Improve Intelligence of E-CRM Applications and Customer Behavior in Online Shopping

Improve Intelligence of E-CRM Applications and Customer Behavior in Online Shopping


CRM refers to the management of a business’ present and potential customer’s interactions as a means of satisfying their needs and wants in an efficient manner. Because of advancement in technological growth, CRM has been integrated into the computer system for ease of management thus resulting in E-CRM. Studies, for this reason, have come up trying to investigate internet application in the whole body of CRM (Kim, Zhao & Yang, 2008). Electronic Customer Relationship Management that is to E-CRM is new and integrates IT into an internal organization as well as external marketing strategies in the bid of fulfilling the customer requirements. CRM as an essential business strategy aims at maintaining and acquiring a new customer over the long term (De, 2012).
There are various channels in which customers can interact within the framework of CRM.E-CRM; as it entail all the CRM that use the net settings, for instance, intranet, internet, and extranet. E-CRM focuses on the IT platform for maintaining and acquiring new customers. Application of E-CRM does not mean other CRM approaches can never be. Companies will have to formulate and implement E-CRM in order to have a competitive edge. The E-CRM strategy is beneficial to companies since it allow them to focus and retain the profitable customers. In addition, application of E-CRM enables the company to understand the client behavior thus allowing effective marketing. The remarkable growth of ERP (Enterprise Resource Planning) in 1990 brought a legacy in the world business. The legacy system was used for the backend operation so that various systems across the firm use information access in firm (Schnepel& Hope, 2010).

The current issue facing the application of E-commerce in the online shopping industry is determining whether prospective customers are responsive to the e-commerce application (Woodcock, Stone, & Foss, 2003). Subsequently, it has been noted that customers become frustrated while attempting to use the E-CRM application. In such a case, the application becomes redundant and thus incapable of meeting the desired objectives. In the effort of using the application in meeting the desired needs, it would be important to measure and ascertain whether the e-commerce application fosters ergonomic relationships (Becker, 2008). Testing the E-CRM application is prone to subjectivity and biases; hence, the measuring methods must avoid such instances. Most of the biases stem from existing customer especially in data collection or the methods of gathering this information (Greenberg, 2001). The problem for the research is to determine whether prospective customers are capable of responding to e-commerce application.
In the present modernized world, E-CRM is a top priority for most business oriented organizations yet there are very many non-clear issues regarding its effectiveness and role in the company setting. Through the use of business intelligent applications like OLAP (online analytical processing server), companies can predict future business performances and prepare with respect to the different conditions. As a means of understanding the above ambiguities, we should view CRM from a strategic and systematic perspective (Adebanjo, 2003). A conceptual framework, in this case, includes all functions affecting customer interaction using the E-CRM applications and relevant areas that require flexibility. The flexible changes are essential to the marketing department since they act as a managerial, strategic approach. This framework and porter value chain is similar since it entails primary and supportive activities with the integrating factor being the E-CRM application. Through its marketing activities, the company acquires customers. Customer retention through E-CRM is essential as it helps in customer retention that later leads to customer expansion and an efficient, competitive edge. With supportive activities like channel, integration and information management E-CRM is used strategically (Kamiloglu&Zarali, 2009). The conceptual framework in Appendix 1 integrates the three essential components that are important in improving the intelligence of E-CRM and customer behavior in online shopping. The three basic components are
1- The consumer’s decision-making process
2- Customer relationship marketing
3- Firm’s performance
Figure 1: Conceptual Framework
Customer Relationship Framework
a). Information management
The process includes the collection of relevant customer data at all interaction points with the exclusion of redundancies and using them to create profiles of different clients. The various tools utilized in this process are; customer database and analytical tools (Lee-Kelley, Gilbert &Mannicon, 2003).
b). Customer Acquisition
Customer acquisition entails essential marketing activities such as advertising and personal selling strategies. Site visitor tracking and click stream databases are useful in identifying customer interests and offering them better products (Alt &Puschmann, 2004).
c). Customer retention
The purpose of customer retention is to capitalize on customer acquisition investments with the primary focus on profitable ones (Richard, Thirkel and Huff, 2007).
d). Customer Expansion
Core customers actively participate in a dual interaction with the CRM. Suggestion or feedback from core customers has proved to be crucial in new product improvement, as well as business processes (McKie, 2001).

Conceptual Framework

Figure 1: Customer relationship framework Of E-CRM Applications And Customer Behavior














Figure 2: Internet customer relationship management




            The approach used for the research will involve exploitation of qualitative and quantitative methodologies. In the case of a qualitative approach, assessment of the findings based on secondary research will be done (SCNeducation, 2001). Assessing these results will help in proving the effectiveness of E-CRM in enhancing positive customer relationships. Analysis of the previously published articles provides a theoretical framework that forms the inspiration for the research. Qualitative approach makes use of journals and books as the source of factual justification.
The research will also employ the scientific research design that makes use of the scientific methodologies. Experimental research designs are central to the inquiry thus proving to be the source of quantitative data. In addition, testing using the scientific methods will be critical in explaining the social, behavioral systems (Becker, 2008). Qualitative research reviews will help in assessing the research findings of both primary and secondary sources. Qualitative research thus proves the intelligence of E-CRM applications and the approaches in online shopping industry. Qualitative approach will examine the effectiveness of E-CRM application in enhancing positive customer relationship (Fjermestad& Romano, 2006).
a). Qualitative Approach
This study’s objective is to understand customer reactions upon interacting with the E-CRM application. Through this methodology, the system static processes can be and corrected with an aim of making it flexible (Dyché, 2002). The study will be objective to the reactions of the customer after using E-CRM application. Under this realization, the qualitative approach thus allows the possibility of developing flexibility (Dyché, 2002). Through the utilization of the integrated approach, scientific method complements the qualitative approach. The scientific method acts as a bridge between the qualitative and quantitative designs. The qualitative design helps in developing a research question that will enable the researcher to design and test the hypothesis (icets&Kachidze, 2012). The rationale for our research is the effect of intelligent E-CRM applications on customer feedback.
b). Quantitative Approach

The mathematical formulas developed compared the different influences of E-CRM to the customers that would like to purchase the online tickets (Powell, 2004). The mathematical formulas will change the construction of ideas by the researcher (Huddleston, 2011). Planning a processing click stream data will create the possibility of building a model that will predict the probability of the current user purchasing an item online. In dividing the click stream data sessions, it is important to specify the criterion that will be for the optimization process. This will be through the identification of similar groups of consecutive pages identified in a continuous stream. The clicks are portioned under several sessions in order to maximize both intra-sessions similarities and intersession differences (Franklin, 2012). The basic approach to be in specifying the differences and similarities will involve identification of the variance used for browsing. A fundamental parameter in such a case will entail assessing the amount of time that is in viewing a page. In cases of random sessions, the assumption of the number of time per page will be ‘µi’, where i will represent the course number. The minimum and maximum section can be calculated using:
C). Mathematical Equations
The research will make use of the proportionate percentage to survey the question. Under this realization, similar bibliographies will be aligned to assess sequence justifications. Proportionate percentage will allow analysis of theoretical assumptions outlined in different journals. Proportionate percentage thus will enable analysis of large volumes of literature (Simon & Shaffer, 2001). The researcher fragmented the resources for review into two main sections. Primary sources and secondary sources were fragmented at the proportionate percentage of 70% and 30% respectively. In addition, the sources were defragmented into two central groups comparing justification of E-CRM as advantageous in comparison to the manual systems (Muther, 2002). The following equation was then applied to the study:

n =solution
b = bibliographical justification level
s =sample
m = module
The z score was 1.645 in the case of 90% bibliographical justification level while 1.96 for 95% justification level. Finally, the z score was 2.575 for the 99% bibliographical justification level. In the formula m represented (.07 = + or – 7%, .05 = + or – 5%, and .03 = + or – 3%). Understandably, s represented the value of journals that were in support of E-CRM influences on aspects of positive customer relationship (Anderson & Kerr, 2002).
Finite Population Correction (FPC) was used to calculate the initial sampling with the variance being at least 5%. The number of units in the population, N is unknown; thus finite population correction factor is not applicable. Sampling proportion is below 5%; hence, the finite population correction factor equals to one hence minimal influence on standard error. FPC was then expressed as follows:
n’= =20.06
From the formula, 21 different sources out of 450 were used. The figure 1.047 was derived from 21/450+1=1.047. Dividing the 21 by 1.047 gave 20.06.

    The answer 20.06 obtained from the above calculations represented the new sample size. The number of the units is in the case of standard error. In such cases, finite population correction factor is applied. The researcher, in this instance, makes use of the finite population correction factor. The formula enables calculation of actual standard error (Hadaya and Cassivi, 2009). The approximate standard error is without the use of finite population correction factor. When using this approach, P value gives the unknown standard error. FPC approach influences the data sample data size. From the sample size, it is clear that a high possibility of people anticipates using the online platform as compared to streaming for a ticket.


CRM analytics used in the study entailed online analytical processing (OLAP), Web analytics, and data mining.

The CRM analytics not only contribute to productive customer relations in terms of improved sales and service

Delivery   but also helps in developing advertisements, planning, and analysis. In the flow chart above, original

Customer data was obtained from internet activities, that is, the numbers of customers present online (Simon &

Shaffer, 2001) the data was then processed in the data warehouses and then analyzed. The process of data

Analysis entailed reporting, and evaluation of customer profiles. The data was then evaluated before critical

Decisions   were made. The research envisaged at the client experience derived from the e-CRM application. The

Figure   below the process of data streaming used for the E-CRM application intelligence.

Figure 3: Data Streaming Process for The Study
















Several electronic customer relationship projects fail globally, implementing these systems, used strategies, and customer oriented thinking is essential for its success (Boone & Kurtz, 2005). With reference to this research process, the approximated response was 80%. It is thus clear that a real experience for an online customer is reliant on an intelligent, convenient and concise application. 45% of the sample population expressed high levels of dissatisfaction with unpleasant experiences while 55% will shift away silently. While using primary methods of research like direct interaction with the customer, the study uses a smaller for ease and relevance of data computation. Despite the earlier frustrations relating to the use of E-CRM application, the study is indicative of a prospective situation in which individuals will make use of e-commerce. Perhaps an essential derivation obtained from the study is the convenience when using the application to conduct online shopping. Nevertheless, the ability of using the application depends on its clarity and conciseness. This would be an important consideration to make especially for the companies when generating the form.





Adebanjo, D. (2003). Classifying and selecting e-CRM applications: an analysis-based proposal. Management Decision41(6), 570-577.

Anderson, K., & Kerr, C. (2002). Customer relationship management. New York: McGraw-Hill

Becker, S. A. (2008). Electronic commerce: Concepts, methodologies, tools and applications. Hershey PA: Information Science Reference.

Boone, L. E., & Kurtz, D. L. (2005). Contemporary marketing. Mason, Ohio: Thomson South-Western.

De, M. M. (2012). Information systems: Crossroads for organization, management, Accounting, and engineering; ItAIS: The Italian Association for Information Systems. Heidelberg: Physica-Verlag.

Dyché, J. (2002). The CRM handbook: A business guide to customer relationship management. Boston: Addison Wesley.

Fjermestad, J., & Romano, N. C. (2006). Electronic customer relationship management. Armonk, N.Y: M.E. Sharpe.

Franklin, M. (2012). Understanding research: Coping with the quantitative – qualitative divide. New York: Routledge.

Greenberg, P. (2001). CRM at the speed of light: Capturing and keeping customers in Internet real time. Berkeley, Calif: Osborne

Huddleston, L. (2011). A Case Study with an Identified Bully: Policy and Practice Implications.(Western Journal of Emergency MedicineScholarship, University of California.

iCETS 2012, &Khachidze, V. (2012). Contemporary research on E-business technology and strategy: International Conference, iCETS 2012, Tianjin, China, August 29-31, 2012, revised selected papers. Berlin: Springer

Khanna, N. (2012). Customer Relationship Management. Retrieved from <>

Kim, C., Zhao, W., & Yang, K. H. (2008). An empirical study on the integrated framework of e-CRM in online shopping: Evaluating the relationships among perceived value, satisfaction, and trust based on customers’ perspectives.Journal of Electronic Commerce in Organizations (JECO)6(3), 1-19.

Kimiloglu, H., &Zarali, H. (2009). What signifies success in e-CRM?. Marketing Intelligence & Planning27(2), 246-267.

Lee-Kelley, L., Gilbert, D., & Mannicom, R. (2003). How e-CRM can enhance customer loyalty. Marketing Intelligence & Planning21(4), 239-248.

McKie, S. (2001). E-business best practices: Leveraging technology for business advantage. New York: Wiley.

Muther, A. (2002). Customer relationship management: Electronic customer care in the new economy. Berlin: Springer

Powell, A. S., & Conference Board.(2004). Shared services and CRM. New York: Conference Board, Inc

Richard, J. E., Thirkell, P. C., & Huff, S. L. (2007).An examination of customer relationship management (CRM) technology adoption and its impact on business-to-business customer relationships. Total Quality Management & Business Excellence18(8), 927-945.

Schnepel, A., & Hope, B. (2010). Success factors for e-CRM system implementations. Saarbrücken: LAP LAMBERT Academic.

SCN Education B.V. (2001). Customer relationship management: the ultimate guide to the efficient use of CRM. Braunschweig/Wiesbaden: Vieweg.

Simon, A. R., & Shaffer, S. L. (2001). Datawarehousing and business intelligence for e-Commerce. A Francisco: Morgan Kaufmann.

Woodcock, N., Stone, M., & Foss, B. (2003). The customer management scorecard: Managing CRM for profit. London: Kogan Page.

Alt, R., &Puschmann, T. (2004, January). Successful practices in customer relationship management. In System Sciences, 2004.Proceedings of the 37th Annual Hawaii International Conference on (pp. 9-pp).IEEE.


Figure 1: Conceptual framework

The conceptual framework was adopted from and is used to show the relationship between different process in the E-CRM applications and its effects on customer behavior. It is a representation of the cause-effect relationship between dependent, independent, and intervening variables.


Figure 1: Customer relationship framework Of E-CRM Applications And Customer Behavior








Figure 2: Internet customer relationship management


Figure 3: Data Streaming Process for the Study

Figure 2 is a flowchart showing the flowchart of involved participants in the data streaming process for use by E-CRM applications. It was adopted from De, M (2012). Information systems.