A psychographic segmentation of Kuwaiti travelers using self-organizing maps
This study identifies distinct market segments of young Kuwaitis intending to travel to Western countries. Applying Viscovery SOMine on survey data from 800 participants, four clusters with distinct travel motivations are examined.
Profiling green consumers with data mining
Attitudes towards ecologically sustainable products are analyzed for the Iranian market. Viscovery SOMine is applied to behavioral and psychographic data and identifies four distinct clusters which differ in demographics and ecological buying behavior.
Profiling Kuwaiti female apparel consumers
The purpose of this article is to examine the influence of self-identity, social interaction and social prestige on female clothing consumers in Kuwait. Viscovery SOMine is used to identify various influences of culture, religion and traditions on purchasing behavior.
Clustering Kuwaiti consumer attitudes towards Sharia-compliant financial products: a self-organizing maps analysis
The connections between sociodemographic background, financial-product quality and attitude towards Sharia-compliant financial products are analyzed for consumers in Kuwait. Viscovery SOMine is used to find clusters of consumers that responded with enthusiasm, sluggishness or rejection, and to provide useful information about these consumer groups.
Egyptian consumers' willingness to pay for carbon-labeled products: a contingent valuation analysis of socio-economic factors
This analysis is concerned with estimating the price premium different customer groups are willing to pay for carbon-labeled products. Viscovery SOMine is used to cluster survey data to find similar consumer groups and analyze their willingness to pay for unlabeled and labeled products.
Combining visual customer segmentation and response modeling
The paper explores customer behavior during sales campaigns. A visual, data-driven and efficient framework for customer segmentation and campaign-response modeling is provided. First, the self-organizing map is used for grouping customers according to characteristics of their purchasing behavior. Then, segment migration patterns are linked to this behavioral segmentation model using feature plane representations. This enables visual monitoring of the customer base and tracking of customer behavior before and during sales campaigns.
A data mining approach for segmentation based importance-performance analysis (SOM–BPNN–IPA): a new framework for developing customer retention strategies
A framework for the development of customer retention strategies and their application to survey data of mobile telephone customers is introduced. It consists of a market segmentation model found using Viscovery SOMine clustering techniques, and a back-propagation neural net (BPNN) on every market segment. Based on the sensitivity analysis on the BPNNs, the effect of specific parameters on customer loyalty is measured and an importance/performance chart is created for each customer group, which can then be used to formulate individual customer retention strategies.
Business client segmentation in banking using self-organizing maps
The aim of this article is to find clusters of business clients in banking with better product affinity and cross-selling properties than provided by ordinary business-client segmentation. For this purpose, Viscovery SOMine is applied to data about Croatian business clients, including survey data from decision makers and ordinary client characteristics.
Strategic knowledge services — catching the context specific strategic meaning
The aim of this article is to get an overview of the demographics and behavior of department store customers by converting large amounts of customer data into actual knowledge about them. Viscovery SOMine is used to group customers according to their shopping behavior and analyze the underlying demographic information.
The impact of skills and demographics on end-user developers’ use of support
This article studies end-user developers' use of various support sources. Viscovery SOMine is used to create clusters considering several demographic, skill and job-related variables.
Visual decisions in the analysis of customers’ online shopping behavior
Advertisements and shopping behavior of online shop customers are analyzed. Viscovery SOMine is used to cluster the customer interactions according to the referring advertisement type and channel and analyze the corresponding sales figures.
Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis
This paper applies a two-level approach that combines Viscovery SOMine's SOM-Ward clustering and decision trees to conduct customer-portfolio analysis for a case company. The resulting two-level model is then used to identify potential high-value customers from the customer base. This hybrid approach provides detailed and accurate information about the customer base for tailoring actionable marketing strategies.
Shades of green: a psychographic segmentation of the green consumer in Kuwait using self-organizing maps
Consumers in Kuwait are analyzed according to their psychographic profile (altruism, skepticism, knowledge, etc.) and their tendency to incorporate ecological values into their buying behavior. Viscovery SOMine is used for visualization, clustering and profiling.
Customer portfolio analysis using the SOM
The purpose of the paper is to illustrate how self-organizing maps can be used for customer portfolio analysis, a category of customer-relation management which serves to identify profitable customers to develop marketing strategies. Customer data were provided by a case company. Viscovery SOMine is used to generate the model, which grouped the customers into segments with similar profiles. The segments are subsequently analyzed in relation to product sales information. The model could potentially be used to adjust marketing efforts to increase the profitability of customers.
Segmenting the market of West Australian senior tourists using an artificial neural network
Neural networks can be used to determine what trade-offs older travelers make as they decide their travel plans. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model created with Viscovery SOMine to describe a consumer–product relationship.
Marketing mix decision making using scanner data and self-organizing maps
A self-organizing-map application is proposed to address the problem of managing a product line with respect to price decisions. Viscovery SOMine is used for the analysis of price elasticity.