late 2024 “Siphoning and discouraging user traffic to PMC’s and other publishers’ websites in this manner will have profoundly harmful effects on the overall quality and quantity of the information accessible on the internet,” the complaint alleges, using the acronym for Penske Media Corp." https://lnkd.in/emDqVEFs

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7 Reasons PMax is Stealing Your Search Impressions We've done a lot of research over the past few months comparing Search & PMax campaigns. While we know search has both better CTRs & conversion rates than PMax, these campaigns can still work together (even for non-retail accounts). However, if these campaigns are not properly managed, PMax can hurt your account performance quite a bit. In our latest article, we look at the most common reasons these campaigns are not playing well together so that you can diagnose and fix any of these issues. I hope you enjoy the latest article :) https://lnkd.in/e3pgbSaj

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Mantis Analysis Reveals 56% of Oscars Content Blocked Due to Blunt Keyword Blocklists! At Mantis, we assessed more than 460,000 page views between Dec 28, 2024, and Jan 27, 2025, and found that a master keyword blocklist used by multiple ad agencies flagged 56% of the views as potentially problematic. That's a significant portion of the audience missed out on! With nearly half of consumers trusting the #Oscars as a key indicator of top films, celebrities and performances, brands are missing out on vital opportunities to engage with an audience that's actively tuned in during one of entertainment’s peak moments. It’s crucial for brands to think carefully about the technology they’re using to protect their advertising efforts. Mantis offers a far smarter, AI-powered solution that helps brands avoid these missed opportunities.   Read the full article by Kendra Barnett including my insights here ADWEEK https://lnkd.in/eVZRsbtF You can also catch this article in AdExchanger https://lnkd.in/et6eGzGn roundup/tuesday-2025-04-03/ and eMarketer https://lnkd.in/ew6C6XRz #KeywordBlocklist #BrandSafety #ContextualAdvertising #MantisResearch #Oscars2025

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I think the future of SEO (still defined as “Search Engine Optimization”) is changing to overall organic growth optimization. I'm not suggesting a new name (enough new names are being kicked around presently, some of which might be short-sighted as a label IMO). I'm thinking more of a natural extension of what we've been doing for decades, forced by more than the biggest search engine switching to AI modes. User habits are shifting. SEOs should already be flexing. The role hasn’t changed, as we're still the bridge between users and the discovery platforms. But the platform is no longer just Google. It’s also AI interfaces, marketplaces, voice, social, aggregators — wherever discovery happens. The old SEO toolbox (keywords, crawlability, links) still matters to algorithms, but it’s table stakes. They are expected. - Real growth comes from building visibility and value into the product itself at launch (channeling Eli Schwartz here). - Real growth comes from understanding how users actually discover, evaluate, and trust brands (and designing for that reality). - Real growth comes from solving for the entire discovery journey, not just ranking keywords. That’s relevance across every organic channel — not just Google's AI-infused SERPs. After the fact SEO bolt-ons to target the ESV metrics is becoming the new old-school. Organic growth means thinking beyond search. It’s strategy, product, UX, and content — aligned.

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eCommerce brands must stop working with email agencies if they expect to "fix" their growth problems. After working with over 200 brands at my Klaviyo Elite Agency, here are the top 5 dirty truths most agencies won't tell you. 1. Email's success is intrinsically tied to acquisition Over 70% of your customer base will likely never buy from you again. Increasing returning customer revenue is a critical part of building a healthy business, but true growth comes from increasing your database of customers to target. That comes from new visitors to your website, not existing ones. 2. Email can't fix poor retention alone Retention rates & churn are largely a bi-product of the lived experience with your product. If the customer experience sucks and your product fails to deliver, no amount of emails can solve this problem. You can use email & lifecycle strategies to diagnose the problem, but it's going to be up to a separate department to drive true change. 3. 80% of email results are driven by brand equity The best marketer in the world can't polish a turd. An agency can only help you if they're working with solid raw materials. That means product market fit, reliable shipping and a strong customer experience. If you have these in place, most people will be able to generate revenue for your brand as they'll be capitalising on trust you've already built with your customers. 4. Arbritrary % of email revenue is irrelevant Many email marketers will argue until they're blue in the face that over 40% of your revenue needs to be driven by email marketing. This is a nonsensical platitude not backed by any meaningful data. It's healthy to have a significant portion of revenue driven from retention channels, but it's also going to be impacted heavily by the stickiness of your product, seasonality, assortment and pricing strategy. A generic % target doesn't give you any indication as to how healthy your strategy is. 5. Short-term revenue strategies come at the expense of long-term profitability If you see an agency with an absurd guarantee, it's inevitable that their "strategy" is going to be hammering your customers with a lot of heavy discounting to achieve revenue targets. Front-loading revenue this way nearly always leads to profit erosion and subsequent drops in returning customer revenue. _______ What did I miss from this list? Would love to hear your thoughts too. #emailmarketing #klaviyo #ecommerce

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People new to MMM: don't make the same mistakes you made with MTA. • MMM: marketing mix modeling • MTA: multi-touch attribution I've been in this industry a long time, and have helped to build out custom multi-touch attribution systems using fingerprinting, DMPs, clickstream parameters, IP matching, and all of the 1PD/3PD bells and whistles you can think. Most brands never got that advanced, instead they just: • Bought a tool • Used Google Analytics • Trusted the tool and used it to make decisions The biggest mistake -- which I'm begging you not to repeat -- is the third bullet above. You can't just blindly trust ANY methodology. And I'm seeing it happening right now at fusepoint and beyond. The measurement gurus (myself included) can be very convincing, and people who aren't as close to this are just eager to finally "get some answers." And while MMM is undoubtedly a much better method than MTA, it isn't without its flaws. In general, you should have healthy suspicion of anything. I'll give you an example: • Many MMM use Bayesian time-series methods (we do too) • Bayesian allows you to inject some "opinions" and priors • Bad priors means a bad model output A highly simplified version of this might be something like: • Run an MMM for a brand with Meta, Google, TikTok • Put ROI "priors" (read: assumptions) for each at --- Meta: 1-2x --- Google: 2-3x --- TikTok: 3-4x • Compute the model (with these priors) The results will be heavily influenced by those ROI ranges that we put in from priors. This is a "good" thing if you have those priors calculated from recent incrementality experiments or a body of historic models, but if you're just putting them in cold it can really invalidate results. For this brand they might have completely different results in reality, even though the model is saying something like: • Meta ROI of 1.5x • Google ROI of 2.5x • TikTok ROI of 3.5x In this case the brand would likely conclude that they should divest from Meta and more heavily reallocate into TikTok. However if the model was allowed to run "cold" with few priors, it might have come back with totally different results. For six months maybe they continue to allocate budgets in this way, and they see that their performance isn't getting any better (or actually worse). So they decide to run some incrementality experiments and see: • Meta ROI is actually 4x • Google ROI is actually 1x • TikTok ROI is really only 2x Completely different from what the assumptions were input as priors to the model. Maybe those assumptions were based on a different brand or some other kind of "opinion" from whoever was running the model. Happens all the time. Takeaways: • All measurement methodologies have flaws • Humans have a tendency to overly trust these things • Develop a healthy skepticism and scientific approach • Keep validating what you're doing is showing up in the P&L Stay scientific out there.

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3 features our competitors have that I respect but Chosenly.com doesn’t (and why): 1) Search volume data (like Profound has) They purchased customer research panel data to show search volumes for prompts. It’s a great way to estimate demand for different prompts. But we haven’t built it yet because those panels don't have reliable data for B2B-specific prompts or bottom-of-funnel queries our customers need. Which is why it works well for their enterprise B2C audience, not our ICP. What we offer instead: We have much more complete leading indicators. We have share clear data points on what % of searches you show up on and what % of times facts about you can make you be the top recommendation. You can use these to estimate the total volume in your space. 2. Blog generation features Some GEO platforms build tools to generate entire blogs. But most are just thin wrappers around Claude or OpenAI with minimal added value. And that’s the thing… doing this well requires significant research and setup that goes way beyond a simple wrapper. Take Lashay Lewis from BOFU.ai. She charges premium rates because her process isn’t just “run a few Claude prompts.” It’s deep and research-driven. And that’s the bar we hold for ourselves, too. If we build a blog generation feature, it needs to be genuinely good. I’d rather ship nothing than ship something half-baked. So what we offer instead: Our content recommendation engine tells you WHAT to create based on actual visibility data. Then you can use your preferred writing process (AI-assisted or not) to execute it better than any pure-AI solution. 3) Daily tracking of LLM visibility Some tools track changes daily, which treats LLM visibility like traditional analytics. Daily tracking makes the graphs look pretty but doesn’t impact the action your team takes. We’ve instead invested in stability of these metrics, and if we ran these daily, we’ll have to 20x our pricing. The reason I still added these to the list is  we build campaign features into Chosenly, we might take inspiration here to track specific campaigns and measure the impact of actions. We'll likely build this in our next iteration once campaign features are live. For now, what we offer instead: We have the best leading indicators in the industry that track if people are lying about you, if you're getting chosen more/less often, and overall visibility shifts. - Ishaan "Proof I'm not just critical of our competitors" Shakunt

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Yext just published "A statistical look at regional and industry variation in local SEO ranking factors". Really interesting, especially this graphic. Here are the quickest takeaways: All effects are weak. Every correlation is small (roughly −0.10 to +0.14), so treat these as gentle signals, not causes. Biggest positives, by industry: Hospitality: Has Business Hours (+0.14, strongest overall), Has Merchant Description (+0.11), Profile Completeness (+0.10), Rating (+0.09). Healthcare: Review Count (+0.12) and New Reviews/Month (+0.11) lead; several review/engagement metrics sit around +0.07 to +0.08. Retail: Review Count (+0.08) and New Reviews/Month (+0.07); smaller bumps from Total Attributes and Photo Count (~+0.05). Financial Services (FINS): Review Count (+0.08), New Reviews/Month and Rating (+0.06), plus modest lifts from Attributes/Photos (+0.05). Food & Dining: Very small signals across the board; nothing much above +0.04. Consistent positives across industries More reviews (total and recent) and higher ratings are positive everywhere. Owner review responses (year/month/week) are positive but small. Consistent negatives Slower average review response time is negative in every vertical (worst in Healthcare at about −0.07). Notable outliers In Hospitality, having a local landing page shows the strongest negative (≈−0.10), and photo count is slightly negative (≈−0.04) - unique compared to other industries. Popular Times (presence of that feature) is more helpful for Healthcare and Hospitality (~+0.07–0.08) than elsewhere. Practical gist Focus on review volume, recency, and ratings everywhere. Respond quickly to reviews (slow replies correlate with worse outcomes). Hospitality: make sure hours, descriptions, and profiles are complete; don’t rely on a local landing page to help visibility. Retail/FINS: keep attributes and photos filled out; keep reviews flowing. Food & Dining: expect weaker gains from these particular profile tweaks, other factors likely matter more. ---- What do you think? #localseo #localseotips #localsearchrankings

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GEO (AI SEO) Is the New Content Stack Forget keyword clusters. The new game is evidence clusters. Backlinko’s Generative Engine Optimization research shows that LLMs favor content ecosystems combining: Community trust (Reddit, Quora) Third-party validation (press, data, docs) Consistent entity markup This isn’t SEO or PR or content — it’s all of them fused. Winning AI visibility requires brand accuracy on branded prompts, topic coverage on category prompts, and verifiable data everywhere else. If you’re still running these functions in silos, you’ve already lost the plot.

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SEO Insight: Google’s AI Overviews are now incorporating location based personalization into their responses. We tested a search for “dental digital marketing strategies” and saw something unexpected: Google’s AI Overview included localized content tied to Cary, NC (our location). When personalization in the SERPs was turned off, the same query generated a more generic, non-localized AI Overview response. This suggests that Google is now blending: • Query Intent: Understanding whether a user may be looking for local or general strategies. • Personalization Signals: Including location and possibly search history to tailor AI-generated answers. • SERP Contextualization: Adjusting AI Overviews dynamically based on user location and potentially other settings. Why this matters for SEOs: • AI Overviews may show different results for different users—even when they search for the same thing. • Local context is bleeding into AI Overviews, which could influence your content strategy. • Testing SERPs in incognito is no longer enough—you may need to test with personalization toggled on and off to understand true visibility. We’ll be keeping a close eye on how location-based data shapes AI Overview content going forward. Have you seen similar location-based shifts in AI Overviews yet? Curious what patterns others are seeing.

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