Data Warehouse Design to Support Customer Relationship Management Analysis

2011 ◽  
pp. 731-752
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song ◽  
Peter P. Chen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Design Decisions ◽  
Long Term Study ◽  
Percent Success ◽  
The Impact

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

scholarly journals Data Warehouse Design to Support Customer Relationship Management Analyses

2009 ◽  
pp. 702-724
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song ◽  
Peter P. Chen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Design Decisions ◽  
Long Term Study ◽  
Percent Success ◽  
The Impact

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Data Warehouse Design to Support Customer Relationship Management Analysis

2008 ◽  
pp. 787-809
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song ◽  
Peter P. Chen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Design Decisions ◽  
Long Term Study ◽  
Percent Success ◽  
The Impact

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Factors Affecting Design Decisions for Customer Relationship Management Data Warehouses

2011 ◽  
pp. 159-191
Author(s):  
Colleen Cunningham ◽  
Il-Yeol Song
Keyword(s):  
Customer Relationship Management ◽  
Data Warehousing ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Warehouses ◽  
Key Factors ◽  
Design Decisions ◽  
Factors Affecting ◽  
Percent Success ◽  
The Impact

Customer relationship management (CRM) is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. Key factors for successfully implementing CRM (e.g., data quality issues, organizational readiness, customer strategies, selection of appropriate KPIs, and the design of the data warehouse model) are discussed with the main thrust of the chapter focusing on CRM analyses and the impact of those analyses on CRM data warehousing design decisions. This chapter then presents a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification/ classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Designing a CRM Framework for online & offline sports footwear retailers

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Rodrigo Cueva ◽  
Guillem Rufian ◽  
Maria Gabriela Valdes
Keyword(s):  
Environmental Changes ◽  
Customer Relationship ◽  
Business Strategies ◽  
Economic Sectors ◽  
Running Shoes ◽  
Brick And Mortar ◽  
Key Factor ◽  
On Line ◽  
The Impact

The use of Customer Relationship Managers to foster customers loyalty has become one of the most common business strategies in the past years.  However, CRM solutions do not fill the abundance of happily ever-after relationships that business needs, and each client’s perception is different in the buying process.  Therefore, the experience must be precise, in order to extend the loyalty period of a customer as much as possible. One of the economic sectors in which CRM’s have improved this experience is retailing, where the personalized attention to the customer is a key factor.  However, brick and mortar experiences are not enough to be aware in how environmental changes could affect the industry trends in the long term.  A base unified theoretical framework must be taken into consideration, in order to develop an adaptable model for constructing or implementing CRMs into companies. Thanks to this approximation, the information is complemented, and the outcome will increment the quality in any Marketing/Sales initiative. The goal of this article is to explore the different factors grouped by three main domains within the impact of service quality, from a consumer’s perspective, in both on-line and off-line retailing sector.  Secondly, we plan to go a step further and extract base guidelines about previous analysis for designing CRM’s solutions focused on the loyalty of the customers for a specific retailing sector and its product: Sports Running Shoes.

Influence of internal and external risks on sustainability of financial system of Аltai krai

2018 ◽  
Vol 35 (4) ◽  
pp. 133-136
Author(s):  
R. N. Ibragimov
Keyword(s):  
Sustainable Development ◽  
Financial Stability ◽  
Management Strategy ◽  
Financial System ◽  
Risk Management Strategy ◽  
The Sustainable Development ◽  
The Stability ◽  
The Impact

The article examines the impact of internal and external risks on the stability of the financial system of the Altai Territory. Classification of internal and external risks of decline, affecting the sustainable development of the financial system, is presented. A risk management strategy is proposed that will allow monitoring of risks, thereby these measures will help reduce the loss of financial stability and ensure the long-term development of the economy of the region.

Data Warehousing Requirements Collection and Definition

2012 ◽  
pp. 261-271
Author(s):  
Nenad Jukic ◽  
Miguel Velasco
Keyword(s):  
Data Warehouse ◽  
Large Scale ◽  
Data Warehousing ◽  
System Development ◽  
Large Scale Data ◽  
Typical Data ◽  
Potential Risks ◽  
Real Scenario ◽  
The Impact ◽  
Scale Data

Defining data warehouse requirements is widely recognized as one of the most important steps in the larger data warehouse system development process. This paper examines the potential risks and pitfalls within the data warehouse requirement collection and definition process. A real scenario of a large-scale data warehouse implementation is given, and details of this project, which ultimately failed due to inadequate requirement collection and definition process, are described. The presented case underscores and illustrates the impact of the requirement collection and definition process on the data warehouse implementation, while the case is analyzed within the context of the existing approaches, methodologies, and best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.

Humanitites Data Warehousing

2008 ◽  
pp. 2364-2370
Author(s):  
Janet Delve
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Data Warehousing ◽  
Numerical Data ◽  
Complex Nature ◽  
Data Warehouses ◽  
Textual Data ◽  
Numeric Data ◽  
First Time ◽  
And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

Long-term prognosis of patients withJ-wave syndrome

Heart ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 299-306
Author(s):  
Tsukasa Kamakura ◽  
Tetsuji Shinohara ◽  
Kenji Yodogawa ◽  
Nobuyuki Murakoshi ◽  
Hiroshi Morita ◽  
...  
Keyword(s):  
High Amplitude ◽  
Long Term Study ◽  
St Segment ◽  
History Of ◽  
Provocation Testing ◽  
Long Term Prognosis ◽  
Electrocardiographic Parameters ◽  
The Impact

ObjectiveLimited data are currently available regarding the long-term prognosis of patients with J-wave syndrome (JWS). The aim of this study was to investigate the long-term prognosis of patients with JWS and identify predictors of the recurrence of ventricular fibrillation (VF).MethodsThis was a multicentre retrospective study (seven Japanese hospitals) involving 134 patients with JWS (Brugada syndrome (BrS): 85; early repolarisation syndrome (ERS): 49) treated with an implantable cardioverter defibrillator. All patients had a history of VF. All patients with ERS underwent drug provocation testing with standard and high intercostal ECG recordings to rule out BrS. The impact of global J waves (type 1 ECG or anterior J waves and inferolateral J waves in two or more leads) on the prognosis was evaluated.ResultsDuring the 91±66 months of the follow-up period, 52 (39%) patients (BrS: 37; ERS: 15) experienced recurrence of VF. Patients with BrS and ERS with global J waves showed a significantly higher incidence of VF recurrence than those without (BrS: log-rank, p=0.014; ERS: log-rank, p=0.0009). The presence of global J waves was a predictor of VF recurrence in patients with JWS (HR: 2.16, 95% CI 1.21 to 3.91, p=0.0095), while previously reported high-risk electrocardiographic parameters (high-amplitude J waves ≥0.2 mV and J waves associated with a horizontal or descending ST segment) were not predictive of VF recurrence.ConclusionsThis multicentre long-term study showed that the presence of global J waves was associated with a higher incidence of VF recurrence in patients with JWS.

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