How to Use AI for a Truly Effective PPC Competitor Analysis

A competitive PPC (Pay-per-Click) analysis involves evaluating how competitors utilize paid advertising on platforms such as Google Ads to identify opportunities to outperform them. This process reveals valuable insights, such as the keywords competitors are targeting, the design and messaging of their ads and landing pages, and possible estimations of their advertising budgets.

Using AI-powered tools can be an efficient alternative for those with limited time or less experience in manual analysis. Access to the right PPC data and a well-structured prompt can extract valuable insights quickly and effectively.

This PPC analysis approach combines data from Ahrefs with the capabilities of AI tools like ChatGPT to deliver a comprehensive view of a competitor’s Google Ads strategy—and to uncover actionable opportunities for improvement.

Using Ahrefs, it’s possible to identify the keywords competitors are bidding on, the countries they’re targeting, and the ad copy and landing pages they’re using—information typically not available through Google Keyword Planner alone.

The platform also provides estimated daily, weekly, and monthly ad spend, offering insight into how aggressively each competitor is advertising and revealing patterns over time.

AI tools like ChatGPT can then analyze this data further by identifying keyword gaps (terms competitors are targeting that are currently missing from your campaigns), highlighting key insights, and suggesting quick-win opportunities. Additionally, these tools can assist in generating a structured PPC action plan—complete with landing page suggestions and improved ad copy—to enhance campaign performance.

The 5-step approach to conducting a PPC competitive analysis

Step 1: Spot Who You’re Competing With in Paid Ads

This is where the groundwork begins—figuring out who you’re up against in the world of Google Ads. In this step, you’ll shortlist competitors and start noticing patterns in their ad spend and targeting strategies.

Start by listing websites you directly compete with. To be thorough, check out the Organic Competitors report in Site Audit. Brands that aim to beat you in organic rankings usually try the same in paid searches.

Once you’ve got the list, open the Paid Search section under the Overview report. Look closely at their ad budget and how they’re spending it. Try to spot trends manually—your brain is better than AI at catching visual cues and shifts in charts.

For instance, a competitor is spending ₹2 lakh a month, which is much lower than their August 2024 peak of ₹10 lakh. They had consistent surges toward the end of the year, too. That suggests they’re likely to push harder between August and December 2025.

Step 2: Pull Data on Competitor Keywords, Ads & Pages

Now that you know who your competitors are, it’s time to dig deeper into their campaigns—specifically the keywords they’re bidding on, their ad copy, and where they’re sending traffic.

Head over to the Paid Keywords section in Site Explorer. From there, download both the Paid Keywords and Ads reports. This data will give you a front-row view into what’s working for them and where they’re putting their money.

Step 3: Gather Your Own Paid Keywords and Ad Info

Already running your own ads? Great—now let’s see how you compare.

Use the same method you used for competitor research: grab your keyword and ad data from Ahrefs or export directly from your Google Ads account. You can even combine data from different sources. The goal is to prepare a side-by-side analysis highlighting what you might be missing or doing better.

Step 4: Collect Competitor Landing Pages (Optional but Super Useful)

Here’s where things get interesting. Analysing your competitors’ landing pages can reveal much about their messaging and strategy.

To do this, check out the Paid Pages section in Site Explorer. Look for URLs with terms like “lp,” “landing,” or those with unusual strings or UTM tags—these are often the high-converting PPC-specific landing pages.

Download these pages as PDFs or clean HTML files. This makes it easier for AI tools to scan and understand what’s happening, helping you reverse-engineer their structure, design, and messaging style.

Step 5: Upload Everything to Your AI Workspace and Run the Analysis

Now that all your data is ready, organize it in one place. Set up a project in your preferred AI tool—this could be ChatGPT or any similar assistant that allows file uploads and contextual conversations.

Upload all your reports and landing pages, then paste your prepared prompt into the chat. Ensure you’re using the tool’s most advanced version available for deeper analysis.

From here, let the AI process your files and generate insights. It can help uncover keyword gaps and ad weaknesses and even suggest improvements for your own campaign, like better headlines, CTA wording, or landing page tweaks.

How to Analyze Competitor Ads on Other PPC Platforms

While tools like Ahrefs make it relatively straightforward to study competitors’ strategies on Google Ads, gathering insights from other PPC platforms requires a slightly different approach.

Social Media Ad Analysis

The best starting point for platforms such as Meta (Facebook and Instagram), TikTok, X (formerly Twitter), and LinkedIn is their official ad libraries. These libraries allow users to search by brand and view:

• Ad creatives (images, videos, and captions)
• Variations of the same ad
• Ad durations (when they ran)
• Reach (for EU-based audiences)
• Some targeting insights, depending on the platform

Each platform must be searched manually for individual competitors, and the level of detail may vary. While these libraries don’t offer in-depth competitive metrics like budget or performance, they still provide enough creative content to observe marketing patterns.

How AI Can Support Creative Analysis

Despite limited raw data, large language models (LLMs) can be used to analyze ad creative themes. To do this, save ad library pages or screenshots as PDFs and upload them to your preferred AI tool. Then, prompt the model with structured queries, such as:

• What products or services are these ads promoting?
• Group the ads by visual theme (e.g., human faces, product interface, icons, illustrations).
• Identify the focal point in each creative (e.g., face, brand logo, text block, CTA button) and rank by frequency.
• Extract and categorize all ad headlines or overlay text by message angle, such as:
  • Benefits
  • Urgency
  • Time-saving
  • Social proof

  Note which angle is most dominant.

• Detect repeated design patterns across creatives.

This helps build a deeper understanding of how competitors communicate visually and strategically.

For Display Advertising Networks

Tools like AdBeat and AdClarity can be effective when analyzing competitors across broader display networks. These platforms often provide:

• Types of ads most frequently used
• Publisher websites where competitors place ads
• Access to archived ad creatives
• Campaign longevity and frequency

Such tools offer a macro-level view of a brand’s display advertising strategy, especially useful for spotting high-performing publishers and ad formats.

Final Thoughts

LLMs (Large Language Models) offer the flexibility to conduct rapid, on-demand analyses, making it easier to experiment and refine strategies. Want to test whether competitor headlines with emotional triggers outperform straightforward product-focused messaging? Simply ask the AI. Interested in understanding how seasonality affects your competitors’ keyword strategy? Re-run the analysis with a new set of parameters.

This approach allows for quick iterations and unconventional insights, helping you uncover opportunities that would be difficult to find by manually sifting through spreadsheets or reports. Embrace the ability to think outside the box, adjust on the fly, and discover valuable marketing insights more efficiently than ever.