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Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV pp 160–193Cite as

Enabling Decision Support Through Ranking and Summarization of Association Rules for TOTAL Customers

Part of the Lecture Notes in Computer Science book series (TLDKS,volume 12380)

Abstract

Our focus in this experimental analysis paper is to investigate existing measures that are available to rank association rules and understand how they can be augmented further to enable real-world decision support as well as providing customers with personalized recommendations. For example, by analyzing receipts of TOTAL customers, one can find that, customers who buy windshield wash, also buy engine oil and energy drinks or middle-aged customers from the South of France subscribe to a car wash program. Such actionable insights can immediately guide business decision making, e.g., for product promotion, product recommendation or targeted advertising. We present an analysis of 30 million unique sales receipts, spanning 35 million records, by almost 1 million customers, generated at 3,463 gas stations, over three years. Our finding is that the 35 commonly used measures to rank association rules, such as Confidence and Piatetsky-Shapiro, can be summarized into 5 synthesized clusters based on similarity in their rankings. We then use one representative measure in each cluster to run a user study with a data scientist and a product manager at TOTAL. Our analysis draws actionable insights to enable decision support for TOTAL decision makers: rules that favor Confidence are best to determine which products to recommend and rules that favor Recall are well-suited to find customer segments to target. Finally, we present how association rules using the representative measures can be used to provide customers with personalized product recommendations.

Keywords

  • Data mining
  • Association rules
  • Recommendation

I. Benouaret and S. Amer-Yahia—Our work is funuded by a grant from TOTAL.

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Authors and Affiliations

  1. CNRS, Univ. Grenoble Alpes, Grenoble, France

    Idir Benouaret & Sihem Amer-Yahia

  2. New Jersey Institute of Technology, Neward, NJ, USA

    Senjuti Basu Roy

  3. TOTAL, Paris, France

    Christiane Kamdem-Kengne & Jalil Chagraoui

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  1. Idir Benouaret
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  2. Sihem Amer-Yahia
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  3. Senjuti Basu Roy
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  4. Christiane Kamdem-Kengne
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  5. Jalil Chagraoui
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Correspondence to Idir Benouaret .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Prof. Abdelkader Hameurlain

  2. Vienna University of Technology, Wien, Austria

    Prof. Dr. A Min Tjoa

  3. INSA Lyon, LIRIS, Villeurbanne Cedex, France

    Prof. Philippe Lamarre

  4. University of Versailles Saint-Quentin-en-Yvelines, Versailles Cedex, France

    Prof. Dr. Karine Zeitouni

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Benouaret, I., Amer-Yahia, S., Roy, S.B., Kamdem-Kengne, C., Chagraoui, J. (2020). Enabling Decision Support Through Ranking and Summarization of Association Rules for TOTAL Customers. In: Hameurlain, A., Tjoa, A.M., Lamarre, P., Zeitouni, K. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV. Lecture Notes in Computer Science(), vol 12380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62271-1_6

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  • DOI: https://doi.org/10.1007/978-3-662-62271-1_6

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