Marketing · Process breakdown
Pull the numbers on rules, let AI draft the narrative, and keep the budget decisions with a marketer who can read a chart.
Marketing campaign reporting is mostly copying. Every Monday someone exports numbers from the ad platforms, the email tool, and analytics, pastes them into a sheet, and writes three lines about what changed. Campaign performance reporting like that is a strong case for marketing reporting automation, because the inputs are structured and the steps never vary. AI only enters at the end, to draft the explanation, and even there a marketer decides what the numbers mean and what to do about them. Start by agreeing on the definitions before automating a single export.
A scheduled workflow collects spend, impressions, clicks, leads, and conversions from each platform through its connector or API and lands them in one table with the same definitions every week. No judgement is involved, so no AI is needed.
Rules compare the pulled totals with the platform dashboards, check date ranges and currencies, and flag gaps or duplicates. Most reporting arguments start with a data problem, not an interpretation problem.
The marketing dashboard and the campaign reporting template fill themselves from the reconciled table, so everyone looks at the same numbers. The layout is fixed; only the data changes.
AI writes the short summary of what moved, which campaigns drove it, and what is worth a look, working only from the figures in the report. Turning a table into readable sentences is a bounded task.
A marketer reads the draft, corrects context the numbers cannot show, such as a launch, a price change, or a tracking issue, and sends it. The report goes out under their name.
Shifting budget, pausing a campaign, or starting a test is a decision about money and priorities. The report informs it; a person makes it.
Automate the data pull and the reconciliation for one channel and run it next to the manual report for a month. Count the discrepancies between the two, note how long the manual version still took, and only then add the AI narrative for that channel.
Automating the paste before agreeing what a lead is. A dashboard that updates itself on inconsistent definitions produces confident wrong numbers every week.
A workflow tool or reporting connector that can call your platforms on a schedule, a place to store the combined data, such as a warehouse or a well-structured sheet, and a dashboard on top of it. Many teams already own most of this inside their analytics or marketing platform. Add an AI step for the narrative only after the numbers are trustworthy, because a fluent summary of wrong data is worse than no summary.
It can describe what changed and suggest likely reasons from the data it sees, such as a spend shift or a drop in click-through rate on one campaign. It cannot see the price change, the competitor launch, or the broken tracking tag. Treat AI explanations as hypotheses for the marketer to confirm or reject, and label them that way in the report so readers do not mistake a guess for a finding.
Reconcile them against the platform totals on every run, alert on gaps, and keep the manual report going for a period so you can compare. Most errors come from date ranges, time zones, currency, and attribution settings, so check those explicitly. Publish the metric definitions next to the dashboard. When someone questions a number, the answer should be a lookup, not a debate.
Illustrative workflow guidance by Arcgent. Each business needs its own assessment. No integration or savings claim has been verified for your systems.
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