Volatility Attribution Split

Volatility Attribution Split is the discipline of sorting every ranking movement into one of three causes before it reaches a client report: algorithm or model change, competitor action, or your own delivery work. Each cause carries a different story and a different commercial consequence, so mixing them produces reports that credit the agency for luck and blame the platform for execution gaps. The split matters because rank tracking now spans two volatile surfaces at once. Traditional SERP positions shift with core updates, while AI citation presence shifts with model refreshes and routing changes that no agency controls. A practical version: tag each tracked keyword set with a cause class weekly, hold a 14-day rolling baseline, and only escalate movements that survive two consecutive checks. Nightwatch and LLMrefs both let teams segment AI citation data from classic SERP data, which makes the split auditable. When a client asks why visibility dropped, the answer should name the cause class, not just the delta.