Building a Deeper Understanding of the Customer Through a Values-Based Customer Segmentation

As businesses strive to understand their customers more comprehensively and deliver personalized experiences, customer segmentation remains a critical strategic tool. Traditionally, customer segmentation has utilized demographic and behavioral criteria, which proved to be helpful but can still be further enhanced to provide a deeper understanding of the customer. A values-based customer segmentation provides a more profound understanding of customers' motivations and values. 

Values-Based Customer Segmentation: A Conceptual Overview

Values-based customer segmentation is a methodology that segments customers according to their shared universal values. As suggested by Schwartz (1992), universal values are broad motivational goals that transcend cultures and situations, including self-direction, stimulation, hedonism, achievement, power, security, tradition, conformity, benevolence, and universalism. This approach goes beyond demographic and behavioral data, zeroing in on the core values that fundamentally influence customers' purchasing decisions.

Enriching Traditional Segmentation Models

Traditional segmentation models, based on demographic and behavioral data, provide useful insights into who the customers are, where they live, and how they behave. However, these models often fail to explain why customers make certain choices.

Values-based segmentation aims to fill this gap, offering a more in-depth understanding of customers' inherent motivations and values. This fundamental understanding enables companies to develop more relevant and effective, personalized products, services, and marketing strategies, that foster stronger emotional connections and tighter customer relationships.

Implementing Universal Values-Based Segmentation: From Initiation to Database Tagging

The process of implementing values-based segmentation begins with understanding what values are relevant among consumers.  Using Schwartz's universal values framework as a baseline proves to be helpful but further exploration of values relevant to one's cultural context is needed. This can be done via qualitative methods such as in-depth interviews, focus group discussions, and ethnography. 

The next step is quantitative data collection, which involves gathering data on customers' values (on top of demographics and behavioral information). This data can be collected through surveys where customers rate the importance of different values and which values they hold. Once the data is collected, cluster analysis can be done to identify groups of customers who share similar values. These groups form the basis of the values-based segments.

After the segments have been identified, profiles for each segment should be developed. These profiles include not only the universal values that characterize each segment but also relevant demographic and behavioral data. This comprehensive profile provides a holistic understanding of each segment.  

The final step in the process is database tagging. This involves updating the customer database with the segment information. Each customer record is tagged with their respective segment, allowing for easy identification and targeted marketing.  Building a data model for these segments is crucial for effectively managing and utilizing the segmentation data.

The data model should be designed to easily store and retrieve segment information, facilitate updates as customers' values change, and integrate with other systems for marketing automation or customer relationship management.

In conclusion, values-based customer segmentation offers a powerful approach to understanding customers on a deeper level. By focusing on customers' core values, this approach enriches traditional segmentation models and enables companies to develop more effective, personalized offerings.

The process of implementing this approach, from understanding customer values to building data models for database tagging, is complex but rewarding. As markets continue to evolve, businesses that can effectively leverage values-based segmentation will likely gain a competitive advantage, fostering stronger, value-based connections with their customers. 

The potential of values-based customer segmentation is vast. It allows companies to not only understand who their customers are and how they behave but also why they make the decisions they do. This deeper understanding of values that drive behavior opens the door to more personalized marketing strategies, innovations in product and service offerings, and ultimately, stronger customer-brand relationships.

Moreover, the successful application of this approach is highly dependent on an effective data model. A well-designed data model ensures that the valuable insights gained from values-based segmentation can be easily stored, retrieved, and utilized. As such, companies should invest in developing robust data models that can support their segmentation efforts.

In the future, as data collection and analysis technologies continue to advance, the ability to understand and cater to customers' values will become increasingly important. Businesses that can successfully implement values-based customer segmentation and manage the resulting data effectively will be well-positioned to thrive in this evolving landscape.