This thesis document outlines the marketing issues and implications related to behavior based personalization of products and services. The paper does a great job of summarizing the evolution of market segmentation, behavior-based personalization techniques, behavior-based applications, and implications for CRM.
The author defined behavior based personalization as a:
[P]rocess that focuses on what consumers have done in the past and then identifies individual tastes or characteristics of the customers.
The author identified five primary modes of behavioral personalization: rule-based, content-based, collaborative filtering, hybrid, and web morphing. Hybrid systems typically use combinations of the rule-based, content-based, and collaborative filtering combined with overlays of demographic and psychographic data. Website morphing takes into account users’ varied cognitive styles of communication and interpretation of data. For example, some users are more visual in processing information, whereas other users are more analytical. As such, a morphing system analyzes a users cognitive style via data interaction and then morphs to meet the users needs (i.e., moving from a verbose marketing message to a more visual marketing message).
An interesting application of behavior based personalization is in the context of cross-selling additional products to current customers. Customer loyalty and brand equity increase the more targeted recommendations appeal to a particular customer. By using a hybrid model, brands can deliver a true one-to-one service.
Finally, behavior based personalization has strong implications for enhancing CRM systems. CRM focuses on creating value for customers by knowing more about their needs and past interactions with a brand. Thus, behavior based data about a customer should feed a CRM system so companies–over time–begin to better understand each customer and deliver more targeted and meaningful products and services to this customer. By focusing on delivering meaningful value to customers, customer loyalty increases. Similarly, companies can use behavior based personalization data within a CRM to model ideal customer attributes, which they can leverage in advertising to acquire new customers with attributes most similar to their ideal customers.
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