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Section-A

problem1) describe the following terms :

a) Distributed decision support systems and marketing information system

b) Functions of decision support system.

problem2) describe the international/multinational/global marketing information system model. Describe different problems inherent in the operation and coordination of a multinational /global marketing information system.

problem3) What is market Analytical system? List and describe different data mining tools and techniques used by this system for supporting marketing decisions.

problem4) “Marketing Intelligent System is not purely a computer-based system. It is a total system that incorporates human processes for interpreting and processing information into intelligence.” Comment on this statement.

Section-B

Case Study: BI Helps Allergan Monitor Sales Data

The 12-year-old Allergan India is the pharmaceutical which specializes in ophthalmic products. A joint venture between Allergan and Nicholas Piramal, Rs 100-crore company offers medication for conjunctivitis, dry eyes and glaucoma and has about 17 percent of Rs 430-crore Indian ophthalmic pharmaceutical market. Like other pharmaceutical business, Allergan knows significance of the extensive downstream strategy. It prides itself on having the largest reach in Indian ophthalmic industry with its network of 10,000 ophthalmologists. Company's primary points of sale are 18 clearing and forwarding agents (CFAs). From there products go to the distributor, and chain goes down a couple of tiers, to the wholesaler, retailer and chemist outlets. Between 900 and 1,200 distributors and about 1.2 lakh small distributors and stockiest constitute secondary sales.

This huge network creates plenty of scope for data inaccuracies. As the company grew, it became increasingly crucial to monitor parameters like DSO (Day Sales Outstanding), that is used to gauge performance of every division and DOH (the Day's On Hand), which is a measure of inventory. It was then that they realized there was the fair amount of inefficiency in the system. That's when we decided to introduce BI tracing the need for RUBIC (Re-usable Business Intelligent Components). The association with Rubik's cube is no accident. The cube represented the two things Allergan wanted from its IT team: a single version or block of truth and multiple sides to look at it.

Which is why like most companies, Allergan, choose to implement BI: to give business more actionable information. But, Allergan also required BI to create a platform that its executives can collectively work off. The year 2006 saw a decision to implement a BI solution that can throw up solutions to this problem. In 14 months, with Mindtree (who had helped build Empower) as their technology partner,

Rajan set up RUBIC. The tool had an SQL 2005 server at the backend and the extraction transformation loading (ETL) tool in the middle. ETL extracts and cleans data then coverts it into the standard format. It then puts data into the local ERP which feeds data warehouse. The warehouse is fed from various transactional level systems. A varietyof analytics, static and dynamic query capabilities were built on this. Reports are available through a presentation layer (on Windows Share Point), and allowed a comprehensive representation of various key business performance indicators.

His solution paid off in a big way. While the rest of the pharmaceutical industry grew between 5 and 6 percent in the last fiscal, Allergan India registered 20 percent growth. Rajan modestly acknowledged that some of that lead is thanks to RUBIC. It also improved company's day sales outstanding (DSO) and its inventory levels. Post-RUBIC, DSO levels dropped by 10 percent and Allergan achieved what few of its pharma peers have managed: it maintains an inventory of less than 20 days. The industry average, says Rajan, is about 45 days.

Figures aside, the biggest advantage RUBIC offers Allergan is the power of informed decision-making. "Earlier, a little bit of guesstimate and a little bit of gut feeling will have seen the decision through. Today, all decisions are data driven," Rajan says.

problem5) Case problems:

a) What are the different sources of data Allergan has? Why we could not use Marketing Information System for analyzing that data?

b) Describe how business warehouse can look at data from various dimensions, and generate as many reports as we want. Give an ex to support your answer.

c) What kind of various mining techniques could be used by Allergan to analyze sales data and what type of strategic decisions they could make.

Marketing Management, Management Studies

  • Category:- Marketing Management
  • Reference No.:- M93511

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