Case Study 4 (Business Intelligence ) - Essay Example

Running Head: case study Data mining by 800-Flowers of the of the of the Question 1-800-Flowers operate under virtual value chain system enabled by information and communication technologies. Because of absence of physical means of interaction between the company and millions of customers worldwide, addressing customer service issues instantly assume prime importance for it. In this regard, use of business analytics and data mining prove to be of immense help due to the speed and ease of access to vast data they provide.

For instance, use of real time analytics can be made to address customer log complaints as soon as it is registered. Similarly, trends and subsequent strategies can be developed for customers using multiple contact channels like call centers, website or mobile internet (Freeland 2003, p. 105). One such organization doing exemplary work in this regard is Ebay- world’s largest online marketplace which also uses information systems to attract and retain customers and has developed technology enabled customer relationship management tools.

Question 2 Cross selling is a sales technique where suggestions and recommendations to a customer are made based on his previous purchase, recorded preferences and other details. Use of data mining is of paramount importance in cross selling technique undertaken by 1-800-Flowers. Related product or service can only be recommended only when previously recorded customer taste and preference is available and which matches the expectation of the customer. For instance, a customer who purchases a specific kind of flowers regularly from 1-800-Flowers can be reminded of latest offers and related product and services in flowers section.

Here data mining helps in recording individual customer details, buying patterns, changing needs, frequency and nature of purchase and even budgetary constraints, if any, which can be deciphered from the price ranges purchased by the customer. Question 3 Data mining helps businesses capture prospective clients by devising new business models as they can predict the patterns and buying behavior and target the model accordingly. Also, even existing customers can be retained and made loyal by providing them personalized and preferred choices.

For instance, insurance companies particularly rely on data mining to establish new business models and selecting target markets based on income and savings patterns (Edelstein 2000). This helps companies develop social and structural bonds with esteemed customers surpassing financial level relationships and also maximizes the repeat business revenues. Banks also rely heavily on customer personal and payment data as it helps them identify the defection rates and devise better marketing strategies.

Other related financial products and services can be up sold to the customers who seem loyal and regular to the bank. Even information acts and other regulations are also levied on the basis of such customer data and significance and intensity of its value to respective entities (Vladimir & Carl 2005). Question 4 Multiple channels refer to the number of ways product or service can be obtained by customers. In case of online and internet based selling, multichannel takes a huge form with multiple issues as well.

Multiple channels in such cases can be direct selling, association with offsite retailers, social networking tools, call centers, mobile selling, and others. In this regard, data mining faces serious challenges related to the privacy and security of customer data through different channels. Also, which channels do customers use the most has also to be decided but on what parameters is dicey. Other concerns arise from establishing parameters of commonality of customer satisfaction across different channels, can the existing or new channel achieve demand-supply match or whether multiple channels are complimenting or cannibalizing the sales of each other.

Further, what technological and organizational inputs are required to develop new channels has also to be devised based on channel’s popularity, ease of access and security features. References Edelstein, H. (2000). Building profitable customer relationships with data mining. SPSS White Paper. Retrieved 14 February, 2011 from http://www.crmodyssey.com/Documentation/Documentation_PDF/Building_Profitable_Customer_Relationships_with_Data_Minig.pdf Freeland, J.G. (2003). The ultimate CRM Hand Book: Strategies and concepts for building enduring customer loyalty and profitability.

New York: Tata McGraw Hill. Vladimir, R & Carl, H. (2005). Payments data could be key to bank prosperity. Electronic Payments Week, 2 (5), 1-2. Retrieved 14 February, 2011 from http://mylibrary.wilmu.edu?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=19243329&site=ehost-live