Mining changes in customer behavior in retail marketing

Chen, Mu-Chen; Chiu, Ai-Lun; Chang, Hsu-Hwa

doi:10.1016/j.eswa.2004.12.033

Cited by 167 publications

(64 citation statements)

References 19 publications

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“…where n is the number of descriptions, m the number of the term vector dimension, df i is the number of all descriptions containing term i, tf i,j is the term frequency, and idf i , the inverse descriptions frequency (Chen, Chiu, & Chang, 2005). The different length of the descriptions is considered by using a length normalization factor in the divisor of the formula.…”

Section: Pre-processingmentioning

confidence: 99%

Technology classification with latent semantic indexing

Thorleuchter

Poel

2013

Expert Systems with Applications

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Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine several technologies it is difficult to identify all corresponding technologies of all research-funding organizations. In this paper, we present an approach to support researchers and to support research-funding planners by classifying applied science research projects according to corresponding technologies of research-funding organizations. In contrast to related work, this problem is solved by considering results from literature concerning the application based technological relationships and by creating a new approach that is based on latent semantic indexing (LSI) as semantic text classification algorithm. Technologies that occur together in the process of creating an application are grouped in classes, semantic textual patterns are identified as representative for each class, and projects are assigned to one of these classes. This enables the assignment of each project to all technologies semantically grouped by use of LSI. This approach is evaluated using the example of defense and security based technological research. This is because the growing importance of this application field leads to an increasing number of research projects and to the appearance of many new technologies.

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Section: Pre-processingmentioning

confidence: 99%

Technology classification with latent semantic indexing

Thorleuchter

Poel

2013

Expert Systems with Applications

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“…AR discovers interesting associations that are often used by businesses such as retail enterprises for decision-making purposes; an example could be to find out which products are frequently purchased simultaneously by different customers [26]. It is one of the most common and widely used techniques in data mining, aimed at finding interesting relations [27,28] or correlations between large data items [29]. AR provides decision-makers at retail enterprises with marketing insights for cross-selling by providing information about product associations [30].…”

Section: Association Rulesmentioning

confidence: 99%

Analytical Business Model for Sustainable Distributed Retail Enterprises in a Competitive Market

Matobobo

Osunmakinde

2016

Sustainability

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Abstract:Retail enterprises are organizations that sell goods in small quantities to consumers for personal consumption. In distributed retail enterprises, data is administered per branch. It is important for retail enterprises to make use of data generated within the organization to determine consumer patterns and behaviors. Large organizations find it difficult to ascertain customer preferences by merely observing transactions. This has led to quantifiable losses, such as loss of market share to competitors and targeting the wrong market. Although some enterprises have implemented classical business models to address these challenging issues, they still lack analytics-based marketing programs to gain a competitive advantage to deal with likely catastrophic events. This research develops an analytical business (ARANN) model for distributed retail enterprises in a competitive market environment to address the current laxity through the best arrangement of shelf products per branch. The ARANN model is built on association rules, complemented by artificial neural networks to strengthen the results of both mutually. According to experimental analytics, the ARANN model outperforms the state of the art model, implying improved confidence in business information management within the dynamically changing world economy.

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“…Customer retention refers to the activity of preventing the existing customers from switching to competitors by enhancing the level of customer satisfaction through one-to-one marketing, loyalty program, complaints management, etc. (Chen et al 2005;Jiang and Tuzhilin 2006). Customer development, the ultimate goal of CRM, aims to maximize the revenue by expanding transaction intensity, transaction value and individual customer profitability through customer lifetime value analysis, up/cross selling and market basket analysis (Etzion et al 2005;Rosset et al 2003).…”

Section: Development Of Crm Strategiesmentioning

confidence: 99%