One of the promises of the experience sampling methodology (ESM) is that it could be used to identify relevant targets for treatment, based on a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life. A requisite for clinical implementation is that outcomes of person-centered analyses are not wholly contingent on the researcher performing them. To evaluate how much researchers vary in their analytical approach and to what degree outcomes vary based on analytical choices, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. The dataset was from a 25-year-old male with a primary diagnosis of major depressive disorder and comorbid generalized anxiety disorder, who completed momentary assessments related to depression and anxiety psychopathology prior to psychotherapy. Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics. Most teams did include a type of vector autoregressive model, which examines relations between variables (e.g., symptoms) over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one advice was similar: both the number (0-16) and nature of selected targets varied widely. This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key methodological issues that need to be addressed in moving toward clinical implementation.