Abstract
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.
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Notes
- 1.
http://www.equiterre.org, whose mission statement includes “Équiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity. We see the everyday choices we all make - food, transportation, housing, gardening, shopping - as an opportunity to change the world, one step at a time...”.
- 2.
Gracieuseté of an entry in the INTOWINE web site, http://www.intowine.com/best-wine-pair-lamb-chops, accessed 1/12/2014.
- 3.
Big fish, because they are higher up in the food chain, contain more toxic substances such as mercury.
- 4.
Analyzed using big data and text mining techniques such as sentiment analysis for instance.
- 5.
Identified as “young married couple with dependent children”.
- 6.
A good number of the studies that helped build these models concern behaviors like smoking, drinking, dieting, recycling, exercising, etc. Thus, the consumption process under study actually starts with the behavior desire.
- 7.
Figure 4.5, p. 97, in [7].
- 8.
There is some debate as to whether perceived behavioral control and self-efficacy are the same thing. Ajzen thinks so (e.g. http://people.umass.edu/aizen/faqtxt.html). Armitage and Conner do not [4].
- 9.
See https://en.wikipedia.org/wiki/Commodity, accessed on 18/8/2015.
- 10.
Marketers would disagree with the manipulative “create”: they prefer the term “recognize”.
- 11.
Typically, marketers produce different variants of the same marketing theme, aimed at different populations.
- 12.
Ignoring, for the time being, the fact that it is the customer’s significant other who likes lamb chops.
- 13.
Which may be characterized, if not defined, by the customer’s membership to, or militancy within, various organizations and associations. In our example, our shopper Chris is member of http://www.equiterre.org.
- 14.
If an unhappy customer leaves a complaint on the company’s portal, the company is able to connect with that customer and remedy the situation.
- 15.
In fact, the property value is/can be mapped to the property value distribution of the category.
- 16.
For example, the cashier just scanned cheese, and they know that the consumer is a DINK, they may suggest them a matching bottle of wine in the mid-to-expensive price range.
- 17.
Knowing the orientation of the site alone does not tell us whether a frequent commentator shares the editorial line of the site: a regular commentator may express systematically contrarian opinions.
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Mili, H. et al. (2016). Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_12
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