Cory Howell ✅

⚡ No, it is not just you - the cost per click (CPC) for Google Ads continues to 📈rise! ✏ There is a great new study covering 4 billion in media spend posted on Media Post showing: ➡ COSTS ARE UP - 13% increases in CPC costs year-on-year in the first quarter of 2024 (compared with a 9% increase YoY in Q4 2023) ➡ COMPETITION IS GROWING - 17% increase in gross search spend year-on-year in the first quarter of 2024 ➡ TRAFFIC GROWTH FROM ADS IS SLOWING DOWN - 4% click growth YoY in first quarter of 2024 (compared with an 8% increase in YoY in Q4 2023) ➡ AVERAGE VALUE OF ORDERS IS NOT INCREASING - 1% increase in AOV (just a slight increase from roughly flat growth in Q4 2023) What does this all mean for you 🤷‍♀️? It means you should have a 📢 FULL FUNNEL digital marketing strategy. Relying on one or two channels puts your business at a disadvantage going forward. You can learn more here -

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Is GEO the new SEO? If you'd have asked VP of Search, Eric Miltsch, over a year ago - you would have gotten a resounding "YES". Always a step ahead of the rest, Dealer Teamwork has been optimizing content for AI-driven search engines that utilize generative algorithms (aka GEO, or Generative Search Engine Optimization) for our dealership clients all along. Check out this blog from May 2023 to learn more! "Future of Search: Explore the Impact of Generative Search Experience (SGE) on Google's Results." The Future is NOW. Dive into the possibilities of SGE & its profitable impact on SEO & advertising. It's all part of Dealer Teamwork's Total Search™ Approach - and it's miles ahead of the game. #SEO #GEO #AI #TotalSearch #BuiltForThat #digitalmarketing #automotivemarketing

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Thought provoking SEO article: What if schema ISN'T a grand system for semantic understanding...but simply a labeling + tagging system. Interesting read from Jono Alderson! He makes an argument that it's possible that structured is really just an advanced tagging system. Instead of structured data being this "magic" concept that helps Google understand the Web, what if it just labels content? He talks about how there's not a lot of evidence of what Google actually does with structured data: 1. Limited reporting & insights 2. Mainly used to power rich results 3. No way to determine if Google actually reads it or not However, he also makes a point that even if schema is only used to label content - it's still extremely useful. Labeling could help machines understand relationships at scale and better connect it to LLMs, knowledge graphs and voice search. He argues whether or not schema actually "understands content" - it's still important. Search and AI needs direct datapoint to label and build relationships.

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Ugh... "Google is hiding search data from advertisers and profiting" Google's recent practice of concealing 20% to 80% of search term data has raised concerns among advertisers. This move aims to boost revenue by nudging advertisers towards purchasing less targeted ad space. Despite being labeled as a privacy measure, this tactic restricts advertisers from optimizing their campaigns effectively. Advertisers can mitigate this challenge by leveraging exact-match keywords, reducing hidden data to less than 20% and gaining better control over their ad spend. Read more about this issue here: https://lnkd.in/evGuPqT9

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GA4 demographic and interest data isn't even directionally accurate. I work with a lot of brands that make decisions based off of the "best data they have available." It sounds like sage advice, except for when you confuse "best" with "available." Just because something is reported doesn't mean it is accurate, or useful. We have clients who frequently look at the information available in GA4 and make IMPORTANT business decisions like: • Customer strategy • Product strategy • Marketing strategy • Logistics strategy It's tempting, I get it. Google is just so gosh darn convincing. And look, to be fair, a good chunk of the time their data is close enough. But, it's very hard to know if your business is one of the ones collecting a bunch of nonsense. I'll give you a recent example: We had a client with GA4 data that suggested: • Their average age was 25-34 • Heavily skewed male (65%) • Interested in technology, video games, etc We then plugged in their Shopify first party data into nova -- where we actually used their encrypted PII to append verified credit card and demographic details -- and you know what we found? Their actual customer was: • 55+ • Slightly skewed female (55%) • Focused on family, vacations, coupons This made sense with their product, which intuitively seemed to cater more to a retiree focused on their family. This caused us to significantly change our messaging, creative, media, and experiments we were running on CRO. Why is GA4 so frequently off? Outside of the handful of people who actually tell Google the truth when they get those little YouTube popup surveys, the vast majority of their data comes from probabilistic modeling. You searched for knitting so you love knitting. You looked at colleges so you're college age. You watch monster trucks getting crushed so you're male. Etc. For bigger brands that cater to more of the mass market and have huge amounts of data are less affected. But if you're a sub $10m/yr brand, especially one that caters to a specific niche? Fair warning. #strategy #marketing #persona #ga4

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Want to get clear on your agency’s profitability? Your agency’s growth isn’t just about winning more projects—it’s about understanding how to deliver them profitably. The Parakeeto Model Service helps agencies take the guesswork out of pricing, capacity planning, and profitability. Our clients are using it to see real-time insights into how their decisions impact the bottom line—and making smarter moves because of it. With hands-on support from our team, this service helps you: 1. Understand the true costs behind your projects 2. Optimize pricing and resource allocation 3. Plan for growth with confidence Ready to make smarter, data-backed decisions? DM us to see how the Parakeeto Model Service can help you scale profitably. #AgencyProfitability #DataDrivenDecisions #OperationalExcellence #ScaleYourAgency #Profitability #Parakeeto #AgencyGrowth #AgencyProfit

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SEM insight re E-comm vs Lead Gen accounts... esp. E-Comm on tROAS. Note: all E-Comm examples were on tROAS w/accurate Rev. data in Google. The biggest pattern I've recognized is: performance improvements to RSAs take a LOT longer to "mature" and prove out in E-Comm accounts. Possibly bc they have to compete against traffic ("inventory") allocations across Pmax, Search, Display AND Shopping. Regardless, the takeaway from the data I've seen is two-fold (and you'll need to hear my explanation): First - when testing anything on tROAS, but esp. in E-Comm accounts, ONLY look at COST (stay with me!). Second - be patient and allow for enough time for Conv. Value (Revenue) to mature. I know this sounds crazy at first, but here's why. In a tROAS system, the algorithm is maximizing Revenue (ideally at an efficiency target, or targetROAS). Your ROAS target is literally just Conv Value / Cost (Revenue / Cost = ROAS). When Cost spikes, Google owes you money. If Google CONTINUES spending more, it KNOWS it can get you that money! Your Return on Ad Spend (ROAS) is achievable, predictably, so long as a) you operate at enough scale, and b) you give the system enough time to achieve your target. Let me repeat: when Google spends more, it's "exploring" new audiences. When Google CONTINUES spending more, based on your improvements and optimizations, it's because Google KNOWS it can find incremental revenue at your target. Below is the past 10 weeks for a mid-sized E-Comm partner of ours. (And we are SO grateful they were smart enough to understand all of this!!!) After 2 weeks, our bucket was DOWN 62.5% in Revenue (relative to Control) but UP ~8% in Spend (or Cost). Client was patient and understood this meant Google OWED our Test bucket 8% more Revenue (at its target)! After 5 weeks, we were DOWN just 14% in Revenue but UP 33% in Cost. After 10 weeks, we're UP 50% in Revenue AND UP 70% in Cost. In a tROAS system, ESPECIALLY for E-Comm accounts, look at Cost only and trust that (continued) increased spend means Google OWES YOU Revenue--and knows it can get it! (Note: this ALL assumes your revenue (or projected revenue) are imported accurately and without too much delay in Google/Bing.) For those who understand algorithms enough to be patient and trust the science behind them, the rewards = improved performance results, are truly incredible at scale. 😊 Contact Traffic by Intent at [email protected] to geek out & learn more! #googleads #googleadwords #googleadvertising #bingads #bingadvertising #microsoftbing #microsoftads #microsoftadvertising #sem #ppc #paidsearch #payperclick #ppcadvertising

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New Customer Acquisition Targets in P-Max – Proceed with Caution 🚦 Overall, the new customer acquisition feature in P-Max campaigns works well, and I’d recommend giving it a try. But here’s the caveat: ⚠️ Most accounts don’t have ideal settings for differentiating new vs. existing customers. And while you can send some of this data to Google, the level of control is limited. So, what happens? 👉 If you have a well-defined audience setup and a solid strategy for bidding higher on new customers (while keeping existing customers in mind), the results can be great. However, too often, people assume “new customers are better” without a proper plan. A common misconception: Including brand terms in P-Max and assuming that bidding higher for new customers will reduce spend on branded terms. In reality, 80-90% of your customers from branded terms might already be new, which can skew your results even further. Takeaway: It’s a simple button to click, but figuring out the *right* settings takes a lot more thought 🙂. Last note: Any changes to new customer acquisition targets will inflate conversion value data in your account. Nothing to do about it, but just be aware it’s happening and ❗remember to adjust ROAS targets accordingly. What’s been your experience with new customer acquisition targets in P-Max?

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If your brand isn't using AMC insights, you're missing out. If you run DSP with Monks, AMC is included with no extra fee. AMC provides great analysis, but more importantly it provides a way for you to actually use this information and improve efficiency. Custom audiences you can't get anywhere else, ideal frequency caps, and path-to-purchase analysis with accompanying recommended budget spend are all things that you can action. If you're running DSP without AMC, you're wasting spend.

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GA3 (Universal Analytics) is going away this June! If you haven't backed up your data yet, don't panic. I have made a new release of the UA-Archive script, which makes it super easy to download everything you need. Just set it up, and it'll do the rest. You'll have all your data in CSV files, ready to use however you want. Try the UA-Archive script and let me know if you face any challenges. Check out the link in the first comment. #UABackup #GA3 #UniversalAnalytics

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The growth of Retail Media Networks (#RMNs) has exploded and has created more fragmentation and complexity to the planning process for marketers. If you are looking for a resource to help guide your RMN strategy, then check out our eBook to learn why scenario planning and advanced analytics is necessary to make data-driven decisions to help maximize your investments in this space. https://bit.ly/4hOj4gE #retailmedia #dataanalytics #scenarioplanning #marketingmix #marketingstrategy

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Not just mobile responsive but make sure your website is conversion focused. Here are a few factors to consider based on mobile users recent engagement statistics: 1. Make CTA buttons easily clickable (with thumbs) 2. Optimize your website’s mobile speed 3. Optimize images & videos 4. Use Responsive Design 5. Simplify Navigation 6. Break your forms into multiple steps & ensure they are mobile friendly 7. Use Mobile-Friendly Fonts 8. Minimize the Use of Pop-Ups 9. Implement Mobile-Specific CTAs 10. Optimize for Voice Search & focus on mobile SEO 11. Personalize Your Content to Your Mobile Users 12. Use Clear and Compelling Headlines 13. Ask for feedback from mobile users Hope this helps!

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How will seo rankings change in Google with #aioverviews heading toward standard operating procedure status? Here's a refresher

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Got an interesting question from a $100M+ online retailer. How can we improve site speed before holiday (without impacting our tracking)? These guys have: - Dozens of marketing pixels and tracking scripts - GA/GTM stack - Some server-side tracking (not all) Here's the quick approach they (and you) can take: Pixel Audit and Cleanup - Strip out unused libraries and pixels - Disable any redundant or outdated events - Hunt down and fix broken pixels You'll also find that analytics and other 3rd party pixels (often) aren't the primary site speed blockers. Instead, some upstream resource. So run your site through Google Lighthouse to identify blockers, and improve your "first contenful paint". Tag Managers Migrate all 3rd party scripts to a tag manager (GTM, Tealium, Adobe Launch, etc.) Benefits: - Better data governance - standardization - script modification - caching - sequencing control Server-Side Tagging Move clientside tracking to server-side capabilities for platforms like Meta, Google, TikTok SS Tagging can reduce browser load (crucial for mobile), but more importantly improves data quality to ad platforms. More data = more signal = smarter algo = increased ROAS, lower CAC. Smart Tag Sequencing - Defer non-critical pixels to load later - Use tag manager features for async loading The TLDR - Ditch unused pixels/libraries - Run your site through Lighthouse, fix blockers - Use a tag manager - Fix any broken tracking - Optimize load sequence - Set up server-side where possible This approach should significantly boost mobile performance without sacrificing your marketing data. Remember, with the holiday rush and heavy mobile traffic, every millisecond counts. What other tracking or site speed tactics would you add to prep for the Black Friday pop? Drop your thoughts below. #ecommerce #siteoptimization #blackfridayprep

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In MMM, it's essential to include price as a variable in your model, even if you’re not specifically reporting on it. Here’s why: Some products are highly elastic, meaning small changes in price lead to significant shifts in demand, while others are more inelastic. However, you won’t know how elastic your products are unless you model it out. This is why it’s so important to collect accurate data about pricing, even if you’re not focusing on it. Price may turn out to have little effect in your model if it hasn’t varied much during the period you're analysing, but you need the data to determine that. When modelling price elasticity, you also need to consider the granularity of your data. For instance, it doesn’t make much sense to look at price elasticity at a national level if you don’t have a national price—many brands have different prices in different regions or for different product categories. In those cases, the elasticity calculated for the overall data set will likely be meaningless. So, the question you need to ask is: What do I need the measurement of elasticity for, and in what context? Only when you have a clear answer to that can you start looking at the right level of data. If you’re trying to figure out the price elasticity of a product within a specific retailer, say Target or Walmart, then having pricing data at the retailer level will make your results actionable and valuable. The more granular your data, the more insightful your elasticity measurement will be. For example, if you have store-level pricing data, you’ll get much better insights than if you’re analyzing Walmart as a whole or aggregating the data at a higher level. Another critical insight you can get from MMM is the interaction between price and media spend. In many cases, we’ve found that the negative impact of price increases can be softened when supported by a strong media campaign. Advertising can reassure customers and reduce the elasticity of demand. This kind of synergy between media and pricing changes is a powerful insight that MMM can identify. There’s no one-size-fits-all answer to this—it depends on the brand and the specific market dynamics. But MMM can help you figure out the optimal timing by measuring the interactions between different variables.

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It's good to see marketers planning to move further from last-click attribution. "MediaMixModeling (MMM) appeals to marketers in an environment where last-click attribution doesn’t show a holistic picture of the customer journey and privacy is paramount. But it has some drawbacks. The model lacks granularity and can be difficult to implement. We expect more marketers will lean into MMM in the coming years, especially following open-source MMM offerings from both Google and Meta." #digitalmarketing #attribution

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