The recommendation, delivered alongside the report
Automated change detection, root-cause surfacing, and recommended-action generation delivered wherever your team already works, not buried in a dashboard nobody opens.
The last step in most analytics workflows - turning a number into a decision - is the one that stays manual even after everything upstream gets automated. A metric moves, someone notices eventually, someone investigates the likely cause, someone decides what to do about it. Automated insight generation closes that gap by pairing each detected change with a plausible cause and a recommended action, delivered automatically instead of waiting for someone to open a dashboard.
It works alongside the reporting and analytics you already have - it's the layer that turns 'this number moved' into 'here's the likely reason and here's what we'd suggest doing about it', delivered to Slack, email, or a dashboard rather than requiring someone to go looking for it.
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 we bring to this
Automated Change Detection
- Monitoring key metrics for statistically significant shifts
- Thresholds tuned to your actual variance, not generic defaults
Root-Cause Surfacing
- Correlating a detected change against related data to surface a likely cause
- Flagged with a confidence level, not stated as certain
Recommended-Action Generation
- A suggested next step attached to each detected change
- Actions 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, not a shared inbox
Scope of work
| Area | What's included |
|---|---|
| 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 |
The engagement, step by step
Metric & Threshold Setup
We identify which metrics warrant automated monitoring and tune detection thresholds against your actual historical variance.
Root-Cause Model 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 & Iteration
We track which recommendations get acted on and which get dismissed, and use that to improve future recommendation accuracy.
Where this fits
- A significant drop in conversion rate gets flagged with a likely cause and a suggested fix before the weekly reporting meeting
- A sudden spend spike on one channel gets surfaced to the person managing that budget within the hour, not at month-end
- A recurring pattern - a metric that dips every time a specific event happens - gets surfaced once and then recognised automatically going forward
Who needs this
Teams that only review metrics on a fixed reporting cadence
If something significant happens between reporting cycles, this catches it before the next scheduled review.
Businesses with the data and dashboards already in place but no action layer on top
If detection already exists but someone still has to manually decide what it means, this is the missing layer.
What changes for you
- Significant changes get flagged and explained before the next scheduled reporting cycle, not after
- A recommended action arrives with the alert instead of requiring a separate investigation
- Recommendation accuracy improves over time as the feedback loop tracks what actually gets acted on
Why work with EASI7 on this
- We tune detection thresholds against your actual data before turning anything on, rather than shipping default sensitivity settings
- Recommendations are flagged with a confidence level instead of presented as certain, which matches how much trust an automated suggestion should actually get
Other services in this area
Reports that explain the number, not just show it
AI-generated narrative summaries and anomaly explanations layered onto your existing dashboards, so reporting time goes to the decision instead of the write-up.
Patterns across channels a human would take days to find
Cross-channel data aggregation and AI-assisted pattern detection that surfaces 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.
Segments and patterns pulled from data you already have
AI-assisted analysis of behavioural, transactional, and support data that turns what you already collect into segments and decisions, not just a bigger spreadsheet.
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.