Organising haute-cuisine service processes : a case study

One of the essential aims of service process organisation is to increase the added value for the customer, thereby increasing customer satisfaction and stimulating consumption. In a haute-cuisine context, customers typically have a higher degree of uncertainty as they often lack the experience of receiving and judging quality in a haute-cuisine setting. This article reports on the application of service process organisation in a haute-cuisine restaurant. The case study shows that there is a significant need to reduce back office activities so that interaction with the customer or customer-facing processes can be increased. This can increase the added value for the customer and can result in higher profits for the restaurants as the customer is either willing to pay higher prices or to consume more. Routines should be implemented that align with segmentation and customer data, while undergoing a retraditionalisation of the service through know-how and interaction. Only interaction with, and integration of, the customer adds significant value that can be further expanded by providing an atmosphere where customer and co-customer have the chance to interact

Segment selection by relationship strength

Relationship management is becoming more important, also in direct marketing. Measuring the strength of relationships is relevant, since relationship strength can be used as a segmentation variable. However, in measuring relationship strength, mostly one or more behavioral indicators are used (e.g. the R/F/M-formula). So, these indicators measure customer quality instead of relationship quality, which is mainly determined by customer perceptions. This paper shows some preliminary results of a relationship audit, which depends on customer attitudes towards the relationship.marketing ;

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

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

Predicting Customer Lifetime Value in Multi-Service Industries

Customer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.forecasting;value;customer relationship management;customer lifetime value;customer segmentation;database marketing;interactive marketing

A new model to support the personalised management of a quality e-commerce service

The paper presents an aiding model to support the management of a high quality e-commerce service. The approach focuses on the service quality aspects related to customer relationship management (CRM). Knowing the individual characteristics of a customer, it is possible to supply a personalised and high quality service. A segmentation model, based on the "relationship evolution" between users and Web site, is developed. The method permits the provision of a specific service management for each user segment. Finally, some preliminary experimental results for a sport-clothing industry application are described

Market Research and Target Market Segmentation in City’s External and Internal Environment

Marketing segmentation have to be massive enough to fulfil the financial necessity of the organization and its products. Segmentation can be picked primarily based on demographics, psychographic, behavioural or geographic place. The segmentation has to be reachable by way of promotional means. Customer contentment, consumer allegiance and consumer retention are critical intermediate goals for education carriers on their ride to higher economic success in the liberalized markets. (Christopher, Payne, & Ballantyne, 2013) suggest the use of segmentation as a way to improve client satisfaction, client loyalty and consumer retention

Perfect and Dynamic Segmentation via the Internet

The paper starts from the hypothesis that traditional approaches to segmentation are seriously flawed because the object of segmentation, the consumer, has dramatically changed over the past 30 years. The New Consumer actively defies segmentation attempts by marketing professionals and thus makes a new approach to marketing strategy necessary. The paper suggests to let the consumers segment themselves instead of doing market research. Thereby the filter between consumer and company is dropped. Self-segmentation is not as radical as it may sound and the paper shows in which industries it has been in use for over thirty years. Companies using self-segmentation let their customers choose/mix their own value proposition from the company’s offerings. This means that they open up the company to the consumers and that the consumers become involved in the value creation process. Thus, self-segmentation, building on Prahalad/Ramaswamy’s co-opting customer competence concept, is more than a marketing tool, it necessitates a re-structuring of organisational structures and a rethinking of the role of marketing as a value-creating activity.Marketing strategy, segmentation, market research, value proposition, new consumer, mass customisation