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💻📜 Featured Article 💻📜

Classifying Customer Attributes with Importance Performance Analysis and Fuzzy Kano

Abstract
Analyzing what consumer needs remains every day’s challenge for every business. Every business entity requires continuous effort as consumers become more demanding and have more access to product/service offerings, leading to more competitive market dynamics and the necessity for more innovative ways of offering products/services. The research aims to recommend a set of customer attributes for the studied company and analyze the selected attributes using a combination of Importance Performance Analysis (IPA) and fuzzy Kano. The research is a case study of a company selling gift vouchers for individual and corporate consumers. The research combines literature study and affinity diagram workshop to identify the required consumer attributes, which are analyzed using the integration of IPA and fuzzy Kano. The results suggest that the studied company should concentrate on several attributes, such as A7-simple requirement during the purchasing process, A10-no administration fee during purchase, A14-cross promotion with various sister brands, and A15-no minimum purchase. The attributes fall under “concentrate here” in the IPA grid while at the same time, those are considered as “effective improving area” in the fuzzy Kano grid. The studied company is also recommended to keep their good work on the attribute of A5-expiry date longer than one year so that it remains their competitive attribute and does not fall into the other inferior quadrants.

Keywords: Customer Attributes, Importance Performance Analysis (IPA), Fuzzy Kano

Read full article: https://journal.binus.ac.id/index.php/commit/article/view/8534

An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors

Abstract
Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step detection, step length estimation, and heading estimation. Among them, the last component is the most challenging since a small heading error accumulates to produce a very large positioning error, especially when the pedestrian holds the smartphone in unconstrained styles such as swinging the phone freely along the pedestrian’s walking direction or putting the phone into the pants’ front pockets. The research proposes an adaptive heading estimation method to deal with heading errors caused by smartphone holding styles. The novelties are described as follows. Firstly, the proposed method attempts to classify the four basic smartphone holding styles using a machine learning algorithm based on simple features of acceleration values to give pedestrians more freedom during the walking period. Secondly, the proposed method adaptively combines the two heading estimation methods, which are calculated from the integrated sensors, to determine the walking direction for different smartphone holding styles. The experimental results show that the proposed heading estimation method achieves average heading errors of less than 30 degrees when testing in two different walking paths with the smartphone held in dynamic styles. It helps to reduce the heading errors by more than 15% compared to previous heading estimation methods.

Keywords: Adaptive Heading Estimation Method, Holding Styles Recognition, Smartphone Sensors

Read full article: https://journal.binus.ac.id/index.php/commit/article/view/9196