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Tag: Ad Optimization

How Ignoring Data Can Derail Your PPC Success

Recently, I found myself captivated by a story shared by Dean Kadi, Head of Paid Growth at One Link Media. He recounted a fascinating experience from a PPC Live podcast that really highlighted what can go wrong when you ignore performance data. It involved a client who overrode a winning ad strategy with new creatives that just didn’t deliver.
Watch this video on YouTube
Dean Kadi’s team had developed an exceptionally successful Meta advertising strategy for a premium woodworking brand, Rubio Monocoat, using user-generated content (UGC). Their intensive testing across creators and formats resulted in a significant ROAS improvement, proving the power of well-tested strategies.
However, the client decided to halt all the high-performing ads in favor of new, heavily branded content. Despite the polished look, these ads didn’t blend well with the Meta platform, and it was clear that engagement and conversion would likely suffer.
The client’s assumption was rooted in a customer survey that praised the brand’s color range, leading them to mistakenly prioritize this over proven data. This is a classic marketing pitfall where assumptions can cloud judgment and overshadow hard-earned data insights.
The most eye-opening moment came when the client expressed a simple wish for their new strategy to be a winner. Dean explained that in paid media, success isn’t driven by preferences or hopes—it’s determined by what resonates with audiences, as clearly shown by performance data.
When facing such situations, Dean advises agencies like us to stay calm, present evidence, and communicate risks effectively. Professionalism and clear documentation can help maintain client relationships while asserting the agency’s expertise.
As expected, the new strategy did not perform well. Underperformance became evident with increasing costs and decreasing campaign efficiency. After eight weeks of this, the client recognized the necessity to revert to the original strategy.
Reintroducing UGC ads quickly turned the tide, proving the original strategy’s effectiveness. Performance metrics showed immediate improvements, reinforcing the importance of data-driven decisions.
The overarching lesson here is that data should be your guiding light in PPC campaigns. Clients sometimes need to see failures themselves before they trust data insights. Consistently providing clear, transparent reports helps rebuild trust and guide future strategies.
Dean also pointed out that many PPC accounts still suffer from poor tracking setups. This issue is a major roadblock to optimizing performance and should be addressed urgently.
Additionally, while AI tools can enhance efficiency, they cannot replace the need for a strong strategy. Human judgment remains crucial for evaluating AI outputs and guiding successful campaigns.
In conclusion, successful PPC is all about balancing data, strategy, and communication. Document recommendations thoroughly, trust your expertise, and let audience data guide your actions. Remember, it’s the audiences who ultimately decide what works.

Inspired by this post on Search Engine Land.

May 15, 2026
Discover How AI is Transforming Google Search Queries

I’ve noticed that Google Search Query Reports are moving towards AI-driven interpretations, reflecting inferred intent rather than exact user searches.

What’s happening. Google has clarified that the search terms in Search Query Reports might not precisely match what users typed. Instead, the system displays the “closest approximation” due to the complexity of modern search behaviors.

What’s behind it. It’s fascinating how heavily AI now influences Google Ads’ matching systems. Rather than depending solely on specific keywords, Google increasingly interprets user intent, context, and behavioral signals to decide which ads to display.

Why we care. For those of us in advertising, Search Query Reports might become less of a mirror reflecting user language and more of a summarized representation of intent. This shift might complicate query analysis, decisions on negative keywords, and strategy around match types.

Discovered by. This update was brought to my attention by Adsquire founder, Anthony Higman, on an official Google help page discussing ad group and asset group prioritization in Google Ads.

The bottom line. Google Ads continues its evolution from keyword matching to AI-driven intent modeling, meaning we might have less insight into the exact searches that activate our ads.

Inspired by this post on Search Engine Land.

May 13, 2026
Mastering Negative Keywords: Your 2026 Strategy Guide

I’ve always believed that negative keywords are more than just a checklist. In 2026, they represent strategic decisions that shape how the algorithm interprets your ad account.

If you’re still viewing negative keywords as a mere maintenance task, you’re missing out. Each exclusion signals who you intend to target, what you’re willing to pay for, and how you expect your campaigns to perform.

Let me share six key decisions that define today’s negative keyword strategy, and explain their growing significance.

Negative keywords help shape our campaigns so the right ad appears in front of the right audience. Achieving alignment between the user’s search query, your ad, and the landing page is crucial for creating an exceptional user experience.

When this alignment is absent, budget is wasted, click-through rates (CTR) decline, Quality Scores suffer, and cost-per-click (CPC) rises. These challenges can make the algorithm seem like it’s working against you.

However, many of us weren’t taught how negative keywords fit into an overall account strategy, only how to add them. Let me delve into these six critical strategic choices.

Determining how aggressive to be with negative keywords is the first decision every account manager needs to make, yet it’s often overlooked.

Are you relentlessly removing every low-performing search term? Are you deliberately allowing space for keyword opportunities? Or do you find yourself somewhere in between?

There isn’t a universal right answer, but it is essential to choose your level of aggression. A growth-focused account may need a less aggressive approach, whereas an efficiency-focused account might require more aggression. This choice should align with the account’s goals and performance metrics.

Using the right match types for negative keywords is crucial. Most advertisers default to one type without understanding why.

Here’s my breakdown:

Use negative exact match for strictly removing specific long-tail variations, negative phrase match for groups of related queries, and negative broad match for eliminating words that indicate a misaligned audience.

A well-thought-out negative keyword strategy utilizes all three match types, each serving a distinct purpose.

When should you add negative keywords? This is a consideration I’ve seen approached in various ways by different account managers.

Some add negatives weekly regardless of data, while others only when conversions drop, or during quarterly reviews. The right approach depends on your goals and data-driven insights.

For growth-focused accounts, trigger addition when a query exceeds three times your target CPA over 90 days without conversion. For efficiency-focused accounts, use a stricter budget-focused trigger.

The timeframe for reviewing data when deciding on negative keywords is another crucial factor.

A 30-day window might be too aggressive unless dealing with short-term promotions. A 90-day window is balanced and often recommended, while a 365-day window may be conservative, excellent for long buying cycles.

Choosing the correct timeframe informs smarter strategic decisions.

The role of AI in campaign sculpting through negative keywords is increasingly pivotal.

Decide how much control you want versus how much you rely on the machine. Some eliminate competitor keywords, yet others let them through for conversions.

While AI holds more information than us, sculpting is necessary for communicating your intent.

In 2026, we have more options than ever for managing negative keywords effectively.

You can conduct a manual review, use AI tools for suggestions, or let AI handle it fully. The key is balancing efficiency with oversight according to the comfort level and stakes of the account.

In every era, a few principles remain true. Keep your search terms report in check, make sure to update negatives as your campaign evolves, and always remain flexible to changes in user intent.

Ultimately, efficient advertising starts with strategic exclusion. What we choose not to target often holds equal importance to what we do target.

Inspired by this post on Search Engine Land.

May 6, 2026
Unlocking New Potential: ChatGPT’s Self-Serve Ads Revolution
I’ve witnessed firsthand how ChatGPT ads are evolving with self-serve buying options, enhanced measurement features, and a vision to create a scalable advertising platform.

OpenAI is stepping up its game with the ChatGPT ads platform by introducing self-serve buying, CPC bidding, and improved measurement methods to invite more advertisers into its ecosystem.

What’s happening. The ChatGPT ads initiative is shifting from a limited pilot to a broader rollout, providing businesses new methods to purchase and manage their campaigns. Advertisers can now access inventory through agency and tech partners or directly via the new beta Ads Manager, which is currently rolling out in the U.S.

This marks a significant move from a controlled test phase to a promising, scalable ad platform.

Why we care. In the past, access to ChatGPT ads was restricted and costly, limiting it to major advertisers. These updates are lowering the entry barriers, allowing SMBs, startups, and diverse brands to experiment with this channel.

By introducing CPC bidding, ChatGPT aligns more closely with established performance platforms, enabling advertisers to optimize for actions rather than just impressions.

Self-serve Ads Manager. With the new Ads Manager, advertisers gain direct control over campaigns, including budgeting, bidding, creative uploads, and performance tracking.

Even though it’s still in beta, it demonstrates OpenAI’s commitment to building a full-service ad platform, beyond a mere partner-led ecosystem.

Between the lines. This approach is not new. Typically, platforms start with high-touch, partner-led campaigns before transitioning to self-serve tools that enhance scalability. ChatGPT is entering this second phase.

CPC bidding arrives. Originally, ChatGPT ads were sold on a CPM basis. The inclusion of CPC enables advertisers to align expenditures with user actions—a critical evolution for performance marketers.

The nature of ChatGPT queries—often exploratory, comparative, and decision-driven—means that clicks could become an effective indicator of user intent.

Measurement catches up. OpenAI is also introducing pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups, and leads.

Notably, this data is aggregated, ensuring no access to individual conversations, emphasizing OpenAI’s commitment to privacy.

Why this is a big deal. Measurement was a major gap in early ChatGPT ads. Without it, justifying ad spend was challenging for advertisers. These updates help bridge that gap, making optimization more feasible.

The ecosystem grows. OpenAI is expanding its network by partnering with agencies like WPP and Publicis Groupe, along with tech platforms such as Criteo and Adobe.

This allows advertisers to buy ChatGPT ads through tools and workflows they are already familiar with.

What to watch:
- How quickly self-serve adoption scales
- Whether CPC performance holds as competition increases
- How measurement evolves to match advertiser expectations
Bottom line. ChatGPT ads are transitioning from an experiment to a platform—and with self-serve tools, CPC bidding, and enhanced measurement, OpenAI is laying the foundation for expansive growth.

Inspired by this post on Search Engine Land.
May 5, 2026
Unlocking Insights: Microsoft Ads Enhances Performance Max Reports
I’m thrilled to share some exciting news from Microsoft Advertising. They’ve made a significant leap in Performance Max reporting by adding conversion and spend data to PMax placement reports. This means I now have a much clearer understanding of how my ad placements are performing, which is fantastic for optimizing my campaigns.

What’s happening. According to Microsoft Ads Product liaison Navah Hopkins, the PMax Website Publisher URL report now includes conversion and spend metrics. This update takes us beyond just seeing where our ads appear; it lets us see actual performance data in action.

This new visibility allows me to pinpoint exactly which placements are driving meaningful results, not just impressions or clicks. It’s a game-changer for understanding what really works.

Why we care. Having this level of detail means I can make smarter decisions about where to allocate my budget. It helps me scale successful inventory and eliminate waste, providing a stronger foundation to trust Performance Max’s capabilities with tangible data rather than estimates.

How advertisers can use it. This update opens several practical doors. I can leverage high-performing placements to shape my Audience Ads strategies, like building remarketing campaigns or targeting audiences based on successful inventory.

At the same time, I can spot placements that aren’t a good fit and exclude them using account-level URL exclusion lists. This not only protects brand safety but also boosts efficiency.

Between the lines. This development further enhances the transparency of automated campaigns. It’s evident that while automation handles much of the heavy lifting, platforms are keen on giving us advertisers clearer insights into what’s effective and where we need to intervene.

What to watch:
- Will this transparency extend even further in PMax reporting?
- How will advertisers balance the power of automation with manual tweaks?
- Could similar reporting features be rolled out across other platforms?
Bottom line. With access to precise conversion and spend data, Microsoft is transforming Performance Max from a black box into an actionable tool, inviting us to make informed decisions and achieve better results.

Inspired by this post on Search Engine Land.
May 1, 2026
Maximize B2B Results: 5 Essential Tips for Performance Max

In the evolving world of B2B marketing, Performance Max has emerged as a powerful, yet often misunderstood, tool. Over the years, I’ve witnessed its transformation from an uncertain trial to a crucial part of my B2B marketing toolkit.

The core principles still hold true: skepticism is essential, first-party data remains invaluable, and experimentation is a must. Google has improved in integrating these elements, making it important for me to adapt my strategies accordingly.

Let me share five best practices that have helped me enhance my Performance Max campaigns effectively.

1. Guide AI with the Right Inputs

In 2022, as Google aggressively promoted automated PMax campaigns, I predicted a surge in AI integration. This shift has indeed occurred, driven by competitors like ChatGPT. AI Max for Search and PMax have taken center stage, with improvements making PMax more viable for the B2B landscape.

Some updates I’ve embraced include search themes for precise targeting, brand exclusions to control costs, and account-level channel reporting, which allows me to see performance across all campaigns. By segmenting conversion metrics, I can identify and optimize on overperforming channels.

Get started with Semrush to ensure your brand shows up where it matters most.

2. Address Persistent Lead Quality Issues

B2B lead quality has always been a concern in search campaigns. PMax’s lack of control has made it even more challenging. To combat this, I’ve relied heavily on offline conversion tracking (OCT). It’s a vital element for successful B2B campaigns.

In addition to OCT, I’ve been using enhanced conversions for leads, along with reCAPTCHA, to reduce low-quality leads from my PMax campaigns.

3. Build Stronger Audience Signals

With the end of third-party cookies and the phasing out of Similar Audiences, I’ve focused on leveraging PMax’s audience signals. By feeding high-quality first-party data to the AI, I’ve managed to target the right prospects efficiently.

Cleansing and segmenting CRM data to create robust audience lists close to revenue points are pivotal to capturing valuable new users.

4. Make Creative a Performance Lever

Creative content plays a crucial role in engaging the right audience. Given YouTube’s significance in PMax campaigns, producing quality video content is more critical than ever. Google’s new tools for AI-generated assets and creative A/B testing have made this process much easier.

Testing these elements helps me identify what truly resonates with my audience and optimize accordingly.

5. Use Reporting to Drive Decisions

Transparency in results has been a sticking point with PMax, but recent reporting updates from Google offer more insights than before. Utilizing search term insights and auction insights provides me with clarity on performance metrics, enhancing my optimization capabilities.

With asset-level reporting, I can see how creative assets perform and make data-driven decisions to boost my campaigns’ success.

Don’t miss out on optimizing your search visibility with Semrush’s comprehensive AI toolkit.

Make Performance Max Work for You

These updates have made PMax a more practical tool for B2B marketers like me, especially when equipped with strong first-party data. I always strive for more control and transparency, balancing Google’s tools, and leveraging every resource available to optimize my campaigns.

Stay ahead by exploring the latest Google releases that add visibility and control, making Performance Max truly work for you.

Inspired by this post on Search Engine Land.

May 1, 2026
Unlocking Insights from ChatGPT Ads: What You Need to Know
I’ve been closely following the fascinating world of ChatGPT ads, and I’m thrilled to share what I’ve learned. Adthena is tracking over 50,000 daily ad placements from more than 600 advertisers. Let’s dive into what this means for our campaigns.

The ChatGPT ad trial is currently live in the U.S., and it’s moving along faster than I expected. Launched on February 9 for users on Free and Go tiers, it now boasts participation from over 600 advertisers.

With a massive user base of 800 million active weekly users, it’s only a matter of time before ChatGPT ads see a global rollout.

I’ve heard from OpenAI that the next wave will expand to Australia, New Zealand, and Canada. From our trialists’ feedback, it looks like the UK might see ads by mid-May.

Tracking these ad placements since their rollout has given me a front-row seat to their evolution. With an index capturing 50,000+ daily placements across sectors like B2B software, ecommerce, fintech, and consumer verticals, here’s what I’ve discovered.

Here’s what ChatGPT ads look like: They appear inline within conversation responses. Imagine asking about the “best weekend getaway” or “top running shoes under $100,” and a sponsored result labeled “Sponsored” pops up alongside the AI’s answer.

Unlike a search bar, ChatGPT is a conversation. Users come already engaged and researching, often on the brink of a decision. The format is more concise than traditional search — just a headline, brief body, and a destination, with no sitelinks or extensions.

Here’s a surprising discovery: what I term the “Double Parked” phenomenon, where a single brand like New Balance appears twice in one ChatGPT response. This unique visibility aspect opens discussions on frequency and owning conversations on the platform.

This is a unique moment with new formats and a largely untapped landscape, presenting data that most competitors don’t yet have. Here’s what I’ve found from over 50,000 daily placements:
1. Headlines often follow a “Brand: Benefit” formula. Think “Betterment: 5.25% APY Cash Account.” This is a common thread among top performers.
2. Most ads begin with the brand name. This reflects the deeper intent of users already engaged in a conversation.
3. Headlines average just 30 characters, peaking at 36. This forces brief, impactful messaging.
4. Body copy uses around 19 words. Usually two precise sentences: one proof point, one call to action.
5. Context mirroring is key. Top ads reflect the user’s query directly, enhancing relevance.
6. The $ symbol boosts conversions. Specific financial figures and rates outperform vague promises.
Brands thriving in this space don’t just reuse their Google ad copy. They tailor ads for an engaged, decision-oriented audience. Leading with the brand, anchoring offers in specifics, and minimizing friction are essential.

The biggest consideration? Visibility. If competitors are in these conversations and you’re not, you miss more than clicks — you miss part of the dialogue itself.

Adthena’s ChatGPT Ads Intelligence offers a solution. It goes beyond impressions and clicks, giving insight into your ad presence, competitors, and performance. Here’s what to expect:

The Ads Performance tab provides live snapshots of your activity, while Topics and Keywords Analysis offers tactical recommendations for improving your ad presence.

Understand competitor strategies through Competitor Creative Analysis, and never miss shifts in the competitive landscape using the Ads Benchmarking tab. This is how you find gaps before your competitors do.

As OpenAI expands, scaling up your ChatGPT presence can provide a head start in a rapidly changing competitive field. Don’t let competitors win the first prompt. Join Adthena’s waitlist or use their free ChatGPT AdBridge to optimize your campaigns and seize the opportunity now.

Inspired by this post on Search Engine Land.
May 1, 2026
Unlock Brand Insights with Google’s New Association Metric

Recently, I discovered Google’s latest addition to their Google Ads arsenal: the Association metric in Brand Lift Studies. This innovative feature reveals how consumers connect brands with essential attributes, bridging the gap between awareness and consideration.

Google is addressing a critical gap by providing advertisers with a clearer view of how their brand is truly perceived—not just recalled.

What’s new. With this update, Google Ads introduces a fresh “Association” metric within Brand Lift Studies. As advertisers, we can specify a concept, category, or attribute, and Google will survey users to determine which brands they associate with these ideas.

How it works. This revolutionary metric evaluates whether audiences link our brand to a desired positioning—such as “premium” or “sustainable”—offering a sophisticated perspective on brand perception.

Why we care. This new metric allows us to measure brand positioning, not just surface-level awareness or recall. It’s crucial to understand if our campaigns genuinely influence how consumers perceive our brand—vital for those targeting specific attributes or categories.

Between the lines. Previously, Brand Lift focused on awareness, recall, and consideration. Now, Association dives deeper, illuminating whether our messaging shapes how people perceive our brand, beyond mere recognition.

The catch. However, there’s a catch: we can only choose three Brand Lift metrics per study. Adding Association requires us to balance the existing KPIs.

The bottom line. Association provides a strategic perspective on brand building, enabling us to measure whether our intended messages resonate with consumers.

First seen. This update was first spotted by Google Ads expert, Thomas Eccel, who shared the news on LinkedIn.

Inspired by this post on Search Engine Land.

April 30, 2026
Unlock the True Power of Google Ads Beyond Just Clicks

A small currency error and unnoticed breakdown in conversion tracking can quickly turn into unnecessary expenses.

Watch this video on Vimeo

On PPC Live The Podcast, I had the opportunity to chat with Pete Bowen, a seasoned Google Ads expert with a keen focus on B2B lead generation.

Pete shared that throughout his career, he learned two pivotal lessons: never neglect the fundamentals, and don’t assume everything around your ads is functioning perfectly just because the campaign appears fine.

The Currency Mistake That Cost 10 Times the Budget

In our discussion, Pete recounted an incident where a South African client’s account was mistakenly set to the UK currency, leading to a spend ten times higher than planned. Initial results looked impressive, but the oversight eventually set unrealistic expectations and cost the client relationship.

Why Checklists Protect PPC Teams

The lesson here is to incorporate learning into a formal process. For instance, implementing a currency check in initial setups can transform frustrating mistakes into reliable, repeatable safeguards.

The Bigger Problem: System Decay

Beyond errors in setup, Pete discussed a more insidious issue: “system decay.” This involves the gradual breakdown of the infrastructure linking ads, tracking, CRM, and sales processes, often without detection.

Why Conversion Data Failures Hurt Performance

If conversion data flow is disrupted, Google’s algorithms miss out on critical optimization feedback, resulting in reduced spending, declining performance, or campaigns that seem to halt unexpectedly.

PPC Managers Need to Look Beyond the Interface

A common error among advertisers is focusing solely on Google Ads. Optimal performance involves the whole journey, from click to conversion to revenue, and any disruption can diminish results.

What to Do When Conversion Tracking Breaks

Priority number one is identifying and fixing the root of tracking failure quickly. Leveraging data exclusions to prevent poor data from affecting optimization is crucial, as is implementing monitoring systems to catch recurring issues early.

The Danger of Optimising for Clicks

Pete highlighted another frequent mistake: prioritizing clicks over outcomes. Without effective conversion tracking, advertisers might end up with significant traffic that yields few leads or sales.

Why Performance Max Needs Strong Tracking

Automation tools like Performance Max can exacerbate this issue if they receive misleading signals. Accurate conversion data is essential before making the most of automated tools.

Why Bid Strategies Need Guardrails

Google’s powerful bidding systems optimize based on the success criteria provided by advertisers. Clear objectives, reliable data, and sensible constraints like CPC limits are needed to prevent extreme results.

Testing AI Features Carefully

With new AI tools, the risk isn’t of premature testing, but of testing without clearly defined success metrics. Beyond just impressions and clicks, the focus should be on impacting qualified leads, sales, and overall revenue.

The Problem with “Always Be Testing”

Pete also challenged the constant testing philosophy. Many accounts lack the data volume to effectively run small tests, so energies are often better directed towards strengthening core practices than chasing minor improvements.

The Key Takeaway

The overarching lesson is that mistakes are valuable if they lead to robust systems. Each error should translate into a checklist, a monitoring strategy, or a preventive measure to ensure it doesn’t recur.

Inspired by this post on Search Engine Land.

April 27, 2026
Mastering Paid Search: What to Optimize When Keywords Matter Less

In today’s digital landscape, I’ve noticed that paid search platforms are evolving to prioritize who sees my ads, often without depending solely on my chosen keywords.

This shift means I need to focus on optimization strategies beyond just keywords, such as leveraging audience data, enhancing landing page context, and understanding conversion behaviors. Recognizing this shift is crucial for me to know where to focus my efforts now.

A decade ago, keywords gave me a sense of control. Back then, hypersegmentation and single keyword ad groups were the norm.

We’d meticulously create unique landing pages for each keyword in every ad group, reveling in the manual process, convinced that we controlled the machine.

Times have changed, and the forecast of Google and Microsoft phasing out keywords feels more real than ever.

With tools like Performance Max and emerging AI Max solutions, along with contextual LLM-driven searches such as ChatGPT, I see the industry leaning towards a keywordless future.

Still, keywords remain vital as they reveal user intent and indicate where users stand in their journey:

If these signals are now managed behind a black box, my role as a marketer is evolving. So, what am I optimizing for?

Dig deeper: Beyond keywords: Mastering AI-driven campaigns

Intent is now inferred from a web of signals, relegating individual keywords to the background. My optimization focus should now be on three main pillars in 2026.

Google now emphasizes customer match and first-party data over mere queries. With Data Manager API integration, it identifies users in auctions matching my key deals.

No longer do I bid on “cloud security.” Instead, I target IT directors (sharing first-party data) investigating SOC 2 compliance, even if they search for something vague like “scaling infrastructure.”

B2B match rates can be challenging, but this is where I must innovate my strategy, broadening one-to-one list matching and collaborating with integration partners.

Clustering individuals by shared pain points and offering on-site experiences help me understand their verified intent before reaching the remarketing list.

My landing page serves as a vital data source. Google’s AI examines it to grasp the nuances of my offerings, making creative assets crucial signals that align with my target themes and keywords.

If my landing page effectively communicates “mid-market manufacturing,” AI identifies relevant users regardless of specific keyword use, transforming my “keyword strategy” into a content strategy.

Opting for a creative approach similar to Meta’s, where Andromeda elevates the creative as a primary targeting signal, is beneficial. These creative inputs define my audience, demanding a balance between creative and technical input.

Journey-aware bidding and value-based bidding mean algorithms now analyze a user’s journey beyond the final click.

Optimization now targets “high-value need states,” feeding the system data about mid-funnel behaviors that result in significant contracts.

Dig deeper: Why better signals drive paid search performance

The most profound change for digital marketers, including myself, is shifting focus from query-level to user-level intent.

While the previously ignored query “how to manage payroll” might not have targeted enterprise SaaS companies, AI now understands if that user is a financial VP at a large firm, indicating commercial intent.

If it’s the right user, the right signals should prompt AI to act on their purchasing stage.

As AI handles matching, my role shifts towards becoming a data architect.

Data quality determines my success. I must feed AI with valuable leads to optimize for value-based bidding effectively.

Assessing the health of my signal, from landing pages optimized for AI readability to correct technical content, ensures Google accurately targets my audience.

I now focus less on micromanaging search terms and more on managing brand exclusions and negative themes.

The future of search is about being the best solution for the right individual at their evolving need state.

Keywords served as training wheels, but it’s time to see how quickly my data can propel me forward.

Dig deeper: Why PPC teams are becoming data teams

Inspired by this post on Search Engine Land.

April 27, 2026

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