Lifetime-value creation through a customized offer adressed to diferent lifestyles

This paper purposes a method for marketing segmentation based on customers‟ lifestyle. A quantitative and qualitative segmentation established by the Whitaker Lifestyle™ Method was created in order to define a concrete and clear identification of the customer, by understanding the behavior, style and preferences of each segment. After conducting 18 in-depth interviews, it was concluded that four main personas characterize the customer base of the company. These four personas will be the support for the creation of „quick-wins‟ that address to the expectations of each lifestyle, projecting a significant impact on the lifetime-value of the company‟s customer base

e-Pontos: Uma solução embarcada de automação comercial aplicada a clubes de fidelidade baseado em tecnologia RFID e esmart card

In practically all vertical markets and in every region of the planet, loyalty marketers have adopted the tactic of recognition and reward to identify, maintain and increase the yield of their customers. Several strategies have been adopted by companies, and the most popular among them is the loyalty program, which displays a loyalty club to manage these rewards. But the problem with loyalty programs is that customer identification and transfer of loyalty points are made in a semiautomatic. Aiming at this, this paper presents a master's embedded business automation solution called e-Points. The goal of e-Points is munir clubs allegiances with fully automated tooling technology to identify customers directly at the point of sales, ensuring greater control over the loyalty of associate members. For this, we developed a hardware platform with embedded system and RFID technology to be used in PCs tenant, a smart card to accumulate points with every purchase and a web server, which will provide services of interest to retailers and customers membership to the club

From supply to demand-driven opportunities : unravelling the role of the customer in opportunity identification processes

Successful identification and exploitation of opportunities has been an area of interest to many entrepreneurship researchers. Since Shane and Venkataraman’s seminal work (e.g. Shane and Venkataraman, 2000; Shane, 2000), several scholars have theorised on how firms identify, nurture and develop opportunities. The majority of this literature has been devoted to understanding how entrepreneurs search for new applications of their technological base or discover opportunities based on prior knowledge (Zahra, 2008; Sarasvathy et al., 2003). In particular, knowledge about potential customer needs and problems that may present opportunities is vital (Webb et al., 2010). Whereas the role of prior knowledge of customer problems (Shane, 2003; Shepherd and DeTienne, 2005) and positioning oneself in a so-called knowledge corridor (Fiet, 1996) has been researched, the role of opportunity characteristics and their interaction with customer-related mechanisms that facilitate and hinder opportunity identification has received scant attention.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Introducing employee social identification to customer satisfaction research: a hotel industry study

The purpose of this paper is to address the concept of linkage research and propose the addition of social identity theory as an important consideration in managing employee-customer interactions and customer satisfaction.

Identification and analysis of moderator variables:investigating the customer satisfaction-loyalty link

Purpose – Research on the relationship between customer satisfaction and customer loyalty has advanced to a stage that requires a more thorough examination of moderator variables. Limited research shows how moderators influence the relationship between customer satisfaction and customer loyalty in a service context; this article aims to present empirical evidence of the conditions in which the satisfaction-loyalty relationship becomes stronger or weaker. Design/methodology/approach – Using a sample of more than 700 customers of DIY retailers and multi-group structural equation modelling, the authors examine moderating effects of several firm-related variables, variables that result from firm/employee-customer interactions and individual-level variables (i.e. loyalty cards, critical incidents, customer age, gender, income, expertise). Findings – The empirical results suggest that not all of the moderators considered influence the satisfaction-loyalty link. Specifically, critical incidents and income are important moderators of the relationship between customer satisfaction and customer loyalty. Practical implications – Several of the moderator variables considered in this study are manageable variables. Originality/value – This study should prove valuable to academic researchers as well as service and retailing managers. It systematically analyses the moderating effect of firm-related and individual-level variables on the relationship between customer satisfaction and loyalty. It shows the differential effect of different types of moderator variables on the satisfaction-loyalty link.

Identification of factors predicting clickthrough in Web searching using neural network analysis

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing

The emergent male shopper : an identification of male supermarket shopper types

Grocery shopping is a routine activity widely considered the responsibility of the female spouse, yet modern social and demographic shifts are causing men to engage in this task. This study develops a retail shopping typology of male grocery shoppers, employing a cluster analysis technique. Five distinct cohorts emerge from the data of eight constructs, measured by seventy one items. One new shopper type emerges from this research. This shopper presented as a younger man, at the commencement of their family lifecycle, attracted by a strong value offer, focusing on price and promotional discounts. Our research offers a contribution to the marketing, consumer behaviour and supermarket retailing disciplines in three ways. By examining and identifying male shopping behaviour in the context of grocery shopping, the development of a retail shopping typology of male grocery shoppers and the extension and employment of a cluster analysis in identifying distinct groups. This research has implications for gender, segmentation studies and consumer behaviour disciplines in regard to grocery shopping. The identification of specific groups of male grocery shoppers will enable grocery retailers to effectively implement important, targeted marketing strategies.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Using a smartphone application for customer-centric banking

The banking industry is under pressure. In order to compete, banks should adapt to concentrating on the specific customer needs, following an outside-in perspective. This paper presents the design of a business model for banks that considers this development by providing flexible and comprehensive support for retail banking clients. It is demonstrated that the identification of customer processes and the consequent alignment of banking services to those processes implies great potential to increase customer retention in banking. It will be shown that information technology – especially smartphones – can serve as an interface between customer and suppliers to enable an alignment of offerings to customer processes. This approach enables the integration of banks into their customers’ lifestyle, creating emotional value added, improving the personal relationship and the customers’ affiliation with the bank. The paper presents the design of such a customer-process-centric smartphone application and derives success factors for implementation.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Key Account Management in Paper and Board Industry- Identification of Key Accounts for Stora Enso Graphical Boards

Tutkimuksen tarkoituksena oli tunnistaa nykyiset sekä potentiaaliset avainasiakkaat case yritykselle. Avainasiakkaat tunnistettiin Chevertonin tunnistamis- ja valintamatriisin avulla, jossa asiakkaan sijoittumista matriisiin arvioidaan asiakkaan houkuttelevuuden sekä toimittajan suhteellisten vahvuuksien avulla. Kriteereiksi avainasiakkaiden tunnistamiseen valittiin asiakkaan vuotuinen ostovolyymi, asiakkaan business-potentiaali sekä case-yrityksen toimittajaosuus. Asiakkaat luokiteltiin avainasiakkaisiin, kehitettäviin avainasiakkaisiin, ylläpidettäviin asiakkaisiin sekä satunnaisiin asiakkaisiin. Tutkimus tarjosi lähtökohdan case-yrityksen uusille avainasiakaspäälliköille sekä osoitti suunnan tulevaisuuden tutkimustarpeille. Aktiivisen tiedonvaihdannan kautta eri myyntikonttoreiden johtohenkilöstön sekä myös yrityksen eri funktionaalisten divisioonien välillä voidaan saavuttaa kilpailuetua kun lähestytään asiakasta toimintojaan järkiperäisesti koordinoineena toimittajana samalla kun asiakkaat keskittävät ostojaan. Jotta yrityksen tavoitteet, markkinamahdollisuudet sekä resurssit olisivat hyvin tasapainossa, tulisi myös asiakaskannattavuutta sekä asiakkaiden strategista merkittävyyttä arvioida ja mitata säännöllisesti tässä tutkimuksessa käytettyjen tunnistuskriteereiden lisäksi.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.