Businesses struggle to quantify the return on investment (ROI) of CRM tools and the economic value of the information they generate. While the conceptual foundations of CRM are widely accepted, implementation faces challenges, particularly in accurately measuring the value of customer information. Existing literature offers theoretical models or complex, impractical approaches to customer valuation. Most studies focus solely on transactional relationships, neglecting the multifaceted value customers bring beyond direct transactions. This research addresses this gap by proposing and empirically testing a new model that quantifies the economic value of information and knowledge generated by CRM tools from a customer value management perspective. The study focuses on B2C environments and aims to offer a practical tool for businesses, particularly SMEs, where the benefits of CRM are significant but often difficult to measure. The research's primary objective is to develop and validate an exploratory model quantifying the economic value of information generated by CRM tools. The three research goals are: 1) to propose and test a new model for measuring the value of business information in CRM environments; 2) to compare this model's performance with traditional methods; and 3) to analyze the influence of each component within the model.

The paper reviews existing literature on customer lifetime value (CLV), customer relationship management (CRM), and the economic value of information. It highlights the limitations of traditional CLV models, which often overestimate customer value and are highly dependent on retention rates. The authors note the lack of practical, quantitative models linking IT tools, information management, and CRM strategies to business performance. While Kumar's Customer Valuation Theory offers a comprehensive framework, it lacks an integrated quantitative model. The paper emphasizes the need for a holistic approach that integrates marketing, information, and technology systems. The work of Glazer (1991), which treats information as an asset, serves as a foundation for the proposed model. The review also identifies the need for a model that considers customer engagement, customer knowledge, and customer social value beyond simple transactional CLV.

The research employs a mixed-methods approach. First, a literature review establishes the theoretical foundations for the VICRM model. Second, an empirical case study is conducted using data from a medium-sized Spanish insurance company. The company's high organizational maturity, data availability, and willingness to participate made it suitable for the study. Data collection involved individual interviews and group workshops with personnel involved in customer information management. A longitudinal approach was used to estimate customer costs and revenues. The study uses a sample of 60 customers, 10 from each of six pre-defined archetypes based on consumption patterns and socio-demographic characteristics. This sample size balances operational feasibility with the research goals. The chosen customers all responded to regular company surveys. Various metrics were operationalized to quantify the constructs within the VICRM model (detailed in Table 2 of the paper). Statistical analysis, including the Scheirer-Ray-Hare test and Mood's median test, were used to compare the VICRM model to a traditional CLV model and assess the heterogeneity of results across customer segments.

The VICRM model yielded significantly different results compared to a traditional transactional CLV model. The VICRM model incorporates three key constructs beyond traditional CLV: customer engagement value (CEV), customer knowledge value (CKV), and customer social value (CSV). The Scheirer-Ray-Hare test showed significant differences in VICRM values across customer segments, highlighting the model's ability to identify heterogeneity within the customer base. The analysis also revealed that engagement value is a key driver of the VICRM, although the median values suggest a greater weight of knowledge value due to extreme values in engagement value. Social value contributions were relatively small due to limited reported social interactions within the system. The results deviate from the Pareto principle, showing a long tail distribution indicating a broader range of customer contributions thanks to IT. The VICRM model proved less regressive than traditional CLV models, with approximately 7 years required to reach 50% of the expected value, compared to the typically faster return of traditional models. A comparison of the VICRM and traditional CLV models indicated that the traditional model significantly overvalues some customers. The VICRM model showed less sensitivity to changes in retention rates than the traditional model, suggesting improved robustness. The VICRM model provided a more homogeneous and robust valuation than the traditional model, reducing sensitivity to outliers. A direct comparison of the two models demonstrated a significant positive correlation but with considerable differences in total economic value (VICRM being significantly lower overall). However, the VICRM identified a group of customers that were undervalued by traditional methods, representing around 23.3% of the sample.

The findings demonstrate the value of integrating customer engagement, knowledge, and social value into customer valuation models. The VICRM model offers a more nuanced and comprehensive assessment of customer worth than traditional transactional CLV models, revealing significant heterogeneity among customers. The model's reduced sensitivity to retention rates improves its robustness and predictive accuracy. The long-tail distribution of customer contributions highlights the importance of considering a broader range of customers when assessing overall business value. The results underscore the need for businesses to move beyond simplistic transactional approaches to capture the full economic value of their customer base and leverage CRM data effectively. The study's findings support the theoretical underpinnings of the VICRM model, demonstrating its practical applicability in the financial sector and offering potential for adaptation to other B2C industries.

This research provides a novel, practical model for quantifying the economic value of customer information in CRM systems. The VICRM model offers a significant improvement over traditional CLV models by incorporating engagement, knowledge, and social value, leading to a more robust and nuanced valuation. Future research could explore the model's applicability in B2B settings, other industries, and investigate potential refinements to the model's constructs and metrics.

The study's sample size (60 customers) limits the generalizability of the findings. The specific metrics used in the model were tailored to the Spanish insurance sector, requiring potential adjustments for other industries. The model's reliance on self-reported data from surveys introduces potential biases. Future research should address these limitations by expanding the sample size, refining the metrics, and exploring alternative data sources to enhance model robustness.