Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence

Customer experience management (CEM) denotes a set of practices, processes, and tools, that aim at personalizing customer’s interactions with a company according to customer’s needs and desires (Weijters et al., J Serv Res 10(1):3–21, 2007 [29]). E-business specialists have long realized the potential of ubiquitous computing to develop context-aware CEM applications (CA-CEM), and have been imagining CA-CEM scenarios that exploit a rich combination of sensor data, customer profile data, and historical data about the customer’s interactions with his environment. However, to realize this potential, e-commerce tool vendors need to figure out which software functionalities to incorporate into their products that their customers (e.g. retailers) could use/configure to build CA-CEM solutions. We propose to provide such functionalities in the form of an application framework within which CA-CEM functionalities can be specified, designed, and implemented. Our framework relies on, (1) a cognitive modeling of the purchasing process, identifying potential touchpoints between sellers and buyers, and relevant influence factors, (2) an ontology to represent relevant information about consumer categories, property types, products, and promotional material, (3) computational intelligence techniques to compute consumer- or category-specific property values, and (4) approximate reasoning algorithms to implement some of the CEM functionalities. In this paper, we present the principles underlying our framework, and outline steps for using the framework for particular purchase scenarios. We conclude by discussing directions for future research.

[1]  Kang Liu,et al.  Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu , 2015, CL.

[2]  R. Bagozzi,et al.  Trying to Consume , 1990 .

[3]  B. Weijters,et al.  Determinants and Outcomes of Customers' Use of Self-Service Technology in a Retail Setting , 2007 .

[4]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[5]  Rohit Verma,et al.  Research opportunities in service process design , 2002 .

[6]  A. Palmer.  Customer experience management: a critical review of an emerging idea , 2010 .

[7]  M. R. Solomon.  Consumer Behavior: Buying, Having, and Being , 1993 .

[8]  Mark Conner,et al.  The theory of planned behaviour: Assessment of predictive validity and 'perceived control , 1999 .

[9]  I. Ajzen,et al.  Prediction of goal directed behaviour: Attitudes, intentions and perceived behavioural control , 1986 .

[10]  R. Bagozzi.  The self-regulation of attitudes, intentions, and behavior , 1992 .

[11]  Suad Alhojely,et al.  Sentiment Analysis and Opinion Mining: A Survey , 2016 .

[12]  John Mylopoulos,et al.  From object-oriented to goal-oriented requirements analysis , 1999, CACM.

[13]  Dawn Iacobucci,et al.  Network Analyses of Hierarchical Cognitive Connections Between Concrete and Abstract Goals: An Application to Consumer Recycling Attitudes and Behaviors , 1996 .

[14]  Larry L. Constantine,et al.  Human Activity Modeling: Toward A Pragmatic Integration of Activity Theory and Usage-Centered Design , 2009, Human-Centered Software Engineering.

[15]  R. Fazio.  How do attitudes guide behavior , 1986 .

[16]  B. Hammond.  Ontology , 2004, Lawrence Booth’s Book of Visions.

[17]  Katherine N. Lemon,et al.  Customer Experience Creation: Determinants, Dynamics and Management Strategies , 2009 .

[18]  R. Bagozzi,et al.  Antecedents and purchase consequences of customer participation in small group brand communities , 2006 .

[19]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[20]  Erik Cambria,et al.  SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis , 2012, FLAIRS.

[21]  Erik Cambria,et al.  Towards an intelligent framework for multimodal affective data analysis , 2015, Neural Networks.

[22]  R. Fisk,et al.  Multilevel Service Design: From Customer Value Constellation to Service Experience Blueprinting , 2011 .

[23]  William D. Wells,et al.  Life Cycle Concept in Marketing Research , 1966 .

[24]  Nuno Jardim Nunes,et al.  Customer experience modeling: from customer experience to service design , 2012 .

[25]  Mary Jo Bitner,et al.  Moving Forward and Making a Difference: Research Priorities for the Science of Service , 2010 .

[26]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[27]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[28]  R. Bagozzi,et al.  The social psychology of consumer behaviour , 2002 .

[29]  Sriram Dasu,et al.  Human issues in service design , 2002 .

[30]  S. Schwartz.  Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries , 1992 .

[31]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[32]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.