Get The Recommendation, Not Just The Report
We detect the change, surface the likely cause, and deliver a recommended action wherever your team already works.
Automated insights are a layer that detects a significant change in a key metric, surfaces a likely cause, and generates a recommended action automatically, delivered to wherever your team already works.
What is Automated Insights?
Automated insights are a layer that detects a significant change in a key metric, surfaces a likely cause, and generates a recommended action automatically, delivered to wherever your team already works. Turning a number into a decision is the one step that stays manual even after everything upstream gets automated - a metric moves, someone eventually notices, someone investigates, someone decides.
Skip it and that gap between a metric moving and someone deciding what to do about it stays entirely manual, no matter how good the detection or dashboards underneath it already are.
It's different from marketing intelligence, which aggregates and pattern-matches across channels, and from a BI tool's built-in alerting, which typically stops at telling you a threshold was crossed rather than adding a likely cause and a recommended next step.
Common problems we solve
Detection without decisions
A metric moves and gets flagged, but someone still has to manually work out what it means.
Changes caught too late
Significant shifts only surface at the next scheduled reporting cycle, not when they happen.
Alerts with no next step
Built-in BI alerting tells you a threshold was crossed but nothing about what to do next.
Recurring patterns rediscovered repeatedly
The same dip tied to the same recurring event gets investigated fresh every time it happens.
Generic alert sensitivity
Default thresholds flag routine variance as if it were a genuine, actionable change.
Everything covered in this service
Automated Change Detection
Monitoring key metrics for statistically significant shifts, with thresholds tuned to your actual variance.
Root-Cause Surfacing
Correlating a detected change against related data to surface a likely cause, flagged with a confidence level.
Recommended-Action Generation
A suggested next step attached to each detected change, grounded in what similar past changes actually responded to.
Multi-Channel Delivery
Delivered via dashboard, email, or Slack depending on urgency, routed to the person actually positioned to act.
Our optimization framework
Threshold Setup
We identify which metrics warrant automated monitoring and tune detection thresholds against your actual historical variance.
Root-Cause Configuration
Related data sources are connected so a detected change can be correlated against likely causes automatically.
Recommendation Logic
Recommended actions are grounded in what similar past changes in your own data actually responded to, not a generic playbook.
Delivery Routing
Each alert type gets routed to the channel and person best positioned to act on it - dashboard, email, or Slack.
Feedback Loop
We track which recommendations get acted on and which get dismissed, and use that to improve future recommendation accuracy.
What actually changes
| Before | After |
|---|---|
| A metric moves and someone eventually notices at the next review | A significant change is detected and flagged as it happens |
| An alert tells you something changed with no context | A likely cause and a recommended action arrive with the alert |
| The same recurring pattern gets re-investigated every time | A recurring pattern is recognised automatically going forward |
| Generic alert thresholds flag routine noise | Thresholds tuned to your own historical variance |
What we report on
Time to flag a significant change
Measured from when a metric shifts to when the alert reaches the right person.
Recommendation acceptance rate
Tracked by how often a suggested action gets acted on versus dismissed.
Root-cause accuracy
Reviewed against how often the surfaced cause matches what actually happened.
Recurring pattern recognition
How reliably a repeating pattern gets flagged automatically after its first occurrence.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
Recommendations are flagged with a confidence level instead of presented as certain, which matches how much trust an automated suggestion should actually get.
How Automated Insights connects with AI search
Automated Insights ├── Marketing Intelligence ├── Predictive Analytics ├── AI-Powered Reporting └── Process Automation
Marketing Intelligence
Aggregates and pattern-matches across channels, feeding the detection this layer acts on.
Predictive Analytics
Forecasts what is likely to happen next, complementing this layer's focus on what just changed.
AI-Powered Reporting
Delivers the underlying numbers this layer monitors into the reports stakeholders already read.
Process Automation
Can execute a recommended action directly once a team is comfortable automating the response, not just the alert.
Related AI Analytics services
Reports That Explain the Number, Not Just Show It
AI-generated narrative summaries layered on your dashboards, so reporting time goes to decisions instead of the write-up.
AI AnalyticsCatch Cross-Channel Patterns Before Your Next Review
We aggregate every channel and surface what's actually shifting in your marketing performance, before it shows up in a quarterly review.
Forecasts based on your actual historical data
Predictive models for revenue, churn, and campaign performance, validated against your own historical outcomes rather than a generic template.
Find the Segments Hiding in Data You Already Have
AI-assisted analysis that turns the data you already collect into segments and decisions, not a bigger spreadsheet.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
Treat it as a well-informed suggestion, not a directive - we attach a confidence level to each one for that reason. Higher-confidence recommendations are grounded in patterns that repeated reliably in your own historical data; lower-confidence ones are flagged as worth a human look before acting.
No - root-cause surfacing works by correlation, and correlation isn't always causation, especially for a genuinely novel event. What we guarantee is that the surfaced cause is the most statistically plausible one given the connected data, clearly flagged as a hypothesis worth checking, not a certainty.
Most built-in BI alerting just tells you a threshold was crossed. This adds the next two steps - a likely cause and a recommended action - so the alert itself is actionable instead of just a notification that something changed.
That feedback gets tracked and folded into the recommendation logic, which is specifically why the feedback loop exists - to make dismissed or incorrect recommendations improve future accuracy rather than repeat the same miss indefinitely.
Any metric with reliable historical data behind it - revenue, conversion rate, churn indicators, spend efficiency, and similar. We confirm exact scope during the metric and threshold setup step.
Depends on how frequently the underlying data updates, but for most connected sources, detection runs close to real time, with delivery routed based on the urgency you configure for that metric.
Getting alerts that tell you something changed but not what to do about it?
We'll add root-cause surfacing and recommended actions to the monitoring you already have.
Ready to get started?
We usually reply within 24 hours.
Automated Insights, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Change Detection | Ongoing monitoring of key metrics with thresholds tuned to your data |
| Root-Cause Analysis | Automated correlation against related data to surface a likely cause |
| Recommended Actions | A suggested next step generated alongside each significant change |
| Delivery & Feedback Loop | Alerts routed to the right channel, with feedback tracked to improve future recommendations |