How Gabe, a B2B Growth Performance Marketer, reduced PPC reporting time by 60% with AI and Coupler.io at Right Percent

Gabe needed faster, smarter ways to analyze performance and make decisions while performance shifts were happening daily. His existing workflow with Supermetrics was slowing him down, making it difficult to keep pace with rapidly changing performance data across key activities:

• daily CPL monitoring

• creative fatigue analysis

• weekly stakeholder reporting

• forecasting

• test analysis

With high spend and many live ads, delays meant missed opportunities and wasted budget.

Using Coupler.io AI Integrations, Gabe connected Meta Ads (Facebook Ads and Instagram Ads) and Looker data directly to Claude for conversational analytics without pushing raw datasets into the LLM.

Coupler.io’s Analytical Engine provides Claude with the data structure (column names, data types, and sample rows) so the model understands what’s available. When Gabe asks a question in plain English, Claude generates a query and sends it to Coupler.io. The Analytical Engine then:

• executes the query

• runs calculations and aggregations

• validates outputs

• returns only the verified results back to Claude

Together, these workflows let Gabe move from manual spreadsheet work to repeatable, on-demand dynamic analysis while keeping accuracy and business context front and center.

1) Daily Performance Health Checks

Challenge: With $40,000+ in daily spend across 50+ live ads, Gabe needs to spot issues before they burn the budget.

Solution: He runs a daily CPL “account health” report in Claude and gets an immediate read on trends and direction.

Impact: What used to take manual stitching across sources now takes under 10 minutes.

2) Weekly Performance Reporting

Challenge: Stakeholder check-ins require clear performance narratives backed by reliable numbers.

Solution: Gabe uses templated reports saved as Claude artifacts that are easily updated, then refined with his strategic context.

Impact: “Rather than madly hacking data together, I can actually spend time strategically thinking about what’s going on.”

See example artifact here.

3) Ad Creative Fatigue Detection

Challenge: Performance degrades over time, but Meta can continue spending on yesterday’s winners. With high daily spend, one slipping ad can waste $5,000+ before it’s caught.

Solution: Gabe runs a 7 and 3 day rolling fatigue analysis to detect early downward trends—faster than static 7-day lookbacks.

Impact: He pauses declining ads sooner, preventing wasted spend and catching issues the platform doesn’t surface quickly.

See example artifact here.

4) Analysis & Optimization

Challenge: Controlled tests require careful interpretation of nuances, anomalies, edge cases, and parameters.

Solution: Gae writes the test context in a short Google Doc paragraph and feeds it into Claude which then provides an educated analysis and next-step recommendations.

Impact: He spot-checks totals against source data and updates his project instructions when data structure issues appear.

5) Reusable Analysis Library

Workflow: Gabe maintains a library of saved artifacts inside his Claude project, each one includes outputs and the logic behind them.

How it works:

• He saves high-value analyses as artifacts using a naming convention

• In new chats, he asks Claude to list project artifacts

• He reruns the analysis with fresh data instantly

Impact: Repeatable, scalable reporting without rebuilding queries from scratch.

See example artifact here.

6) Financial Forecasting for Stakeholders

Challenge: Executives need reliable “where will we land?” forecasting, usually dependent on complex spreadsheet models and constant manual refreshes.

Solution: Gabe created forecasting templates that project end-of-month and end-of-quarter performance from current spend and trends, runnable on demand.

Impact: Forecasting that used to take hours now happens in minutes and can be refreshed as conditions change.

7) Ad Hoc Lookback Analysis

Challenge: Meetings trigger questions like “What happened here?” Previously, this required spreadsheet wrangling and time-consuming manual data pulls.

Solution: Gabe writes the business context and hypotheses in plain language, then uses Claude to generate the analysis grounded in Coupler.io-queried data.

Impact: Structured performance data + unstructured business context produces analysis that’s both fast and decision-ready.

See example analysis here.

Because analyses that once took hours now take minutes, Gabe runs them far more frequently, often daily. He’s built a repeatable system spanning health checks, reporting, fatigue detection, test analysis, forecasting, and deep-dive lookbacks. He validates accuracy with domain expertise and spot-checking, using Claude Opus 4.5 with extended thinking enabled for more rigorous analysis.

Outcomes:

• ⏱ 60% time savings on data analysis and reporting

• 📊 Daily insights in minutes instead of hours

• 💰 Faster decisions on campaigns with $1M+ monthly spend

• 🔄 Scalable analysis that maintains quality as accounts grow

• 📈 Reliable forecasting for C-suite stakeholders