TY - GEN
T1 - In-store journey model based on ID-POS data and in-store journey data
AU - Ishimaru, Yutaro
AU - Morita, Hiroyuki
AU - Goto, Yusuke
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers 17K00441. The authors would like to appreciate to Trial Holdings, Inc. for providing the data.
Publisher Copyright:
© 2020 ACM.
PY - 2020/10/31
Y1 - 2020/10/31
N2 - ID-POS data has been analyzed in many retail stores for several decades, and the results have been used to support decision making such as sales promotion and item arrangement in the stores. Such analysis has effect on various business performance like total sales. Although the data is so useful, it is difficult to grasp extent of customer's interest about items that were not purchased, and to identify that it is planned purchase or not. Therefore, we need to use in-store customer journey data to reveal that complementary. In this paper, we propose an in-store journey simulation model, and carry out the agent-based simulation using actual in-store customer journey data acquired by using the Bluetooth beacons. From several computational experiments, we show that our model reproduces in-store customer journey and evaluate our model from the viewpoint of difference between our results and actual data.
AB - ID-POS data has been analyzed in many retail stores for several decades, and the results have been used to support decision making such as sales promotion and item arrangement in the stores. Such analysis has effect on various business performance like total sales. Although the data is so useful, it is difficult to grasp extent of customer's interest about items that were not purchased, and to identify that it is planned purchase or not. Therefore, we need to use in-store customer journey data to reveal that complementary. In this paper, we propose an in-store journey simulation model, and carry out the agent-based simulation using actual in-store customer journey data acquired by using the Bluetooth beacons. From several computational experiments, we show that our model reproduces in-store customer journey and evaluate our model from the viewpoint of difference between our results and actual data.
KW - Agent Based Simulation
KW - ID POS data
KW - In-store journey model
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U2 - 10.1145/3429395.3429409
DO - 10.1145/3429395.3429409
M3 - Conference contribution
AN - SCOPUS:85098455755
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 7th Multidisciplinary in International Social Networks Conference and the 3rd International Conference on Economics, Management and Technology, MISNC 2020 and IEMT 2020
PB - Association for Computing Machinery
T2 - 7th Multidisciplinary in International Social Networks Conference, MISNC 2020 and the 3rd International Conference on Economics, Management and Technology, IEMT 2020
Y2 - 31 October 2020 through 2 November 2020
ER -