--- title: ChatGPT for Google Ads Analysis Workflow type: article created: '2026-04-05' updated: '2026-04-05' source_docs: - raw/2025-10-10-client-health-pulse-check-internal-93456393.md tags: - ai-tools - chatgpt - google-ads - workflow - analysis - performance-marketing layer: 2 client_source: null industry_context: null transferable: true --- # ChatGPT for Google Ads Analysis Workflow A practical workflow for rapidly analyzing Google Ads campaigns using ChatGPT. Instead of manually reviewing screen after screen of campaign data, you export structured data files and screenshots, feed them to ChatGPT, and receive synthesized insights and prioritized recommendations in minutes. This approach was demonstrated live during the [[clients/asymmetric-applications-group/_index|Asymmetric Applications Group]] internal strategy session using the Adavacare senior living account as a worked example. --- ## Why This Workflow Exists Google Ads surfaces enormous amounts of data across campaigns, ad groups, keywords, locations, and assets. Manually synthesizing that data into actionable insights is time-consuming and easy to do inconsistently. ChatGPT can identify patterns, surface anomalies, and generate recommendations across multiple data exports in a fraction of the time — freeing account managers to focus on judgment calls rather than data wrangling. > "There's so much data in Google Ads that it's hard for you or me as a human to go through every screen, screen by screen by screen, and figure out what the hell is going on. But the way ChatGPT thinks — it can go into this stuff and very quickly figure out patterns and things that you wouldn't see." — Mark Hope --- ## Core Workflow ### Step 1: Export Data from Google Ads Navigate to each relevant screen in Google Ads and download the data as an Excel file using the **Download** button. Key screens to export: | Screen | What You Get | |---|---| | **Campaigns** | Budget, impressions, clicks, conversions, CPC by campaign | | **Keywords** | Match types, bids, status flags (e.g. "below first page bid"), conversion data | | **Ad Groups** | Performance breakdown within campaigns | | **Locations** | Geographic targeting performance | Set the date range before exporting — **last 30 days** is a standard starting point, but you can extend the window for trend analysis. ### Step 2: Upload to ChatGPT and Request Analysis Open ChatGPT and start with a simple prompt: ``` Analyze this campaign screen. ``` Attach the downloaded Excel file. ChatGPT will return: - **Reporting period and campaign summary** — what's enabled, budget totals, campaign types - **Key insights** — which campaigns are over/underperforming and why - **Performance rankings** — winners and laggards on CPC, CTR, conversion rate - **Recommendations** — specific actions (e.g. reallocate budget, raise bids on high-intent keywords, fix conversion tracking) You can then drill deeper with follow-up prompts: - *"Dive deep into the keyword data."* - *"Why do you recommend raising bids by 15–25%?"* - *"Which specific keywords should we prioritize?"* ### Step 3: Add Context About the Client ChatGPT performs better when it understands what the account is actually trying to accomplish. After the initial analysis, provide a brief context prompt: ``` This is [Client Name]. They are a [business type] operating in [geography]. Their primary goal is [goal — e.g. generate qualified leads for senior living tours]. Their main competitors are [X, Y, Z]. ``` This shifts the recommendations from generic best practices to situation-specific strategy. ### Step 4: Build a Multi-File Project for Deeper Analysis For ongoing accounts, create a **ChatGPT Project** (not just a conversation) and load all exported files into it. As you add more data over time, ChatGPT can cross-reference across files and identify trends that wouldn't be visible in a single export. **To set up:** 1. Create a new Project in ChatGPT — name it something like `[Client] Google Ads` 2. Add files via **Add Files** within the project 3. Continue adding new exports each week/month — the model gets progressively more context --- ## Supplementing with Screenshots You don't always need to download a file. For dashboards and visual reports (e.g. Google Search Console, Ahrefs), you can take a screenshot and paste it directly into ChatGPT. The model can read charts and tables from images. **Useful screenshot sources:** - **Google Search Console** → Performance → Queries (organic keyword visibility) - **Ahrefs** → Organic traffic trends, domain authority - **Google Ads Dashboard** → High-level performance graphs > "You don't have to download anything. You get a little better information if you download the data, because then it's got details, but you can just give it screenshots." — Mark Hope --- ## Cross-Channel Analysis The real power emerges when you combine data from multiple sources in a single ChatGPT session or project. In the Adavacare example, combining Google Ads exports with Google Search Console data surfaced this insight: > *"You're winning on brand, you're invisible on intent-driven discovery — which is where you make money."* That cross-channel synthesis — paid + organic together — would have taken a full day to produce manually. With this workflow it took roughly 20 minutes. **Recommended data sources to combine:** - Google Ads exports (campaigns, keywords, ad groups, locations) - Google Search Console (organic queries, CTR, impressions) - Ahrefs or SEMrush (domain authority, backlink profile, competitor gaps) --- ## Turning Analysis into a Client Deliverable Once ChatGPT has produced a strategy or recommendation set, you can pipe it directly into **Gamma** to generate a polished presentation: 1. Copy the ChatGPT output 2. Open Gamma → **New** → **Paste in text** 3. Let Gamma generate a deck with slides, visuals, and a 90-day rollout timeline 4. Edit slides as needed (replace AI-generated images with actual client photos, adjust any inaccurate claims) 5. Share via Gamma's **Publish** link or export to PDF This produces a client-ready presentation — complete with strategy, channel recommendations, and timeline — in under 30 minutes total. --- ## Caveats and Quality Control ChatGPT is a starting point, not a final answer. Always review output before acting or sharing: - **Verify campaign counts** — ChatGPT will only analyze what you gave it. If you have 7 campaigns but only exported 3, the analysis is incomplete. - **Check recommendations against client context** — e.g. a geo-modified keyword suggestion may not make sense if the location name is ambiguous (a street name vs. a city). - **Ask for reasoning** — if a recommendation seems off, ask *"Why do you recommend that? What's your reasoning?"* ChatGPT will explain, and you can evaluate whether it applies. - **Cross-check with Gilbert or the performance team** — use ChatGPT output to generate questions and hypotheses, then validate with the specialist before making changes. --- ## Related - [[knowledge/ai-tools/gamma-presentation-workflow|Gamma for Client Presentations]] - [[knowledge/ai-tools/ai-tool-selection-guide|Which AI Tool to Use When]] - [[knowledge/operations/role-definitions|Role Definitions — Performance Marketing]] - [[clients/asymmetric-applications-group/meetings/2026-04-05-client-health-pulse-strategy-session|Source Meeting — Client Health Pulse & 2026 Strategy Session]]