Power BI

Tracks the RTO orders trend, monitors RTO percentage against total orders, analyzes delivery timeline impact (0–3, 4–7, 7+ days), identifies high-RTO tiers, and evaluates RTO timing (Before / On / After EDD) — used to reduce RTO and improve delivery efficiency.

Tracks RTO % across COD vs Prepaid, BAU vs Sale, and monthly trends — used to identify how payment behavior affects RTO and to bring it down.

Highlights the top RTO-contributing SKUs so the team can isolate problem products and minimize losses driven by individual items.

Evaluates geo-level RTO patterns (pincode, city, state) across COD and prepaid orders — used to detect high-risk areas and tailor delivery strategy by region.

Analyzes RTO across order value buckets to identify which price ranges have higher return rates and where to tighten policies.

Tracks RTO % by pickup location (warehouse) and city tier — surfaces operational and regional risk concentrations across the dispatch network.

Compares RTO performance across courier partners using RTO % and TAT — used to negotiate, route smarter, and hold partners accountable on returns.

Analyzes order quantum across TAT days from order to delivery and RTO — used to identify delays and optimize delivery timelines end-to-end.

Tracks how many orders fall under each NDR (Non-Delivery Report) attempt and the corresponding RTO percentage — used to identify risk levels and intervene early.

Identifies damage, loss, and disposal events across warehouse, SKU, and courier — quantifies the GMV impact and helps reduce operational losses.

Analyzes order volume across TAT days and delivery stages, broken down by state — used to spot regional delays and improve delivery efficiency state by state.

Analyzes order status distribution and percentages — delivered, cancelled, RTO, destroyed — to track performance across the funnel and identify where losses happen.

Analyzes order distribution before and after EDD (Estimated Delivery Date) across delivery zones — used to identify delays and improve overall delivery performance.

Analyzes order distribution across dispatch TAT buckets by warehouse — used to identify slow dispatch points and improve dispatch efficiency.

Analyzes customer retention cohorts, repeat purchase behavior, and AOV / AOQ trends — used to design retention strategies and measure their impact.

Tracks cohort-wise retention % month-on-month — pinpoints drop-off points and enables targeted interventions to improve customer retention over time.

Tracks customer acquisition count and percentage by state — used to understand which regions are contributing most to growth and where to invest next.

Analyzes SKU-wise customer lifetime value across time buckets — used to identify high-value products and shape product strategy around them.

Analyzes complaints across categories, products, and issue types alongside complaint ratios — used to identify root causes and improve the customer experience.

Analyzes month-on-month complaint trends, new vs existing customer split, complaint ratio, and post-complaint purchase behavior — measures the customer impact of complaints over time.

D2C CRM performance analysis built on Power BI — covers CRM agent throughput, conversion, and revenue impact across the direct-to-consumer pipeline.