Data quality in customer relationship management (CRM): Literature review

Marijana Petrović

doi:10.5937/straman2002040p

Abstract

The aim of this paper is to examine challenges that organizations face when they start to deal with quality of customer data more seriously in order to manage their customer relationships better. Research extracted from the literature review has identified some problems with the quality of customer data as well as suggestions for their solutions. The author found that challenges regarding the quality of data used in customer relationship management are reflected in: decentralized data storage, inconsistencies in input and storage, inadequate integration of different data sources, different data defects, and their tendency in quality deterioration over time. In addition, problems have been identified in the high costs of maintaining data quality, as well as new challenges in the form of big data and open data. Possible improvement solutions have been suggested through a number of tools and frameworks by different authors.

Highlights

A key source of the company's competitive advantage lies in its ability to dynamically respond to changes (Adamik et al, 2018)
Problems have been identified in the high costs of maintaining data quality, as well as new challenges in the form of big data and open data
Problems of logical consistency of data entry (in many organizations there is no common language of logically compatible data that would affect customer relationship management (CRM) (Alshawi, Missi & Irani, 2011), as well as inconsistencies in how information is stored in different units, which occurs because in CRM, almost everyone in the organization is in touch with the application

Summary

Introduction

A key source of the company's competitive advantage lies in its ability to dynamically respond to changes (Adamik et al, 2018). Missi et al (2005) cite the basic types of data that organizations collect about customers: demographics (gender, age, marital status, education level, home ownership, etc.) that are very stable and not very expensive, but the problem is that we can hardly get them on an individual basis with a high level of accuracy; behavioral data (types of purchases, payments, customer service activities, etc.) that are the easiest to predict, but are the most difficult and expensive to obtain from external sources; psychographic data (opinions, lifestyle, personal values, etc.) that can lead to improvement and be used to determine a customer's life stage, but the weakness is that they indicate behavior that may be highly, partially, or marginally related to the right behavior (Missi et al, 2005) In addition to these types of data, Zahay et al (2011) emphasizes the contact information of the users, which forms the basis for marketing efforts, as well as personalization i.e. the ability to tailor marketing communications to the individual customer. Eppler & Helfert (2004) split the costs into those caused by low data quality (verification, reentry, compensation, low reputation, wrong decisions, sunk costs) and those to improve data quality accuracy (training, monitoring, development and usage standards, analysis, reporting, plan repair and implementation) (O 'Brien, 2015)

Solutions

Problems

Findings

Conclusion