Philippines staffing research
Do business-name changes predict more wrong-record calls?
A source comparison that keeps identity accuracy separate from campaign conversion.
Published September 9, 2026. This note examines whether a reported business-name change is associated with later wrong-record or corrected-record outcomes. It describes a study design for a defined outsourced-calling workflow. It does not report a measured effect or promise a business result.
Methodology: define the observation unit as one released company contact record with a completed verification attempt. Set the eligibility rule, start point, end point, and exclusions before looking at outcomes. Keep unsuccessful and unresolved records in the denominator.
Data collection should cover record ID, source name, source date, former name, reported name, dial result, review decision, and suppression state. Use pseudonymous identifiers when the analysis does not need a person's name. Restrict access, set a retention period, and keep consent and suppression records in their authoritative systems.
Analysis: report counts and denominators across source, record age, name-change state, reviewer outcome, and later dial result. Show missing fields and unresolved cases. Use medians or interval bands for skewed elapsed times. Publish uncertainty estimates only when the sampling design supports them.
Scope and inference limits: A name difference is not proof of a legal name change, bad data, consent, or commercial intent. Assignment may also differ by customer mix, source age, call lane, season, owner availability, and workflow changes. Treat those as competing explanations.
Quality control: pilot the codebook on a small set of eligible records. A second reviewer should classify ambiguous cases without seeing the first decision. Report disagreements, exclusions, and any rule changed after the pilot.
Limitations: missing audio, late notes, inconsistent timestamps, small groups, and incomplete owner outcomes can distort the result. Show how many records each limitation affects. Do not fill a missing outcome with the most likely disposition.
Use the finding narrowly. It may justify a clearer field, a revised queue rule, or a prospective test. It cannot justify a claim about an individual caller or contact without evidence from that case.
Sources: NIST Privacy Framework (https://www.nist.gov/privacy-framework); FTC Telemarketing Sales Rule (https://www.ftc.gov/legal-library/browse/rules/telemarketing-sales-rule); AAPOR Best Practices (https://aapor.org/standards-and-ethics/best-practices/); Philippines National Privacy Commission, Data Privacy Act guidance (https://privacy.gov.ph/data-privacy-act/).