Home News New Article: Analytics for Customer Engagement
New Article: Analytics for Customer Engagement
Wednesday, 11 August 2010 15:17

Frank Block, FinScore, has co-authored an article on JSR due to publication in fall 2010 with the title "Analytics for Customer Engagement" on the current state-of-the art of marketing analytics including important topics such as

  • opportunities and organizational aspects with respect to data collection for customer engagement
  • how key behavioral manifestations of customer engagement (WOM, cocreation, complaining behavior) can be included in customer engagement models
  • how to overcome existing barriers in marketing practice to introduce analytical models for customer engagement

Here is the summary of an article that appears in the Journal of Service Research:

The quantification of marketing actions and their results is an essential part of the marketing function. So far, analytics have concentrated on direct (tangible) customer outcomes, such as current and future transactions with the firm, and neglected the additional value of customer engagement. Beyond direct customer outcomes, customer engagement includes behavioral manifestations that have rather indirect impacts on firm performance, such as word-of-mouth (WOM) referrals, participation in the firm’s activities, suggestions for service improvements, customer voice, participation in brand communities, or revenge activities; all of these actions affect the brand or firm in ways separate from the influence of purchase. Neglecting behavioral manifestations of this kind can lead to highly biased perceptions of a customer’s contribution to a firm. For example, failing to incorporate WOM in the customer lifetime value (CLV) calculation could lead to an underestimation of the CLV by up to 40%. Thus, it is essential to establish measures and models that account for key behavioral manifestations of customer engagement.

The main objective of this article is to discuss how existing knowledge and modeling approaches from transaction research may be leveraged to build a model in the extended context of customer engagement. Moreover, taking into consideration the increasing ease of interacting quickly online and the resulting customer engagement opportunities (e.g., customer cocreation) requires a clearer view of the capabilities of analytical methods to deal with large data sets. Therefore, this article:

  1. Reviews opportunities and organizational aspects with respect to data collection for customer engagement. Greater database size and complexity requires aggregated data, working with subsets, or the adaptation of methods from computer science. Real-time computation and new data sources will prompt the implementation of large-scale text mining techniques.
  2. Offers a brief overview of “traditional” models for dealing with customer transactions to discuss how key behavioral manifestations of customer engagement (WOM, cocreation, complaining behavior) can be included in these models.
  3. Discusses how to overcome existing barriers in marketing practice to introduce analytical models for customer engagement. The core aspects pertain to (a) data quality, the size of the databases, and new types of data; (b) data ownership, such that clear responsibility drives data quality and access to data; (c) model complexity, mitigated by standardization; (d) ownership of modeling tools; (e) usability of the results, which requires fast delivery of results together with clear interpretations of findings; and (f) integration into company processes.

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