QWRY connects your order management, customer database, catalogue, supplier records, and logistics data automatically, without manual pipelines. Operational intelligence that used to take a team of engineers becomes a question you ask in plain English.
E-commerce and marketplace platforms generate rich data (orders, returns, reviews, supplier performance, logistics events) across systems that were never designed to talk to each other. Cross-system questions require engineering work every time.
Order management, CRM, catalogue, inventory, and supplier databases each hold part of the picture. A customer's full purchase history, return rate, and lifetime value requires joining four systems manually.
On B2B marketplaces, correlating seller order fulfilment with return rates, review scores, and dispute history requires joins that no single system supports. Seller risk surfaces only after damage is done.
Category trends, regional demand shifts, and seasonal signals exist in order data, but extracting them requires an analyst, a data engineer, and a pipeline that takes weeks to build and breaks on every schema change.
A schema change, a new seller onboarding flow, or a new returns category breaks the dashboard. The data team spends more time maintaining pipelines than building intelligence.
Each query joins orders, customers, catalogue, suppliers, and logistics data, with verified SQL behind every answer, inside your infrastructure.
Join purchase history, return rates, support interactions, and product reviews into a single customer view. Segment by cohort, geography, category preference, and churn risk, in one query.
Correlate seller fulfilment rates with return rates, review scores, and dispute frequency. Surface sellers whose quality issues are eroding category NPS before intervention becomes expensive.
Identify emerging demand signals, stockout patterns, and category growth trends from order and search data. Surface which categories need inventory investment before they lose the sale.
Join return records with order details, product attributes, seller, and logistics provider. Identify whether high return rates are driven by product quality, description mismatch, or delivery damage.
Correlate delivery SLA performance with seller, pincode, logistics partner, and order value. Identify the combinations driving the most customer complaints and late deliveries.
Reconcile gross merchandise value with net revenue, returns, commission, and settlement data across payment gateways and seller accounts. Automated, from source to ledger.
QWRY's engine samples order IDs, customer IDs, and product SKUs across your databases and discovers the joins automatically, with confidence scores. No mapping documents. No schema diagrams.
Hundreds of millions of order records, millions of customer profiles, thousands of sellers. QWRY's warehouse and query engine are built for the data volumes that e-commerce generates at production scale.
Natural language queries return verified SQL results. A category manager asking "which seller cohort has the highest return rate in electronics this quarter?" gets an answer in seconds, not a ticket to the data team.
GMV figures, return rate percentages, and seller rankings all carry their source SQL and execution trail. Your finance team and external auditors can independently verify every number the platform returns.
Customer playbooks and technical deep-dives from QWRY deployments in e-commerce and B2B marketplaces.
Inside the data unification playbook: cross-joining seller payout records, order management exports, and warehouse fulfilment logs, surfacing systematic discrepancies in 18 days.
Read the case studyHow qwry probabilistically resolves customer identity across systems that use (email, phone, cookie, loyalty card) without requiring a master data management project.
Read the deep-diveMost forecast models are trained on order history alone. The real signal (what customers browsed, what they returned, what they abandoned) lives in a different system and never makes it to the model.
Read the postA direct technical conversation with the founding team. No sales deck. Tell us about your data sources and ; we'll show you what becomes possible without a data engineering sprint.
Email goes directly to the founding team. First response within one business day.