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Tag: Automation

Transforming PPC with Claude Skills for Automation Success

Have you ever felt like you’re living in an ‘AI Groundhog Day’? Despite the wealth of AI tools we can use, many of us find ourselves stuck in a loop, manually prompting AI again and again. If we aim to truly automate PPC tasks, we need to move beyond this cycle.

Picture this: you open a chat window, carefully craft a prompt, and paste in your context. The result is fantastic! Yet, an hour later, the cycle repeats. If this sounds familiar, you’re still entrenched in manual work, albeit with a digital twist.

To harness AI effectively, I’ve realized we must transition from being doers to orchestrators. This means moving away from one-off prompts and starting to build robust systems. My book, “The AI Amplified Marketer,” delves deeper into how the human element remains crucial even as AI evolves rapidly.

Today, I’ll guide you on using Skills, an emerging AI capability, to enhance efficiency in managing PPC.

What’s a Claude Skill?

Many of us marketers have tried ChatGPT’s Custom Instructions—a broad directive for AI behavior. A Claude Skill, however, is more precise, dictating specific instructions to ensure consistent and predictable outcomes aligned with my expectations.

Recently, while rating search terms, I noticed AI’s inconsistency. One session yielded letter grades, another a percentage, and another, a numerical scale. This variability can disrupt workflows, confusing tools and team members alike.

A Skill eliminates this inconsistency, ensuring that every time, the results format remains unchanged. This evolution transforms AI from an unreliable assistant to a steadfast team member.

The latest capabilities in Claude allow a Skill to morph your comprehensive PPC strategy into an executable AI playbook, coordinating tasks among various tools and subagents efficiently.

Whether it’s auditing accounts or analyzing search query reports, Skills encapsulate your expertise into scalable systems for your team to deploy with AI seamlessly.

How to Build Your First AI Skill

Starting a new Skill might seem daunting, but it’s quite straightforward. In a chat with your AI, you can upload an audit checklist, a SOP, or a workflow blueprint, and instruct Claude to formulate it into a Skill.

Intriguingly, Claude employs a specialized protocol to construct Skills, guaranteeing outputs that are structured, adhere to best practices, and align with Anthropic’s architecture.

Technically, a Skill is stored as a Markdown (.md) file, serving as the playbook for the task at hand. Concerned about data privacy? You can save this locally or opt to share it in a cloud repository for easy team access and updates.

You don’t need to start from scratch. Platforms like GitHub offer pre-built Skills that you can experiment with and tailor to your needs.

How to Use a Skill in PPC

To get started with a Skill, make sure you have some available in your account.

Simply tell the AI the specific task you wish to accomplish. If a suitable Skill exists, the AI will apply those instructions to carry out the task.

Keep in mind, having competing skills could disrupt consistency. For instance, two skills performing Google Ads audits might randomly select different methodologies, thwarting the predictability.

PPC Skills Need Real-Time Data

While a Skill defines powerful logic, without real-time data, its application remains theoretical. Consider crafting an analysis to review search terms over the past 14 days—it’s great in concept, but without active data pulling from Google Ads, it remains incomplete.

Previously, this required manually downloading CSVs from interfaces. It worked, but was slow and the data became outdated immediately.

Enter the Model Context Protocol (MCP), bridging AI Skills to live data sources seamlessly. Using protocols like Optmyzr’s MCP, Skills can dynamically access and apply live Google Ads data, converting static instructions into an adaptive, responsive tool. (Disclosure: I’m the cofounder and CEO of Optmyzr.)

From Grunt Work to System Oversight

Integrating Skills with MCP transforms AI from assistantship into management. Tasks like search term analysis can shift from hands-on processes to automated oversight, with the AI undertaking everything from data pulling to implementing results.

Incorporating capable logic (Skills) with real-time data (tools) nurtures a practical system ready to shoulder routine tasks, enabling me to focus more on strategy orchestration.

4 PPC Skills You Can Build Today

Ready to jump into action? Here are four PPC Skills to inspire you:

1. Search Term Mining

This Skill guides AI in evaluating search query reports to target waste and opportunities.

Without tools, it requires manual CSV uploads and report implementation. However, with MCP, the necessary data is automatically sourced and applied directly in your Google Ads account.

2. Ad Copy Generation

Using a landing page and keywords, this Skill generates ad copy tailored to user intent and value propositions.

Manual editions involve copying assets, whereas MCP integrations can identify underperforming ads, generate new copy, and even initiate ad experiments autonomously.

3. Account Auditing

This Skill performs a checklist to spot issues like missing ad extensions or budget constraints.

Manually, it reports findings, but with MCP, it remedies problems directly, such as applying existing extensions to appropriate ad groups.

4. Budget Reallocation

Analyzing comparative data, this Skill identifies budget shifts to maximize returns.

Without tools, it suggests reallocations; with MCP, it dynamically analyzes and implements these changes, optimizing budgets promptly.

The Future of Your Role: From PPC Doer to PPC Designer

The fusion of Skills and tools allows us to depart from mere AI collaboration to AI-driven responsibilities. Instead of juggling tasks, our focus shifts to designing automated systems, crafting Skills, and setting the course for relentless efficiency.

As technology melds development and user-friendly interfaces, we’re at the cusp of a paradigm where non-developers design systems. It’s time to innovate and welcome AI as a genuine ally.

The End of Endless Prompting

The cyclical nature of endless prompting confines us to manual execution. By harnessing Claude Skills, we’re revolutionizing our approach to PPC—from mundane tasks to sophisticated system design. This transition embodies the essence of an AI-amplified marketer, fostering a dependable, efficient partner that channels our expertise into thriving systems.

The journey begins by viewing your daily routines through a designer’s lens. What process is ripe for crafting your inaugural Skill?

Inspired by this post on Search Engine Land.

April 14, 2026
Google Ads Shifts Focus: Performance Planner Changes

As someone deeply invested in the world of digital advertising, I’ve noticed that Google is making a significant change. They’re moving away from impression-based planning and encouraging us to adopt more conversion-focused strategies.

Recently, I learned that Google’s Performance Planner tool has refined its scope. They’re now emphasizing conversion-focused campaign types, leaving behind the traditional impression-based planning style.

What’s happening? Last month, Performance Planner stopped supporting planning for Display and Video campaigns. This adjustment also means that metrics like impression share, top impression share, or absolute top impression share are no longer viable on their platform.

Why this matters to us. This shift away from impression-focused planning affects how we forecast and optimize campaigns concentrated on brand awareness. Google’s push towards conversion-focused and automated strategies challenges us to rethink our approach to upper-funnel tactics.

The bigger picture. It’s evident that Google Ads is prioritizing automation and performance-driven results. They are aligning their tools more with campaign types like Search, Shopping, App, Demand Gen, Local, and Performance Max.

How it’s working now. We can continue using the Performance Planner for supported campaign types, but any plans that included Display or Video campaigns, based on impression share metrics, are no longer editable or viewable.

What I’m watching. I’m curious about how we’ll adapt our planning and forecasting strategies for upper-funnel channels like Display and Video now that they lack native support in Google’s tools.

Bottom line. Ultimately, Google’s focus on performance-driven planning means that impression-based strategies might soon be a thing of the past. It’s time to embrace the shift towards conversions.

Inspired by this post on Search Engine Land.

April 9, 2026
Explore Google’s New Developer Hub for Ad Tools and Insights

I’ve recently discovered some exciting news from Google that’s perfect for those of us who rely on their ad tools and measurement resources. Google has just launched a developer hub that’s set to make our tech-driven advertising tasks a lot smoother.

The new Developer Hub centralizes everything into one easy-to-navigate destination, which promises to simplify our experience when building, automating, and scaling ad campaigns.

What’s Happening. Google is introducing the Advertising and Measurement Developers Hub. This centralized site is designed to give us seamless access to an array of tools and resources across their ad ecosystem. Say goodbye to hunting for documentation in multiple places!

The Hub organizes resources for products like the Google Ads API, Google Analytics, and publisher tools such as AdMob and Google Ad Manager into convenient categories including advertising, tagging, and measurement.

How It Works. It features a streamlined homepage where I can quickly access documentation, blog updates, and community channels. Plus, there are dedicated sections to explore products, connect with support, and engage with Google’s developer relations team.

Why We Care. For anyone deep into using Google’s tools, like me, this is a game-changer. The ease of access to advanced tools for automation, tracking, and optimizing campaigns can really boost efficiency. This new hub makes it nearly effortless to take advantage of Google’s robust ad tech ecosystem.

The Big Picture. As our advertising efforts increasingly lean on automation and APIs, Google is bolstering the infrastructure to support developers and technical users managing complex integrations.

Zoom In. New features I think are worth noting include a ‘meet the team’ section, a centralized support page with links to Discord and GitHub resources, and a media hub featuring content like Ads DevCast.

What to Watch. It’ll be interesting to see if this hub becomes the go-to entry point for developers across Google’s ad products, especially as more AI and measurement tools roll out.

Bottom Line. Google is betting big on developer support with this hub, anticipating that it will drive innovation and adoption within its ad tech ecosystem.

Dig Deeper. For more details, check out the full story on the Google blog: Introducing the Google Advertising and Measurement Developers Hub!

Inspired by this post on Search Engine Land.

April 8, 2026
Boost Your Ad Campaigns with Google’s AI Text Rule Cloning

I’m excited to share some fantastic news for advertisers using Google Ads! They’ve introduced a new feature that lets us scale AI-generated ads quickly while keeping our brand’s voice consistent and under our creative control.

Google is granting us more influence over AI-generated ad copy, paving the way for us to expand our campaigns efficiently without compromising our brand consistency.

What’s happening: Google Ads is testing a beta feature where we can reuse text guidelines from existing campaigns. This means we don’t have to start from scratch each time, simplifying the process of maintaining brand rules.

How it works: With just one click, I can apply the approved tone, style, and messaging rules from one campaign to another, keeping AI-generated ads on-brand and cutting down on setup time.

Why we care: This feature is a game-changer, allowing me to launch campaigns faster while ensuring brand consistency across various accounts with multiple campaigns running at once.

Between the lines: It’s clear there’s an increasing demand among us marketers to “train” AI systems. This shift allows us to turn brand guidelines into reusable inputs, steering automation with more precision.

Bottom line: AI is accelerating the ad creation process, but what sets us apart is maintaining control, and Google is starting to return more of that control to us advertisers.

First spotted: This update first came to my attention through Paid Media expert Arpan Banerjee, who shared his find on LinkedIn.

Inspired by this post on Search Engine Land.

April 7, 2026
Mastering Paid Search: Strategy Over Keywords

In my extensive three-decade career, I’ve witnessed keywords dominate the landscape of paid search. However, in today’s world, they have become just a part of a larger puzzle. What truly drives performance now is strategy.

I remember spending weeks meticulously researching keywords, crafting strategies around them, and managing every aspect, from bid adjustments to audience targeting. It was the foundation of success in this industry.

We used to focus heavily on precise placements, structured URLs, and audience targeting, primarily with Google’s influence leading the charge. Our profession thrived on the tactical control this model offered.

We enjoyed the ability to identify which queries triggered ads and make informed decisions to optimize budgets accordingly. Sometimes we would even segment ad groups intricately to maximize returns.

What Changed Across Platforms

Now, advertising has embraced a significant shift: automation, driven by AI, has taken over critical tasks like bidding and creative assembly. While keywords remain relevant, they serve as just one of many signals that AI systems use.

With tools like AI Max for Search, Google has transformed keywords from being the focal point to just signals in guiding ad delivery. It’s fascinating how AI now uses elements like existing keywords and landing page content to enhance performance.

Advertisers employing AI Max often experience notable gains, with some campaigns seeing up to 27% more conversions. Integrating it with other tools like Performance Max can further amplify reach across various platforms.

Dig deeper: Google Ads no longer runs on keywords. It runs on intent.

The New Primary Levers

When I mention strategy as the new keyword, I mean focusing on specific inputs shaping ad performance. These include conversion data quality, a critical factor for systems like Google’s Smart Bidding, which relies on quality data to optimize campaigns.

We now prioritize which conversions hold the most value. It’s a shift from purely manual adjustments to strategic evaluations that highlight what truly matters for campaign success.

First-party data, enriched and well-structured, is paramount. It’s akin to the foundational keyword research of the past, vital for driving performance on today’s platforms.

Creative assets have evolved beyond mere deliverables; they’re now strategic signals that AI uses to target effectively. These visuals and messages have become an integral part of how we engage audiences.

The quality of landing pages and websites has also taken on new importance. AI determines relevance based on post-click experiences, emphasizing the need for seamless user journeys.

Dig deeper: In Google Ads automation, everything is a signal in 2026

What It Means for Practitioners

Our roles have adapted to these changes. It’s less about managing keywords or bids manually and more about creating strategic frameworks that guide AI systems effectively.

Subject-matter experts like us now focus on ensuring data quality, defining creative strategies, and identifying when human intervention is necessary.

We guide AI through a careful mix of conversion architecture, audience signal quality, and creative frameworks rather than traditional methods of keyword lists and bidding.

It’s crucial to understand how these advanced systems and platforms operate, as well as to emphasize the signals that matter most. Building strong first-party data and strategic frameworks will enhance AI capabilities and redefine the future.

Embracing this evolution, practitioners focusing on strategy over technical execution positions will find themselves best equipped to thrive in this changing landscape.

The keyword list remains, but our primary focus now is on strategy.

Dig deeper: 4 times PPC automation still needs a human touch

Inspired by this post on Search Engine Land.

April 3, 2026
Unveiling Auto-Applied Google Ads Experiments: Speed Up Your Results

I recently discovered that Google Ads now includes an auto-apply setting for its experiments feature, which is activated by default. This means that once an experiment determines a winning variant, it can automatically implement that change without waiting for manual review. A real time-saver, but there’s more to consider.

Here’s how it works: as advertisers, we can select between two modes when evaluating results – directional outcomes or statistical significance with varying confidence levels of 80%, 85%, or 95%. However, it’s reassuring to know there’s a safety net; if any chosen success metric performs significantly worse during testing, the system won’t proceed with automatic changes.

Why it matters to me. Experiments are incredibly powerful within a Google Ads account, allowing us to test ideas without risking the existing campaign’s performance. While automating the application of results could streamline testing phases, this process eliminates a crucial checkpoint where we often catch unintended outcomes that might impact active campaigns.

The potential pitfall. One limitation is that experiments currently accommodate only two success metrics. This might mean that a third, important metric could suffer unnoticed if it’s not one of the chosen ones, as the system’s guardrails only protect what we’ve explicitly instructed Google to watch, not every significant factor.

The takeaway. While the auto-apply feature serves as a helpful shortcut for straightforward tests, when conducting significant experiments, it’s worth going the extra mile for manual review. It’s best to let the experiment play out fully, ensure accuracy and thoroughness, and examine all data before making a final call.

First observed by professionals. This update did not go unnoticed; it was first picked up by Google Ads specialist Bob Meijer, who shared his insights on LinkedIn.

Inspired by this post on Search Engine Land.

April 1, 2026
Explosive Growth: Automated Traffic Surpassing Human Activity

I recently came across an intriguing report by HUMAN Security revealing a seismic shift in internet traffic dynamics. Automated traffic is accelerating at a staggering rate, outpacing human traffic growth by eightfold. Machines aren’t just passively scrolling; they’re actively engaging online!

In 2025, automated traffic surged by 23.5% compared to the previous year, while human traffic increased by a mere 3.1%. This data, outlined in the State of AI Traffic report, paints a compelling picture of our digital evolution.

AI-driven traffic is spearheading this transformation, with its monthly volume skyrocketing by 187% year over year. Notably, AI agents and agentic browsers, such as OpenAI’s Atlas and Perplexity’s Comet, experienced an astonishing growth of nearly 8,000%!

As defined in the report, automated traffic encompasses all internet activity generated by software systems rather than humans. This includes classic automation like search engine crawlers and monitoring bots, as well as more sophisticated AI-driven traffic.

Matthew Prince, Cloudflare’s CEO, foresaw this trend, predicting that bots might overtake human web usage by 2027. A bold forecast, but one that seems increasingly plausible.

Why we care. The landscape of search is evolving beyond mere human interaction. AI agents now delve into discovery, comparisons, and transactions, dynamically influencing Google’s evolving results and other AI-powered interfaces.

The details. HUMAN categorizes AI-driven traffic into three main types:

1. Training crawlers, which still dominate AI traffic at 67.5%, though their lead is waning as scrapers and agents gain traction.

2. Real-time scrapers, which are essential for AI searches and live answer engines, boosted by nearly 600% in 2025.

3. Agentic AI systems, which autonomously execute tasks, are smaller in share but growing rapidly and proving to be highly disruptive.

AI agents behave more like users. These systems are becoming more sophisticated, engaging in navigation, logging in, and conducting transactions. In 2025:

– 77% of agentic activity was observed on product and search pages.

– Close to 9% involved account-level interactions.

– Over 2% reached checkout processes.

About the data. HUMAN examined over a quadrillion interactions from 2022 to 2025, using aggregated, anonymized data from its customers. The report classified AI-driven traffic into three categories using user-agent strings, infrastructure signals, and activity characteristics. However, self-reported bot identities may not fully capture AI-driven activity.

Bottom line. The digital world is shifting from being solely human-focused. Discovery is no longer restricted to search engines. Optimizing content now involves deciding which machines can access and act upon it.

The report. For deeper insights, you can check out the 2026 State of AI Traffic & Cyberthreat Benchmark Report.

Inspired by this post on Search Engine Land.

March 26, 2026
Transform Your Webflow Experience with Profound Agents Integration

I’m thrilled to share that Profound Agents now seamlessly integrate with Webflow. This new capability transforms your CMS into an active automation endpoint, streamlining processes and boosting efficiency.
This integration is designed to elevate how you manage content, providing newfound ease and automation right at your fingertips. It marks a significant step forward in optimizing digital workflows, empowering me to focus more on creativity and less on manual tasks.

Inspired by this post on Try Profound Blog.

March 23, 2026
Navigate Google’s New Rule on Duplicate Lookalike Lists
I recently discovered an important update from Google affecting how I run Demand Gen campaigns using Lookalike user lists. Starting April 30, Google will block creating duplicate Lookalike lists via the Google Ads API and return an error code for any breaches.

This update might seem quiet, but its implications are significant, especially for those of us utilizing automated systems or third-party tools. Google is now enforcing a uniqueness check to prevent duplicates that have identical seed lists, expansion level, and country targeting.

Why do I care about this change? An unaddressed error could disrupt the workflow of my campaigns if I don’t update my integrations in time.

Here’s what I plan to do:
- Audit my current Lookalike lists and reuse those that already align with my goals instead of creating new ones.
- Update my API error handling processes to catch the new DUPLICATE_LOOKALIKE error code in versions v24 and above, or RESOURCE_ALREADY_EXISTS in older versions.
The bottom line is, while this change is housekeeping, the deadline is firm. I need to ensure my campaigns are technically prepared before the end of April to maintain stability in Google’s systems.

If you’re interested in a deeper dive, I highly recommend checking out Google’s blog post detailing these changes: Upcoming changes to Lookalike user lists in the Google Ads API, starting April 30, 2026.

Inspired by this post on Search Engine Land.
March 23, 2026
Boost Your Google Business Profile with AI-Driven Review Replies
I’ve been intrigued by Google’s latest test in the Google Business Profile: AI-generated responses to customer reviews. This innovative tool offers businesses the ability to create suggested replies to reviews, which I can then review, tweak, and manually submit.

Why It Matters to Me. Engaging with customer reviews significantly impacts conversions and trust. However, I’m aware of the risks associated with generic AI replies, especially for negative reviews where sincerity is crucial. Personalized, quality responses are more influential than merely replying for the sake of it.

What I Saw. Here’s a sneak peek of how the feature appears:

The Details I’ve Discovered. It seems Google is conducting a limited roll-out of this ‘Reply to reviews with AI’ feature within the Google Business Profile.
- It generates proposed responses to customer reviews.
- I can review and modify these suggestions before submitting.
- The availability fluctuates across different accounts and reviews.
- The feature is spotted in the U.S., Brazil, and India, but not yet widely in Europe.
Initial Impressions. Some users, like me, noticed prompts targeting older, unanswered negative reviews.
- In one test I observed, it’s possible to generate AI responses in bulk.
- I’ve read mixed reports on automation—some claim bulk responses still need a review, while others experienced fully automated replies that require no edits.
How I First Learned About It. This feature caught my attention first through LinkedIn, thanks to Chandan Mishra, a freelance local SEO specialist, and it was further amplified by Darren Shaw, founder of Whitespark.

Inspired by this post on Search Engine Land.
March 20, 2026

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