How to Automate Marketing Reporting with AI: Daily Digests, Zero Effort

A fully automated daily digest combining GA4 traffic, Stripe revenue, and campaign performance — delivered to Slack before standup, with a human-readable summary.

The Make scenario

Scheduled daily trigger at 8am. Step 1: pull yesterday’s metrics from GA4, Stripe, and your ad platforms via their respective Make modules. Step 2: aggregate the data into a single JSON payload. Step 3: ChatGPT module summarizes — “yesterday’s traffic was X with Y% week-over-week change, revenue was Z, the top performing campaign was…” Step 4: post the formatted digest to a Slack channel.

What stays human

Decisions on what to do with the data. The automation reports; humans react.

AI Solution Overview

Automating marketing reporting with AI eliminates the manual process of collecting, compiling, and sharing performance data across platforms. By integrating analytics, revenue, and campaign metrics into a single daily digest, teams can focus on interpreting results and making decisions rather than spending time on repetitive data gathering. This workflow leverages Make (formerly Integromat) for orchestration, connects to GA4, Stripe, and ad platforms via their APIs, and uses ChatGPT for natural language summarization. The result: a concise, actionable report delivered to Slack before the workday begins, with zero ongoing manual effort required.

Step-by-Step Automation Breakdown

Step 1: Schedule and Trigger the Workflow

• Tool: Make (Scenario Scheduler)
• Action: Set a daily trigger for 8:00am local time. This initiates the workflow automatically, ensuring that the latest data is gathered and reported before the team’s daily standup.

Step 2: Extract Data from Core Sources

• Tool: Make integrations for GA4, Stripe, and Ad Platforms (Google Ads, Facebook Ads, etc.)
• Action: For each source, configure modules to fetch the previous day’s key metrics:
  • GA4: Sessions, users, bounce rate, goal completions, week-over-week change.
  • Stripe: Total revenue, number of transactions, refunds, new subscriptions.
  • Ad Platforms: Spend, impressions, clicks, conversions, top-performing campaign.

  Use date filters to ensure only yesterday’s data is pulled. Each module outputs structured data for aggregation.

Step 3: Aggregate and Structure the Data

• Tool: Make (JSON Aggregator/Composer modules)
• Action: Combine the raw metrics from all sources into a single JSON object. This object provides a unified view of traffic, revenue, and campaign performance, making it ready for summarization.

Step 4: Generate a Human-Readable Summary

• Tool: Make ChatGPT Module (OpenAI integration)
• Action: Pass the aggregated JSON to the ChatGPT module with a prompt such as: “Summarize this data for a marketing team. Highlight traffic, revenue, and campaign performance. Note any significant changes or anomalies.” The output is a concise, plain-English summary, tailored for non-technical stakeholders.

Step 5: Deliver the Digest to Slack

• Tool: Make Slack Module
• Action: Format the summary and key metrics into a Slack message. Post this message to the designated channel (e.g., #marketing-daily-digest) before the team’s standup. Optionally, attach the raw JSON or a CSV for deeper inspection.

Realistic Time and Effort Savings

Manual daily reporting typically requires 30-60 minutes per day, depending on the number of platforms and complexity of metrics. This includes logging into each service, exporting data, compiling spreadsheets, and writing summaries. By automating the process:

• Initial setup: Expect 2-4 hours to configure Make scenarios, connect APIs, and test the workflow. More if custom metrics or complex data transformations are needed.
• Ongoing effort: Near zero. Once set, the workflow runs unattended. Occasional maintenance may be required if APIs change or new data sources are added.
• Team impact: Frees up 2-5 hours per week per analyst or marketer. Reduces context switching and reporting errors. Ensures everyone starts the day with the same, up-to-date information.
• Quality of insights: AI-generated summaries surface trends and anomalies that might be missed in manual reports. The standardized format makes it easier to compare performance over time.

This automation does not replace strategic analysis or decision-making. It removes the mechanical work of data collection and reporting, allowing humans to focus on interpreting results and planning actions. For teams handling multiple brands or markets, the time savings and consistency scale with each additional workflow instance.