I’ve discovered that shifting toward Demand Gen in Google Ads transforms the focus from simple keyword targeting to more visually-driven advertising. Relying on outdated methods not only wastes money but also limits the potential of what Demand Gen can achieve. To thrive, I need to see things like a social advertiser rather than just a search advertiser.
At SMX Next, Jack Hepp from Industrious Marketing shared valuable insights on why many businesses, particularly in the B2B sector and lead generation, find demand gen campaigns challenging, while also providing strategies that are applicable to ecommerce.
In transitioning to Demand Gen, I see Google’s move from intent-driven to discovery-focused campaigns. This involves reaching users casually browsing on platforms like YouTube, Gmail, or Discovery feeds rather than those actively searching for my offerings. This approach means that visual assets now play the role that keywords once did.
Aligning campaign strategies to fit this model requires abandoning old tactics. Here’s what I need to avoid:
Expecting bottom-of-funnel CPAs from mid-funnel traffic.
Employing imprecise, broad targeting.
Running dull, uninspired creative.
Lack of optimization know-how without negative keywords.
Seeing success demands that I adopt a mindset similar to social advertising.
Demand Gen structure consists of campaigns governed by broad parameters (like bidding strategies and conversion goals) and ad groups that dictate audience specifics. Each ad group learns independently, which allows for finely tuned audience segmentation.
When crafting interruption-based creative, my goal is to catch attention in the first 3-4 seconds. It’s about highlighting a specific pain point and offering a solution in a way that turns casual browsers into engaged prospects.
Ensuring my visual content aligns with the customer journey is crucial:
Cold audiences benefit from educational material.
Warm audiences engage with case studies and webinars.
Hot audiences are ready for demos or purchase offers.
When my creative addresses specific problems with bold visuals and compelling headlines, the engagement naturally increases. For instance, targeting specific challenges like cybersecurity for small businesses makes my ads stand out.
Bidding in Demand Gen focuses on campaign-specific goals. To gather the necessary data, I aim for significant monthly conversions and budget accordingly to enable optimal performance.
Even small budgets can work if strategically planned. By directing efforts at mid-funnel activities, I can achieve the necessary conversions for meaningful insights.
In building the right audiences, it’s about balance. I avoid extremes of too broad or too narrow segments and focus on custom segments complemented by lookalike data, optimizing as success dictates.
Aligning the messaging of my creative with the buyer’s stage ensures Google effectively targets potential customers. This strategy steering focuses more on creative, audience, and the offer itself.
Using targeted exclusions efficiently helps me concentrate effort on engaging users without overly restricting potential reach. It’s a strategic rather than blanket approach.
Optimization in Demand Gen focuses on creatively testing different formats and refining audience targeting. I continually test offers to match audience readiness and optimize post-click experiences to enhance campaign effectiveness.
In a real-world application, a telecommunications company achieved impressive outcomes by clearly defining its offer, targeting, and creative messages. The results highlighted the critical importance of aligning these elements for Demand Gen success.
Here are the key takeaways for any campaign I plan next:
Align creative content with my target customer’s stage in their journey.
Identify and target audiences at appropriate points in their journey.
Continuously test and refine both creative elements and offers to amplify impact.
In a recent episode of PPC Live The Podcast, I got the chance to sit down with Emina Demiri Watson, the Head of Digital at Vixen Digital based in Brighton. She opened up about one of the more challenging experiences an agency can face: choosing to let go of a client who made up a significant portion of their revenue. Imagine a client that accounts for 70% of your income, and then having to say goodbye. This is what Emina bravely tackled.
Over approximately three months, it became clear that the relationship with this client was worsening. It wasn’t an overnight decision; it evolved from a once-healthy dynamic to something toxic. The leadership team at Vixen made the tough call to prioritize their company culture over the immediate financial gain provided by this client. It was a decision not driven by a difficult client but by a deteriorating relationship that impacted the entire team.
When they finally analyzed the situation, the reality hit hard. Vixen discovered they had a serious issue with client concentration — one client dominated their revenue structure. This wasn’t apparent until they examined the figures closely, underscoring the importance of having well-organized financial tracking systems.
Emina also highlighted several red flags agencies should watch for in client relationships. It’s not just about declining campaign performance; watch for shifts within the client’s business, such as restructuring, team changes, or security breaches that can impact lead conversions. It’s crucial to understand what’s happening on the client’s end to maintain a healthy partnership.
The road to recovery for Vixen Digital involved three key strategies: properly monitoring client concentration, adhering to their core values, and being patient with rebuilding revenue. Losing the client allowed them to re-focus on pitching new business and reconnecting with the industry, activities that had previously been sidelined.
In discussing mistakes observed during account audits, Emina noted common issues such as using broad match without adequate audience safeguards and neglecting negative keyword lists. These errors often lead to ineffective targeting, especially problematic for businesses targeting niche, high-value audiences.
Emina’s view on AI is refreshingly realistic: the key misstep is overhyping it. In the PPC world, we’ve been navigating automation for years, which positions us well to question AI’s supposed magic. Her advice to the team is to use AI tools like Claude for preliminary research but never to replace critical thinking.
If you’re grappling with the idea of ending a deteriorating client relationship, Emina’s straightforward advice is to return to your values. Prioritize commercial goals if that aligns with your mission, but if preserving company culture and team morale are paramount, it may be time to let go.
Data serves as more than just a report card; it’s the roadmap for our performance marketing strategies. To make the most of this roadmap, I’ve learned it’s necessary to go beyond Google Analytics 4’s default tools.
If I were to rely solely on GA4’s built-in reports, I’d find myself juggling multiple interfaces and struggling to tell a clear story to stakeholders. That’s where Looker Studio becomes a game-changer for me.
Looker Studio allows me to transform raw GA4 and advertising data into interactive dashboards that provide decision-grade insights and drive campaign improvements.
In this guide, I’ll show you how to use GA4 and Looker Studio effectively for PPC reporting by comparing their roles, highlighting recent updates, and sharing specific use cases—from budget pacing visualizations to waste-reduction audits.
GA4 vs. Looker Studio: How They Differ for PPC Reporting
GA4 serves as my ultimate reference point for website and app interactions, offering insights into user behavior, clicks, page views, and conversions through a flexible, event-based model. It’s integrated with Google Ads, pulling key ad metrics into its Advertising workspace. However, GA4 primarily focuses on data collection and analysis, not on creating client-ready reports.
Conversely, Looker Studio is my go-to for creating comprehensive reports. It connects to over 800 data sources, allowing me to build interactive dashboards that consolidate all my data in one place.
Data Sources
While GA4 primarily focuses on on-site analytics, its late 2025 update allowed native integration for platforms like Meta and TikTok, enabling automatic imports of cost, clicks, and impressions. However, I find it to be somewhat rigid, requiring strict UTM matching and lacking the capability to clean campaign names or import specific conversion values.
In contrast, Looker Studio allows me more flexibility in blending data sources and connecting to platforms that GA4 doesn’t support natively, such as LinkedIn or Microsoft Ads.
Metrics and Calculations
GA4 has improved its reporting UI, now enabling up to 50 custom metrics per standard property, which is quite an upgrade from the previous limit of five. However, these metrics can often be static.
Looker Studio, on the other hand, lets me perform real-time calculations on my data through calculated fields. This allows for dynamic data manipulation, such as computing profit by subtracting cost from revenue, without altering the source data.
Data Blending
Looker Studio lets me blend multiple data sources to create richer insights. Even though enterprise users on Looker Studio Pro can utilize LookML models for robust data governance, the standard free version still offers flexible data blending capabilities to align ad spend with downstream conversions.
Sharing and Collaboration
While sharing insights in GA4 often requires granting property access or exporting static files, Looker Studio offers live web links that update automatically. I can even schedule the automatic email delivery of PDF reports for free.
The enterprise features in Looker Studio Pro provide advanced delivery options to Google Chat or Slack, although standard email scheduling is accessible to everyone.
Here’s why Looker Studio transitions from being simply helpful to absolutely essential for PPC teams like mine.
1. Unified, Cross-Channel View of PPC Performance
Managing multiple ad platforms, I find that a Looker Studio dashboard acts as my single source of truth, blending intent-based Google Ads data with awareness-driven Meta and Instagram Ads to provide a holistic view.
For example, with Looker Studio, I can normalize data and discover that X Ads drove 17.9% of users, while Microsoft Ads drove 16.1%, enabling me to allocate budgets based on actual blended performance.
2. Visualizing Creative Performance
In sectors such as real estate, visuals sell the clicks. Saying “Ad_Group_B performed well” doesn’t resonate with clients.
Utilizing the IMAGE function in Looker Studio, I can display the actual image of a luxury condo or HVAC promotion directly in the report table alongside the CTR, providing clients with a clear view of which creative elements are driving results.
3. Deeper Insight Into Post-Click Behavior
Effective reporting extends beyond the initial click. By integrating GA4 data with my Looker Studio reports, I can link ads to subsequent actions.
For instance, I might notice that a Cheap Furnace Repair campaign has a high CTR but a 100% bounce rate. Looker Studio enables me to visualize engaged sessions per click alongside ad spend, validating that lead quality is more crucial than sheer volume.
4. Custom Metrics for Business Goals
Every enterprise has unique KPIs. While a real estate firm might track tour-to-close ratios, an HVAC enterprise might prioritize seasonal efficiency.
Looker Studio allows me to create these unique formulas just once, with automatic updates. I can even bridge data gaps and calculate return on ad spend (ROAS) by dividing CRM revenue by Google Ads costs.
5. Storytelling and Narrative
Data alone lacks context. With Looker Studio, I can add text boxes, dynamic date ranges, and annotations, transforming numbers into compelling narratives.
An example is using annotations to explain metrics fluctuations. If cost per lead spiked in July, I might annotate, “Seasonal demand surge + competitor aggression,” preempting client queries and turning the report into a powerful strategic resource.
Use Cases: PPC Dashboards That Drive Real Insights
These dashboards extend beyond basic metrics, providing actionable insights for immediate implementation.
The Budget Pacing Dashboard
Concerned about overspending? Standard reports reveal what’s been spent but don’t indicate its relationship to the monthly budget cap.
With bullet charts in Looker Studio, I set targets to align with linear monthly spend. For instance, if halfway through the month, the target line aligns with 50% of the budget. This visual helps stakeholders see real-time pacing to ensure budget compliance.
The Zero-Click Audit Report
High spending without conversions is a costly mistake, especially in service industries.
By creating a dedicated table to highlight wasteful spending — showing keywords with conversions at zero and a cost exceeding a set threshold — I can quickly identify and pause ineffective keywords, demonstrating proactive budget management internally and to clients.
Geographic Performance Maps
For local services, my geographic location is critical. While GA4 provides local reports, Looker Studio takes visualization to the next level.
In Looker Studio, I build geographic performance pages that shade areas based on cost per lead rather than mere traffic volume, helping me identify that while City A drives more traffic, City B yields leads more efficiently.
Getting the Most Out of GA4 and Looker Studio in 2026
To maximize success with GA4 and Looker Studio, I’ve learned a few essential tips.
Watch Your API Quotas
One of the main technical challenges today involves managing GA4 API quotas. If a dashboard has excessive widgets or draws too many concurrent viewers, charts might break or fail to load.
For heavy reporting demands, I consider extracting GA4 data to Google BigQuery first, then connecting Looker Studio to BigQuery, which bypasses API limits and greatly enhances report speed.
Enable Optional Metrics
Different stakeholders have varied needs. By enabling the “optional metrics” feature in charts, I provide viewers the convenience of toggling between metrics, such as changing a chart from clicks to impressions, without editing the report each time.
Validate and Iterate
Initially, I spot-check report numbers against the native GA4 interface to validate data and ensure attribution settings are correct.
Once I’ve established data trust, I treat the dashboard as a living product, continuously iterating on design per actual stakeholder use and needs.
I recently discovered that Google is changing how it attributes app campaign conversions. Instead of relying on the date when someone clicks on an ad, Google now ties the conversion to the actual install date of the app.
What’s Changing: Previously, Google linked conversions to the ad interaction date. Now, they’ll match the day of the app installation, aligning more closely with Mobile Measurement Partners (MMPs) like AppsFlyer and Adjust.
Why This Helps:
– This change reduces discrepancies between Google Ads and MMP dashboards, making life easier for mobile marketers who often deal with mismatched data.
– With Google’s old 30-day attribution window, many conversions were reported too late, hindering Smart Bidding’s access to the timely signals necessary for effective learning.
– By using the install date for attribution, Google’s algorithms will receive fresher, more accurate data, which could speed up optimization cycles and stabilize performance.
Why We Care: While it might seem technical, this change significantly affects how Google’s machine learning optimizes campaigns. The previous 30-day gap between ad clicks and conversion credit was a bottleneck. Now, Google’s machine learning gets the conversion data just when it needs it—right with the app install.
This shift should lead to smarter bidding and faster campaign optimization, helping to resolve the frustrating discrepancies between Google Ads and MMP reports. If you’ve ever been puzzled by inconsistencies between Google and platforms like AppsFlyer or Adjust, this update directly addresses that problem.
Between the Lines: Most advertisers don’t adjust their attribution window settings, leaving Google’s default 30-day window as is. Unfortunately, this was delaying crucial conversion signals that machine learning needs for improved bidding.
The Bottom Line: This seemingly minor tweak in attribution logic could have a significant impact on app campaign performance. I encourage mobile advertisers to monitor their data in the coming weeks for any shifts in conversion reports and optimization behaviors.
First Spotted: This update was first noticed by David Vargas, who shared a message about it on LinkedIn.
I’m thrilled to share that Google has just launched its Scenario Planner, an incredibly user-friendly, no-code tool. This planner empowers me to transform Marketing Mix Model insights into practical budget and ROI forecasts effortlessly.
Google’s new Scenario Planner allows me to test various budget scenarios and forecast ROI using Meridian’s marketing mix model, all without requiring any data science expertise.
What’s new? The Scenario Planner makes complex MMM outputs accessible and actionable:
Intuitive, code-free interface: Testing different budget allocations and viewing ROI estimates is a breeze without needing to write any code.
Forward-looking planning: I can simulate investment scenarios and stress-test strategies, which moves beyond mere retrospective reporting.
Digestible insights: These technical model outputs are visualized in clear, easy-to-understand formats, making them highly usable for my strategy decisions.
Why do we care? With these predictive marketing insights at my fingertips, I can test budgets, foresee potential returns, and adjust campaigns in real-time. This helps me plan smarter and optimize every dollar I spend.
Closing the MMM actionability gap. The Scenario Planner effectively bridges the “usability gap” long existing in Marketing Mix Models, which previously required specialized skills. According to Harvard Business Review, nearly 40% of organizations face challenges in turning MMM outputs into actionable decisions.
Bottom line. By combining the rigor of MMM with an easy-to-use, interactive interface, Scenario Planner empowers me to plan more strategically, optimize spending, and make confident, data-driven decisions without having to rely on technical experts.
I’ve recently come across an interesting study highlighting a significant shift in search click dynamics. It turns out that text ad clicks have dramatically increased year over year, while the traditional organic clicks in major verticals have taken a sharp decline.
This transformation isn’t solely due to AI Overviews for sure. Google’s expansion of paid search real estate is playing a pivotal role here. In the U.S., data reveals a steep drop in classic organic click share across product categories like headphones, jeans, greeting cards, and online games between January 2025 and January 2026.
The numbers are quite telling. Classic organic click share fell significantly across these categories, making way for text ads, which emerged as the biggest beneficiaries, gaining a notable share of clicks.
Why does this shift matter to us? As digital marketers, it’s no longer just AI-powered features that we’re contending with. Text ads have won substantial ground, capturing about one-third of the clicks in several product categories. For brands seeing a dip in organic visibility, increasing paid efforts seems to be a necessary strategy.
Numbers tell the story. When diving into four main verticals, text ads showed consistent click-share increases. Classic organic lost between 11 to 23 percentage points, while text ads gained anywhere from 7 to 13 percentage points across the board. Paid click share has doubled in several key product categories.
Comprehensive breakdown: Classic organic click shares have seen a year-over-year decline across all verticals. For instance, headphones lost dramatically, shrinking from 73% to 50%, and even organic-heavy areas like online games dropped by double digits. Such declines emphasize the urgent need for many brands to reassess their search strategies.
Data shows that text ads inched forward share-wise in every industry examined. For instance:
Headphones: Rose from 3% to 16%
Online games: Up from 3% to 13%
Jeans: Climbed from 7% to 16%
Greeting cards: Up from 9% to 16%
Moreover, Product Listing Ads (PLAs) are further supporting this change in product sectors:
Headphones: Increased from 16% to 36%
Jeans: Went from 18% to 34%
Greeting Cards: Rose from 10% to 19%
AI Overviews have seen a diverse impact. While the presence of Google AI Overviews on SERPs has certainly increased, the extent varies significantly across sectors:
Headphones: 2.28% → 32.76%
Online games: 0.38% → 29.80%
Greeting cards: 0.94% → 21.97%
Jeans: 2.28% → 12.06%
Zero-click searches remain significant but stable. Even though the overall zero-click rates haven’t seen dramatic changes, online games have witnessed a noticeable uptick:
Headphones: 63% (unchanged)
Jeans: Down from 65% to 61%
Online games: Up from 43% to 50%
Greeting cards: Increased from 51% to 53%
Brands adapt by increasing paid presence. In the headphones market, for example, companies like Amazon boosted paid clicks by 35% despite losing organic traffic, while Walmart increased theirs nearly sixfold.
In the jeans sector, Gap saw a 137% growth in paid clicks, rising to become the leading paid player.
For online games, CrazyGames quadrupled its paid clicks, and Arkadium entered the paid scene after a significant drop in organic clicks.
These shifts have led to a self-reinforcing cycle, as pointed out by Aleyda Solis, the study’s author. Organic share declines, competition increases, and brands continuously boost their paid-search budgets.
Study insights. This study was conducted using Similarweb data, thoroughly examining the SERP composition and click patterns for the top 5,000 U.S. queries in the areas of headphones, jeans, and online games, alongside the top 956 greeting card-related queries. Over time, it has highlighted a marked shift in click distribution among classic organic results, text ads, PLAs, zero-click searches, and AI Overviews.
If you’re curious about deeper insights, you can check out the full study by Aleyda Solis.
I’ve just discovered an incredibly beneficial update from Google Ads that I’m excited to share. Now, we can see precisely where our Performance Max campaigns are running through the “Where ads showed” report. This change opens up a new world of clarity and optimization possibilities that were previously inaccessible.
What’s New? This update allows me to see exactly where my PMax ads are appearing across Google’s network, including search partners, display, and other placements. By tracking impressions by placement type and network, I can now understand the detailed performance of my campaigns like never before.
Why It Matters to Me This is a game-changer for anyone managing PMax campaigns. It brings much-needed visibility into where ads are appearing, including Google Search Partners and beyond. With access to placement, type, and impression data, I can optimize budgets and make informed decisions rather than relying on guesswork. It transforms previously opaque reporting into actionable insights.
User Reaction Digital marketer Thomas Eccel shared his experience on LinkedIn, expressing that the report was once a blank page but now displays real data.
“I finally see where and how PMax is being displayed,” he wrote, highlighting the significance of this update for clarity.
He also noted how Google Search Partners are now no longer a “blurry grey zone.”
The Bottom Line For me, and many other marketers, this update offers actionable visibility into PMax campaigns, helping us understand placement performance, optimize spend, and pinpoint which networks are yielding results — all within one comprehensive report.
I’ve recently discovered that Google Ads has introduced an impressive new Results tab within their Recommendations section. It’s designed to help advertisers like you and me see the actual performance impact of applied suggestions, especially when it comes to bid and budget adjustments.
After applying any bid or budget recommendation, Google analyzes the campaign’s performance one week later. It then compares the results against a baseline estimate, showing us the incremental lift such as additional conversions from raising a budget or tweaking targets. It’s a fantastic update for those of us wanting concrete data on recommendation outcomes.
Wondering where to find this information? You can spot the impact reporting right in your account’s Recommendations area. There’s a handy summary callout with recent results on the main page, plus a dedicated Results tab providing a detailed breakdown categorized by Budget and Target recommendations, with helpful filters.
Why is this an important update? As an advertiser, I’m thrilled because this lets us see whether Google’s automated recommendations truly deliver incremental results, not just predicted boosts. This is crucial for assessing the real business value of these platform suggestions.
But what should we expect going forward? The Results tab reports a seven-day rolling average, measured over 28 days following a recommendation. It zeroes in on the campaign’s primary bidding objective, be it conversions, conversion value, or clicks.
This feature introduces an added layer of accountability to automated recommendations, especially as we’re relying more on platform-driven optimizations. I find it reassuring to know there’s now more transparency.
Interestingly, this was first shared by Hana Kobzová, founder of PPCNewsFeed, who took to LinkedIn with a screenshot of the help doc.
Although there isn’t a live help doc yet, a Google spokesperson mentioned they’re running an early pilot. It’s exciting to be on the cutting edge of these developments!
I’ve noticed some exciting changes coming to Google Demand Gen campaigns. Starting in March 2026, Lookalike audiences will no longer be the rigid framework we’re used to. Instead, they’ll serve as optimization signals, ushering in a new era of AI-driven campaign enhancements.
Google is updating its Help documentation to reflect this transformation where Lookalike segments shift from strict targeting to a more flexible, AI-enhanced recommendation model.
Understanding the Transition. Previously, I would choose a specific similarity tier (narrow, balanced, or broad) to dictate exactly who my campaigns targeted. That’s changing.
Now, Google will use these tiers as signals. The system will intelligently expand its reach beyond my chosen Lookalike lists to engage users predicted to convert.
Behind the Change. This transition turns Lookalikes from a barrier into an enabling tool. It allows Google’s automation to use intent signals to explore audience performance well beyond predefined limits.
Interaction with Optimized Targeting. The new Lookalike-as-signal approach resembles Optimized Targeting but doesn’t replace it. When they’re layered, Google mentions it could further expand my reach.
In practice, this means multiple automation signals will be at play, providing the algorithm more freedom to either reduce CPA or boost conversion rates.
Opting Out. If I prefer the traditional Lookalike approach, I can opt out via a dedicated form, preserving the old targeting behavior. Absent that, campaigns automatically switch to the new format.
Why This Matters. This update affects the control I have over ad targeting in Google Demand Gen campaigns. Lookalike audiences will now guide rather than confine targeting, significantly influencing scale, CPA, and performance.
Additionally, it indicates an industry-wide move toward automation, similar to shifts driven by Meta Platforms. I’ll need to test thoroughly, rethink strategies, and decide whether to embrace the added reach or opt out for tighter targeting.
Industry Context. Google’s strategy echoes a broader trend toward AI-first audience expansion, aligned with similar adaptations from Meta in recent years. The advertising landscape is increasingly prioritizing machine-led optimization over detailed manual control.
The Reasoning. According to digital marketer Dario Zannoni, there are two main reasons for Google’s shift:
Stringent Lookalike targeting can limit scale and hinder performance in conversion-focused campaigns.
The complexity of maintaining high-quality similarity models makes automation a more viable option.
The Bottom Line. For performance marketers like me, this marks another step towards automation-centric strategies. Reduced control might be daunting, but similar platform changes have historically yielded performance gains. A fresh testing cycle is on the horizon as I examine the impact of expanded Lookalike signals on CPA, reach, and conversions.
Observed and Shared. Dario Zannoni initially highlighted this update on LinkedIn.
I’ve recently discovered an exciting development in Google Ads that’s set to revolutionize how we track and measure our advertising success. The platform is now testing a beta feature that allows us to link external data sources directly into the conversion action settings. This move aims to strengthen the bridge between our first-party data and campaign measurement.
How does this work, you might ask? In the conversion action details, a new section titled “Get deeper insights about your customers’ behavior to improve measurement” encourages us to connect our external databases to our Google tag, offering a seamless integration experience.
This integration supports platforms like BigQuery and MySQL, with the primary goal of enriching our conversion metrics and enhancing performance signals. Notably, this feature is highlighted within the data attribution settings and is gradually being rolled out in its Beta phase.
Why do we care? The ability to directly integrate these data sources reduces the hassle of syncing offline or backend data with ad measurements. This beta feature from Google Ads simplifies connecting first-party data to conversion tracking, improving our measurement accuracy and campaign optimization.
By harnessing the power of platforms like BigQuery or MySQL, we’re able to incorporate richer customer data into our signals, crucially offsetting any data loss resulting from recent privacy changes. In practical terms, this means smarter bidding, clearer attribution, and the potential for a stronger ROI.
Beneath the surface, embedding these data connections directly within conversion settings—rather than relying on separate pipelines—democratizes advanced measurement tactics, making them accessible not only to large enterprises but to advertisers like you and me.
As ad platforms compete for superior measurement accuracy, these native data integrations are emerging as a pivotal advantage, particularly for brands heavily investing in proprietary customer data.
