<p>Traditional profile analyses summarize multivariate person data with overall mean levels and relative patterns, but existing methods often blur these sources of variation or reduce each individual to a single best-fitting profile. Segmented Profile Analysis (SEPA) offers a unified, ipsatized singular-value decomposition (SVD) framework that decomposes individual profiles into orthogonal level (LE) and pattern (PE) effects and, crucially, introduces plane-wise segment profiles (summaries of each person's response pattern within each variable-contrast dimension) as primary person-oriented objects. Within each low-dimensional plane, SEPA defines a projected response pattern (segment profile), domain–person cosines that index variable-by-variable alignment, and plane-fit correlations that summarize how closely an individual’s pattern follows the plane’s domain structure. Across planes, SEPA aggregates information via singular-value weighting to yield an overall segment profile while preserving contrast-specific signal. A practical workflow combines ipsatized SVD, parallel analysis for PE dimensionality, marker-domain rules, bootstrap confidence intervals, and subspace-stability diagnostics. Using multivariate cognitive data from the Woodcock–Johnson IV, SEPA identifies interpretable marker domains, reveals distinct pattern facets across planes, and provides person-oriented indices that can be carried into standard regression models. Simulation studies examine the stability of segment profiles and cosines under varying sample sizes and variance structures. SEPA thus supplies a reproducible, geometry-based foundation for person-centered measurement that connects Q-type factor analysis, biplot methods, and contemporary within-person profiling in multidomain assessments.</p>