What type of questions you can ask Kaiya?

These are all in the shape Kaiya Agent Mode expects: compound, multi-metric, multi-dimension, sometimes cross-table (Orders ↔ Products ↔ Channels), and they lead naturally to a plan of SQL → Python → summary.

1. Variance & contribution (what moved, when, who drove it)

• “How did Cancellation Rate change WoW in Q3, and which regions/products/channels explained the delta?”

• “From 2025-07 to 2025-08, break down the +1.8pp cancellation increase by Customer Segment and Marketing Program; flag low-volume segments.”

2. Cohorts & retention (who sticks, who churns, why)

• “By Tenure (months) cohorts, how does sensitivity to Delivery Lead Time change over time?”

• “For customers acquired via Paid Search in H1, what % reordered in 30/60/90 days vs. Organic?”

3. Anomaly detection (spikes, dips, outliers)

• “Detect weeks in Q3 where cancellations spiked >2σ above trend and show the drivers.”

• “Which 10 stores had abnormal return rates in Sept vs their own 3-month baseline?”

4. Correlation & association (what moves together vs. noise)

• “Are pre-payment orders less correlated with cancellations than pay-on-delivery, controlling for channel?”

• “Which factors are most associated with high cancellation — Delivery Lead Time, Reschedule Count, Age Group, or Income Bracket?”

5. Causal probes (is X likely driving Y?)

• “Using weather-delayed orders as an instrument, what’s the IV estimate of lead time on cancellations?”

• “Did the 10% shipping-fee increase cause the rise in cart abandons for Mobile users?”

6. Forecasts with intervals (what’s next, with confidence)

• “Forecast weekly cancellations for Q4 by channel with 80/95% bands; highlight channels above band.”

• “Project new-patient starts for NeuroDrug-X by payer segment for the next 6 weeks, using FY24 access shifts.”

7. Segmentation & clustering (meaningful groups)

• “Cluster branches on cancellation rate, lead time, reschedule count, and product mix; profile each cluster.”

• “Group customers by purchase frequency, discount sensitivity, and channel mix; label ‘high-risk churn’ segment.”

8. Benchmarking & cohorts (this group vs. peers)

• “Rank channels by total cancellations with confidence bands; surface top-10 outliers vs. target.”

• “Compare West vs. All Regions for Q3 delivery SLAs — gaps, variance, and top offending SKUs.”

9. What-if scenarios (change inputs, see outcome)

• “If average delivery lead time drops from 5 → 3 days for Marketplace orders, what’s the expected % drop in cancellations?”

• “What happens to Q4 revenue if we cap cancellations at 2% for Premium segment but leave others unchanged?”