CRMdataanalyse

CRM data analyse refers to the systematic examination of data stored in customer relationship management (CRM) systems to extract actionable insights about customers, sales processes, marketing campaigns, and service interactions. The goal is to understand customer behavior, predict outcomes, and optimize interactions along the customer lifecycle.

CRM data typically includes contact and account information, opportunities, activities, notes, and case histories. Integrations with ERP, marketing automation, website analytics, and support systems enrich the dataset. Analysts apply descriptive analytics to dashboards and reports, diagnostic analytics to identify drivers, and predictive analytics to forecast sales, churn, or lifetime value, with prescriptive analytics suggesting actions. Data preparation—cleaning, deduplication, normalization, and lineage documentation—supports reliable insights.

Common metrics include customer lifetime value, churn rate, conversion rate, average deal size, pipeline velocity, win rate, CSAT, and Net Promoter Score (NPS). Use cases span segmenting customers for targeted campaigns, personalizing communications, forecasting revenue, optimizing pricing, reducing churn, and guiding cross-sell and upsell as well as improving service.

Tools range from CRM platforms such as Salesforce, Microsoft Dynamics, and SAP CRM to business intelligence tools like Tableau and Power BI, plus data integration and data warehousing solutions. Successful CRM data analysis requires governance over data quality and lineage, privacy compliance (for example, GDPR), and clear ownership and access controls.

Emerging trends include AI-driven insights, real-time analytics, analysis of unstructured data from notes or emails, and event-driven triggers that automate next-best actions.