Historical Data Analysis im CRM nutzt vergangene Daten, um Muster zu erkennen, Trends zu identifizieren und zukünftige Entwicklungen vorherzusagen. Diese Analyse verwendet Time-series Analysis, Trend Detection und Pattern Mining. Data Archaeology recovered Lost Insights. Longitudinal Studies tracked Long-term Changes. Seasonality Analysis identified Cycles. Anomaly Detection found Historical Outliers. Comparative Analysis benchmarked Periods. Evolution Tracking showed Development Paths. Causal Analysis understood Past Drivers. Legacy Data Integration incorporated Old Systems. Trend Extrapolation predicted Futures. Learning Extraction informed Current Strategies. Dies führt zu evidenzbasierten Entscheidungen und vermiedenen historischen Fehlern.
