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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).

Yesterday’s trends are reoccurring today as organizations continue to leverage their data resources by developing and deploying data mining technologies to enhance their decision-making capabilities (Eckerson & Watson, 2001). To address this need, organizations are implementing organizational data mining (ODM) technologies, which are defined as technologies that leverage data mining tools to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). ODM spans a wide array of technologies, including but not limited to e-business intelligence, data analysis, CRM, predictive analytics, dashboards, web portals, etc.

As a result of these marketplace trends, organizations must begin implementing customer-centric metrics as opposed to solely adopting product-centric metrics (Cutler & Sterne, 2001). This scenario has triggered increased interest in the implementation and use of customer-oriented ODM technologies such as CRM systems. CRM can be defined as the adoption, through the use of enabling technology, of customer-focused sales, marketing, and service processes (Forsyth, 2001). Customer relationship management is the process that manages the interaction between a company and its customers. The goal of customer relationship management is to create a long-term, profitable relationship with all of an organization’s customers. It is more than just a software package— it is a technology-enabled business process. CRM vendors label these packages as CRM systems because their main goal is to analyze customer behavior and identify actionable patterns. This information is then used to improve goods and services offered to customers while increasing profitability through better relationships. CRM software provides the functionality that enables a firm to make the customer the focal point of all organizational decisions. CRM technologies incorporate some of the best-in-class processes for features such as customer service, product configuration, lead management, database marketing and customer analysis.

Customer relationship management has become a key process in the strengthening of customer loyalty and in helping businesses obtain greater profit from low-value customers. The manner in which companies interact with their customers has changed greatly over the past decade. Customers no longer guarantee their loyal patronage, and this has resulted in organizations attempting to better understand them, predict their future needs, and decrease response times in fulfilling their demands. Customer retention is now widely viewed by organizations as a significant marketing strategy in creating a competitive advantage, and rightly so. Research suggests that as little as a 5% increase in retention can provide a 95% boost in profits, and repeat customers generate over twice as much gross income as new customers (Winer, 2001).

Most companies now realize and understand the value of collecting customer data but are faced with the challenges of using this knowledge to create intelligent pathways back to the customer. Most data mining technologies and techniques for recognizing patterns within data help businesses sift through the meaningless data and allow them to anticipate customers’ requirements and expectations while more profitably managing channel partnerships and similar relationships. These technologies also enable companies to maintain customer privacy and confidentiality while gaining the benefits of profiling, calculating the economic value of the CRM system, and discovering the key factors that make or break the CRM project. By integrating these data mining tools with CRM software, organizations are able to analyze very large databases to extract new customer insights for stronger and more profitable relationships.

Data mining by itself is not a business solution; it is just an enabling technology. However, by assimilating data mining technology with customer relationship management, organizational data can be transformed into valuable knowledge to enhance business decisions that optimize customer interactions. For example, consider a catalog retailer that wants to determine to whom they should send current information about new products. The information integrated into the data mining and CRM process is contained in a historical database containing prior customer transactions (such as sales and returns) along with their demographic and lifestyle attributes. By assimilating these two technologies, this retailer is in a better position to optimize each customer interaction by predicting the characteristics of prospects and/or existing customers who would be most likely to make a purchase.

Similarly, electronic customer relationship management can be defined as the process of acquiring a thorough understanding of an organization’s online visitors and /or customers in order to offer them the right product at the right price. E-CRM analytics is the process of analyzing and reporting online customer/visitor behavior patterns with the objective of acquiring and retaining customers through stronger customer relationships. Prior research has found that in order to understand online customers, a company must integrate its data from both online and offline sources (Mena, 2001). More recent research (King & Burgess, 2008; Chen & Chen, 2004) has also concluded that system and data integration are critical success factors in e-CRM and CRM initiatives.

In a similar light, our research and analysis also demonstrates that a company cannot thoroughly understand its customers if it neglects integrating its customers’ behavioral data from both online and offline channels. In order to have this complete customer viewpoint, it is imperative that organizations integrate data from each customer touch-point. Our research elaborates on this key issue of integrating data from multiple sources and its enabling role in facilitating successful and value-creating e-CRM analytics.

In exploring these issues, we first conduct a literature review and provide a foundation for our research. Then we present our research framework and associated propositions. Next we detail the research methodology utilized in our study, and lastly we present and discuss our findings and their organizational implications.

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.

More recent studies have shown favorable CRM outcomes with data integration, and from the opposite view, significant failure rates of CRM projects that ignore it. Technical issues such as capturing the wrong customer information, using misleading metrics and underestimating the difficulties involved in data mining, data cleansing and data integration are major barriers in implementing and managing successful CRM projects (Jain, Jain & Dhar, 2007; Kale, 2004; Missi, Alshawi & Fitzgerald, 2005).

Companies approach consumers through various marketing channels. Traditionally, each channel or functional area has been managed separately, and all data pertaining to a channel is housed in its own system in a proprietary format (Eckerson & Watson, 2001; SAS Institute, 2001). Technically, data integration can be defined as the standardization of data definitions and structures through the use of a common conceptual schema across a collection of data sources (Heimbigner & McLeod, 1985; Litwin, Mark & Roussopoulos, 1990). This implies that data is accessible across functional areas, making data in different corporate databases accessible and consistent (Martin, 1986). For example, if a traditional “bricks and mortar” company deploys a website and decides to integrate the web data with its legacy systems, it has to consider various technological and design issues such as data requirements, data quality, data inconsistencies, synchronization, security, etc. Once these issues are addressed, an organization must present the data in a way that is consistent and conducive to viewing across heterogeneous enterprise departments (Johnson, 2000). In a B2C company, an example of data integration might be creating an integrated customer database to enable the sales and manufacturing departments to access a single source of customer information even though they each require their own view of the customer.

The volume of data available to organizations is growing exponentially. We generate more new data every month than humanity has created from its beginning to the year 2000 (Hardy, 2010). Challenges arise when determining which piece of information about a particular customer is accurate, up-to-date and relevant. In deciding on which parts of the data should be used for analysis, the issues of incompatible data formats, metadata inconsistencies and conflicting levels of data granularity must be resolved. This is a complex and continuous procedure that requires a significant amount of resources.

Although data integration is such a complex challenge, organizations successfully tackling this issue have derived great benefits from it. For example, Staples Inc. integrated all customer and sales data from their store, catalog and online efforts into a common database (SAS Institute, 2001). Integrating all this information allowed Staples’ marketers to monitor and predict how customers migrate from one channel to another and how they utilize the channels to get what they need. Staples can identify what products are purchased at a store versus their Staples Direct catalog or through their online store. This valuable information gives Staples an edge over its competition and allows marketers to target specific products to customers through preferred channels and perform cross- and up-selling to customers across multiple channels.

A recent report from Forrester Research (Ostrow, 2009) forecasts interactive marketing (which includes mobile marketing, social media, email, display advertising and search marketing) to grow over the next five years. Of these online mediums, social media marketing is projected to grow at an annual rate of 34% —from $716 million in 2009 to $3.1 billion by 2014. By then, social media will be a bigger marketing channel than both email and mobile, but only a fraction of the size of search or display advertising ($31.6B and $16.9B, respectively). Consequently, some of this growth comes at the expense of offline advertising. Forrester estimates that online advertising will grow from 12% of total marketing spend in 2009 to 21% by 2014, thereby reducing the amount spent on offline advertising.

This finding raises a number of allocation questions. How do organizations determine which marketing media to use, where their customers spend most of their time, and what their customers’ lifestyles are?  To better answer these questions, online marketers must build a 360-degree (holistic) view of their customers in order to track purchasing behaviors, preferences, likes and dislikes. This holistic view requires organizations to integrate their data to track every customer transaction (customer purchases, returns and complaints) in all customer touch-points (stores, email, mobile, search marketing, social media and direct mail).

Forrester Research predicts online retail sales will account for 8% of all U.S. retail sales in 2014, up from 6% last year. More impressive is that by 2014, more than half of total retail sales (53%) will be affected by the web—for example, consumers going online to do product research or contact customer service (Engleman, 2010). In another survey, e-business executives report rising costs of acquiring customers online—current online acquisition costs total half of store acquisition costs, an increase from one-third of the cost reported a year ago. To minimize these marketing costs, organizations should concentrate on satisfying and serving existing customers and understanding the engagement of those customers with their companies (Johnson & Davis, 2009). These findings suggest that if you want to compete in today’s marketplace and increase profitability in the coming years, you need to go beyond web cookies and meta-tags—you need to build an integrated offline and online customer profile.


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:

  • Over 40% of marketers using social media sites Twitter, LinkedIn, Facebook and company blogs have generated a customer from that channel (HubSpot, 2010).

  • Over half (51 percent) of consumers are using the Internet before making a purchase in shops, educating themselves on the products and best deals available (Bazaarvoice, 2010).

  • Brands with the highest "social media activity" (including reviews) increased revenues by as much as 18% (Bazaarvoice, 2010).

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.

In a prior survey of 800 information technology executives by the Meta Group, four out of five companies did not have a 360-degree view of their customers even though 92% of the firms surveyed ranked increasing customer knowledge as a top priority (Cooke, 2000). This study reported that although business and information technology managers in these companies are interested in obtaining customer knowledge, a number of serious obstacles prevent them from doing so, i.e. building the right data architecture and obtaining useful analytical tools to integrate and use this data effectively.

For successful CRM analytics, an enterprise-wide, customer-centric data repository should be utilized rather than a channel specific data repository (Beck & Summer, 2001; Swift, 2002; Johnson, 2000). Vasset (2001) suggests an enterprise-wide, customer-centric data warehouse should be the foundation of any CRM initiative. A common trend in many organizations today is the management of data and other information in independent silos by different departments and teams. In addition, these teams sometimes leverage different tools for data quality, data integration, data governance and other data management tasks. The Data Warehousing Institute (TDWI) has defined unified data management (UDM) as a best practice for coordinating diverse data management disciplines and aligning them to business goals. UDM encompasses many disciplines, including data integration, data quality and master data management. A recent TDWI Best Practices Report based on 179 respondents found that the leading two benefits of organizations that practice UDM are better business decisions and better data quality (Russom, 2010). This leads us to our last proposition:

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. 


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Note: This article has been updated from the original work E-CRM Analytics: The Role of Data Integration that appeared in the July-Sept 2003 issue of the Journal of Electronic Commerce in Organizations, 1(3), Copyright 2003, Idea Group Inc.

SOURCE: E-CRM Analytics: Leveraging Data Integration for Prospective Customer Insight and Breakthrough ROI—Part 1

  • Christopher BarkoChristopher Barko
    Christopher is a senior market information consultant in the Home Loans Data Management and Reporting business at Bank of America. Previously he worked for the bank as a marketing information manager in their Direct Marketing Center of Excellence. His most recent consulting engagements include website development for a leading Internet company and building affordable and robust business intelligence and reporting solutions for small businesses. His business and IT experience spans many years in a variety of consulting, business intelligence, data management, software engineering and analyst roles for a number of Fortune 500 organizations. His current interests include data mining, analytics and open source business intelligence and how these technologies improve decision making. His first book, Organizational Data Mining, a collaborative effort with Dr. Nemati, was published in 2003. His articles have also appeared in many professional and scholarly journals.
  • Ashfaaq Moosa
    Ashfaaq is a data analyst at Upromise Inc., a firm that helps families save for college. He has worked on various customer-focused data integration and data mining projects for various industries including retail, real estate, and financial.
  • Hamid NematiHamid Nemati
    Hamid is Associate Professor of Information Systems at the Information Systems and Operations Management Department of the University of North Carolina at Greensboro. Before coming to UNCG, he was on the faculty of J. Mack Robinson College of Business Administration at Georgia State University. He also has extensive professional experience as a consultant and has consulted with a number of major corporations. Dr. Nemati is the author of several books, and his most recent books are Pervasive Information Security and Privacy Developments: Trends and Advancements and Applied Cryptography for Cyber Security and Defense: Information Encryption and Cyphering. His research specialization is in the areas of decision support systems, data warehousing, data mining, knowledge management, and information security and privacy. He has presented numerous research and scholarly papers nationally and internationally and his articles have appeared in many professional and scholarly journals.


Recent articles by Christopher Barko, Ashfaaq Moosa, Hamid Nemati



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