klantanalyse

Klantanalyse is the systematic examination of customer data to understand who customers are, how they behave, and what value they represent for a business. The term is commonly used in Dutch-language contexts and encompasses a range of techniques from descriptive analytics to predictive modeling. The goal is to inform marketing, product development, and customer experience initiatives by turning data into actionable insights.

Methods used in klantanalyse include customer segmentation, where customers are grouped by shared characteristics; cluster analysis methods such as k-means or hierarchical clustering; and RFM analysis (recency, frequency, monetary value) to identify active and valuable customers. Additional approaches include churn analysis, lifetime value modeling, propensity scoring, and, in some cases, conjoint analysis to gauge preferences. Data sources typically cover CRM systems, transactional records, website and app analytics, surveys, and social media.

The analysis process generally follows defining objectives, collecting and cleaning data, integrating multiple data sources, feature engineering, selecting appropriate models, validating results, and interpreting findings for decision-making. Outputs often take the form of customer segments or personas, targeting rules, dashboards, and key performance indicators such as customer lifetime value, churn rate, and campaign ROI. Recommendations may include personalized offers, channel-specific campaigns, product recommendations, and pricing or service changes.

Applications span retail, e-commerce, telecommunications, finance, and other sectors, in both business-to-consumer and business-to-business contexts. Limitations include data quality, representativeness, privacy concerns, and potential biases. Responsible klantanalyse emphasizes data governance, compliance with privacy laws (such as GDPR), and transparency in how models influence decisions.