The Practice of an Optimal Pricing Strategy for Maximizing Store Profits Using PRISM

The purpose of this paper is to introduce a process for implementing optimal pricing that uses PRISM to maximize store profits. PRISM is a system and process that uses data mining technology to process large volumes of data, then develops a probability model for customer purchases, and which then uses a heuristic approach to identify the pricing pattern that will maximize store profits. For this paper, we used customer purchase data from Japanese supermarkets to identify the optimal pricing pattern for curry roux, which would maximize store profits.2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, October 9-12, 2016

Differences in retail strategies on the emerging organic market

Abstract: Purpose – The organic product market can be considered as an emerging market. Since the 1990s it has experienced rapid growth, and supermarket chains have become the sales channel with the largest market share and are the main driver for further growth. However, different supermarket retail groups have very different strategies concerning the marketing of organic products. The purpose of this paper is to gain insight into the different strategies of retailers who are active in the organic product market and to explain the drivers which may underlie them. Design/methodology/approach – The strategies of the three most important Belgian retailers that market organic products, and in particular organic beef, are analyzed. Data were collected through interviews with the retailers' staff and through observations in retail outlets. Also, GfK-household panel data which recorded all purchases of 3,000 Belgian households and a postal survey with 529 respondents were used as data sources. Findings – The different strategies used by retailers to market organic foods are associated with the overall characteristics and marketing strategies of the retail groups. Some retail groups have clear “first mover” advantages from engaging in the organic product line, while for others an adaptive strategy is more appropriate. Research limitations/implications – The insights from this paper will help the understanding and facilitate the development of future strategies for organic and other high-value or premium products, which will be of interest to researchers and stakeholders who are active in these markets. Practical implications – The retail sector is not a single homogeneous block, but instead consists of retailers who pursue quite different strategies. This concept may have major implications for the future development of high-value markets. Originality/value – Existing relevant theories were applied to the adoption of the organic product line, a segment in the portfolio of retailers that is becoming more important. The empirical material collected sheds new light on the drivers behind retail strategies

Modeling the Product Space as a Network

In the market basket setting, we are given a series of transactions each composed of one or more items and the goal is to find relationships between items, usually sets of items that tend to occur in the same transaction. Association rules, a popular approach for mining such data, are limited in the ability to express complex interactions between items. Our work defines some of these limitations and addresses them by modeling the set of transactions as a network. We develop both a general methodology for analyzing networks of products, and a privacy-preserving protocol such that product network information can be securely shared among stores. In general, our network based view of transactional data is able to infer relationships that are more expressive and expansive than those produced by a typical association rules analysis

Experiencing the sense of the brand: the mining, processing and application of brand data through sensory brand experiences

Purpose - This article aims to develop an integrative framework based on a convergence of embodiment, ecological and phenomenological theoretical perspectives, to explain the multiple processes involved in the consumers’ mining, processing and application of brand-related sensory data through a sensory brand experience. Design/methodology/approach – This research adopts a qualitative method by using face-to-face in-depth interviews (retail managers and customers) and focus group interviews (actual customers) with 34 respondents to investigate sensory brand experiences in the context of Chinese shopping malls. Findings - Results show that the brand data mined through multisensory cues (visual, auditory, olfactory, tactile and taste) in a brand setting are processed internally as sensory brand experiences (involving sensory impressions such as fun, interesting, extraordinary, comforting, caring, innovative, pleasant, appealing, convenient), which influence key variables in customer-brand relationships including customer satisfaction, brand attachment, and customer lovemarks. Originality/value – This study has implications for current theory on experiential marketing, branding, consumer-brand relationships, consumer psychology and customer experience management

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

The Productivity Slowdown, Measurement Issues, and the Explosion of Computer Power

macroeconomics, Productivity Slowdown, Measurement Issues, Computer Power