A Latent Topic Analysis and Visualization Framework for Category-Level Target Promotion in the Supermarket

Deciding when and which products to recommend to whom is always an essential issue for retailers. In this study, we propose a mixed framework with two components to capture customer buying behavior and its changes over time and visualize these results to better help retailers choose and target products strategically for marketing. In this framework, a topic model is first used to extract customer’s purchase behavior instead of association rules or K-means as mainly used in market field. To automatically choose the optimal number of topics, we implement an approach proposed by Koltcov et al. on point-of-sale (POS) data in the supermarket. Meanwhile, to grasp the change of topics over time, we divided monthly POS data in half and applied the topic model with Renyi entropy separately. The results suggest that splitting data might be a better way to understand customer behavior. Second, we consider how to develop an effective way to visualize the results of the topic model, which is essential, because in a supermarket context, simply knowing which product categories are included under which topics is not enough to support how a supermarket promotes their products. To address this, we design a three-layer visualization approach to better interpret the topic model results and to help retailers design target promotion strategies. The design of visualization was overlooked by studies related to the use of topic models on supermarket data. Finally, to demonstrate the usefulness of our proposed framework, we conduct a simple scenario-based analysis between our framework and other models, such as Latent Dirichlet Allocation (LDA) and the Dynamic Topic Model (DTM). The results show that for most periods, our proposed framework outperforms LDA and DTM.

How did Japan cope with COVID-19? Big Data and Purchasing Behavior

Japan has been recognized as having successfully controlled the spread of the coronavirus pandemic. This study aims to gather insights to combat the spread of infection in our daily lives by observing our purchasing behavior. We use Point of Sales (POS) data from supermarkets, convenience stores, home centers, drug stores, and electronics retail stores for a nationwide analysis. Our analysis revealed the following. First, the Japanese people actively prevented the spread of infection by voluntarily wearing masks, using alcohol-based disinfectants, and gargling. Second, they willingly stayed home during the semi-lockdown. Third, they continued to purchase infection prevention essentials during periods of both low and high levels of infection. We conclude that continuing to wear masks, wash and sanitize hands, and gargle, along with spending more time at home to maintain safe distancing, will be effective in reducing the spread of the virus and combatting the pandemic. Finally, the infections and deaths were primarily concentrated in the metropolitan area and Kansai region, where the nature of the spread of the infection was different from that in small and middle-sized prefectures.

Automated analysis of lexical features in Frontotemporal Degeneration

We implemented an automated analysis of lexical aspects of semi-structured speech produced by healthy elderly controls (n=37) and three patient groups with frontotemporal degeneration (FTD): behavioral variant FTD (n=74), semantic variant primary progressive aphasia (svPPA, n=42), and nonfluent/agrammatic PPA (naPPA, n=22). Based on previous findings, we hypothesized that the three patient groups and controls would differ in the counts of part-of-speech (POS) categories and several lexical measures. With a natural language processing program, we automatically tagged POS categories of all words produced during a picture description task. We further counted the number of wh-words, and we rated nouns for abstractness, ambiguity, frequency, familiarity, and age of acquisition. We also computed the cross-entropy estimation, which is a measure of word predictability, and lexical diversity for each description. We validated a subset of the POS data that were automatically tagged with the Google Universal POS scheme using gold-standard POS data tagged by a linguist, and we found that the POS categories from our automated methods were more than 90% accurate. For svPPA patients, we found fewer unique nouns than in naPPA and more pronouns and wh-words than in the other groups. We also found high abstractness, ambiguity, frequency, and familiarity for nouns and the lowest cross-entropy estimation among all groups. These measures were associated with cortical thinning in the left temporal lobe. In naPPA patients, we found increased speech errors and partial words compared to controls, and these impairments were associated with cortical thinning in the left middle frontal gyrus. bvFTD patients’ adjective production was decreased compared to controls and was correlated with their apathy scores. Their adjective production was associated with cortical thinning in the dorsolateral frontal and orbitofrontal gyri. Our results demonstrate distinct language profiles in subgroups of FTD patients and validate our automated method of analyzing FTD patients’ speech.

The effect of the sequence of image distortion correction on the accuracy of connection point generation

In this paper, the UAV photogrammetry data of two hills and two flat sampling areas are selected. The image data are processed by pixel Grid software through different operation methods, and the effect of the two methods is compared. The results show that: after the distortion correction, the image is rotated manually or the flight angle in the pos data is directly changed, which has no obvious effect on the error in the number of iterations of the automatic connection point. When the distortion correction is not corrected, the photo is rotated, and the angle of the Omega, Phi and Kappa angles is obtained from the UAV autopilot system, and makes the corner element higher precision when the automatic connection point is generated. Add Kappa to the POS data by 90, and then correct distortion at the same time choice of rotating pictures; Set the Omega, Phi is 0, Kappa angle from the IMU, this method avoids the influence of the angle deviation caused by the tilt or jitter of the flight process, so it is better to line elements when the automatic connection point is generated.

A Mechanism Design Approach to Vendor Managed Inventory

This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage inventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help institute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically determine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit improvements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer. This paper was accepted by Gad Allon, operations management.

PENGARUH KUALITAS JASA, NILAI PELANGGAN DAN CITRA MEREK TERHADAP KEPUASAN PELANGGAN (Pengguna Jasa Kurir Kantor Pos cabang Bogor)

Pada era informasi sekarang ini, alat komunikasi semakin berkembang dengan pesat dan memunculkan banyak inovasi baru dalam penggunaannya. Memasuki banyak perubahan pada era digital sekarang ini membuat perusahaan berlomba-lomba untuk meningkatkan layanan dan memenangkan hati pelanggan. Pos merupakan salah satu sarana komunikasi dan infomasi yang mempunyai suatu peran penting dan strategis dalam mendukung pelaksanaan pembangunan, serta meningkatkan hubungan antar bangsa dalam bentuk layanan pengiriman surat dan paket, logistik dan jasa keuangan.Tujuan dari penelitian ini adalah (1) untuk mengetahui dan menganalisis pengaruh Kualitas Jasa terhadap Kepuasan Pelanggan, (2) untuk mengetahui dan menganalisis pengaruh Nilai Pelanggan terhadap Kepuasan Pelanggan, dan (3) untuk mengatahui dan menganalisis pengaruh Citra Merek terhadap Kepuasan Pelanggan. Jumlah responden dalam penelitian ini berjumlah 100 responden yang diambil dari orang-orang yang sudah pernah menggunakan layanan jasa Pos. Data penelitian diolah menggunakan SPSS versi 22.Hasil dari penelitian ini sebagai berikut : (1) Kualitas Jasa berpengaruh negatif dan tidak signifikan terhadap Kepuasan Pelanggan dengan nilai t-hitung sebesar 0,937, lebih kecil dari t-tabel sebesar 1,984. (2) Nilai Pelanggan berpengaruh positif dan signifikan terhadap Kepuasan Pelanggan dengan nilai t-hitung sebesar 2,513 lebih besar dari t-tabel sebesar 1,984 dan, (3) Citra Merek berpengaruh positif dan signifikan terhadap Kepuasan Pelanggan dengan nilai t-hitung sebesar 4,775 lebih besar dari t-tabel sebesar 1,984.Kata Kunci : Kualitas Jasa, Nilai Pelanggan, Citra Merek, dan Layanan Jasa PT. Pos di Kota Bogor.