E-CRM Analytics: Leveraging Data Integration for Prospective Customer Insight and Breakthrough ROI—Part 1
Originally published January 26, 2011
This article is the first segment of a two-part series exploring the significant role of data integration in electronic customer relationship management (e-CRM) analytics. In this article, we introduce e-CRM, provide a foundation for our research, propose our hypotheses and present our new framework. In the second part of the series, we will detail our research methodology and discuss our findings and their organizational implications.
In today’s globally competitive marketplace, organizations of all sizes can no longer ignore the value of business intelligence (BI) technologies and the competitive advantage they offer through optimal, or at the very least enhanced, decision making. These decision support technologies provide business value by discovering analytical insights and incorporating them into organizational processes. This value creation process requires the integration of various technologies and data—a challenging and complex endeavor for even the experts. Although we have a growing arsenal of robust programming APIs along with web-based data standards and universal communication protocols, many technologies remain disjointed. From search engines results and social networks to XML data sources to data warehouses and government databases to software-as-a-service (SaaS) applications hosted in the “cloud” in geographically dispersed data centers, the integration of these technologies to improve decision making is a growing but necessary challenge in creating business value (Kavanagh, 2009).
Research Foundations and Framework
Many studies (Brancheau, Janz & Wetherbe, 1996; Neiderman, Brancheau & Wetherbe, 1991; Brancheau & Wetherbe, 1987; Dickinson, Leithesier, Wetherbe & Nechis, 1984; Ball & Harris, 1982; Martin, 1982) have shown that data has been ranked as one of the top priorities for information services (IS) executives. With the growth of web-based technologies, the collection and storage of data—both internal and external t— has increased dramatically. Internal data refers to data generated from systems within an organization, such as legacy and online transactional processing (OLTP) systems. External data refers to data that is not generated by systems within an organization, such as government census data, industry benchmark data, consumer psychographic data and economic data. For instance, consumer demographic and psychographic data is available for each of the 200+ million adults in the United States, and product-based data is available for the millions of businesses in the United States. If this data is collected, integrated and formatted properly, it can prove to be immensely beneficial to a firm in better understanding its customers (Rendlemen, 2001). External data should be leveraged in a CRM system to the extent that it adds additional value to the existing internal organizational data.
Extensive research and case studies have shown that data integration is one of several critical factors in successful CRM implementations. To realize measurable business value, firms must combine physical resources (such as computers and networks) and informational resources (online and offline customer databases, call records, email correspondence and other customer service interactions) in their CRM systems (Foss, Stone & Ekinci, 2008). With today’s demanding customers communicating through multiple marketing channels, organizations must be cognizant of customer preferences to optimally manage their delicate yet vital relationship with them. This leads us to our first two propositions:
Proposition 1: The more data sources a company integrates, the better the customer insight, thus creating more value for the company.
Proposition 2: Integrating online data with data from the firm’s offline operations will lead to better customer insight, thus creating more value for the company.
Timeliness of data is an important component of user satisfaction (Doll & Torkzadeh, 1988; Ballou, Wang, Pazer & Tayi, 1998; Adams & Song, 1989). Users need to have up-to-date information about customers’ needs and preferences (Swift, 2002) to thoroughly understand and satisfy those needs. Traditional customer-centric measures such as recency, frequency and monetary statistics should be captured and incorporated into CRM analytics. Without integrated data (from online and offline sources), these statistics will not accurately represent the customer.
A recent survey of 231 online marketers by an innovative Internet marketing company found that businesses that blog multiple times a day acquire more customers than those who blog less frequently. In fact, 100 percent of companies who blog multiple times a day have generated customers from their blog compared to 90 percent of respondents who blog daily and 69 percent of respondents who blog two or three times a week (HubSpot, 2010). This finding shows the additional value obtained by frequently updating and refreshing marketing and e-CRM data.
Traditionally, it was acceptable for organizations to update their customer database on a monthly or quarterly basis. But in today’s fast-paced electronic economy where critical decisions are made daily, companies strive for more current information, requiring systems to update their databases much more frequently (daily, hourly, or in real time). This leads us to our next proposition:
Proposition 3: Data that is more frequently refreshed will lead to better customer insight, thus creating more value for the company.
Past experiences or product quality are not the only reasons why customers make purchases. There are factors external to an organization such as new marketplace competitors, economic factors, competitor promotions, online social media and other similar factors that alter our buying preferences. The explosive growth of social media and its user-generated content are now becoming more effective at driving sales than traditional marketing channels. Consider the following statistics that support the growing importance of leveraging online and external data sources:
In his book Web Farming (1998), Richard Hackathorn advocates that organizations must integrate external data into their data warehouse to gain a complete picture of its business. Sources of external data may include government databases, customer demographic and lifestyle data, online customer preferences, census data, geographic data and weather data. This leads us to our next proposition:
Proposition 4: Integrating external data with internal data will lead to better customer insight, thus creating more value for the company.
In many instances, companies focus their limited resources on their core competencies and outsource many remaining business functions, sometimes retaining the services of application service providers (ASP) and specialized hosting partners to manage online and ecommerce functions (Eckerson & Watson, 2001). Whether an organization’s business processes are performed in-house or outsourced, the collaboration and integration of systems and data from multiple functional areas is complex and difficult. A prior Data Warehousing Institute Industry Report (Eckerson & Watson, 2001) found that organizations are challenged when integrating web technologies into their existing legacy and IT systems. Some of the reasons behind this challenge are scalability issues, managing large clickstream databases, immaturity of technology, lack of experience, and the complexity of modeling web data for analysis. But despite the integration challenges, the benefits realized are significant.
Proposition 5: Deploying an enterprise-wide data warehouse as the CRM backbone will lead to better customer insight, thus creating more value for the company.
Research in customer relationship management is growing as it is gaining greater acceptance within organizations. Customer relationship management has received considerable attention from researchers in many diverse disciplines. Although there is a growing pool of literature that addresses many aspects of the application of customer relationship management for business solutions, there are few scholarly publications that focus on the study of customer relationship management from an e-commerce perspective. Given the complexity of the issues involved in data integration, the enormous benefits that electronic customer relationship management can offer, and the role data integration plays in achieving e-CRM’s goals, we developed an e-CRM Value Framework (Figure 1) to study data integration issues and their impact on the overall value attained from e-CRM projects. Through this framework, we empirically test our five propositions to determine the impact each factor has on creating e-CRM value for an organization. The results of our analysis reveal that four of the five factors support this new framework and have a significant influence on creating value for an organization.
Adams, Carl R. & Song, Jae Hyon (1989), “Integrating Decision Technologies,” MIS Quarterly, June, pp. 199-209.
Rendleman, J. (2001), "Customer Data Means Money, " Information Week, August 20.
Russom, Philip (2010), "Unified Data Management: A Collaboration of Data Disciplines and Business Strategies,” Industry Best Practices Report, April 2010, The Data Warehousing Institute.
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