Step 1: Start with FootprintIQ's TikTok scanner. Enter the target username and review the confidence-scored results. The scanner checks for exact matches, common variations, and known aliases, providing a prioritised list of probable matches with supporting evidence for each.
Step 2: Extract and document profile intelligence. For confirmed TikTok matches, systematically record all publicly available data: display name, bio text, profile and banner images, post history summaries, engagement metrics, and any linked external URLs or accounts. This documentation creates a structured evidence base for correlation analysis.
Step 3: Expand the search radius. Use any identifiers surfaceed on TikTok — email addresses, website URLs, phone numbers, linked social accounts — as seeds for secondary FootprintIQ searches. Each new identifier opens additional investigation pathways that may reveal platform registrations not connected to the original username.
Step 4: Perform network analysis. Examine the TikTok account's social connections for overlap with known accounts on other platforms. Mutual followers or shared group memberships create network-level correlations that complement identifier-based matching and increase overall attribution confidence.
Step 5: Synthesise and report. Combine all intelligence into a structured report that distinguishes between confirmed findings (multiple corroborating signals), probable findings (strong but unverified correlations), and possible findings (single-signal matches requiring additional investigation). FootprintIQ's automated reporting tools generate compliance-ready documentation in multiple formats.