I’m excited to share that Meta is set to expand Threads ads to all users worldwide beginning next week. This move opens up new opportunities for advertisers to engage with over 400 million users.
Threads, which rivals the platform X, has seen rapid growth since its debut in July 2023. With its soaring popularity, CEO Mark Zuckerberg has high hopes that Threads could reach 1 billion users in the near future.
Advertiser Access. Advertisers have already been testing Threads ads in the U.S. and Japan. As of last April, global advertisers gained access. Meta helps streamline campaign expansions to Threads through its Advantage+ program, supporting various ad formats like image, video, and carousel. This can all be managed alongside campaigns on Facebook, Instagram, and WhatsApp within Business Settings.
Third-Party Verification. Meta is ensuring brand safety by extending third-party verification tools from Facebook and Instagram to Threads. Although ad delivery will start modestly, this scaling should ensure more confidence in the brand’s safety across the platform.
Why This Matters. With Threads integrating into Meta’s vast ad ecosystem, there’s an exciting opportunity for you to leverage this dynamic social platform. Early participation can give brands an edge as Threads offers a range of advanced ad formats and verification measures to avoid challenges like deepfakes.
Bottom Line.Meta’s global rollout of Threads ads is a pivotal moment for advertisers. It not only offers a channel on a rapidly expanding platform but also includes enhancements like brand-safety verification, making early adoption a strategic advantage.
I’ve recently discovered that OpenAI is moving ahead with plans to introduce ads in ChatGPT, aiming for a launch as soon as February. This move signals a quicker than anticipated step into the vast world of advertising.
What’s happening. It’s fascinating to learn that OpenAI has begun testing these ads with selected advertisers. Unlike the traditional pay-per-click model, they are opting for a pay-per-impression (PPM) approach. The initial test appears limited, with advertisers spending less than $1 million each, and there are currently no self-service buying options.
Why we care. The introduction of ads within ChatGPT could revolutionize the advertising landscape by integrating with conversational AI. However, it seems that OpenAI’s initial model focuses on securing revenue over allowing advertisers to measure ad performance. Although this PPM model limits traditional performance tracking, it offers a unique opportunity for brands to access a protected, intent-driven space, potentially influencing the future of conversational advertisements.
Getting involved at this stage might just provide advertisers with the chance to shape formats, pricing strategies, and standards before ChatGPT advertising fully scales up.
The backdrop. Just last week, OpenAI officially announced its advertising plans. This accompanies the launch of ChatGPT Go, their $8/month ad-supported plan. It’s interesting to note that free users will see ads, unlike Plus, Pro, or Enterprise users – at least for the time being.
Why impressions. The PPM model secures revenue for OpenAI without user interactions with the ads, but it does offer advertisers limited insight into how their ads perform. OpenAI hinted that user engagement, like follow-up questions regarding sponsored products, could act as a future signal of interaction and possibly, an additional monetization strategy.
The tension. OpenAI CEO Sam Altman has often described ads as a “last resort.” This raises questions about whether escalating infrastructure costs are speeding up OpenAI’s advertising venture. While CFO Sarah Friar claims that revenue growth matches the increase in compute expenses, critical details about profitability remain elusive.
Between the lines. Ads will be positioned at the bottom of ChatGPT responses, clearly marked and separated from organic content, which seems like a careful rollout prioritizing user trust while exploring commercial possibilities.
Bottom line.OpenAI is transitioning swiftly from policy to practice with these impression-based ads, focusing on scale and revenue. Yet, numerous questions about their efficacy, transparency, and future steps linger.
As I delve into the world of digital advertising, I realize that AI is more than just a buzzword; it’s a fundamental component of our strategies in 2026. Especially with video ads, where visuals speak louder and clearer than text, leveraging AI has become crucial not just for creating content but for innovating how we connect with audiences.
The power of video in advertising is undeniable as it allows consumers to process information rapidly. With the drop in creative costs, using video is more viable and impactful than ever. The real question I find myself asking is not if PPC teams should use AI, but how to optimize its usage to maximize results and ensure our content remains compelling and governed well, safeguarding against pitfalls like hallucinations that might disrupt performance.
Why has AI adoption in PPC alone become insufficient to enhance performance? Nearly 90% of marketers now integrate AI for creating or modifying video ads—a testament to its widespread use, though it does not guarantee success. Being successful in this domain now hinges more on our ability to feed AI the right creative inputs, data signals, and monitoring practices instead of relying on outdated manual bidding strategies.
Here are five AI-backed strategies that I believe are key to enhancing video PPC campaigns effectively:
1. Embrace Modular Asset Libraries Over Perfection
Historically, we have approached video production with a mindset tailored for TV-style advertising. However, in this new age of Performance Max, providing a rich library of modular assets allows AI to dynamically craft video experiences, tailored to user behavior, device, and intent. Flexibility in creative elements does not hinder, but rather enhances, performance by offering multiple hooks, bodies, and CTAs that AI can creatively assemble.
2. Move Beyond Keywords to Intent Orchestration
In today’s AI-driven ad environment, keywords are more about nuances rather than triggers, aimed at helping systems understand audience themes. Rather than allowing AI to optimize within broad, unguided targets that may reduce quality, it’s imperative to guide it toward understanding and targeting true intent, using negative keywords and first-party data to inform its decisions.
3. Optimize With Value-Centric Data
One common pitfall we face is feeding generic or low-value conversion signals to AI systems, which misdirects efforts toward less fruitful outcomes. By aligning AI optimization strategies with value-based conversions through enhanced and offline data imports, we can refine how AI perceives and prioritizes user actions, ensuring a focus on quality over mere quantity.
4. Opt for Lift Measurement Over Last-Click Attribution
In assessing the impact of AI-driven video formats like YouTube Shorts, adopting advanced attribution models becomes crucial since traditional models fall short. By employing media mix modeling or simple tests that monitor consistency in spend and revenue growth, we can better understand and demonstrate the true value ads deliver across channels.
5. Cater to Silent Viewers
Many viewers start by watching videos on mute, especially during initial discovery phases. Therefore, ensuring that visual elements of a video are clear and engaging without the necessity of sound can effectively maintain audience interest and ensure message retention from the first visual frame onward.
Shaping the Future of PPC
The role of the PPC manager resembles that of an architect, structuring the framework in which AI operates. The emphasis has shifted from direct control to strategic input planning and data management, allowing for scalable and efficient AI-guided campaigns that propel brands toward success.
I’ve come to realize that misinterpreting churn can lead to flawed assumptions about customer lifetime value (CLV). By analyzing retention over time, I can better identify which customers truly drive profit.
In my experience, CLV is often viewed as a static metric, but in reality, it is shaped by how different customer types behave and churn over time. One critical dynamic to understand is the “shakeout effect.”
The shakeout effect is when early churn filters out lower-value customers from a cohort, leaving a smaller, more stable group with higher engagement and predictable purchasing behavior.
In this article, I’ll delve into the shakeout effect in CLV analytics, explore why it occurs, and discuss how marketers should consider it when evaluating churn, retention, and long-term profitability.
What is the shakeout effect in CLV analytics?
Imagine I have a new group of customers. Over time, the “bad” customers—those likely to drop—leave, while the “good” ones remain. These customers have lower drop rates, better engagement, and more predictable purchasing patterns.
This decreases overall churn propensity over time, known as the shakeout effect, and results from heterogeneity among customers.
Typically, analysts use one-year windows or the entire purchase history; the timeframe can vary.
For businesses with monthly subscriptions, analyzing the window after the first 30 days is crucial. No purchases after this period often indicate churn.
When assessing overall churn probability over time, I look for trends like the one in this example.
Breaking out retention rates across dimensions like UTM medium reveals heterogeneity. For example, email as a first touch shows higher retention, around 27% after 500 days, compared to Google’s 18%.
Why should the shakeout effect matter to marketers?
In my view, not all customers are equal in terms of CLV. Many businesses lose money on new customers who churn before achieving a CLV sufficient to cover acquisition costs.
Profitability is typically concentrated in a small segment of loyal customers.
If I ignore the shakeout effect and don’t analyze churn adequately, I risk overestimating long-term churn or CLV by misjudging early losses.
A strategic view incorporates the Lorenz curve and the Pareto principle—often, 80% of CLV comes from 20% of customers.
Identifying this loyal core, understanding their demographics and preferences, can generate insights to engage similar potential customers.
How to identify heterogeneity in your CRM
I’ve found that ranked cross-correlation analysis (RCC) is an effective way to explore CRM data and understand CLV drivers.
Initially, I check if features in the data exhibit significant variance in CLV.
For instance, customers with above-average CLV often show frequent purchases, subscribe to newsletters, and make recent or initial product-related purchases.
Further, I find visualizing CLV distribution by dimensions like purchase frequency and geo provides valuable insights.
For B2B, I consider job title, vertical, and account types in my analysis.
Advanced statistical methods, while beyond this discussion, can further refine these insights.
Recently, I discovered that Google is offering advertisers more control over data flow, which is especially helpful when user consent is limited.
Driving the news. There’s a new tool out called Data Transmission Control, appearing in Google Ads. This enhancement builds on Advanced Consent Mode by providing a more detailed approach to managing how advertising, analytics, and diagnostic data are shared.
What’s new. As an advertiser, I can now independently adjust the flow of advertising data, behavioral analytics, and diagnostic data. If ad_storage consent is not given, I have two choices: either allow limited data with identifiers removed (which still supports conversion modeling), or entirely block the data until consent is obtained. Interestingly, I can still allow behavioral analytics even if ad data is restricted, or choose to block it completely.
Where to find it. I found the setting hidden within Data Manager → Google Tag (Manage) → Manage data transmission. It’s easy to overlook if you’re not looking carefully.
Why we care. Traditionally, Consent Mode was all about reflecting user choices. Now, with Data Transmission Control, I can decide—right down to the tag level—what data flows when there’s no consent, aligning more closely with privacy-focused strategies.
It’s empowering to have this degree of control, especially when trying to balance privacy compliance against performance metrics, which is crucial in markets with strict regulations.
Key details. It’s important to note that Consent Mode must be enabled for this feature to function. It’s set up via the user interface in Google Ads, Google Analytics, or Campaign Manager 360, and applies only to Google tags. If the feature isn’t enabled, everything stays the same, but once consent is given, data transmission resumes automatically.
First seen. This update was first reported by Google Ads expert Thomas Eccel, who shared his insights on LinkedIn.
The bottom line. The introduction of Data Transmission Control provides a subtle yet powerful way for me to ensure tighter data collection control without fully losing out on valuable measurement capabilities.
Navigating a shaky economy and the rise of AI tools transforming entry-level jobs, my career in marketing sometimes feels precarious.
Yet, there’s hope for those ready to seek it.
As a marketer, embracing adaptability, critical thinking, and thoughtful AI integration means I can streamline workflows, refine strategies, and invest time in impactful initiatives.
This AI era is still unfolding, but over a decade as a marketing leader has highlighted enduring patterns.
Within my teams and our partnerships, certain PPC experts are better prepared to thrive as AI reshapes our roles.
1. Understand the tools, but think beyond them
The influx of new AI tools is overwhelming. What I’ve learned is to focus on understanding which tools to test and why.
Testing just for the sake of it leads nowhere.
Without a clear goal, knowing a tool in isolation holds little value.
Choosing tools wisely is just the beginning. Measuring results effectively and integrating tools thoughtfully into broader strategies is equally crucial.
I’ve seen AI tools embraced only to be neglected or cause issues when poorly integrated.
Thriving marketers in this era are strategists, not just users. They test with purpose and understand a tool’s role in the marketing mix.
2. Be a stubbornly critical thinker
AI tools can deliver outputs, but what’s next?
I’ve often seen outputs accepted without question. Standout marketers dig deeper, questioning assumptions and interpreting results.
Critical thinking also involves understanding ad platforms and algorithms as they evolve.
Experienced marketers, who have witnessed changes in ad systems, understand their impact on performance.
New marketers can develop this understanding by exploring platforms thoroughly.
3. Balance curiosity with discipline
Curiosity drives learning and creativity. However, balancing it with discipline is essential in an AI-driven world.
The abundance of tools and ideas can easily distract without a focused strategy.
Discern between what’s interesting and what’s truly impactful for defined business outcomes, such as driving pipeline or improving retention.
4. See the whole picture
AI excels at optimization.
However, it struggles with context, where I can set myself apart from both tools and peers.
AI may suggest strategies, but it won’t show how they fit into a company’s overall strategy.
Successful marketers view AI outputs through the lens of business objectives and audience behavior, beyond mere tool features.
5. Develop technical depth (not just surface skills)
While AI automates campaigns, it can’t substitute deep technical expertise.
On my team, those who excel dig deeper, addressing KPIs and comprehending the underlying reasons for performance.
Marketers successful in this era blend technical precision with creativity, interpreting data beyond surface-level insights.
This technical fluency builds trust and enables marketers to catch and correct AI missteps.
6. Stay skeptical of automation
Overconfidence in automation is risky.
This isn’t about mistrust but about careful management.
Just because AI can do something doesn’t mean it should without consideration.
Smart marketers establish guardrails, testing automation wisely and validating outcomes to support human insight.
7. Take ownership and accountability
AI can’t take responsibility. Anything shared with a client, be it AI-generated or not, is my responsibility.
This approach is vital.
In using AI for various tasks, accountability distinguishes professionals.
Before deploying AI-driven work, ensure it’s accurate, on-brand, ethical, and insightful.
If any of these aspects are uncertain, reconsider before risking your professional reputation.
8. Champion AI governance and brand safety
AI governance is essential for today’s marketers.
AI features from platforms present real risks concerning privacy and brand safety.
I ensure my brand’s integrity by setting clear AI usage guidelines internally and externally.
Responsibilities include reviewing data, establishing approval processes, and aligning AI content with brand standards.
Relying solely on IT for governance without direct involvement poses significant risks.
9. Measure what matters
AI can track everything, but not all metrics are valuable.
I focus on metrics that relate directly to business outcomes.
This often involves moving beyond basic metrics to assess comprehensive performance.
I’ve observed many cases where shifting away from surface-level successes leads to stronger results.
AI accelerates progress, but direction should align with genuine business goals.
10. Sharpen your soft skills
With AI leveling technical playing fields, human skills are the key differentiators.
In this automated landscape, it’s hard to showcase unique platform techniques. Instead, soft skills like emotional intelligence, storytelling, and communication are irreplaceable.
Marketers who hone these skills will preserve the human edge that turns AI capabilities into tangible brand value.
The mix that still defines great marketers
AI is transforming the marketing landscape.
The most successful marketers blend technical expertise with adaptability, critical thinking, accountability, and creativity in this new era.
Having worked closely with Google’s Shopping platform, I’ve seen the evolution of their policies first-hand. Recently, they’ve made significant updates for 2026, allowing advertisers more creative ways to engage with consumers through various promotions.
Google’s updated Shopping policies are expanding eligibility criteria for promotions, offering merchants greater flexibility starting next year. This change is a game-changer for those of us looking to leverage newer promotional formats like subscriptions and localized payment incentives.
What brought this change? Google is enhancing its promotion guidelines to include more types like subscription discounts, common promotional abbreviations, and specific payment offers in Brazil. These updates aim to align better with current consumer purchasing behaviors.
Why it matters to me. Promotions are vital for standing out in Shopping results, impacting both visibility and conversion rates. With these updates, I now have the chance to use new promotion formats that resonate with today’s buyers, particularly for subscriptions and cashback deals. The expanded flexibility reduces the risk of disapproval and makes my Shopping ads far more compelling during critical decision-making moments.
If you’re like me and rely on subscriptions or local payment incentives, this policy update opens up new avenues for visibility and conversion on Google Shopping.
What’s new? Now, promotions can be linked to subscription fees, with possibilities for free trials or discounts on initial billing cycles. Setting these offers up is straightforward: select “Subscribe and save” in Merchant Center or use the subscribe_and_save redemption option in promotion feeds. Examples could be offering a free first month or a significant discount for the first few subscription periods.
Additionally, there’s a relaxation on language restrictions. I’m excited that common promotional abbreviations like BOGO, B1G1, MRP, and MSRP are now supported, making it easier to reflect real-world retail lingo without risking ad disapproval.
In Brazil only, Google is now accommodating promotions linked to specific payment methods, including cashback deals associated with digital wallets. Merchants need to opt for “Forms of payment” in the Merchant Center or use the forms_of_payment redemption restriction. As of now, there are no plans to expand this to other markets.
Reading between the lines. These policy changes indicate Google’s intent to better align promotional strategies with modern retail dynamics — particularly focusing on subscriptions and localized payment methods, thereby reducing hurdles for merchants like me.
In summary. By broadening the categories for promotions, Google allows us as advertisers to compete on added value, not just pricing, with upcoming Shopping policy updates set for January 2026.
Entering into the world of PPC advertising for 2026, I realize how easily we can be misled by trends. AI, creative scaling, and marketing models promised us efficiency, but often ended up costing more than delivering. So how can we reset our PPC priorities as we step into the new year?
In 2025, PPC advice revolved heavily around AI and glittering new tools, sounding both promising and expensive. We found ourselves succumbing to platform narratives rather than aligning with business needs, causing budgets to balloon without corresponding efficiency gains.
As 2026 dawns, it’s high time to break free from these outdated beliefs. This article highlights three PPC myths that looked appealing in theory and quickly spread in 2025 but often led to poor decisions.
My objective is straightforward: rethink priorities and avoid repeating costly mistakes.
Myth 1: AI Outshines Manual Targeting
We’ve been told countless times to trust AI for targeting while manual structures are deemed obsolete. But is that truly the case?
The truth depends on conditions. AI thrives on volume and quality signals. Without these, the AI delivers no meaningful results, just automated processes that mask poor performance.
For instance, ecommerce brands often find value in feeding purchase data back into Google Ads, assuming they generate enough conversions. Only then does outsourcing targeting to AI hold potential.
If your campaigns struggle with low conversions or rely primarily on lead optimization, manual intervention may still be necessary.
How to Reset Priorities
Before turning everything over to AI, there are critical questions to ask:
Are campaigns optimized against a business-level KPI like CAC or ROAS?
Do the ad platforms receive sufficient conversion data?
Are conversions reported promptly, with minimal delay?
If any answer is no, consider revisiting PPC fundamentals for 2026. Do not hesitate to apply traditional methods when needed. In 2025, I turned around a client’s fortunes by using match-type mirroring structures, even though it contradicted the common best practices.
The success was based on historical performance data:
Match Type
Cost per Lead
Customer Acquisition Cost
Search Impression Share
Exact
€35
€450
24%
Phrase
€34
€1,485
17%
Broad
€33
€2,116
18%
Here, Google Ads did exactly what it was told—focus on lower cost per lead, disregarding business impact like KPIs.
I regained control by focusing on high-performing audiences with unsaturated potential, via exact match keywords. If you’re unfamiliar with traditional structures, advanced semantic techniques can offer an excellent starting point without over-reliance on automation.
Myth 2: More Ads Lead to Better Results
This myth frustrates me as it sounds logical but rarely pans out. The argument is simple: more creative variation equates to better ad auction performance. But more often, it increases creative costs without the promised results, helping agencies more than advertisers.
Creative volume adds value only when backed by high-quality conversions. Without them, extra ads only mean more materials rotating meaninglessly.
How to Correct Course
True value still lies in creative diversification that matches messages to audiences and contexts. This isn’t a novel concept. The same principles apply:
Have a strategic approach to creative testing; testing without intent is wasteful.
Plan measurement in advance to avoid setting yourself up for failure.
Ensure business-level KPIs are present in enough volume to make a difference.
When resources are tight, rotating ads without direction is common. Focus on Conversion Rate Optimization (CRO) instead:
Enhance tracking for better performance.
Refine customer journeys to boost conversion rates and signal volume.
Align higher-margin products with more efficient spending.
Explore new networks or channels with saved creative budget.
Myth 3: MMM Will Offer Clear Clarity
Finding 10 marketers who believe GA4 is effective is challenging, indicating Google’s missteps. The misalignment with ad platform data breeds mistrust, leading to the belief that advanced solutions are needed. Yet, this often results in higher costs with average outcomes.
Most brands don’t have the scale required for Marketing Mix Modeling (MMM) to yield insightful results. Instead, it’s best to master existing tools.
The usual brand setup looks like this:
Concentrated media spend across a handful of channels, mainly Google and Meta, with YouTube, LinkedIn, or TikTok as extras.
Reliance on a narrow but consistent customer base, risking long-term stability.
Marginal marketing impact beyond the core audience.
In such settings, MMM adds abstraction, not clarity. Staying grounded in fundamentals remains vital, not modeling complexities.
Strategies to Add Value Instead
Before considering advanced tools, ensure you’re getting the basics right:
Stand out clearly from competitors.
Boost margins, even with simple budget plans.
Build a strong data foundation, emphasizing tracking, CRO, and conversion paths.
Expand your channel or network options.
Align creative execution with genuine customer pain points.
Smooth out any marketing execution kinks.
While advanced tools gain importance with complexity, deploying them too soon obscures accountability rather than offering real insights.
The True Issue Lies in Misuse
The thread linking these PPC myths isn’t the capabilities like AI, creativity, or analytics—it’s how they’re misused. Platforms fulfill the roles they are set for, optimizing within the provided signals and limitations.
Business fundamentals are what break in these scenarios, rather than AI fixing our problems.
Instead of pursuing the next shiny distraction, 2026 should be about focusing on core business strategies and executing with precision for profitable scaling.
Looking to expand your reach beyond Google Search? Demand Gen campaigns push your ads to ideal audiences across YouTube, Discover, and Gmail.
As someone deeply involved with Google Ads, I spend most of my time optimizing Search, Shopping, or Performance Max campaigns. It’s understandable, as the Google SERP is foundational to Google Ads. But there’s a significant opportunity within your Google Ads account that many overlook.
I firmly believe Demand Gen is the most undervalued campaign type in Google Ads, and this needs to change.
If you’ve been cautious about trying Demand Gen or have written it off due to past failures, consider this your nudge to incorporate it into your 2026 strategy. Demand Gen offers a transformative approach to using Google’s ecosystem for growth through paid advertising.
To understand Demand Gen, move away from a keyword-centric mindset. Think of it as running Meta (Facebook or Instagram) ads but leveraging Google’s platforms instead.
Where traditional Search campaigns react to a user’s query, Demand Gen focuses on the user themselves, distributing creative content—images or videos—based on user characteristics rather than their immediate actions or searches.
Demand Gen can place your ads on Google’s various “owned and operated” properties, including:
YouTube (Shorts, In-stream, In-feed)
Gmail
Discover
Google Maps (coming soon!)
I advise starting with all these channels activated but opting in or out of specific channels as desired.
While the Google Display Network is an option, it’s wise to prioritize Google-owned properties where intent signals are more robust.
In Demand Gen, targeting moves away from content and instead utilizes Google’s extensive audience targeting capabilities:
Lookalikes: Build audiences mirroring your converters, similar to Meta.
Remarketing: Re-engage past visitors or customers.
In-Market, Life Events & Affinity segments: Reach people based on interests or behaviors.
Detailed demographics: Target based on user demographics.
Custom Segments: Focus on search terms or websites/apps users frequent.
However, combined segments aren’t compatible with Demand Gen; you can only exclude your data segments.
Demand Gen supports a versatile range of ads: standard image ads, carousel image ads, and video ads. If you’re in ecommerce, integrate your Google Merchant Center feed for product-based ads.
Unlike Video campaigns, which aim for impressions or views, Demand Gen targets clicks or conversions using these bid strategies:
Maximize Clicks
Maximize Conversions
Maximize Conversion Value
Target CPC
Target CPA
Target ROAS
You must choose a conversion category, whether it’s a purchase or another action like a YouTube subscription.
What’s more, Demand Gen uniquely permits the Target CPC strategy, allowing control over CPC in a space dominated by AI-driven bidding. This manual control is beneficial for tightly managed budgets.
Demand Gen surpasses standard Display campaigns in several ways:
1. Inventory Quality:
It primarily serves on authenticated Google-owned properties, ensuring higher engagement compared to random web placements typical in Display campaigns.
2. Spam Reduction:
Higher audience and inventory quality reduce the likelihood of spam leads, a crucial factor for lead generation.
3. The Cost Reality:
While CPCs in Demand Gen often exceed Display, the quality justifies the price. Plus, it remains cheaper than Search campaigns, with CPCs typically between $0.50 to $2.00.
Demand Gen isn’t a black box; it provides transparent reporting similar to Performance Max:
Asset-level reporting: Analyze text, image, and video performance.
Audience insights: Understand who engages with your ads.
Channel segmentation: Control where ads appear (YouTube, Discover, Gmail) and tailor placements accordingly.
Placement reporting: Inspect YouTube placements to refine targeting.
Feeling ready to launch a Demand Gen campaign? Here’s my advice for structuring a test:
For smaller businesses:
With a tight budget ($5-40/day), go simple.
Targeting: Use your “Google Engaged” remarketing audience and a Custom Segment of top-performing search terms.
Why: Capture high-intent users yet to convert with Demand Gen’s cost-effective inventory.
For ecommerce businesses:
Creative reigns supreme! Run one Demand Gen campaign with and one without your product feed.
Why: Test whether product ads or lifestyle visuals better drive engagement. Results will reveal optimal strategy.
For larger businesses:
If budget allows, Demand Gen should be a strategic staple, not just a test. Treat it as an “always-on” layer for targeting specific audiences.
Targeting: In-Market, Life Events, Detailed demographics, Affinities.
Why: This approach keeps your brand visible and top-of-mind among your target audience.
In conclusion, Demand Gen stands out by bridging high-intent Search with social storytelling, offering superior quality over Display and cost-efficiency compared to Search. Will Demand Gen make it into your strategy this year? If growth beyond the search bar is your goal, it absolutely should.
This article is a part of Search Engine Land’s ongoing series, Everything you need to know about Google Ads in less than 3 minutes. Each edition, curated by Jyll, highlights a different Google Ads feature to maximize your results swiftly.
I recently discovered that Google is enhancing Vehicle Ads with a click-to-call feature. This update gives potential car buyers a direct and seamless way to connect with dealers, turning search behavior into swift, live conversations.
Why does this matter? Vehicle Ads typically attract buyers who are already showing a strong intent to purchase. Removing obstacles with the new click-to-call feature meets shoppers at the precise moment they’re ready to engage with a dealership.
The big picture reveals a shift in automotive advertising towards instant human interaction. Buyers are more interested in real-time conversations rather than filling out additional forms. With call-enabled Vehicle Ads, connecting search to dialogue has never been easier.
In this evolving landscape, advertisers now bear a greater responsibility. Since the ad itself has become a conversion point, the quality of call handling, as well as staffing levels, can greatly affect performance. Dealers who prioritize phone interactions as a main conversion method will prevail, while those who do not may experience a decline.
Credit goes to Google Ads specialist Thomas Eccel for spotting this update first and sharing it on LinkedIn.
The bottom line is simple: Vehicle Ads have not only gained more visibility but have also come closer to facilitating actual sales.
