Information Verification: Ensuring Key Facts and Figures Stand Up to Scrutiny During Interviews

In market research, industry surveys, and business news interviews, the interview process is not merely about gathering opinions and data—it is an ongoing exercise in information verification. When faced with critical content such as corporate operating data, market shares, capacity utilization rates, product prices, and customer structures, professional researchers must simultaneously cross-check the interviewee's responses against existing materials, promptly identify discrepancies in statistical calibers, logical inconsistencies, and potential errors, and prevent unverified information from directly entering research conclusions.

High-quality information verification begins with thorough pre-interview preparation. Prior to the interview, the QYResearch research team typically establishes a foundational information dossier on the target company, systematically collating data from the company's official website, annual reports, regulatory filings, investor materials, government project announcements, industry association data, tender announcements, and disclosures from upstream and downstream enterprises. On this basis, key indicators such as corporate revenue, product sales volume, average price, production capacity, major customers, and market share are organized into an "evidence matrix," clearly delineating which data points already have public support, which still require confirmation through the interview, and which exhibit apparent contradictions.

Once the interview commences, QYResearch researchers need to perform real-time calculations and logical checks on the data provided by the interviewee. For example, when a company indicates that its annual sales volume for a certain product category is 100,000 units at an average selling price of RMB 5,000, the corresponding sales revenue should be approximately RMB 500 million. If this result deviates significantly from the company's disclosed business revenue, immediate follow-up questions are required to determine whether the data includes taxes, whether it only covers the domestic market, whether it encompasses after-sales business, or whether the average selling price is affected by the product mix of different specifications. Through on-the-spot calculations, vague statements such as "strong market performance" can be converted into actionable information with clear boundaries and statistical definitions.

Cross-verification also entails validating the same facts within the context of the industrial chain. Shipment volumes provided by manufacturers can be corroborated against the procurement scale of major customers, end-product output, upstream raw material consumption, and channel inventory changes; a company's claim of being designated for a certain vehicle model or project also requires further confirmation of designation timing, mass production milestones, supply regions, unit quantity per vehicle, and actual delivery status. For highly sensitive data such as market share, it is advisable to simultaneously reference competitor interviews, industry expert assessments, and publicly available market data, avoiding conclusions based solely on a single respondent's subjective estimates.

The crux of real-time verification does not lie in directly challenging the interviewee, but in progressively reconstructing the facts through professional, specific questions. When data from different sources diverge, researchers should prioritize inspecting statistical calibers, including calendar year versus fiscal year, orders versus revenue, production capacity versus actual output, shipments versus end-user sales, tax-inclusive versus tax-exclusive prices, and the consolidation scope of parent companies and subsidiaries. Many apparent data conflicts are not essentially errors in the information itself, but rather differences in timeframes, product definitions, or statistical boundaries.

For information that cannot be fully confirmed on-site, the QYResearch research team should clearly document the data source, interview time, respondent identity, calculation process, and items requiring further verification, and continue to check through secondary sources after the interview. Only data that is supported by corporate documents, industrial chain evidence, or multiple independent interviews should be incorporated into the formal report; information lacking sufficient evidence should be labeled as estimates, ranges, or data pending confirmation, with corresponding credibility levels assigned.

In an environment of rapid information dissemination and increasingly complex data sources, the value of market research has extended beyond merely "finding data" to assessing whether the data is authentic, whether it is comparable, and whether it can support business decisions. Shifting verification efforts forward to the interview site can significantly reduce rework later on, improve research efficiency, and facilitate timely detection of the underlying reasons behind changes in corporate operations and market structure adjustments.

The outcome of a professional interview should not be a simple record of numbers and opinions, but rather an information chain forged through multi-source comparison, logical deduction, and evidence tracing. Real-time cross-verification is precisely the critical link that transforms interviewee statements into reliable market intelligence, and it is also the key standard that distinguishes high-quality industry research from ordinary information compilation.

Kang Qi | Market Research Analyst at QYResearch

Research Focus: General Industrial Machinery and Equipment, and Related Industrial Chains

Mr. Kang Qi specializes in technology and market research in the fields of general industrial machinery, intelligent manufacturing equipment, and core components, with a focus on industrial pumps, vacuum pumps, compressors, blowers, industrial fans, valves, heat exchangers, refrigeration and HVAC equipment, environmental test chambers, automatic helium mass spectrometer leak detection systems, vacuum chambers, helium gas recovery systems, airtightness testing equipment, flow and pressure testing instruments, as well as mechanical basic components such as bearings, gearboxes, precision reducers, couplings, lead screws, guide rails, and seals.

In the fields of intelligent manufacturing and industrial automation, his research topics include industrial robots, collaborative robots, mobile manipulators, AGV/AMR, machine vision, smart sensors, servo motors, stepper motors, linear motors, motion controllers, PLCs, industrial control systems, digital twins, predictive maintenance, industrial artificial intelligence, and flexible manufacturing cells. He also tracks popular technology trends including SMT placement equipment, wafer bonding and debonding equipment, temporary bonding, hybrid bonding, thermal compression bonding, advanced packaging, 2.5D/3D integration, HBM, glass substrates, panel-level packaging, precision die bonding, and semiconductor metrology equipment.