Factors influencing customer relationship management system failures

¶ … Failure of it Systems

Evaluation of the Failure of Information Systems in Managing Customer Relationships

The failure of information systems and information technologies influences how any practice serves and grows trust with clients over time. Customer Relationship Management (CRM) systems specifically fail the majority of the time due to project leaders failing to take into account change management strategies, inflexible business processes, and losing sight of customers (Kale, 2004). Additional factors include focusing solely on technology, lack of management support, and undervaluing data analysis (Petouhoff, 2006) (Kale, 2004). The intent of this dissertation is to evaluate the factors that lead to CRM systems failing, often leading to enterprise-wide information systems also failing over time due to lack of customer focus (Petouhoff, 2006). Many practices and firms choose to create their enterprise systems, frameworks and platforms around their served market needs in an attempt to attain customer centricity (Shumanov, Ewing, 2007). The main objective of this study is to create a balanced framework with will mitigate the risks involved with managing CRM implementations throughout practices and firms globally.

Objectives

The proposed objectives for this dissertation's research are as follows. First, to determine the level of causality in a firm's financial performance relative to their approach to CRM and it system implementation will serve as the foundation of this analysis. This can be achieved through the use of secondary data, providing all companies included in the sample are publicly-traded and their financial statements are on file with government agencies. The second objective is to determine how each of these publicly-traded firms organized their CRM implementations. Many choose to be highly functional in scope, choosing to align CRM and it systems by department (Shumanov, Ewing, 2007). While this is the most efficient approach to getting a CRM system up and running, it often is short-sighted in that it does not take into account rapid shifts in strategies, markets and customers (Kale, 2004). The second objective therefore is to research how each of the companies in the analysis chose to implement their CRM and it systems over time. This will be used as the independent variable in the analysis of how much of an impact these systems had on corporate profitability. Once this data is captured, the third objective of building an equilibrium-based model that conceptually defines the relationship of companies who chose to invest from a strategy standpoint over those that were functionally-driven and the differences in profitability as a result. This model will also be used as the basis of creating a four-step maturity model that will attempt to show how over time strategy-based implementations are more effective at creating collaboration within and between firms choosing this approach.

Benefits

The most significant benefit of this study is determining what factors contribute to the success or failure of CRM systems, taking into account implementation planning and strategies to mitigate resistance to change. The success or failure of CRM system implementations is often with the area of change management and alignment of these systems to business strategies (Foss, Stone, Ekinci, 2008). The proposed research will show through an equilibrium model and maturity model how best to manage CRM systems implementations.

Structure

The structure of the proposed dissertation will be aligning with ISM guidelines including literature review, theory, methodology, analysis and conclusions.

Methodology

The methodology will be entirely based on secondary research as the sampling frame will be publically held companies who have recently implemented CRM systems in the last five years. Statistical Package for the Social Sciences (SPSS) will be used for analyzing the data.

Critical Analysis

Using the series of SPSS routines the data will be analysed to determine the level of causality and strength of predictability, and will also take into account spending on infrastructure and ERP systems to determine their effects on profitability. The coding of the specific variable for functional vs. strategy-based will also is completed at the interval scale so the statistical analysis can be completed using parametric routines and tools.

Resources

The use of Mergent Online for historical profitability figures, the U.S. Securities and Exchange Commission's many files including the 10K, 8K and 10Q of each firm will be used. A data set will be created and analysed using SPSS to determine causality.

Project Timeframe

It is anticipated that the data set will take approximately two months to produce, with the final dissertation completed in the 4th quarter of 2010.