CRM Using Data Warehousing at essay

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It is important to realize that the level of technology investment at present is sufficient for the current project; this proposal does not necessarily advocate any additional spending on pure technology.

What this analysis pinpoints as a major area of concern is the slow and very gradual transformation of FAC when it needs to move much faster towards its customer goals. The lack of speed and urgency in the shift can be partially attributed to the lack of cohesion and consistency of overall IT strategies to support the next generation of CRM business strategies. At present the IT systems and platforms are sufficient yet they will need to change over time to stay current. Figure 1 illustrates how the ETL structure exists today at FAC, highlighting the role of the data warehouse. FAC will need to streamline this architecture if they are going to continually improve.

Figure 1: Topology of an Enterprise Data Warehouse and its relationship to Business Intelligence Based on analysis of the following sources: (Raisinghani, 2000)

(Shahzad, Mustafa, 2010)

Implications and Recommendations for First American Corporate Strategy

In order for FAC to continually grow and attract new customers while holding onto their existing ones, the existing CRM system needs to be augmented to better align with the strategic priorities and needs of the comp[any. While the many accomplishments and advances are impressive, there is significant room for improvement in the following areas. What is missing from the entire strategy today is the dimension of trust, both within the comp[any for the system, and most importantly, outside the company as FAC aspires to be the trusted advisor for their clients.

The long-term success of the entire initiative however rests on how well FAC can gain and keep the trust of its employees. With 100% turnover in certain areas and a common 25 -- 30% turnover rate in others, FAC has a very significant cost drain going on in the area of change management alone. Based on these factors the first and most significant recommendation is to appoint a senior level executive, potentially a C-level manager who can arbitrate the inherent conflicts that occur when implementing CRM systems and also navigate beyond the often complex problems of process re-engineering and process elimination if necessary (Power, 2009). A leader of this initiative at the C-level will also ensure it stays on track from a prioritization standpoint, not deviating into other areas of the organization as well. One of the most powerful aspects of having a C-level executive be the champion of these programs is the ability to create a greater level of urgency for fully accomplishing all Phase 5 deliverables including having complete production testing and validation of the warehouse done as well. Ultimately the C-level sponsor will be the internal trusted advisor for employees on this project and has the potential to completely re-order the adoption rate or acceptance rate while also significantly increasing the level of faith employees have in it as well (Fay, Zahay, 2005).

The second recommendation based on this analysis centers on creating a more effective approach to unifying the data surrounding personas of the customer and their needs. Today there is a wealth of data within the company, with multiple audiences being served by them as diverse as direct and indirect users. What needs to happen is that the lifecycles of each customer segment need to be more accurately and effectively understood, and analytics need to be used to better understand preferences, unmet needs, perceptions of FAC and the ongoing evaluation of substitutes. What is challenging for FAC right now is the breath of data available, yet only partially used frameworks for putting it into context. For a CRM system to deliver the total value it is capable of, it must have a full 360-degree view of the customer (Chan, 2005). This equally applies to data used internally in addition to the market-based and services-based information shared with customers (Fay, Zahay, 2005). By concentrating more on this aspect of the TCS, the opportunity exists to deliver a consistently higher level of performance to customers as well.

The final recommendation is to more closely align the many CRM systems and strategies to corporate objectives. Today the level of accomplishment is exceptional, yet there are many manually-based process workflows in place which introduce the potential of errors. Instead of concentrating on the tactical however, TAC needs to better align their CRM systems to the intended customer experience consequences they are attempting to create and deliver. Only then will TAC be able to attain the full value of these powerful systems and analytics that have the potential to redefine their business.

References

Bernoff, J., & Li, C. (2008). Harnessing the power of the oh-so-social web. MIT Sloan Management Review, 49(3), 36-42.

Chan, J.O. (2005). Toward a unified view of customer relationship management. Journal of American Academy of Business, Cambridge, 6(1), 32-38.

Cunningham, C., Il-Yeol Song, & Chen, P.P. (2006). Data warehouse design to support customer relationship management analyses. Journal of Database Management, 17(2), 62-84.

Dver, A. (2003). The future of CRM technology. Information Management, 13(7), 22-22.

Fay, C.P., & Zahay, D. (2003). Understanding why marketing does not use the corporate data warehouse for CRM applications. Journal of Database Marketing & Customer Strategy Management, 10(4), 315-326.

Fay, C.P., & Zahay, D. (2005). Why doesn't marketing use the corporate data warehouse? The role of trust and quality in adoption of data-warehousing technology for CRM applications. The Journal of Business & Industrial Marketing, 20(4), 237-244.

Hay, D. (2003). Analyze that?; ensuring data quality for successful CRM. Information Management, 13(11), 22-22.

McKim, B., & Hughes, A.M. (2001). How to measure customer relationship management success. Journal of Database Marketing & Customer Strategy Management, 8(3), 224-231.

Raisinghani, M.S. (2000). Adapting data modeling techniques for data warehouse design. The Journal of Computer Information Systems, 40(3), 73-77.

Tim O'Reilly. (2006, July). Web 2.0: Stuck on a Name or Hooked on Value? Dr. Dobb's Journal,…[continue]

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