B.Sc. (Computer Science) Sem 6 Customer Relationship Management Mar 2025 Question Paper - Mumbai University | munotes

Questions asked in this paper

• 2) Figures to the right indicate marks

1. Q3 Illustrations, in-depth answers and diagrams will be appreciated
2. Q4 Mixing of sub-questions is not allowed
3. Q1 Attempt ANY FOUR from the following: (20M)
   • (a) Define CRM and explain how it helps in better decision making
   • (b) Explain Peppers and Roggers model of CRM
   • (c) Explain how customization helps in building relationships?
   • (d) Write short note on Lead management
   • (e) Describe the key factors that will drive the profitability of customer insights
   • (f) Differentiate between analytical and operational CRM
4. Q2 Attempt ANY FOUR from the following: (20M)
   • (a) Define Customer Portfolio Management and explain cross selling and up selling with example
   • (b) How time series helps in Forecasting Sales in organization from time to
   • (c) Explain how internal and external environment are examined by SWOT
   • (d) Enlist and explain Key features of Data mining
   • (e) “Internet and Event Marketing helps in promoting the
   • (f) Explain any 5 functionalities offered by service automation Software
5. Q3 Attempt ANY FOUR from the following: (20M)
   • (a) Explain some common techniques used in Big data analytics
   • (b) Write short note on Structured and Unstructured Data
   • (c) How service level management controls level of service offered to
   • (d) With neat labelled diagram explain the key phases of CRM project design
   • (e) What is market analytics? Explain with example
   • (f) Enlist and explain Customer Service Standards
6. Q4 Attempt ANY FIVE from the following: (15M)
   • (a) Write short note on customer retention
   • (b) Mention and explain any 3 tools used for analysis of structured data
   • (c) Enlist the benefits of customer and market segmentation
   • (d) Explain any three loyalty management offers
   • (e) Write short note on asset management
   • (f) Explain in short sentimental and predictive analytics

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