Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda

Although social media use is gaining increasing importance as a component of firms’ portfolio of strategies, scant research has systematically consolidated and extended knowledge on social media marketing strategies (SMMSs). To fill this research gap, we first define SMMS, using social media and marketing strategy dimensions. This is followed by a conceptualization of the developmental process of SMMSs, which comprises four major components, namely drivers, inputs, throughputs, and outputs. Next, we propose a taxonomy that classifies SMMSs into four types according to their strategic maturity level: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. We subsequently validate this taxonomy of SMMSs using information derived from prior empirical studies, as well with data collected from in-depth interviews and a quantitive survey among social media marketing managers. Finally, we suggest fruitful directions for future research based on input received from scholars specializing in the field

S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM

Advance online in 2016</p

The cumulative effect of satisfaction with discrete transactions on share of wallet

Purpose - The purpose of this paper is to propose a theoretical model for how consumers aggregate satisfaction with individual service encounters to form a summary evaluation of satisfaction, and further examines its effect on customers' share of category spending (share of wallet (SOW)). Design/methodology/approach - The data used consist of 10,983 completed surveys from 1,448 customers whose transaction-specific satisfaction with a retailer and their subsequent purchase behaviors in the category were tracked for more than four transactions. Mixed effects models were employed to test the relationship between the cumulative effect of satisfaction with multiple service encounters on SOW. Findings - Cumulative satisfaction is a weighted average of satisfaction with specific encounters, with weights decaying geometrically so that more recent encounters receive more weight. More recent transaction-specific satisfaction levels tend to have greater influence on customers' next purchase SOW allocations; this, however, is only the case for customers who are less than highly satisfied, with a rating of 8 or lower on a ten-point scale. Additionally, the impact of transaction-specific satisfaction on SOW is not linear. Highly positive transaction-specific satisfaction levels have a greater impact on SOW than negative levels. Practical implications - Many companies monitor satisfaction across multiple service encounters. This study shows how one can aggregate these measures to arrive at a cumulative effect, and highlights the importance to discriminate between first, more and less recent encounters and second, low vs high levels of satisfaction to better understand customers' spending among different providers. Originality/value - Using a longitudinal data set with real customers, this paper identifies a new measure for taking into account the cumulative satisfaction, identifies the positivity bias, and shows how recency affects the relationship between satisfaction and SOW

A longitudinal examination of customer commitment and loyalty

Purpose - This study aims to provide the first longitudinal examination of the relationship between affective, calculative, normative commitment and customer loyalty by using longitudinal panel survey data. Design/methodology/approach - Repeated measures for 269 customers of a large financial services provider are employed. Two types of segmentation methods are compared: predefined classes and latent class models and predictive power of different models contrasted. Findings - The results reveal that the impact that different dimensions of commitment have on share development varies across segments. A two-segment latent class model and a managerially relevant predefined two-segment customer model are identified. In addition, the results demonstrate the benefits of using panel survey data in models that are designed to study how loyalty develops over time. Practical implications - This study illustrates the benefits of including both baseline level information and changes in the dimensions of commitment in models that try to understand how loyalty unfolds over time. It also demonstrates how managers can be misled by assuming that everyone will react to commitment improvement efforts similarly. This study also shows how different segmentation schemes can be employed and reveals that the most sophisticated ones are not necessarily the best. Originality/value - This research provides the first examination of models for change in customer loyalty by employing survey panel data on the three-component model of customer commitment (affective, calculative, and normative) and considers alternative segmentation methods

Perceptions are relative: an examination of the relationship between relative satisfaction metrics and share of wallet

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