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.

Analyzes CRM % trends over time with multiple filters (date, SKU, user type, payment mode, UTM, campaigns, and order status) — used to identify key drivers and optimize CRM performance.

Detailed CRM breakdown across products, variants, campaigns, platforms, and user types — with key metrics (orders, revenue, DRR, AOV, AQV, customers, ACV) tracked over time.

Analyzes inventory quantity and value across shelf life and ageing buckets by warehouse — used to spot at-risk stock early and tighten inventory management.

Detailed inventory breakdown by warehouse, SKU, batch code, and ageing bucket with quantity and value — enables precise inventory tracking and control.

Tracks Days of Holding (DOH) across warehouses and SKUs — surfaces stock availability, overstock, and replenishment needs at a glance.

Tracks fill rate across warehouses to optimize dispatch from the nearest location — drives faster delivery and a better customer experience.

Fill rate at SKU + warehouse level — identifies low-performing locations, surfaces order distribution patterns, and accelerates stock movement to lift dispatch efficiency.

SKU + warehouse-level visibility of DRR, current stock, DOH, and PO pipeline (open / approved / in-transit / delivered) — drives accurate, timely inventory replenishment.

PO lifecycle visibility across SKUs and warehouses — ordered, dispatched, pending, GRN units, fill rate, and short quantities — to track fulfillment performance end-to-end.

Tracks PO dispatch delays by days and warehouse — used to identify bottlenecks and ensure dispatch SLAs hold.

Monitors in-transit PO units by days and warehouse — flags delivery SLA breaches, surfaces logistics delays, and ensures TAT holds.

Tracks GRN-pending units by days and warehouse after delivery — ensures timely stock inwarding and improves inventory accuracy.

Tracks projected demand against actual sales — measures achievement levels, isolates underperforming and overperforming SKUs, and refines future demand planning.

Day-level view of website sales across products, campaigns, customer types, and time (day / hour) — tracks orders, units, revenue, AOV, DRR, and customers to surface trends and growth opportunities.

Product-level performance over time using DRR, AOV, and average selling price — identifies which products are growing, which are declining, and where to act on product strategy.

SKU-level availability and how many days each product was unavailable (out-of-stock or low demand) — quantifies the revenue lost and prioritizes product availability fixes.

Day-on-day performance across Google, Meta, and CRM — analyzes revenue, AOV, and campaign-level performance to identify which sources are driving growth and which need optimization.

Tracks channel-wise conversion rate (CR %) and sessions across UTM sources — pinpoints which channels are driving better conversions and engagement.

Day-on-day pending orders against SLA timelines across warehouses — surfaces delayed dispatches, identifies bottlenecks, and improves SLA adherence.

SKU + warehouse-wise pending units — identifies which products are stuck in dispatch and for how long, helping prioritize delayed SKUs.

Tracks orders where the AWB number is not yet assigned — flags processing delays so shipments can move quickly to dispatch without hurting delivery timelines.

Tracks the full discount journey from MRP to final selling price — including MRP discount, coupon discount, and net discount at SKU, order, category, variant, and channel level.

Month-on-month and year-on-year growth or decline across SKUs, product categories, sub-categories, and variants — identifies high performers and laggards for product strategy decisions.

Customer purchase journey from 1st to 6th order — product / variant selection, time gap (TAT) between orders, and recency of last purchase — to drive precise upsell and cross-sell at the right lifecycle stage.

End-to-end retail operations from PO creation to final delivery — total ordered units, fulfilled units, fill rate across warehouses, products, and distributors, plus dispatch and delivery TAT to monitor distributor performance.