Marketing
StandardPeriodic review
AI Analytics Agent
An assistive agent that turns your approved marketing data into scheduled, plain-language summaries and change alerts — so decisions are faster, while interpretation and strategy stay with people.
Request an architecture review AI readiness diagnosticIn short
An AI Analytics Agent pulls the marketing data you already trust — analytics, ad platforms, CRM — on a schedule, checks it is complete, computes the KPIs your team agreed on and writes a short, plain-language summary that a non-analyst can read. It flags notable changes against thresholds you set and answers questions over approved metrics. It is assistive by design: it summarises and surfaces, it does not decide budgets, claim causation or act on anomalies. It is for teams drowning in dashboards nobody reads. The safest first step is a short review of your data sources, agreed KPIs and reporting cadence.
Best for: Marketing lead / CMO · Business owner tracking marketing spend · Growth / performance manager
Typical context: Companies running several marketing channels · Teams with dashboards but no time to read them · Owners who want plain-language marketing reporting
When leadership usually needs this
There is no clarity on which channel actually drives results.
Dashboards exist but nobody reads them.
Weekly and monthly reports are assembled by hand.
A drop in performance is noticed too late.
Non-analysts cannot interpret the numbers.
Data lives in several tools that nobody reconciles.
Questions like "how did last month go?" take hours to answer.
Reporting is inconsistent between people and periods.
The team reacts to noise instead of real change.
What the agent actually does
- Pulls approved data from connected sources on a schedule.
- Checks the data is present and internally consistent before reporting.
- Computes the KPIs and goals your team agreed on — no invented metrics.
- Detects notable changes against thresholds you define.
- Drafts a concise, plain-language summary for humans to review.
- Answers questions over the approved metric set.
- Notes data gaps and tracking issues rather than papering over them.
Inputs and outputs
Inputs and required data
Events / triggers
Scheduled reporting runs (daily/weekly/monthly)On-demand questionsThreshold breaches
Source data
Web analytics (e.g. GA4)Ad platformsCRM outcomes where connected
Business rules
Agreed KPIs and goalsAlert thresholdsReporting cadence and recipients
System access
Read access to analytics and ad accountsLeast-privilege credentialsDefined metric definitions
Outputs and business actions
- A scheduled plain-language performance summary
- Change / anomaly flags against agreed thresholds
- Channel and campaign summaries
- Answers to questions over approved metrics
- A note of data gaps and tracking issues
- Consistent reporting across periods
How the workflow works
1
Pull data
The agent reads approved sources on the agreed schedule.
2
Validate
It checks the data is present and internally consistent.
3
Compute KPIs
It calculates the agreed metrics — nothing invented.
4
Detect change
It compares against thresholds to find notable movement.
5
Draft summary
It writes a concise plain-language report.
6
Human review
A person reviews and interprets before it is circulated.
7
Flag gaps
It records tracking issues instead of hiding them.
Human oversight
Automated
- Data pulls
- KPI computation
- Change detection
- Draft summaries
- Gap notes
Requires human approval
- Circulating a report externally
- Changing metric definitions or thresholds
- Any budget or strategy action
Escalation
- Unusual or ambiguous movements are flagged for a human to interpret, not acted upon.
Ownership: Interpretation, attribution judgement and every spend decision stay with people. The agent prepares the picture; the team decides what it means.
Systems and integrations
Commonly integrated
GA4 / web analyticsGoogle Ads / Meta AdsGoogle Sheets / Looker StudioEmail for delivery
Possible via API / webhook
CRM outcome data via APIAdditional ad platformsData warehouse / BigQuery
Requires technical assessment
Offline / in-store dataCustom attribution modelsRegulated or consent-restricted data
Qualitative business impact
Reporting a non-analyst can actually read
Notable changes surfaced sooner
Hours saved on manual report assembly
More consistent reporting across periods
Fewer reactions to noise, more to real change
Actual impact depends on process quality, data quality, integration scope, user adoption and governance.
When it is not suitable
Tracking is broken or untrustworthy — fix measurement first.
There are no agreed KPIs to report against.
You expect certain causal attribution or guaranteed insight.
You want autonomous budget changes — this agent does not decide.
There is essentially one channel and one simple report.
Risks, constraints and governance
Confident but misleading conclusions
It reports agreed metrics and flags change; it does not claim causation, and a human interprets before circulation.
Garbage-in from broken tracking
A data-readiness check precedes launch; the agent flags gaps rather than hiding them.
Over-trust in an automated summary
Summaries are drafts for human review; definitions and thresholds are explicit and owned.
Privacy / consent on analytics data
Read-only least-privilege access and a defined data scope set during the review.
Alternatives and simpler options
A well-built BI dashboard (Looker Studio / Power BI)
Better when: People will actively explore data themselves.
Native platform reports and alerts
Better when: One or two channels and simple needs.
A part-time analyst
Better when: Deep, bespoke interpretation matters more than cadence.
AI Analytics Agent vs a BI dashboard
| Dimension | AI Analytics agent | BI dashboard |
|---|---|---|
| Output | Plain-language summary + alerts | Interactive charts |
| Who reads it | Non-analysts, on schedule | People who explore data |
| Change detection | Proactive, threshold-based | You have to look |
| Interpretation | Human, always | Human, always |
| Best when | Nobody has time to read dashboards | The team wants to self-serve |
Implementation sequence
- Process discovery — what decisions the reporting should support.
- Data and tracking audit — sources, quality, metric definitions.
- Architecture design — KPIs, thresholds, cadence, recipients.
- Pilot scope — one report and one channel set first.
- Integration — connect analytics and ad sources (read-only).
- Testing — validate metrics against a known period.
- Launch — start the scheduled summaries.
- Monitoring and improvement — refine thresholds and definitions.
What affects scope and price
Number of data sources and accountsData quality and tracking readinessNumber and complexity of KPIsReporting cadence and audiencesDepth of change detectionDelivery format (email, Looker Studio, Sheets)Languages for the summaries
Exact scope and price are defined after a short architecture review.
Frequently asked questions
No. It is assistive: it summarises approved data and flags change. Every interpretation and spend decision stays with your team.
The analytics service sets up measurement, tracking and dashboards. The AI Analytics Agent is an ongoing layer that reads that data and reports it in plain language.
A dashboard waits for you to explore it. The agent proactively summarises on a schedule and flags notable change — useful when nobody has time to read charts.
It reports what changed and where; it does not claim certain causation. That interpretation is a human judgement it supports, not replaces.
Then measurement comes first. The agent flags data gaps rather than papering over them, and a data-readiness check precedes launch.
There is no fixed price — scope depends on sources, data quality, KPIs and cadence. Exact scope and price follow a short architecture review.
Turn your data into a report people read
We start with a short review of your data sources, agreed KPIs and reporting cadence. You get a clear scope and a safe first step — no full implementation is assumed before the review.
Request an architecture review AI readiness diagnostic