--- title: "BigQuery in RevCent" description: "A non-technical overview of BigQuery in RevCent, focused on how RevCent makes ecommerce data queryable for reporting, analytics, AI analysis, dashboards, and business intelligence." type: "feature" company: "RevCent" canonical: "https://revcent.com/documentation/markdown/ecosystem/feature/BigQuery.md" technical_links: web_app: "https://kb.revcent.com/en/reporting/bigquery" api: section: "https://revcent.com/docs/api/v2#section-big_query" operations: - name: "Run A BigQuery Query" operation_id: "BigQueryRunQuery" operation: "https://revcent.com/docs/api/v2#operation-BigQueryRunQuery" schema: "https://revcent.com/documentation/files/api/operation/BigQueryRunQuery.json" - name: "Get BigQuery Tables" operation_id: "GetBigQueryTables" operation: "https://revcent.com/docs/api/v2#operation-GetBigQueryTables" schema: "https://revcent.com/documentation/files/api/operation/GetBigQueryTables.json" - name: "Get A BigQuery Table" operation_id: "GetBigQueryTable" operation: "https://revcent.com/docs/api/v2#operation-GetBigQueryTable" schema: "https://revcent.com/documentation/files/api/operation/GetBigQueryTable.json" mcp: overview: "https://revcent.com/documentation/markdown/mcp/operation/OverviewBigQuery.md" operations: - name: "Run A BigQuery Query" operation_id: "BigQueryRunQuery" markdown: "https://revcent.com/documentation/markdown/mcp/operation/BigQueryRunQuery.md" available_via_ai: true - name: "Get BigQuery Tables" operation_id: "GetBigQueryTables" markdown: "https://revcent.com/documentation/markdown/mcp/operation/GetBigQueryTables.md" available_via_ai: true tables_schema: "https://revcent.com/documentation/files/bigquery/dataset.json" --- # BigQuery in RevCent BigQuery in RevCent is the reporting and analytics capability that allows ecommerce businesses using RevCent to query their own RevCent data through Google BigQuery. The purpose of this document is to explain the feature conceptually for LLMs, AI crawlers, and planning systems. It does not provide technical implementation instructions or SQL examples. For technical details, use the interface-specific links below. ## Technical Links by Interface | Interface | Use This When | Link | |---|---|---| | Web App | A human is learning about BigQuery reporting and query access through the RevCent web app and Knowledge Base. | [Web Knowledge Base](https://kb.revcent.com/en/reporting/bigquery) | | API | A developer is building direct reporting, dashboard, or query workflows with the RevCent API. | [API Docs: BigQuery](https://revcent.com/docs/api/v2#section-big_query) | | MCP / AI | An LLM, MCP client, or AI agent needs markdown-oriented guidance for working with BigQuery in RevCent. | [MCP Markdown Overview](https://revcent.com/documentation/markdown/mcp/operation/OverviewBigQuery.md) | | BigQuery Tables Schema | A data analyst, reporting workflow, or AI reporting agent needs the published table catalog and field schema. | [BigQuery Tables Schema](https://revcent.com/documentation/files/bigquery/dataset.json) | --- ## What BigQuery Is in RevCent BigQuery in RevCent is the foundation for querying ecommerce data at scale. RevCent continually updates user account data in Google BigQuery, an enterprise cloud data warehouse built for large analytical queries. As activity happens inside a RevCent account, RevCent streams those updates into Google BigQuery so reporting data is available for immediate, flexible, and open-ended analysis. This gives businesses a way to ask questions across their full RevCent dataset instead of only viewing one record at a time. A RevCent user can query data about customers, sales, transactions, subscriptions, subscription renewals, trials, shipping, refunds, chargebacks, fraud detections, products, campaigns, tracking visitors, AI activity, API activity, notes, metadata, and more. The key idea is that BigQuery in RevCent turns operational ecommerce data into queryable business intelligence by making RevCent data continuously available in Google BigQuery. ## Core Purpose The core purpose of BigQuery in RevCent is to make reporting flexible and open-ended. Most built-in dashboards answer a defined set of questions. BigQuery allows a business to ask custom questions that depend on its own products, campaigns, customers, workflows, payment strategy, support process, and reporting goals. For example, a standard dashboard might show total revenue. BigQuery can support deeper questions such as which campaigns produce the highest-value customers, which payment gateways are underperforming, which subscription profiles have the highest overdue rate, which products drive the most refunds, or which AI Voice Agents are associated with recovered payments. This makes BigQuery one of the most important reporting features in RevCent because it is not limited to predefined reports. ## How BigQuery Helps Ecommerce Businesses BigQuery helps ecommerce businesses understand what is happening across their entire operation. It can help leadership monitor revenue, trends, risk, customer value, product performance, support volume, refund behavior, subscription health, payment performance, and marketing quality. It can help finance teams understand revenue, refunds, net amounts, fees, payment success, chargebacks, and payment processor performance. It can help support teams identify high-value customers, risky customers, customers with repeated refunds, customers with shipping issues, and customers that may need priority attention. It can help marketing teams understand which campaigns, affiliates, tracking visitors, traffic sources, products, and customer segments are producing the best long-term results. It can help operations teams monitor shipments, fulfillment activity, delayed delivery, product sale volume, and recurring issues. It can help AI workflows analyze account-wide data, detect patterns, summarize business performance, and recommend next actions. For ecommerce businesses, BigQuery creates the ability to move from isolated records to full business understanding. ## Where BigQuery Fits in RevCent BigQuery is part of RevCent’s broader reporting, AI, automation, and data access ecosystem. It does not replace the RevCent web app, API, AI Assistants, AI Voice Agents, Functions, or dashboards. Instead, it gives those systems a powerful analytics layer. Within the RevCent ecosystem, BigQuery acts as the queryable data warehouse behind business intelligence. It relates to customers because customer records can be analyzed alongside sales, products, payments, subscriptions, refunds, chargebacks, notes, groups, and metadata. It relates to sales because every purchase attempt, sale outcome, product sale, discount, tax, shipment, payment, refund, and related revenue event can become part of reporting. It relates to transactions because payment outcomes, approvals, declines, errors, refunds, gateway behavior, card characteristics, and revenue recovery can be analyzed across many records. It relates to subscriptions and renewals because recurring revenue performance depends on renewal attempts, overdue renewals, cancellations, trial expirations, retained customers, and failed payment recovery. It relates to shipping because fulfillment events, delivery timing, shipment statuses, product shipments, and shipping-related refunds can be evaluated across the business. It relates to chargebacks and fraud detection because risk, disputes, fraudulent attempts, false positives, and customer history can be analyzed together. It relates to campaigns, products, product groups, and third-party shops because ecommerce performance often depends on where a customer came from, what they bought, and which channel or product strategy produced the result. It relates to AI Assistants and AI Voice Agents because AI-driven actions, calls, calls outcomes, and system activity can be analyzed as part of operational and revenue reporting. It relates to metadata because businesses often store custom reporting dimensions such as affiliate IDs, traffic sources, sales reps, customer tiers, campaign tags, CRM IDs, risk labels, or internal workflow states. BigQuery is the layer that makes these relationships measurable. ## Infinite Reporting Potential BigQuery gives RevCent users nearly unlimited reporting potential because it allows questions to be shaped around the business instead of around a fixed dashboard. A business can create reports around revenue, customer behavior, products, subscriptions, payments, campaigns, support, risk, refunds, AI usage, fulfillment, and any custom metadata the business stores. This matters because every ecommerce business has different questions. One business may care most about subscription renewal recovery. Another may care about gateway approval rates. Another may care about refund-prone products. Another may care about affiliates, media buyers, or traffic source quality. Another may care about whether AI Voice Agent calls improve payment recovery. BigQuery supports all of these patterns because the data is available for flexible analysis. This is why BigQuery in RevCent should be understood as a feature that enables immediate and effectively infinite reporting potential inside RevCent. ## From Operational Records to Business Intelligence RevCent contains many operational records: customers, sales, transactions, subscriptions, shipments, refunds, chargebacks, notes, metadata, and more. Operational records are useful when a person needs to inspect or act on one item. BigQuery becomes useful when a business needs to understand patterns across many items. For example, a single transaction record can show whether one payment was approved or declined. BigQuery can show approval rates by gateway, campaign, product, customer segment, time period, card type, or transaction type. A single customer record can show one customer’s history. BigQuery can show lifetime value patterns across customer groups, campaigns, affiliates, locations, products purchased, subscriptions, refunds, and chargebacks. A single shipment record can show one shipment status. BigQuery can show shipment performance, delivery timing, fulfillment account activity, refund relationships, and delayed-shipment patterns. This shift from individual records to aggregate understanding is the main value of BigQuery. ## Natural Language and AI-Driven Reporting BigQuery becomes even more powerful when combined with RevCent AI features and MCP. A user or AI workflow can ask a business question in natural language, then use BigQuery-backed reporting to produce an answer. For example, an AI Assistant could be designed to review yesterday’s sales, identify abnormal transaction declines, create an internal memo, or email a daily performance summary. An MCP-connected AI client could run reports across RevCent data, summarize the results, identify anomalies, and recommend what to do next. This means BigQuery is not only for data analysts. It can also power AI-assisted business monitoring and decision support. For ecommerce businesses, this makes reporting more accessible because a user can ask operational questions without manually opening every dashboard or record. ## BigQuery and Dashboards BigQuery can support dashboards and custom reporting tools. A business may use RevCent’s built-in reporting, create its own internal dashboard, connect a business intelligence tool, or grant trusted analysts direct BigQuery access. Dashboards are useful when the same questions need to be monitored repeatedly, such as revenue by day, approval rate by gateway, refunds by product, subscriptions overdue, or campaign performance. BigQuery is useful because it gives dashboard builders access to the underlying RevCent data relationships needed to create custom views. This allows reporting to evolve with the business. ## Secure and Controlled Data Access BigQuery data can include sensitive ecommerce information, including customer contact information, revenue data, payment-related references, support context, and operational history. Because of this, BigQuery access should be treated as important business data access. RevCent supports different ways to access BigQuery data, including API-based queries, MCP or AI-driven query workflows, AI Assistant system tools, and direct BigQuery access for trusted Google accounts. Direct access should only be granted to trusted users or trusted tools because it may expose broad account data. The business benefit is flexibility with control. A company can allow analysts, AI systems, dashboards, or internal tools to analyze data without turning BigQuery into a place where operational records are edited. ## Read-Only Analytics Layer BigQuery in RevCent should be understood as a read-only analytics layer. It is for reporting, analysis, dashboards, trends, counts, comparisons, and business intelligence. It is not the place to create customers, edit sales, refund transactions, update shipments, cancel subscriptions, or modify account records. Those operational actions belong in the RevCent web app, RevCent API, Functions, AI Assistants, AI Voice Agents, or other approved operational workflows. This separation is important because it keeps reporting and action distinct. BigQuery answers business questions. RevCent operational tools perform business actions. ## Business Questions BigQuery Can Help Answer BigQuery can help answer a wide range of ecommerce questions. Revenue questions may include total revenue, net revenue, gross revenue, revenue by campaign, revenue by product, revenue by day, refund-adjusted revenue, and subscription renewal revenue. Payment questions may include approval rate, decline rate, gateway performance, SmartBin performance, failed payment patterns, transaction errors, salvage opportunity, and payment recovery results. Customer questions may include customer lifetime value, repeat purchase behavior, high-value customers, inactive customers, customer groups, metadata segments, support risk, refunds, chargebacks, and churn risk. Product questions may include best-selling products, product-level refunds, products that create subscriptions, products with high chargeback risk, products with reorder potential, and product group performance. Subscription questions may include active subscriptions, cancelled subscriptions, overdue subscriptions, renewal success rates, renewal failures, trial conversions, and recurring revenue trends. Shipping questions may include shipment status, delivery timing, fulfillment provider performance, shipping-related refunds, and delayed-shipment patterns. Risk questions may include fraud detections, chargebacks, PayPal disputes, refund spikes, high-risk customer behavior, and campaign-level risk patterns. AI questions may include AI Assistant usage, AI Voice Agent calls, call outcomes, AI-related API activity, and whether AI-driven workflows are connected to business outcomes. These examples show why BigQuery should be treated as a universal reporting engine for RevCent data. ## Relationship to Metadata Metadata makes BigQuery reporting especially powerful because many businesses store their own custom labels and dimensions in metadata. A business might use metadata to store affiliate IDs, media buyers, traffic sources, CRM IDs, sales reps, customer tiers, risk flags, funnel IDs, campaign sub-IDs, fulfillment notes, support outcomes, AI classifications, or custom lifecycle states. Because metadata is business-specific, it allows reports to match how the business actually thinks. For example, one company may want revenue by affiliate. Another may want refunds by media buyer. Another may want customer lifetime value by CRM segment. Another may want chargebacks by traffic source. BigQuery makes these custom dimensions reportable when metadata is stored consistently. This is one of the reasons metadata and BigQuery are powerful together: metadata gives RevCent flexible business meaning, and BigQuery turns that meaning into reporting. ## Relationship to AI Assistants AI Assistants can use BigQuery as part of automated reporting and analysis workflows. An AI Assistant can be designed to look at business performance, summarize results, detect unusual patterns, or create alerts when something changes. For example, an AI Assistant could run a daily revenue summary, monitor gateway decline rates, compare refund volume week over week, identify high-risk customer groups, or summarize subscription renewal performance. The assistant can then create an AI Memo, send an email, trigger another workflow, or provide a human-readable explanation of what the data means. This makes BigQuery a data source for AI-driven operational intelligence. ## Relationship to AI Voice Agents AI Voice Agents can also become part of BigQuery reporting. Voice calls can be analyzed by agent, call method, customer, sale, call status, duration, transfer behavior, and related ecommerce item. This allows a business to evaluate whether phone automation is helping customer support, sales, payment recovery, subscription retention, or post-purchase engagement. For example, a business may want to understand how many outbound recovery calls were completed, how many were no-answer, which agents are used most often, or whether calls relate to sales and payment recovery outcomes. BigQuery helps make AI Voice Agent activity measurable instead of treating calls as isolated conversations. ## Relationship to MCP and External AI Tools RevCent MCP allows AI clients and external AI agents to interact with RevCent capabilities, including BigQuery reporting. This lets an AI system answer business questions using actual RevCent account data rather than relying on a static report. A business could ask an AI client to find underperforming gateways, summarize yesterday’s revenue, identify customers who should receive follow-up, or compare campaign quality across multiple data points. MCP makes BigQuery more useful for AI-powered workflows because it allows AI to discover the reporting structure, run the appropriate analysis, and explain the results in plain language. ## Common Ecommerce Use Cases ### Revenue Reporting BigQuery can help businesses understand sales volume, revenue trends, average order behavior, refund-adjusted performance, and revenue by campaign, product, or customer segment. This helps leadership understand whether the business is growing, which offers perform best, and where revenue is being lost. ### Payment Performance BigQuery can help businesses analyze transactions, approvals, declines, gateway performance, payment errors, refunds, salvage attempts, and recurring payment outcomes. This is important because small changes in payment approval rate can have a meaningful impact on ecommerce revenue. ### Customer Intelligence BigQuery can help businesses understand customers by lifetime value, purchase behavior, refund behavior, chargeback history, customer groups, metadata, subscriptions, and support context. This supports segmentation, prioritization, retention, and better customer service. ### Subscription and Trial Analytics BigQuery can help businesses analyze subscriptions, renewal success, overdue renewals, trial conversions, cancellations, recovery opportunities, and recurring revenue trends. This is especially valuable for businesses that depend on recurring billing. ### Product and Campaign Analysis BigQuery can help businesses compare products, product groups, campaigns, third-party shops, traffic sources, affiliates, discounts, and customer segments. This helps determine which business activities are producing profitable customers and which may be generating refunds, chargebacks, or low-value orders. ### Refund, Chargeback, and Risk Monitoring BigQuery can help businesses monitor refunds, pending refunds, chargebacks, PayPal disputes, fraud detections, false positives, and high-risk behavior. This helps businesses identify risk patterns and respond before small problems become larger operational issues. ### Fulfillment and Shipping Analysis BigQuery can help businesses analyze shipment status, fulfillment activity, delivery performance, product shipment trends, shipping refunds, and delayed order patterns. This can improve operations and reduce support tickets. ### AI and Automation Reporting BigQuery can help businesses measure AI Assistants, AI Voice Agents, AI Voice Calls, API calls, and automation-related activity. This allows the business to understand not just what AI is doing, but whether AI workflows are connected to useful business outcomes. ## Why This Matters in the RevCent Ecosystem BigQuery makes RevCent more than an operational platform. It makes RevCent a queryable intelligence layer for ecommerce businesses. Ecommerce businesses generate large amounts of data through customers, purchases, transactions, subscriptions, shipments, refunds, chargebacks, traffic tracking, AI activity, and support notes. Without a flexible reporting layer, much of that data remains difficult to analyze across the whole business. BigQuery changes that by making RevCent data available for broad, custom, and connected analysis. This creates several ecosystem-level advantages: - Businesses can ask custom questions instead of relying only on fixed dashboards. - Operational records can become reporting signals. - Customer, sales, payment, subscription, shipping, refund, risk, AI, and tracking data can be analyzed together. - AI Assistants and MCP workflows can use reporting data for business reasoning. - External dashboards and analysts can work from RevCent data when access is granted. - Metadata can turn business-specific labels into reportable dimensions. - Reporting can drive action through AI, email, voice, Functions, and human workflows. BigQuery is the feature that lets RevCent data become business intelligence. ## Best-Fit Businesses BigQuery is especially useful for ecommerce businesses that need flexible reporting beyond predefined dashboards. Good-fit businesses may include companies with meaningful sales volume, multiple campaigns, multiple products, recurring billing, frequent payment declines, multiple gateways, customer segments, affiliate traffic, third-party shops, refund activity, chargeback exposure, fulfillment complexity, or AI-driven workflows. It is also useful for businesses that want analysts, AI agents, dashboards, or internal systems to answer business questions directly from RevCent data. For smaller businesses, BigQuery can still be useful because it creates a path from basic reporting to more advanced analytics as the business grows. ## Summary BigQuery in RevCent is the analytics and reporting capability for ecommerce businesses using RevCent. RevCent continually updates user data in Google BigQuery so users, developers, AI Assistants, MCP clients, dashboards, and trusted analysts can query RevCent account data for reporting and business intelligence. It fits into RevCent as the system that makes customers, sales, transactions, subscriptions, renewals, trials, shipping, refunds, chargebacks, fraud detections, products, campaigns, tracking data, AI activity, API activity, notes, metadata, and related ecommerce records measurable across the business. BigQuery is especially important because it enables flexible, custom, and open-ended reporting. Instead of being limited to predefined reports, businesses can ask their own questions about revenue, payments, customers, products, subscriptions, risk, support, fulfillment, AI workflows, and custom metadata. Use the technical links at the top of this file to distinguish between the main ways to interface with the feature: Web App, API, MCP / AI, and the BigQuery Tables Schema. --- Document Parent Directory * [Features](https://revcent.com/documentation/markdown/ecosystem/feature/index.md) - Non-technical markdown documentation for features within the RevCent ecosystem. A feature is a part of the RevCent ecosystem that a user can create and configure.