18,207 research outputs found

    Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

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    Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

    THE BORDER BETWEEN BUSINESS INTELLIGENCE AND PSYCHOLOGY- SEGMENTATION BASED ON CUSTOMER BEHAVIOR

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    In today’s economy, marketers have been facing two challenging trends: fierce competition between companies offering essentially similar products, and dealing with customers that are increasingly informed and demanding, but less and less loyal. Under these conditions, it has become imperative for managers and for marketing professionals to invest in business intelligence in order to find patterns in the consumers’ behavior that could predict their future buying decisions. In this report we have presented how Decision Support Systems, data analysis and customer segmentation can help companies to know their customers better in order to predict (and influence) their future actions. At the same time, we have argued that Business Intelligence should meet psychology and neurology halfway, and accept that there is a very high emotional subconscious component that produces a high degree of unpredictability in consumers’ behavior.DSS, business intelligence, consumer behavior, segmentation, buying decision process

    Using Customer Relationship Trajectories to Segment Customers and Predict Profitability

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    A central premise of relationship marketing theory is that economic benefits flow fromretaining customers. However, the early research focus on the duration of the relationship may obscure other important aspects of the interactions with the customer that drive profitability. Borrowing from the branding literature, where different types of customer relationships have been described (but not empirically examined), we study the patterns of business customers’ buying behavior, or trajectories that characterize customer-firm relationships over time, and their impact on profitability. We develop a finite mixture model relating customer relationship trajectories to profitability over a three year period. Our analysis yields five segments, or types of customer-firm relationships, for this dataset. We find key determinants of profitability vary across types of customer relationship. Interestingly, in none of these segments does duration predict profitability.marketing ;

    Is cross-category brand loyalty determined by risk aversion?

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    The need to understand and leverage consumer-brand bonds has become critical in a marketplace characterized by increasing unpredictability, diminishing product differentiation, and heightened competitive pressure. This is especially true for fast moving consumer goods (FMCG) manufacturers and retailers. Knowing why a customer stays loyal to a brand in multiple product categories is necessary for deriving suitable marketing strategies in the context of a brand extension, yet research on the motives, characteristics, life styles and attitudes of cross-category brand loyal customers has been investigated only in a limited number of studies. We will fill a gap in the literature on cross-category brand choice behavior by analyzing revealed preference data with respect to brand loyalty in several categories in which a brand competes. Provided with purchase and corresponding survey data we investigate the product portfolio of a leading nonfood FMCG brand. We segment consumers on the basis of their revealed brand preferences and, focusing on consumers’ risk aversion, identify cross-category brand loyal customers’ personality traits as determinants of their brand loyal purchase behavior.cross-category brand loyalty, risk aversion, share of category requirements, customer segmentation

    Database Marketing In Travel And Tourism

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    An increasing number of organisations are developing customer databases in a bid to get closer to their customers and gain competitive advantage. This report investigates the practice of database marketing among different travel and tourism sectors, including airlines, hotels, museums and tour operators, and draws on UK and international examples. It compares direct marketing and database marketing and examines the different levels of sophistication at which database marketing can be practiced, the role of customer loyalty schemes, the ways in which a database can be segmented, the role of consumer data profiling companies and current developments in database marketing. The use of database marketing for customer retention and business acquisition is also investigated. In order to ensure true customer relationship building it is vital for the industry to leverage the information on their databases and provide customer recognition through the delivery of personalised service. Business acquisition through customer retention is likely to be a key strategy in future through the use of data-mining and cross-selling techniques. The report concludes that organisations must create a new marketing environment by moving away from transaction marketing towards the principles of customer relationship management
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