Abstract
Retaining customers has been considered one of the most critical challenges among those included in Customer Relationship Management (CRM), particularly in the grocery retail sector. In this context, an accurate prediction whether or not a customer will leave the company, i.e. churn prediction, is crucial for companies to conduct effective retention campaigns. This paper proposes to include in partial churn detection models the succession of first products' categories purchased as a proxy of the state of trust and demand maturity of a customer towards a company in grocery retailing. Motivated by the importance of the first impressions and risks experienced recently on the current state of the relationship, we model the first purchase succession in chronological order as well as in reverse order, respectively. Due to the variable relevance of the first customer-company interactions and of the most recent interactions, these two variables are modeled by considering a variable length of the sequence. In this study we use logistic regression as the classification technique. A real sample of approximately 75,000 new customers taken from the data warehouse of a European retail company is used to test the proposed models. The area under the receiver operating characteristic curve and 1%, 5% and 10% percentiles lift are used to assess the performance of the partial-churn prediction models. The empirical results reveal that both proposed models outperform the standard RFM model.

Abstract
Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products' first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products' sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.

Abstract
Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies. Journal of the Operational Research Society (2012) 63, 982-990. doi: 10.1057/jors.2011.82 Published online 26 October 2011

Abstract
A good relationship between companies and customers is a crucial factor of competitiveness. Market segmentation is a key issue for companies to develop and maintain loyal relationships with customers as well as to promote the increase of company sales. This paper proposes a method for market segmentation in retailing based on customers' lifestyle, supported by information extracted from a large transactional database. A set of typical shopping baskets are mined from the database, using a variable clustering algorithm, and these are used to infer customers lifestyle. Customers are assigned to a lifestyle segment based on their purchases history. This study is done in collaboration with an European retailing company.

Abstract
This paper provides a review of the literature on applications of the Driving forces, Pressure, State, Impact, Response (DPSIR) framework to fisheries. The interpretation given to each DPSIR category differs in existing studies, and as a result the indicators used to support fisheries management also vary considerably. This impairs comparisons concerning the state of different fishery systems, and does not provide a common base of knowledge concerning potential management measures that can be adopted in a given context. This paper clarifies the interpretation of each DPSIR category and proposes a set of indicators that can be applied in fishery contexts. The set of indicators proposed is also classified according to sustainability dimensions. It is argued that organising the indicators according to the DPSIR framework and sustainability dimensions (ecologic, economic, social and governance) is a positive contribution to serve as a guideline for future applications to adopt standardized indicators and improve fisheries management.

Abstract
Design and production of small series for specific customer target groups in collaborative networks can help companies to increase their competitiveness. This paper aims to describe a new framework of services for collaborative networking for on-demand productions. After describing the business processes that need to be supported by innovative tools for partner search and collaborative production planning, we describe the developed services and how they are integrated to ease the communication between different activities. © 2012 International Federation for Information Processing.