Week 9: Synchronization

The article discusses about two cases, 3M and Rewiring Thomson Financial. In the case of 3M the fragmentation was very awful as sales representative of each division need to collect all the information from customer by calling them individually and then store all that information in its own database. As a result it was impossible for the company to retrieve the profit from an individual customer. Each business units had to maintain and update its customer database separately which increases the expense of the company. The same situation arose when the company moved online. In fact 40% of the customers address was invalid. Thus the company decided to build its own database and spend $20 million to create a global data warehouse. By synchronizing and collecting all the data in the single data warehouse the company can redesign, reaggregate, and reconfigure its offerings to better fulfill the needs of groups of customers or to capitalize on market opportunities. Thus it can be said that if the company has a strong organizational culture it can overcome any single issue.

On the other hand, the case of Thomson Financial followed the customer scenario model discussed but in a slightly different way. The company segmented its customers into three groups which are portfolio managers, equity analysts, and traders. The company then studied each segment in details and based on the result of this study, it directed packaged products to the appropriate segment. Thomson Financial traditionally offers its customer a fragment set of product controlled by the isolated business unit. To solve this problem they created a set of tailored offering individual customer segmentation. In addition, organizations need to organize themselves to meet how their customers think about them and their products.

Thus from the article it can be said that synchronization can lead a major issue for the organization, so to resolve the issue organization should adopt a strategy which overcome all the issues. To make your organization more responsive and efficient you need to break down the walls between your units, rather than manage the information.

Week 8: Customer Data Integration

Organizations fail to achieve ROI from the CRM investments as the organization have no faith on their customers and organization struggle to integrate new channels and point of interaction with marketing sales and service functions. The failure to create a consolidated view of the customer relationship affects basic project objectives, such as a real-time, closed-loop process bridging analytical,
operational and collaborative CRM functions. Data integration plays a vital role at the time of development of CRM system; many CRM systems fail due to inadequate data management and integration capabilities. To resolve the customer related issues organizations will adopt CDI (customer data integration) technologies to resolve data primacy and accuracy issues related to system record of customers.


Solving CRM data integration requires standardization and interoperability. There are many barriers to achieve success for CRM data integration that includes department-centric applications containing master data such as cross-functional information as individual units maintain separate customer lists for local business processing. Customer master files affect CRM implementation, delay in goal, traditional integration method which add more complexity as new organization requires some system change, interface modifications and data quality diagnostics. Some organizations might solve customer data quality issue by using enterprise architecture techniques and approaches. The organization facing such data integrity problems should adopt CDI (customer data integration) solutions to resolve the issue; earlier adoption of solution will help the organization to gain success in business.

The CDI market consists of processes and technologies for recognizing customer at any touchpoint. There are three categories within the CDI technology market:
1) Infrastructure platform packages
2) Third-party providers
3) Service bureaus and data brokers

CDI delivers the required infrastructure to consolidate and augment transactions into a single customer view. These solutions include foundational components to create a panoramic view of a customer to leverage across CRM functions. CDI also supports collaborative and operational CRM.

CDI helps the organization in synchronizing the data achieving closed-loop CRM processing by sharing customer intelligence across the three primary CRM domains that is analytical, collaborative and operational. Merging CDI solutions with downstream data warehousing helps the organization to integrate a closed-loop CRM business intelligence environment.

Thus to conclude a well-structured CDI strategy has a set of planning, implementation, and ongoing operational requirements that align with the overall CRM strategy.

Week 7 Knowledge Management

The case study presented by David Finnegan applies a processual analysis (Pettigrew, 1997) to the implementation of a Customer Relationship Management (CRM) system from a knowledge management perspective to a contemporary situation within IBM. In the articel it describes about the specific areas neglected in previous CRM studies - sub-cultures, psychological contracts, how tacit knowledge is surfaced and transferred, and with what effects on implementation. It investigates how the system stakeholders and the system itself evolved through encountering barriers, sharing knowledge, finding new uses, inventing work-arounds. The following are some of the issues:

1. End-user involvement.
2.Support from the management and its side-effect.
4. political issue on sharing knowledge.
3.Development schedule (part of resource issues)
5.Data quality and migration issues.
6User training.
7.Data mining and Integration.
8.Lack of customer clear-vision.

The above issues are very similar to the Data warehousing issues. It is said by many authors that around 60% to 80% of the CRM project fails due to strategic issues. In the case, IBM is a company very experience in IT development, and has enough resource and specialist to overcome most technical problems. However, they still encoutner the issues mentioned above and struggle introducing CRM to the organizationi.

Week 6 : Case study Based on article

The case study applies a processual analysis to the implementation of Customer Relationship Management system from a knowledge management perspective to a contemporary situation within a City Council. The main focus of the article is sub-cultures, psychological contracts, how tacit knowledge is surfaced and shared, and with what effects on implementation. David Finnegan and Leslie Willcocks entitled “Knowledge Issues in the Introduction of CRM Systems: Subculture Interactions, Tacit Knowledge sharing and Psychological Contracts”. Their article specifically discusses the knowledge management issues in CRM implementation in Birmingham City Council (BCC).

Issues leading towards the failure of BCC

• Political issues
• Lack of end-user support
• Internal communication
• User involvement
• Data accuracy and consistency
• User training (Lack of training to new staff)
• Selection of Vendor
• Lack of management support
• Loosing of key staff

To conclude, knowledge-sharing culture is critical in implementing CRM system. Thus, organisations implementing CRM should have knowledge management initiatives in place in order to facilitate knowledge sharing between employees, improve staff retention, and prevent knowledge loss when experienced staffs leave the organisations.

Week 5: Data mining

Data mining is powerful tool in finding large set of data. The article discuss about techniques of data mining such as Decision tree, Neural network and CART methodology. Comparing all the three techniques it seems that adoption of decision tree is the most simple technique, but it is not as useful as neural network.In case of decision trees result can be derived according to the design of the tree, so if there is no adequate design of the tree than the result will be different. The back point of decision tree is, one decision tree can find out one patter. There are different splitting rules which could lead to different number of terminal tree nodes, that may also lead to different results from the same data.

A part from decision tree, neural network is one of the other data mining techniques which has been frequently used. Neural network could assist experts in credit card risk assessment, it is generated as a black box; users just feed the training data to construct it and testing data to exam it. The major difference between both the techniques is decision tree are simple and easy to understand, but on other side neural network is bit complicated. With Neural Network, a considerable amount of time and money need to be spent in training as it would require the analyst to have background understanding of Neural Network such as:

How to best design its algorithm ?
How to maintain to get the accurate outcome?

From the article it is seen that both the tools had been widely adopted by the organization but than also it is necessary to have a further research on both the techniques to get an accurate results.

Week 4: Data mining and Knowledge Discovery

‘Data mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules’. (Berry & Linoff 2000) In data mining, data sampling is very important. Data mining is not only automatic process but it is automatic as well as manual process which involves human interaction, because without human interaction and participation organization cannot build the software that is required for gathering the useful information for data mining. Algorithms pattern for proper interaction with organizations data mining, data warehousing is very important. In the presentation of Raman lyer, the author believes that data mining is discovered due to the major gap between disk capacity and the process ability to maintain the data. Data mining can be used for classification, estimation, segmentation, association, forecasting, text analysis and advanced data exploration. Lecturer in the lecture gives an idea about Data mining and Knowledge discovery which can be used in cross pointing the organization. It is said that data mining is the part knowledge discovery. To put in nutshell, Knowledge discovery are more than data mining. People can discover knowledge from various sources as it is and end product of a chain. It is believed that we cannot use the information unless it is not accurate or to the point, and if we cannot use the information that it is not a part of knowledge. Thus the information which is useful can only be said as knowledge. To increase the knowledge any one needs information and data. In short, data mining can also be seen as part of knowledge discovery. This is because knowledge can be gain through different methods, and data mining is one of the methods for gaining the knowledge. Thus in my opinion data mining plays an important role.

Week 3 seminar

In the last week lecture an interested seminar presented by Barry Schwartz, author of The Paradox of Choice discuss about the choice of the customer by giving the best examples. The main focus of the seminar is to convey the message to the industry that by producing a variety of goods will not fulfil the customer needs, along with the variety there should be quality and service which plays a major part for the customer satisfaction. As per the tendency of human nature it can be said that smaller the choice better will be the selection for the product. More option there are, the easier it is to regrate any thing at all the disappoint about the option that you choose. When there are lots of alternative it is easy to the amount of alternative you regret, that make it less satisfy than the one which you have chosen. Even though a person chooses the best from the available option, he/she will never get satisfy, because the expectation of the person will be different from the one they choose. For example buying jeans, in past jeans come in one flavour so there is no expectation, but now when there are many flavours there is more expectation. Human expectation is too high in today’s life; the secret of happiness is low expectation. When there is one style of jeans available you can blame the whole world because there is no other option, but when there are multiple options and person is not satisfied of the thing he/she had purchased than they are the only one who are blamed. Thus it can be said that higher the expectation, higher will be confusion for person.

Now a days as the expectations of the person increases the need for the CRM also increases because it helps in decreasing customer confusion. For example a customer want to buy a mobile phone there are many options and plan available, which makes customer confuse even though he/she is sure which mobile he wants to buy and for what plan, at this point of time CRM is helpful for taking the decision. If the product is not good or suitable for the customer, still CRM tries to convince the customer, it inturns result in to waste of time and money.

In conclusion, a good understanding of the role of CRM is critical in marketing the products to their customers, which is clearly depicted in the example of mobile phone. Thus, CRM is rubbish but it is helpful in increasing customer satisfaction.