I’ve recently stumbled upon some fascinating global research data that highlights a tech gap silently draining team speed, revenues, and competitive edge. The Storyblok Global Speed-to-Market Benchmark Report explores these issues comprehensively.
This rapidly evolving world demands a new pace, driven by cutting-edge AI and technology, and constant shifts in digital trends have redefined how we handle go-to-market (GTM) strategies.
In today’s marketplace, everyone, from customers to organizations, expects top-notch deliveries with speed. Unfortunately, only 22.5% of teams consistently meet these soaring speed-to-market expectations, revealing a disconcerting gap between ambition and actualization.
One might ask, what’s holding us back?
The Global Speed-to-Market Benchmark survey involved several GTM teams who shared insights on where processes are stalling or facing delays and what steps would truly improve speed-to-market in today’s fast-paced business environment.
The survey uncovered four significant bottlenecks largely tied back to technological hiccups or dependencies. The approval process, for instance, emerged as the most substantial bottleneck, with over 50% of teams identifying it as a major hurdle. This includes enduring multiple rounds of content revisions largely driven by disorganized feedback systems, exacerbating inefficiencies.
The practical solution? A well-configured CMS, particularly a headless one, allows for an organized and efficient content review process by decoupling content from presentation. This ensures stakeholders have access to a central content repository, thereby minimizing review confusion and delays.
Equally problematic is the overreliance on developers, where 38% of teams require developer input for most GTM operations. This not only slows marketers but also distracts developers from more critical tasks. A modern tech stack enabling team autonomy can mitigate this issue, allowing each team to concentrate on their core functions.
Moreover, compounding tech limitations, including complex deployment and outdated systems, further warrant an overhaul. Tech bottlenecks often operate silently, but they demand attention and timely solutions for improved GTM cycles.
I also noticed how post-launch firefighting issues are rampant, affecting 79% of teams. This inefficiency stems from fragmented systems, where constant developer intervention is necessary, further delaying launch processes.
Addressing these challenges involves refining the tech stack, especially choosing a CMS that aligns with modern delivery needs. This results in smoother launches, improved efficiency, and fewer post-launch issues.
The cost of slow GTM delivery is undeniable, leading to lost revenue and missed market opportunities, while also impacting team morale and increasing turnover risks. Interestingly, there’s a visible discrepancy between executive priorities and the requisite support for improved speed-to-market capabilities.
Armed with data, teams can make a compelling business case for change, drawing attention to specific bottlenecks and their ramifications, thus bridging the leadership alignment gap.
Overall, overcoming GTM challenges requires adopting adaptive technology stacks that align with today’s fast-paced demands. By doing so, we not only keep up with competition but also foster a resilient, engaged team poised for success.
For the complete analysis and strategies, the full Storyblok Global Speed-to-Market Benchmark Report is an invaluable resource.
As someone exploring the ins and outs of Microsoft Advertising, I’ve discovered an update that’s sure to enhance our campaign analysis. Microsoft is now allowing us to customize columns with all conversion metrics, providing us with deeper insights and aligning reports with our unique business goals.
What does this mean for us? Well, according to Navah Hopkins, our go-to expert at Microsoft, we can now build custom metrics by leveraging the full spectrum of conversion data available in the platform. This means we can track all conversions and primary conversions, enabling us to tailor our reporting to meet our specific objectives more closely.
Please note the new image showcasing Microsoft’s enhanced custom columns feature. It’s a visual reminder of how these updates can transform our analytical capabilities.
Why am I excited about this? Because the standard reporting often doesn’t mirror how we truly measure success. By giving us the tools to expand custom columns, Microsoft allows us to define metrics that truly matter—be they lead quality, revenue, or a combination of conversion actions.
This flexibility is crucial for managing a variety of conversion types or navigating complex marketing funnels. Now, I can create custom columns, using ratios and metric combinations such as cost per qualified lead or conversion rates focused on primary goals.
Moreover, I appreciate that the revenue and ROAS calculations will now reflect the values that align with my conversion goals, providing more accurate insights directly linked to business outcomes.
What does this change imply for us in a broader sense? It represents a shift toward a more flexible and advertiser-defined measurement approach, instead of relying solely on standardized platform metrics.
This update highlights the ongoing demand for improved reporting customization as campaigns become increasingly automated and intricate.
So, what should we keep an eye on? I’ll be observing how advertisers like us utilize these custom metrics to guide optimization decisions, whether consistency in reporting improves across teams, and if similar flexibilities will roll out in other areas of the platform.
Bottom line? With Microsoft giving us more control over how we measure success, custom columns are evolving into a vital asset for campaign analysis. Read more about this update here.
When I first heard about Google Analytics introducing their new Task Assistant, I was intrigued. This tool promises to be a game-changer for those of us who want to maximize our use of Google Analytics without needing deep technical know-how.
It’s exciting to see Google simplify such a complex product. Task Assistant is designed to help advertisers and analysts like me gain more value from our data effortlessly.
What’s New. With the rollout of Task Assistant, Google Analytics offers a guided workflow tool that surfaces tailored recommendations. This means improving property setup, data collection, and reporting is easier than ever.
How It Works. Located in the left-hand navigation, Task Assistant organizes recommendations into clear categories like connecting accounts and enhancing reporting. I can mark tasks as complete or skip items not aligning with my goals, making the setup more flexible.
Why We Care. Identifying gaps in tracking quickly helps ensure I’m working with reliable data. Task Assistant minimizes the risk of missed insights or inaccurate reporting, allowing for confident optimization of campaigns and budgets.
Between the Lines. Analytics platforms, as powerful as they are, can be underutilized due to poor configuration. I’m glad Google is turning setup into a step-by-step process rather than leaving it as a daunting manual audit.
The Bottom Line.Task Assistant is all about making Google Analytics more actionable. It guides users toward better data quality and effective measurement, all with less guesswork.
I recently discovered that back in December, Google introduced read more links for certain search result snippets on Google Search. Now, Google has shared some best practices to help us utilize these ‘Read More’ links effectively.
Digging into the Best Practices: To find these new insights, you can check out the documentation posted here. It outlines three essential tips:
Ensure the content is instantly visible to human visitors, not tucked away behind tabs or expandable sections.
Avoid using JavaScript that governs the user’s scroll position as the page loads. Let your users control their browsing experience.
If you’re calling history API functions or modifying window.location.hash on page load, don’t strip away the hash fragment. This could lead to issues with deep linking.
Visualizing the Concept: Google provided an image illustrating these links. Here’s a glimpse of how they appear:
Let me show you an example of these snippets in action:
Why It Matters to Us: The introduction of read more links adds an alluring touch to search result snippets. The potential for increased website clicks can be significant. Therefore, reviewing these best practices becomes essential for attracting even more visitors to our site.
Ultimately, driving more traffic is always a win, so optimizing your site with these tips could prove beneficial.
I’ve always been fascinated by the evolving landscape of AI and its impact on search optimization. Recently, I’ve been diving deep into platform updates, proprietary research, and the latest optimization strategies emerging from the AEO category.
One article that caught my eye is “9 Top ChatGPT Optimization Tools for Better Visibility” by Emily Axelsen, which was published on October 10, 2025. It offers incredible insights into boosting visibility using ChatGPT.
Julia Olivas also provides a deep dive into crafting an LLM-friendly content strategy, which she explores in “AEO & AI Content Marketing,” released on December 19, 2025. Her insights are invaluable for anyone looking to align with AI advancements.
Understanding the differences in optimization strategies with the article “AEO & GEO vs SEO” by Daria Erzakova, published on August 20, 2025, also expanded my perspective significantly.
In addition to these, various other posts delve into AEO research frameworks, technical foundations, and social optimization. I personally found the analysis in Michael Saltz’s “Social Optimization Suite” from March 17, 2026, to be enriching, emphasizing the importance of owning conversations that truly matter.
Even more, on March 16, 2026, Julia Olivas published about the necessity of having a social media agency adept in AEO, adding depth to my understanding of agency capabilities in today’s digital world.
The timeline of “LLM Data Wars: Deals, Restrictions & Platform Power Plays (2023-2026)” by Julia Olivas, published on March 9, 2026, reveals intriguing narratives about the competitive landscape of AI platforms.
Mostafa Elbermawy’s study on March 5, 2026, explores the power of social platforms and content types in shaping AI visibility, adding more context to these discussions.
For those interested in AI PR, Michael Saltz’s “From Mentions to Citations” on March 4, 2026, provides a fresh perspective on how PR strategies are evolving in the AI era.
The guide on schema markup by Ollie Martin, published March 2, 2026, is comprehensive for anyone looking to enhance AI search. It’s a must-read if you’re diving into AI search optimization.
Lastly, Daria Erzakova’s work on aligning social, SEO, PR, and content for AI search dominance, from February 20, 2026, encapsulates a forward-thinking strategy for today’s digital landscape.
I’ve noticed that when I rely too heavily on micro-conversions, my PPC campaigns don’t quite perform as expected. This often leads to distorted CPA and ROAS figures. Here’s how I’m learning to refine my approach to micro-conversions and align my strategies with real revenue.
AI-powered ad bidding systems are remarkably advanced, yet I find myself grappling with conversion tracking that isn’t as evolved. While ad platforms nudge me to keep track of multiple actions, I’ve heard from experts that it’s actually more beneficial to zero in on final outcomes.
From my experience, neither approach is entirely foolproof. Both over-signaling and under-signaling can impact PPC campaigns negatively. Too many vague micro-conversions can introduce noise, steering the bidding process toward less valuable actions, hampering the actual results. Conversely, with too few signals, the system lacks sufficient data for learning.
This issue becomes particularly apparent in my work with Performance Max and similar setups. The optimization here leans heavily on whatever signals I provide, irrespective of their true business value.
I started reflecting on how micro-conversions can overshadow real conversions, leading me to explore why these bidding systems operate this way and how to create a conversion framework that better aligns signal volume with actual business impact.
The Myth of a ‘Data-Hungry’ PPC Algorithm
I had always believed that algorithms thrive on data, a notion reinforced by platform guides and numerous PPC articles. They often imply that more signals inherently equate to better learning.
Yet, I’ve realized that while bidding systems need a certain signal density, they don’t necessarily gain from indiscriminate micro-conversion logging. More data doesn’t equate to better data.
When I add low-intent or weakly related actions, performance can degrade. The system might start optimizing for actions not aligned with real revenue.
It’s clear to me that these machine-learning systems assess frequency, consistency, and predictability without discerning the strategic relevance of a signal.
My account often contains a blend of meaningful actions like purchases and others less significant, like pageviews. Without a value hierarchy, the algorithm treats all signals as viable targets, leaning toward easy, frequent actions that offer little business value.
As I adjust my approach, I’m finding the need to streamline my focus. By applying disciplined strategies and value-based bidding, I can align my signal structures more effectively with my business outcomes.
In my experience, navigating long sales cycles is like orchestrating a complex symphony, with people, timing, and operations all playing vital roles. I’ve learned that when I value leads appropriately, I can give paid media platforms the clarity they need to perform better.
In these extended sales journeys, much of the action post-lead submission revolves around the human element. If I focus my campaign optimization efforts solely on sales outcomes, I’m essentially allowing ad platforms to react based on the sales team’s monthly performance, which often overlooks lead quality—a dilemma no amount of tweaking can resolve.
The advice to “optimize the full funnel” suggests monitoring media expenditure through to revenue generation. However, beyond capturing leads, the factors that drive sales often exist outside the realm of paid media—it’s tied to the sales team composition, their workload, and other myriad factors beyond your control with targeting or creative updates.
When My Sales Team Becomes the Signal
With over 15 years in financial services marketing under my belt, I’ve seen this phenomenon extend beyond industries like mortgages or insurance. If human interactions are a key part of your sales process, this will resonate with you.
Picture someone like Dave in your organization. For example, in my case, Dave is a talented mortgage advisor, but in your world, he might be your leading enterprise sales rep, an outstanding business development manager, or the star project estimator.
Dave isn’t just successful because he gets better leads. His natural gift for establishing connections, asking insightful questions, and reassuring clients enables him to close deals at a rate far exceeding his peers.
But Dave isn’t omnipresent. He deserves vacations, he might pursue new career opportunities, or your company may recruit more like him. Consequently, the composition of your sales team is in constant flux. A surge of seasoned closers one month might juxtapose a shortfall the next, influenced by recruitment drives or personnel departures like Dave moving on with two coworkers.
This variability can lead to targeting conundrums. When conversion rates plummet as a junior rep fills in during Dave’s absence, algorithms may misinterpret it as a targeting issue rather than a staffing concern.
If my campaigns are programmed to optimize towards sales, the algorithm might surmise, “Targeting malfunctioning—these clicks now yield lower quality conversions; time to redirect spending.”
Such assumptions can lead to previously effective keywords being disabled, active audience engagement dwindling, and overall account performance declining, despite leads remaining unchanged.
There’s more at play than merely the sales team’s structure. Imagine this scenario:
During Q4, workloads often intensify as everyone races to finalize deals by year-end. Response times may surge from two days to over a week, prompting impatient clients to look elsewhere.
Market dynamics could shift abruptly, leading to the withdrawal of your most competitive product. Or, summer vacations reduce staffing, resulting in some leads growing cold long before follow-up. Then, in September, everything stabilizes again.
These are just typical examples of everyday operational hiccups. Be it budget sanctions being stalled, fluctuating product ranges, or project delays, each can uniformly distort your conversion metrics.
The algorithm may misinterpret targeting effectiveness when, in reality, your team is simply juggling leads from other originations.
When Dave Becomes Unstoppable: The Santa Claus Rally
The Santa Claus Rally, often referred to as the December Effect, is a fascinating instance I’ve witnessed where human actions can throw algorithmic targeting for a loop.
Every December around the third week, something peculiar unfolds in the financial services arena: lead-to-sale conversion rates soar, with uplifts skyrocketing up to 150% compared to usual weeks.
Optimizing for sales might lead the algorithm to deduce, “This week’s strategy is phenomenal!” Yet, reality hits during the holiday week, plummeting conversion rates to fractions of their regular levels.
None of this is attributable to paid media strategies. By week three, individuals like Dave enter ‘goal-accomplishment’ overdrive. They’re motivated by year-end bonuses, pushing through one last campaign before the break—swiftly reaching out to leads, following up assertively, and converting deals they might usually spend longer nurturing. Dave’s productivity hits a new high.
With the advent of the holiday week, everyone checks out mentally. Customers stop answering calls, and Dave finally uses his PTO. Meanwhile, those still working spend more time planning family events than business goals.
The lead attributes, targeting, and ad placements remain consistent. The program simply adjusts bids and valuations based on the seasons, reflecting when Dave and team take their much-deserved vacations.
So, if I find that sales-focused optimization skews due to uncontrollable factors, I wonder where this optimization boundary should be drawn. How can I curb this distortion while ensuring the right leads?
The answer lies in finalizing control at lead submission—but evaluating leads isn’t about counting them. It requires ascertaining their probability of conversion and the financial worth of the final sale.
An issue with high-value industries is their frequently low sales numbers, making it nearly impossible for automated systems to gather meaningful insights. Lead valuation counters this by providing a greater volume of conversion events as opposed to sparse sales data.
Consequently, automated bidding performs efficiently, facilitating campaign testing and audience analysis, while maintaining data accuracy. Optimizations draw from lead quality before Dave—or the sales crew—steer the wheel.
Importantly, while downstream conversions or revenue may be imported into platforms powerfully, it only succeeds if volume is ample, conversion delays are short, and sales processes are stable.
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I begin with a robust analysis of historical data, preferably spanning a year, although six months can suffice. My goal is to discern which leads converted and assess their value, identifying any shared characteristics evident at inquiry.
For financial endeavors, relevant metrics might include loan value or terms. In a B2B context, relevant dimensions might involve business size or industry. Construction projects often boil down to scope and immediacy.
Afterward, I categorize leads by their conversion probability and typical deal size, then assign an estimated revenue value.
The checkpoint for accuracy is straightforward: ensure that your leads’ cumulative projected value closely mirrors actual generated revenue over a timeline. If discrepancies exist, the model needs adjusting. It’s prudent to revisit these models routinely, ideally quarterly, in response to dynamic campaign and operational changes.
For instance, I might qualify a high-probability lead at $850, a median lead at $420, and lesser-chance leads at $120.
Upon formulating this, conversion tracking is configured to relay anticipated values back to platform conversion actions, thereby deploying value-based bidding (like Google Ads’ target return on ad spend) to guide the algorithm towards valuable leads.
The advice to “optimize the full funnel” resonates as common sense till we grasp how much we can’t control. For instance, I can shape targeting, craft compelling creatives, enhance landing pages, and streamline initial form engagements. Thereafter, it’s primarily on Dave or the sales team and extraneous factors far removed from my campaigns.
Expecting an algorithm to optimize for invisibles misleads it into chasing erroneous audiences from flawed assumptions.
Instead of ceasing post-lead tracking, I recommend sustained monitoring, as it sheds light on areas of triumph and those needing rectification. Consider these pointers:
With steady lead quality and declining sales, it’s an operational challenge, not a paid media dilemma.
Simultaneous drops in both lead quality and sales might prompt campaign evaluations.
Sudden sales surges with stagnant lead quality often indicate Dave excelling, not improved targeting.
Such detailed insights are invaluable but shouldn’t dictate optimization strategy.
Develop robust lead value assessments, convey expected valuations back to your systems, and allow algorithms to excel at identifying optimal leads. Leave other aspects to Dave’s capable hands.
It’s essential to delineate where your control ceases, marking where optimization should logically end.
As someone who’s been following OpenAI’s journey, I’m excited to share that they’re laying the groundwork for ChatGPT’s advertising business. These early steps reveal that OpenAI has more work to do to measure up against major players like Google when it comes to performance and ROI.
What’s happening. OpenAI has started testing an Ads Manager dashboard with a select group of partners, confirmed by sources at ADWEEK. This tool, aimed at marketers, allows for real-time campaign launching, monitoring, and optimization, drawing parallels with the established digital advertising management platforms.
Why it matters to me. OpenAI is building a self-serve advertising ecosystem around ChatGPT with the Ads Manager, in preparation for AI assistants becoming a significant channel. As conversational search becomes more prevalent, I believe it’s crucial for marketers like us to consider visibility in AI-driven responses, expanding beyond traditional platforms like Google Search.
Getting in on this early means we could gain unique insights into performance, formats, and optimization strategies within this fresh advertising landscape.
How it works now. For now, early testers are receiving weekly CSV performance reports, which include metrics like impressions and clicks. It’s evident that the ads product is in its initial stages, and more advanced analytics and tools are likely as the program matures.
The challenge: Initial tests indicate click-through rates for ChatGPT ads are lagging behind those of Google Search, marking a significant hurdle for OpenAI as they strive to showcase the value of advertising within conversational AI.
The cost of entry. Reports suggest that some early advertisers are being asked to commit a minimum of $200,000 in spend, significantly raising the stakes for OpenAI to deliver demonstrable performance and ROI.
Between the lines. Building an effective ad ecosystem entails more than just ad inventory. As marketers, we expect comprehensive reporting, optimization tools, and reliable performance — areas where established platforms like Google have a considerable head start.
I’ve recently discovered some exciting updates in Google Analytics that I think are real game-changers for marketers like me. They’ve introduced AI-generated insights on the Home page, alongside a new cross-channel budgeting feature in beta. These changes help me quickly identify key performance shifts and optimize how I spend my paid budgets.
What’s happening. The introduction of these AI-generated insights right on the Home screen means I can now see the top three changes that occurred since my last visit. This includes notable updates, performance anomalies, and those tricky seasonality trends—all without sifting through the detailed reports.
This feature is all about speed and convenience. Instead of spending time manually scanning dashboards, it offers me a quick snapshot of what’s changed and why it could matter.
Cross-channel budgeting (Beta). As a marketer, I find the new cross-channel budgeting feature incredibly useful. It allows me to track performance across various paid channels and optimize my investments based on the results I get.
While access to this feature is currently limited, I’m eagerly looking forward to broader availability in the near future.
Why I care. These updates make it easier and faster for me to spot performance changes and directly link insights to budget decisions. The automated insights reduce the time I spend combing through reports, while cross-channel budgeting helps me allocate spending more strategically across various channels.
Together, these features streamline my analysis process and enhance how quickly my team and I can adapt our strategies.
Bottom line. In combining Generated insights and cross-channel budgeting, Google Analytics aims to reduce reporting friction and improve decision-making. This means faster answers and more control over how I allocate budgets across channels.
When I first heard about Performance Max, I was skeptical. It seemed like an unfinished product, but over the past 18 months, Google has made significant improvements in transparency and control. If you haven’t revisited Performance Max since its early days, now is the perfect time to take another look.
As I learned from Mike Ryan at SMX Next, the advancements are worthy of attention.
Taking a Fresh Look at Performance Max
Performance Max evolved from Smart Shopping campaigns, introduced with much excitement in 2019. Yet, industry experts quickly pointed out issues with transparency and control, which Google is only now beginning to address.
Smart Shopping took away vital controls critical for managing campaigns effectively. Essential features like promotional controls and search term reporting vanished, leaving many of us feeling limited.
Fortunately, Performance Max reintroduces much-needed functionality, enhancing what was once lacking.
Understanding Performance Max Search Terms
In my experience, search terms are crucial for understanding the effectiveness of our campaigns. With Performance Max, Google has added a unique match type that brings detailed and scriptable data, allowing us to optimize with precision.
Search Term Insights vs. Campaign Search Term View
Initially, Google introduced search term insights, grouping queries into categories. Unfortunately, these lacked depth as they didn’t provide essential cost data.
The game-changer, though, is the new campaign-level search term view, offering access to more metrics and clearer visibility on performance.
While these insights are only available at the search network level, they offer significant improvement over past limitations.
Search Theme Reporting
Through Performance Max, I’ve realized search themes act as a positive targeting method. By checking conversion data and the source of traffic, I can ascertain the value of search themes, identifying whether they contribute effectively or remain underutilized.
Search Term Controls and Optimization
Negative Keywords
At first, negative keywords in Performance Max were limited, which was frustrating. But now, they are fully supported and much more robust, giving me the control I need to fine-tune performance.
Brand Exclusions
While Performance Max tends to favor brand queries because of their high intent, I’ve noticed that using negative keywords provides a stronger solution for ensuring optimal performance without leakage.
Optimization Strategy
My strategy involves identifying non-performing search terms with higher-than-average clicks but zero conversions, making them strong candidates for exclusion. This approach prevents overcorrection while maintaining a focus on impactful terms.
Modern Optimization Approaches
Instead of spending countless hours manually reviewing search terms, I leverage automation. Using the API for high-volume accounts and scripts for mid-range volumes significantly optimizes my workflow.
Channels and Placements Reporting
Channel Performance Report
One of the tools I now rely on is the channel performance report, offering insights across different networks like Discover and Display. Though interpreting some diagrams can be tricky, it provides valuable data on how different channels perform.
Channel and Placement Controls
Placement Exclusions
Through API and Report Editor data, I focus on excluding specific placements that seem irrelevant or pose risks, particularly in sensitive content areas like politics and children’s videos on YouTube.
Tools for Placement Review
For reviews, especially in other languages, I’ve found that using Google Sheets’ translation function is effective. It helps me quickly determine the relevance of YouTube placements without relying on external systems.
Search Partner Network
The inability to opt out of the Search Partner Network can be frustrating. However, I mitigate this by prioritizing exclusions where performance is subpar compared to the Google Search Network.
Device Reporting and Targeting
Device Analysis
Analyzing device performance provides deeper insights into how specific products perform across different devices. This often reveals advantages or challenges when compared to competitors.
Device Targeting Considerations
Splitting campaigns by device can hurt data volume, impacting machine learning effectiveness. It’s crucial to weigh the benefits of splitting against the potential for data fragmentation.
Conclusion
Reflecting on Performance Max’s evolution, it’s evident that Google has made impressive strides in offering advertisers like myself more control and transparency. While it’s not without flaws, it’s a far more effective tool for ecommerce success now than ever before.
The key lies in understanding available data, using modern tools to streamline processes, and applying performance insights strategically to achieve the best results.
