Statistics for managers, Multiple regression analysis
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The project analyzed sales data from Netambit Insurance Broking Pvt Ltd over a period from July 2010 to December 2012, using graphical techniques and multiple regression analysis via Minitab to assess factors affecting insurance policy sales. The data revealed a significant dip in sales during April due to customers' financial strains after the financial year's first quarter. The regression equation identified policy count and brokerage as crucial predictors for premium, thus suggesting that reducing brokerage could potentially increase sales volume.
Statistics for managers, Multiple regression analysis
- 1. Statistics For Managers Live-Project On Multiple Regression Analysis
- 2. Project Introduction • Under this project, we collected sales data from an insurance brooking company “NetAmbit Insurance Brooking Pvt Ltd.”, for consecutive 30 months starting from July-2010 to December-2012. • By using Graphical data representation technique, we made histogram and scattering diagrams for the available data. • We identified independent variables, & thereafter used “Minitab” Statistical software tool to obtain Multiple regression equation.
- 3. Data Overview • Data Obtained, included: • Number if Insurance Policies sold monthly • Premium received (month-wise) • Brokerage received (month-wise) • Interval : July-2010 to December-2012 • In specified Interval, the company generated an approximate Revenue of 400 Crores.
- 4. Collected Data (Brief) Month (MMM-YY) Insurance Policy (Unit Sales) July – 2010 235 August – 2010 2822 September – 2010 2618 October – 2010 4246 November – 2010 5532 December – 2010 6441 January – 2011 4978 February – 2011 6103 Mar – 2011 12787 April – 2011 11226 May – 2011 11426 June – 2011 13880 July – 2011 13640 August – 2011 13647 September – 2011 14413 October – 2011 13694 November – 2011 16191 December – 2011 20256 January – 2012 17962 February – 2012 18280 March – 2012 21810 April – 2012 12999 May – 2012 18855 June – 2012 19924 July – 2012 18683 August – 2012 19541 September – 2012 19941 October – 2012 19199 November – 2012 19844 December - 2012 22756
- 5. Unit Policy Sales 0 5000 10000 15000 20000 25000 2010 2011 2012 jan feb march april may june july aug sept oct nov dec Started & Hired; Invested in Technology; JFM exponential increase; Approx flat in mid-yr; high in yr-end; drop after JFM
- 6. Problem Sales Figure shows a dip during the month of April. Why?
- 7. Regression Line 0 5 10 15 20 25 30 35 0 5000 10000 15000 20000 25000 Highly ???????
- 8. Depth Analysis: Jan-12 to Dec-12 0 2 4 6 8 10 12 14 0 5000 10000 15000 20000 25000 April Highly ???????
- 9. Depth Analysis: Jan-11 to Dec-11 0 2 4 6 8 10 12 14 0 5000 10000 15000 20000 25000 April Highly ???????
- 10. Depth Analysis: Jul-10 to Dec-10 0 1 2 3 4 5 6 7 0 1000 2000 3000 4000 5000 6000 7000 April Not Applicable; Company started operations from July Highly ???????
- 11. Cause of Problem Independent Variable Name Cause New Financial Year As April marks the beginning of a new Financial year, people do not generally feel a need to purchase life insurance policy and therefore the sales show a downward trend during the month of April JFM ended *(JFM = Jan + Feb + Mar) Most of the people tend to purchase the policies by the end the financial year, in the month of JAN-MAR for Tax planning, which becomes a prime reason for low sales in the month of April
- 12. Premium paid during JFM months Premium for policies like LIC, etc. have to be paid By the end of a financial year which impacts the financial position of the customers affecting their ability to buy new policies during this period. Now the propensity to save is more evident. Impact of other expenses People are burdened with other expenses in the beginning of a new financial year which also affects the sale of life insurance policies. Some of the expenses may be the academic fees of children, interest on loans, Independent Variable Name Cause of Problem Cause of Problem
- 13. Limitation Faced • We rationally came up with independent variable, as discussed before. • But Multiple Regression Analysis using MINITAB, required discrete numeric values available in Companies data. • So, we had to take following numbers to determine multiple regression equation: • Number of Policies sold • Premium Received • Brokerage Received
- 14. Multiple Regression Equation Equation Obtained using MINITAB: Premium = 405048 + (3735 x Policy count) + (3.45 x Brokerage) Predictor Coef SE Coef T P Constant 405048 217536 1.86 0.063 Policy count 3735.3 137.8 27.11 0.000 Brokrage In 3.4499 0.1123 30.73 0.000 S = 3498572 R-Sq = 94.2% R-Sq(adj) = 94.2%
- 15. Conclusion and Solution • Being Insurance brooking company, sales see a dip during April, because customers have spent enough money during JFM months and they tend to save money after that. • Hence in this case, as a brooking company, I know that principal cost of booking a policy cannot be decreased by policy issuing firm, though my brokerage charges can be decreased by my side, so the grand total amount paid my customer is decreased, and as a result policy count will increase. • This can be said as P-value for brokerage is 0.00, making this independent variable as a significant predictor of premium.
- 16. Thank You !!! A live-project presentation by Abhitanjay Chaudhary (A0102214093); Owais Ashraf (A0102214161); Rajat Sharma (A0102214079); Anant Prakash Gupta (A0102214086)