AI Competitor Ad Spend: Semrush Analytics & Insights

AI Competitor Ad Spend: Semrush Insights offers Marketing Managers a crucial edge in 2026, moving beyond basic data to predictive intelligence for optimizing ad budgets. Traditional competitive analysis often delivers static reports, but the integration of AI within platforms like Semrush now transforms this into a dynamic, actionable process. You can pinpoint exactly which competitor campaigns are driving results, dissect their creative strategies, and even forecast future performance to allocate your ad spend more effectively.

Unmasking Competitor Ad Spend with Semrush AI

The digital advertising landscape constantly shifts, demanding more than just reactive adjustments from Marketing Managers. Understanding what your rivals are doing, how much they are spending, and the impact of those investments is no longer a luxury; it's a strategic imperative. In 2026, relying solely on manual data aggregation or generic reporting tools means operating with a significant blind spot. AI-powered analytics, particularly within platforms like Semrush, provides the granular detail and predictive insights necessary to stay ahead. This approach allows you to move from simply observing competitor activity to proactively shaping your own strategy based on their successes and failures.

The Shifting Sands of Digital Advertising in 2026

Marketing Managers in 2026 face a paradox: more data than ever, yet often less clarity. The sheer volume of ad creatives, channels, and audience segments makes manual analysis prohibitive. Competitors are launching hundreds of campaigns annually across Google Ads, Meta, TikTok, and emerging platforms like xAI's Grok Ads (as of 2026). Without AI, identifying patterns in their ad copy, visual assets, landing page experiences, and budget allocations becomes a monumental, often impossible, task. The market rewards agility and precision, penalizing guesswork.

💡 Tip: Focus your initial Semrush AI analysis on your top 3-5 direct competitors who consistently target similar audience demographics or keywords. This narrows the data scope and yields more immediate, actionable insights.

Consider a scenario where a competitor pivots their ad spend to a new platform or a different creative format. Traditional tools might show a sudden spike in their estimated budget, but AI can quickly contextualize this, identifying the specific ad groups, keywords, and even the sentiment of the ad copy that correlates with the shift. This isn't just about knowing what they did, but why it might be working, or failing, for them. This level of understanding informs your own budget adjustments, creative refreshes, and channel diversification, preventing costly trial-and-error.

A New Intelligence Framework for Ad Strategy

The core mental model for AI-driven competitor ad spend analysis involves three distinct phases: Observation, Interpretation, and Prediction. You move beyond simply collecting data to actively deriving strategic implications and forecasting future outcomes.

1. Observation (Data Collection & Structuring): This phase is where Semrush excels, drawing in vast quantities of competitor ad data: estimated spend, active ads, keywords, landing pages, and geographic targeting. AI's role here is to structure this unstructured data, tag creative elements, categorize ad copy themes, and identify anomalies that a human might miss. For example, Semrush's Ad Research tool (part of the Competitive Research suite) pulls estimated PPC budgets and active ad copy, while its Display Advertising tool focuses on display network creatives.
2. Interpretation (Pattern Recognition & Insight Generation): Once data is structured, AI models analyze it for patterns. This includes identifying correlations between specific ad creative elements (e.g., use of human faces vs. product shots) and estimated performance, detecting shifts in keyword strategy, or uncovering emerging messaging themes. Semrush's AI capabilities, often integrated into its reporting or through custom dashboards, can highlight "winning" ad copy elements or identify underperforming campaign structures based on historical data. This is where you start to understand why certain ads perform better for your competitors.
3. Prediction (Forecasting & Strategic Recommendation): The most advanced phase, where AI takes interpreted patterns and forecasts future outcomes. By analyzing historical spend trends, seasonal variations, and competitor strategic shifts, AI can predict potential changes in their ad budgets, identify future keyword opportunities, or even suggest optimal timing for your own campaign launches. This predictive power allows Marketing Managers to proactively adjust their strategies, rather than reactively chasing competitor moves.

This framework ensures that every piece of competitor ad data you collect is filtered, analyzed, and in the end translated into a tangible advantage for your own ad campaigns. It's about building a solid, adaptive ad strategy informed by the market's leading players.

Core Workflows: From Raw Data to Actionable Insights

Using AI for competitor ad spend analysis with Semrush isn't about running one report; it's about integrating multiple tools and AI capabilities into iterative workflows. These workflows enable Marketing Managers to translate raw data into specific, actionable strategies for optimizing their own ad budgets and creative direction.

Workflow 1: Identifying High-Value Competitor Campaigns

This workflow focuses on pinpointing your rivals' most effective paid campaigns and understanding their underlying mechanics.

Procedure:

1. Competitor Domain Input: In Semrush, navigate to the "Advertising Research" tool. Enter a key competitor's domain.
2. Overview & Top Keywords: Review the "Overview" report for estimated monthly budget, top paid keywords, and current ad count. Pay close attention to keywords with high volume and low competition, or those where your competitor is consistently ranking in top positions.
3. Ad History Deep Dive: Move to the "Ad History" report. Filter by date to see seasonal trends or recent shifts. Here, you'll see actual ad copy used historically.
4. AI Pattern Identification (Manual Augmentation): This is where AI truly shines. Export the ad copy data (e.g., to a CSV). Use an AI large language model (LLM) like Claude 3.5 Sonnet or GPT-4o with advanced prompt engineering marketing techniques.

• Prompt Example:

Analyze the following ad copy snippets from [Competitor Name]'s historical PPC campaigns. Identify common themes, unique value propositions, calls-to-action, and emotional triggers. Group similar ads and categorize them by their apparent target audience segment or product focus. Pay close attention to any shifts in messaging over time and hypothesize why these changes occurred based on estimated performance data (if available).

[Paste ad copy snippets here]

• Expected Output: The AI should return clusters of ad copy, identifying phrases like "free trial," "save 20%," "boost productivity," or "solve X problem." It will highlight consistent themes (e.g., "efficiency focus in Q1, cost-saving focus in Q2") and suggest the underlying strategy. This helps you understand which angles resonate for them.

5. Landing Page Analysis: For the most successful ad groups identified, click through to their associated landing pages within Semrush's Ad Research. Use an AI browser extension (e.g., Perplexity AI's web summarizer) to quickly analyze the landing page's content, conversion elements, and unique selling propositions. This reveals the full conversion funnel your competitor is using.

Outcome: A detailed understanding of your competitor's most impactful ad campaigns, including their messaging, target keywords, and landing page strategy. This directly informs your own campaign planning, allowing you to either counter their strengths or exploit their weaknesses.

Workflow 2: Deconstructing Ad Copy and Creative Strategies

Beyond just identifying campaigns, this workflow focuses on the granular details of ad creatives and copy, using AI to extract actionable insights.

Procedure:

1. Display Advertising Review: Navigate to Semrush's "Display Advertising" tool. Enter your competitor's domain. This provides access to their display ad creatives (banners, video ads).
2. Creative Export & Visual Analysis: Export a selection of their most frequently run or recently updated display ads. For visual analysis, use an AI vision model (e.g., GPT-4o's image analysis).

• Prompt Example (for image analysis):

Analyze this display ad creative from [Competitor Name]. Describe the primary visual elements, color palette, emotional tone, and any implied messaging conveyed by the imagery. Identify the target audience based on visual cues (e.g., demographics, lifestyle). Note any consistent branding elements.

• Expected Output: A description of the ad's visual strategy, identifying recurring use of specific colors, imagery (e.g., "diverse group of young professionals smiling," "minimalist product shot on a white background"), and the emotional impact.

3. Ad Copy Dissection: For PPC ad copy (from Workflow 1) and display ad text, use an LLM for deeper linguistic analysis.

• Prompt Example (for text analysis):

Examine the following ad copy variants from [Competitor Name]. For each, identify the headline's hook, the body's main benefit, and the call-to-action. Analyze the sentiment and tone (e.g., urgent, empathetic, authoritative). Suggest which psychological principles (e.g., scarcity, social proof, urgency) are being employed.

• Expected Output: A breakdown of ad copy effectiveness, highlighting compelling headlines, persuasive language, and specific CTAs. You'll see patterns like "always uses numbers in headlines" or "focuses on pain points before offering a solution."

4. A/B Test Hypothesis Generation: Based on the AI's analysis, generate hypotheses for your own A/B tests. If a competitor consistently uses social proof ("Join 10,000+ users"), you might test that against a feature-benefit headline.

Outcome: A rich understanding of your competitor's creative playbook. This allows you to develop more impactful ad copy and visuals, informed by what's demonstrably working in your market, without directly copying.

Workflow 3: Forecasting Performance and Budget Allocation

This advanced workflow combines Semrush's historical data with AI's predictive capabilities to inform your future ad budget decisions.

Procedure:

1. Historical Spend Trends: In Semrush's "Advertising Research" for your competitor, export several months (or even a year) of historical estimated budget data. Look for trends, seasonality, and significant spikes or drops.
2. Market Trend Overlay: Complement this with broader market data. Use Semrush's "Market Explorer" to identify overall market trends, growth rates, and seasonal demand for your industry.
3. AI for Predictive Modeling: Feed this combined data into an AI model (e.g., a custom Python script using libraries like Prophet for time-series forecasting, or a more accessible tool like Google Sheets with an AI add-on for basic predictions).

• Prompt Example (for a data scientist or via a data-savvy LLM):

Given the following historical monthly ad spend data for [Competitor Name] and relevant industry market growth trends, forecast their estimated ad spend for the next 6-12 months. Identify any seasonality or significant anomalies that could impact future spending. Additionally, suggest how these predicted trends might influence optimal budget allocation for [Your Company] across key channels like PPC and display.

[Paste historical spend data and market trend data here]

• Expected Output: A projected ad spend curve for your competitor, highlighting peak seasons or anticipated budget increases. The AI might also suggest that if competitor X is expected to increase PPC spend by 15% in Q3, you should consider increasing your own budget by 10% to maintain share of voice, or shift focus to a different channel where they are less active.

4. Optimizing Ad Budgets with AI: Use these forecasts to adjust your own ad budget. If a competitor is predicted to pull back on a specific keyword group due to diminishing returns (as identified by AI in Workflow 1), you might increase your bid there. Conversely, if they're predicted to dominate a new channel, you might strategically reallocate resources to avoid direct, costly competition.

Outcome: A data-driven budget allocation strategy that anticipates competitor moves, rather than reacting to them. This ensures your ad spend is optimized for maximum impact and competitive advantage. This is where predictive ad performance marketing truly comes into its own for marketing manager analytics.

The AI-Enhanced Semrush Arsenal for Marketing Managers

Semrush, a leading competitive intelligence platform, has been steadily integrating AI capabilities to augment its already solid data. For Marketing Managers, understanding which tools within Semrush are AI-enhanced and how to best use them, often in conjunction with external AI models, is key to unlocking advanced competitor ad spend analysis.

Semrush Competitive Research Toolkit: Beyond Basic Reports

Semrush offers an expansive suite of tools, but for AI-driven competitor ad spend analysis, the core focus is on its Competitive Research category. These tools provide the foundational data that AI then processes and interprets.

• Advertising Research: This is your primary entry point. It provides estimated PPC budgets, top keywords, ad copy, and historical data for any domain. While the core data collection isn't "AI" in the generative sense, Semrush's algorithms for estimating spend and identifying keyword intent are sophisticated, constantly refined, and use machine learning to provide accurate competitive intelligence.
• Key Data Points: Estimated monthly budget, number of paid keywords, traffic cost, top paid keywords, ad copy examples, ad history.
• Display Advertising: Crucial for understanding competitor visual strategies. It uncovers display ads, publishers, and landing pages used by competitors across various ad networks. Semrush's ability to categorize and present these creatives efficiently is supported by underlying AI for image and text recognition.
• Key Data Points: Display ad creatives (images, videos), ad types, publishers, geographic targeting, audience demographics.
• Market Explorer: Offers a high-level view of market trends, competitor market share, and audience demographics. While not directly AI-driven for ad spend, its market insights provide crucial context for interpreting competitor strategy. AI can then be applied to this context to predict market shifts.
• Key Data Points: Market size, growth trends, traffic generation strategy, audience interests.

These tools, especially in their 2026 iterations, offer increasingly refined data segmentation and visualization, laying the groundwork for external AI analysis.

Integrating AI Models for Deeper Pattern Recognition

While Semrush provides the data, the true "AI competitor ad spend analysis" often involves piping that data into external large language models (LLMs) or specialized AI tools. This is where advanced prompt engineering marketing comes into play.

1. Generative AI for Content Analysis (e.g., GPT-4o, Claude 3.5 Sonnet):

• Use Case: Analyzing exported ad copy, landing page text, or even video ad transcripts for themes, sentiment, psychological triggers, and calls-to-action.
• Workflow: Export raw text data from Semrush (e.g., ad copy from Advertising Research). Paste it into the LLM with a detailed prompt (as shown in Workflow 1 & 2). The model can classify, summarize, extract entities, and even suggest A/B test variations based on competitor insights.
• Prompt Pattern: Analyze the following [type of content] from [competitor]. Identify [specific elements]. Categorize by [criteria]. Suggest [implications].

2. Vision AI for Creative Analysis (e.g., GPT-4o's vision capabilities, dedicated image analysis APIs):

• Use Case: Interpreting display ad creatives, social media visuals, or even video thumbnails.
• Workflow: Screenshot or download competitor ad creatives. Upload them to a vision-enabled AI model.
• Prompt Pattern: Describe the visual elements, color scheme, and emotional tone of this image. What message is it trying to convey? Who is the likely target audience?

3. Predictive Analytics Tools (e.g., Google Sheets with AI add-ons, specialized forecasting software, custom Python scripts):

• Use Case: Forecasting competitor ad spend, identifying seasonal trends, predicting market shifts.
• Workflow: Export historical numerical data (spend, traffic, keyword volume) from Semrush. Input this into the predictive tool.
• Prompt Pattern (or configuration): Forecast [metric] for the next [period] based on [historical data]. Identify [seasonal components/anomalies].

The key is treating Semrush as your primary data source and AI as your advanced analytical engine. This combination significantly outperforms either tool used in isolation, providing a truly predictive ad performance marketing capability.

Pricing Tiers and Feature Ceilings (as of 2026)

Understanding Semrush's pricing structure and its AI integrations is crucial for Marketing Managers planning their analytics stack. The platform offers several tiers, with AI capabilities often expanding at higher levels or through specific add-ons.

• Pro Plan (~$129/month, billed annually): This entry-level plan provides essential competitive research, including Advertising Research and Display Advertising. It's suitable for individual Marketing Managers or small teams needing basic competitor ad data. The AI integration at this level is primarily embedded within Semrush's existing algorithms for data estimation and categorization. You'll get thorough reports, but deep generative AI analysis will require manual export and external LLM use.
• Ceiling: Data limits on reports, number of projects, and keyword tracking.
• Guru Plan (~$249/month, billed annually): The most popular choice for growing marketing teams. This plan expands data limits, adds access to historical data (crucial for AI forecasting), and includes more advanced competitive intelligence features like Market Explorer. At this tier, Semrush begins to offer more direct AI-powered insights within its dashboards, such as "Smart Recommendations" or "Opportunity Spotting" that use machine learning to highlight anomalies or high-potential keywords.
• Ceiling: Expanded limits, but still has project and keyword tracking caps. Full API access for custom AI integrations might be limited.
• Business Plan (~$499/month, billed annually): Designed for larger agencies and enterprises. This tier offers the highest data limits, full access to historical data, and often includes API access (as of 2026). API access is critical for Marketing Ops leads or data scientists who want to build custom AI models that directly ingest Semrush data for advanced predictive analytics or bespoke dashboards. This is where smooth integration of Semrush data with your internal AI stack becomes feasible.
• Ceiling: Highest limits, dedicated support, but the cost scales significantly. Custom AI requires internal development resources.

External AI Tools: Pricing for external LLMs (GPT-4o, Claude 3.5 Sonnet) typically operates on a token-based model, which can range from a few cents per 1,000 tokens for basic use to several dollars per 1,000 tokens for advanced models with vision capabilities. For extensive competitor ad spend analysis, where you're processing hundreds of ad creatives or thousands of lines of ad copy, these costs can accumulate, so efficient prompt engineering is vital. Many Marketing Managers find the free tiers or basic subscription plans of these LLMs sufficient for initial analysis.

🎯 Pro move: For larger teams, consider the Semrush Business API. It allows you to programmatically pull competitor ad data directly into your internal data warehouses or custom AI applications, automating data pipelines for continuous predictive ad performance marketing. This enables truly dynamic competitor ad strategy AI.

Navigating Common Pitfalls in AI Ad Spend Analysis

While AI offers unprecedented power in competitor ad spend analysis, Marketing Managers must be aware of common pitfalls. Missteps can lead to flawed insights, misallocated budgets, and missed opportunities. Recognizing these challenges and implementing specific fixes ensures your AI-driven strategy remains solid and effective.

Over-Reliance on Aggregate Data Without Granular Context

A frequent mistake is taking Semrush's estimated ad spend figures at face value without digging into the underlying campaigns, keywords, and creative types. Aggregate numbers can be misleading; a competitor's high overall spend might be concentrated in a few low-performing brand campaigns, or a seemingly low spend could be highly efficient on niche, high-converting keywords.

Fix: Always drill down.

• Filter by Top Keywords: Instead of just looking at total spend, identify the top 10-20 paid keywords where your competitor is consistently ranking. Analyze the specific ad copy associated with these keywords.
• Segment by Ad Type: Distinguish between search ads, display ads, and video ads. A competitor might have a high display budget but a low search budget, indicating different strategic priorities.
• Historical Context: Use Semrush's "Ad History" to see how campaigns evolved over time. Did they increase spend on a particular keyword after a product launch? This contextualizes the numbers and reveals intent.
• AI for Granular Insights: Use AI models (as detailed in Workflow 1) to identify specific campaign themes and messaging within the ad copy. This moves beyond "they spent X" to "they spent X on campaigns focused on Y benefit for Z audience."

Misinterpreting AI-Generated Recommendations

AI models, especially generative LLMs, can produce highly convincing outputs. However, their recommendations are based on patterns in the data they were trained on and the specific prompts you provide. They lack true business context or an understanding of your brand's unique value proposition. Blindly following an AI's suggestion without critical human review is a recipe for strategic drift. For instance, an AI might suggest mirroring a competitor's aggressive pricing ad copy, without considering your brand's premium positioning.

Fix: Treat AI recommendations as hypotheses, not directives.

• Human Oversight is Non-Negotiable: Every AI-generated insight, from a suggested ad copy theme to a predicted budget shift, must be reviewed by an experienced Marketing Manager.
• Cross-Reference with Internal Data: Compare AI insights with your own performance data. If an AI suggests a competitor is excelling with a certain keyword, check your own performance on that keyword.
• Inject Your Brand Voice: When using AI to draft ad copy ideas based on competitor analysis, always refine it to align with your brand's unique voice, tone, and positioning. Use advanced prompt engineering marketing to explicitly instruct the AI on brand guidelines and desired sentiment.
• Test and Learn: Implement AI-derived strategies as A/B tests. Don't roll out a full campaign based solely on an AI recommendation. Use small-scale experiments to validate the insights.

Data Latency and Maintaining Real-Time Relevance

Competitive ad data is dynamic. What was true last week might not be true today. Semrush's data is updated regularly, but there's always a slight latency between a competitor launching a new campaign and that data appearing in your reports. Relying on stale data can lead to outdated strategies, especially in fast-moving industries.

Fix: Implement a continuous monitoring and refresh strategy.

• Scheduled Reporting: Set up weekly or bi-weekly automated reports in Semrush for your key competitors. This ensures you're always working with relatively fresh data.
• Alerts and Notifications: Configure Semrush alerts for significant competitor changes (e.g., new ad campaigns, large budget shifts, new top keywords). This provides near real-time notifications of critical shifts.
• API for Automation (Business Plan): If on a Semrush Business plan, use the API to pull data programmatically into a dashboard that refreshes daily. This is the closest you can get to real-time competitor ad strategy AI.
• Prioritize Recent Data for AI: When feeding data to an LLM for analysis, emphasize recent timeframes. For example, "Analyze ad copy from the last 30 days" rather than "Analyze all historical ad copy." This ensures your AI insights are based on the most current competitor actions.
• Focus on Trends, Not Just Snapshots: Look for patterns and trends over time (e.g., a competitor consistently increasing spend on a particular channel) rather than making decisions based on a single data point.

By proactively addressing these pitfalls, Marketing Managers can ensure their AI-driven competitor ad spend analysis remains a powerful, reliable tool for optimizing ad budgets and outperforming the competition.

Activating Your AI-Driven Competitor Strategy

Moving from analysis to implementation is where the real value of AI competitor ad spend insights is realized. Marketing Managers need a clear path to integrate these findings into their daily operations, ensuring that the time spent on analysis directly translates into improved ad performance and a stronger competitive position.

Building Your First AI-Augmented Ad Strategy Brief

The most immediate application of your AI-driven insights is to enhance your ad strategy briefs. Instead of generic directives, you'll provide specific, data-backed recommendations.

Procedure:

1. Synthesize AI Insights: Review all the outputs from your AI workflows (identifying high-value campaigns, deconstructing creatives, forecasting spend). Consolidate the most impactful findings.

• Example: "Competitor A increased spend on 'eco-friendly [product category]' keywords by 20% in Q2, consistently using imagery of nature and a 'sustainable choice' CTA. Their landing pages emphasize certifications."

2. Formulate Strategic Hypotheses: Translate these insights into testable hypotheses for your own campaigns.

• Example: "Hypothesis: By increasing our budget on 'sustainable [product category]' keywords by 15% and testing ad copy with nature imagery and a 'certified green' CTA, we can capture a share of this growing segment."

3. Draft Ad Copy & Creative Direction with AI: Use your LLM with specific instructions, incorporating the competitor insights while maintaining your brand voice.

• Prompt Example:

Draft three variations of a Google Search Ad for [Your Product/Service], targeting [Specific Keyword/Audience]. Incorporate the following insights from competitor analysis: [List 2-3 key competitor insights, e.g., "competitor X uses urgency in headlines," "competitor Y highlights ROI in body copy"]. Ensure the tone is [Your Brand's Tone, e.g., "authoritative and trustworthy"] and the call-to-action is [Your CTA, e.g., "Get a Free Demo"].

• Expected Output: Ad copy variations that blend competitor-proven tactics with your unique brand messaging, ready for A/B testing.

4. Allocate Budget Strategically: Based on your AI-driven forecasts, recommend specific budget shifts across channels or campaigns.

• Example: "Reallocate 10% of our general brand awareness budget to targeted PPC campaigns on 'eco-friendly [product category]' keywords, given competitor activity and market trend predictions."

5. Define Measurement & KPIs: Clearly state how you will measure the success of these AI-augmented strategies, focusing on metrics like CTR, Conversion Rate, CPA, and ROI.

Outcome: A complete ad strategy brief that is highly specific, data-driven, and proactively addresses competitive dynamics, leading to optimizing ad budgets with AI.

Sustaining the Competitive Edge with Continuous Monitoring

AI competitor ad spend analysis is not a one-time project; it's an ongoing process. To maintain a competitive edge, Marketing Managers must establish a system for continuous monitoring and iterative refinement.

1. Automated Reporting & Alerts: Tap into Semrush's automated reporting features to receive weekly or monthly updates on competitor ad spend, new ads, and keyword shifts. Configure alerts for significant changes (e.g., a competitor launching a new product and suddenly increasing their ad budget by 50%).
2. Regular AI Review Sessions: Schedule a dedicated time (e.g., monthly or quarterly) to revisit your AI-powered analysis. Re-run your LLM prompts with updated data from Semrush to identify new trends or shifts in competitor strategy. This helps you keep your marketing manager analytics sharp.
3. Refine Prompt Engineering: As you gain experience, refine your advanced prompt engineering marketing techniques. Experiment with different prompt structures, temperature settings, and model parameters to extract even more nuanced insights from the raw data. Share effective prompts within your team to build a collective intelligence.
4. Iterative Strategy Adjustments: Use the continuous insights to make small, incremental adjustments to your ad campaigns. This agile approach allows you to quickly capitalize on new opportunities or counter competitor moves. For example, if AI identifies a new, high-performing creative type for a competitor, you might quickly develop and test a similar (but uniquely branded) creative.
5. Feedback Loop with Performance Data: Consistently compare your AI-informed strategies against your actual campaign performance data. This feedback loop is crucial for validating AI insights and improving the accuracy of your future analyses. If an AI-suggested strategy didn't perform as expected, analyze why. Was the AI's interpretation flawed? Was your implementation weak?

By embedding AI competitor ad spend analysis into a continuous cycle of observation, interpretation, prediction, and action, Marketing Managers can transform their ad operations. This approach ensures you're not just reacting to the market, but actively shaping your success through intelligent, data-driven decisions.

Next Steps: Your Immediate Action Plan

Start your AI-driven competitor ad spend analysis this week by setting up a dedicated Semrush project for your top three direct competitors. Configure automated weekly email reports for their Advertising Research and Display Advertising data. Simultaneously, identify a key ad copy segment from one of your competitor's top-performing campaigns (as identified by Semrush) and use a free tier of a generative AI model like Claude or ChatGPT to deconstruct its core messaging, then draft three alternative headlines for your own campaigns based on those insights.