"Ask a question, get a chart" doesn't cut it.

If you want results you can trust and repeat, “ask a question, get a chart” doesn't cut it.

The workflow that gets you actionable insights needs good assumptions, branching approaches, and validation loops.

1) Pick a structured attribution analysis method (then layer if needed)

Choose the right method from a diverse toolkit:

* Metric decomposition (top-down): break a KPI into drivers (e.g., Revenue = Traffic × Conversion × AOV) and quantify contribution.

* Dimensional slice-and-dice: localize where the change happened (geo, device, channel, segment), then drill down.

* Driver tree / causal chain: trace breakpoints through dependencies (sessions → conversion → orders → revenue).

* Change-point / anomaly analysis: find when the shift started; align to launches/incidents; compare pre/post distributions.

* Funnels / cohorts / baselines: use when the metric is driven by conversion or retention, or when seasonality matters.

2) Multi-agent planning before writing queries

The platform uses multiple parallel agents to propose different plans (strategy, data audit, attribution, pipeline design), then reviews them to select the most robust plan.

3) Build a pipeline (dbt-backed) instead of one-off analysis

The agent creates a modular data pipeline:

* cleaning + standardization

* outlier detection / anomaly checks

* multiple analysis branches if needed

This keeps the analysis reproducible and operational.

4) Validation loops

Before producing insights, the agent checks:

* deltas reconcile (explained vs observed)

* grain/dimensionality is correct (no join explosions)

* no unexpected nulls/missingness

* results are stable to window/segment tweaks

* outputs align with business logic

If checks fail, the agent loops back, adjusts, and reruns.

5) Insights + viz, report assumptions and further suggestions

After executing and validating the pipeline, the agent generates:

* quantified drivers + supporting evidence

* visuals appropriate to the method (waterfall, ranked deltas, funnel comparison, cohort curves)
* report of all assumptions used (column & metric definitions, cleaning done)

* suggestions for further analysis

6) Store logic + schedule it (recurring analysis)

Finally, the platform gives you:

* business logic: dbt project, pipeline code, semantic layer file

* insights, viz, validations, and assumptions

* all data output at every step of the pipeline, available for download

* scheduling/triggers available if necessary

That’s how we handle complex recurring analysis at Yorph: structured methods + branching pipelines + validation loops, not one-off answers.