Data Quality Of E-Commerce Products Results In Rs 5k Cr Loss
Study found that 14 per cent SKUs fail to meet an 80 per cent data quality threshold.
Chennai : Poor product data quality results in real and notional loss amounting to an annual loss of around Rs 5,000 crore in the e-commerce and quick commerce ecosystem.
Of this Rs 5,000 crore in lost revenue, Rs 2,000 crore is nominal, representing the gross margin that sellers and platforms would have earned on those transactions under normal conditions. This is the value of sales that do not happen — transactions that are suppressed, abandoned, or never initiated — because product information is incomplete, inaccurate, or inconsistently structured at the point of purchase, finds a study by GS1 India and Kanvic Consulting.
Further Rs 1,900 crore represents the real cost on the direct operational expense of processing returns that are attributable to inaccurate or misleading product information. This includes reverse logistics, handling, reprocessing, and write-offs.
The remaining economic impact of around Rs 1100 crore comes from the excess inventory holding costs arising from slower sell-through, courier overcharges triggered by incorrect weight and dimension data, compliance-related write-offs, and incremental customer service overhead. These costs are harder to isolate at the transaction level precisely because they are embedded within everyday operational metrics: inventory turnover ratios, courier reconciliation reports, customer service queues. Their relative invisibility does not diminish their scale — it explains why the problem has persisted for so long.
The study assessed 510 SKUs across eight leading platforms and found that 14 per cent fail to meet an 80 per cent data quality threshold — a benchmark aligned with common marketplace enforcement practices. Critically, 27 per cent of SKUs fail on completeness alone, and 23 per cent fail on accuracy. These are not small sellers failing to update their catalogues. Large brands, which achieved an average data quality score of 89 per cent, still saw 11 per cent of their SKUs fall below threshold. Scale and brand maturity, the data confirms, do not automatically translate into data quality governance. E
