Using fuzzy Indicators in customer experience analytics

Abstract

The aims of this study is to propose a model for managing customer experience analytics focused on value generated in an online market, this study to explore touch points experience, measured with conventional indicators and fuzzy indicators, using to a structural equation model analysis and Mamdani inference method. The investigation has delved deeper into the nature of the value of the experience construct, the results revealed of the empirical study confirm regarding how the experience value is related with the key touch points of the customer/company relationship. Very few studies in the reviewed literature about the conceptualization on customer interactive experience focused on value generated in an online environment. This study becomes more relevant today, where, after the pandemic, the value of the online customer experience becomes more important.

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“…The fuzzy rules transform essentially membership degree values into the effective fuzzy rule results based on the fuzzy logic operations [21]. The common fuzzy rules include the Mamdani fuzzy rule and Takagi Sugeno Kang fuzzy rule (TSK fuzzy rule) [22]. The Mamdani fuzzy rule has the simple expression, and is often used in the imprecise fuzzy inference model.…”