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MKT 798 (Zheng, Mandi, Mei)

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Page 1: MKT 798 (Zheng, Mandi, Mei)
Page 2: MKT 798 (Zheng, Mandi, Mei)

AGENDA

● SANOFI

● METHODOLOGY

● FINDINGS

● RECOMMENDATIONS

Page 3: MKT 798 (Zheng, Mandi, Mei)

● $33,770 million euros in 2014.● 4th Largest Pharmaceutical Company in Revenues

Worldwide.

SANOFI Profile

Page 4: MKT 798 (Zheng, Mandi, Mei)

SANOFI Profile

Page 5: MKT 798 (Zheng, Mandi, Mei)

Lantus® • A long-acting human insulin analog, market leader for branded insulins

(IMS source). • Available in more than 120 countries worldwide.• one of the best-selling drugs in the world at $7.6 billion in 2013 sales.

Backgrounds

Page 6: MKT 798 (Zheng, Mandi, Mei)

Toujeo● Follow up to Lantus: improved / less risk● Strategies:

○ New patients VS Loyalty patients○ Lower price VS Normal price○ More rebates VS Less rebates

Backgrounds

Page 7: MKT 798 (Zheng, Mandi, Mei)

Methodology

○ Linear Regression

○ Ordinal Logistic Regression Models

○ Multinomial Regression Models

Page 8: MKT 798 (Zheng, Mandi, Mei)

Parameter Estimates(Lantus)Variable Label D

FParamet

erEstimate

Standard

Error

t Value

Pr > |t|

Intercept Intercept 1 1.15150 0.15888 7.25 <.0001Medicaid Rebate C Medicaid

Rebate1 0.000002

247.79268

E-72.87 0.0046

Medicare Rebate

C Medicare Rebate

1 0.00002433

0.00000608

4.00 <.0001

LongTermCare Rebate

C LongTerm CareRebate

1 0.00002644

0.00000360

7.35 <.0001

Parameter Estimates(Lantus)Variable Label DF Paramet

erEstimate

Standard

Error

t Valu

e

Pr > |t|

Intercept Intercept 1 1.50096 0.18599 8.07 <.0001Speaker

ProgramsC Speaker Programs

1 0.00001039

0.00014234

0.07 0.9419

Sales Force Expense

Sales Force Expense

1 0.00000404

0.00000248

1.63 0.1054

rebate rebate 1 0.00000418

8.306757E-7

5.03 <.0001

Parameter Estimates (Toujeo)

Variable Label DF ParameterEstimate

Standard

Error

t Value

Pr > |t|

Intercept Intercept 1 1.49652 0.09062 16.51 <.0001

medicaidRebate

medicaidRebate

1 0.00000596

0.00000111

5.38 <.0001

medicareRebate

medicareRebate

1 0.00014704

0.00002737

5.37 <.0001

LongTermCareRebate

LongTermCare Rebate

1 0.00003762

0.00000621

6.06 <.0001

Parameter Estimates (Toujeo)Variable Label DF Parameter

EstimateStandard

Errort

ValuePr > |t|

Intercept Intercept 1 1.42672 0.25109 5.68 <.0001

Vouchers Vouchers 1 0.00045002 0.00030286

1.49 0.1411

Sales ForceExpense

Sales ForceExpense

1 -0.00000343

0.00000217

-1.58 0.1189

speakerPrograms

speakerPrograms

1 -0.00002051

0.00003894

-0.53 0.5999

Samples SSamples 1 0.00009594 0.00003661

2.62 0.0104

rebate rebate 1 0.00000930 0.00000169

5.50 <.0001

Linear Regression

Page 9: MKT 798 (Zheng, Mandi, Mei)

Ordinal Logistic Regression - Lantus Rebates Programs

Odds Ratio Estimates

Effect Point Estimate

95% WaldConfidence Limits

Medicaid 2 vs 1 1.383 0.746 2.566Medicaid 3 vs 1 4.101 1.928 8.722Medicare 2 vs 1 2.199 1.182 4.089Medicare 3 vs 1 3.636 1.510 8.759

Long Term Care 1 vs 2 >999.999 <0.001 >999.999

Long Term Care 3 vs 2 5.933 2.952 11.924

c 0.731

Net Income(From: Net Income

New)

Net Income (Into: Net Income New)

1 2 3 4 Total

1 (70.6%) 3618.00

94.50

52.50

10.50

5125.50

2 (17.6%) 199.50

94.50

147.00

94.50

5125.50

3 (20.4%) 157.50

115.50

105.00

136.50

4924.50

4 (49%) 84.00

42.00

136.50

2423.00

4924.50

Total (39.5%) 7839.00

3316.5

4221.00

4723.50

200100

Table of _From_ by _Into_

Page 10: MKT 798 (Zheng, Mandi, Mei)

Marketing Programs

Ordinal Logistic Regression - Lantus

Odds Ratio Estimates

Effect Point Estimate

95% WaldConfidence Limits

Speaker 2 vs 1 2.727 0.484 15.360Speaker 3 vs 1 0.549 0.272 1.109

Sales Force 1 vs 2 1.180 0.651 2.138Sales Force 3 vs 2 4.016 1.126 14.322

Rebates1 vs 2 >999.999 <0.001 >999.999

Rebates 3 vs 2 3.676 2.087 6.475

c 0.681

Net Income(From: Net Income

New)

Net Income (Into: Net Income New)

1 2 4 Total

1 (70.6%) 3620.00

52.78

84.44

4927.22

2 (8.3%) 2614.44

42.22

1810.00

4826.67

3 (0%) 105.56

42.22

2614.44

4022.22

4 (62.8%) 105.56

63.33

2715.00

4323.89

Total (37.2%) 8245.56

1910.56

7943.89

180100.00

Table of _From_ by _Into_

Page 11: MKT 798 (Zheng, Mandi, Mei)

Findings -Lantus

Rebates● Medicaid: Higher expense, higher net-income● Medicare: Higher expense, higher net-income● Long term: Cluster 3 might has higher probability brings

Higher net income when expense is around $49888 (Cluster Center)Marketing programs ● Speaker program: Cluster 2 are more likely to results in

higher income compared to Cluster 1.● Sales Force : Higher expense, higher net-income

Page 12: MKT 798 (Zheng, Mandi, Mei)

Ordinal Logistic Regression - Toujeo Rebates Programs

Odds Ratio Estimates

Effect Point Estimate

95% WaldConfidence Limits

Medicaid 2 vs 1 7.397 0.746 2.566Medicaid 3 vs 1 4.554 1.928 8.722Medicare 1 vs 3 0.124 1.182 4.089Medicare 2 vs 3 >999.999 <0.001 >999.999

Long Term Care 2 vs 1 >999.999 <0.001 >999.999

Long Term Care 3 vs 1 5.564 2.541 12.183

c 0.79

Net Income(From: Net Income

New)

Net Income (Into: Net Income New)

1 3 4 Total

1 (82.7%) 4321.50

94.50

00.00

5226.00

2 (0%) 3216.00

178.50

10.50

5025.00

3 (36.7%) 115.50

189.00

2010.00

4924.50

4 (61.2%) 31.50

168.00

3015.00

4924.50

Total (45.5%) 8944.50

6030.00

5125.50

200100.00

Table of _From_ by _Into_

Page 13: MKT 798 (Zheng, Mandi, Mei)

Marketing Programs

Ordinal Logistic Regression - Toujeo

Odds Ratio Estimates

Effect Point Estimate

95% WaldConfidence Limits

Voucher 1 vs 3 1.678 0.6 4.694

Voucher 2 vs 3 3.019 1.109 8.223

Sales Force 2 vs 1 0.677 0.237 1.934

Sales Force 3 vs1 <0.001 <0.001 >999.999

Speaker 2 vs 1 0.640 0.196 2.084

Sample 3 vs 1 2.503 0.999 6.272

Rebates 2 vs 1 8.802 3.238 23.922

Rebates 3 vs 1 >999.999 <0.001 >999.999

c 0.809

Net Income(From: Net Income

New)

Net Income (Into: Net Income New)

1 2 3 4 Total

1 (66.7%) 2022.47

88.99

22.25

00.00

3033.71

2 (37%) 1112.36

1011.24

55.62

11.12

2730.34

3 (42.9%) 22.25

88.99

910.11

22.25

2123.6

4 (36.4%) 00.00

11.12

66.74

44.49

1112.36

Total (48.3%) 3337.08

2730.34

2224.72

77.87

89100.00

Table of _From_ by _Into_

Page 14: MKT 798 (Zheng, Mandi, Mei)

Findings -ToujeoRebates● Medicaid: Higher expense, higher net-income● Medicare: Cluster 1 might has slightly higher probability to

bring higher net-income compared to cluster 3. The cluster center for Cluster 1 is $1136.● Long term: Cluster 3 might has higher probability brings

Higher net income when expense is around $21287 (Cluster Center)

Marketing Programs• Vouchers: Cluster 3 has higher probability brings Higher

net income when expense is around $739 (Cluster Center)

Page 15: MKT 798 (Zheng, Mandi, Mei)

Recommendations

● Reposition Lantus to focus more on lower income patients.

● Control the marketing costs for Lantus and spend the money on the expenditures for Toujeo.

● Minimize voucher cost for Toujeo.

Page 16: MKT 798 (Zheng, Mandi, Mei)

● More data on:

-Target customer purchase behaviors

-Lantus’ Competitions

Next Step

Page 17: MKT 798 (Zheng, Mandi, Mei)

Thank you!

Page 18: MKT 798 (Zheng, Mandi, Mei)

Medicaid

● In all states, Medicaid provides free or low-cost care for some low-income people, families and children, pregnant women, the elderly, and people with disabilities.

● In some states the program covers all adults below a certain income level.

● Medicaid programs must follow federal guidelines, but coverage and costs may be different from state to state. Some Medicaid programs pay for your care directly. Others use private insurance companies to provide Medicaid coverage.

Appendix

Page 19: MKT 798 (Zheng, Mandi, Mei)

Appendix

Score Test for the ProportionalOdds Assumption

Chi-Square DF Pr > ChiSq

9.8106 12 0.6326

Model Fit Statistics

Criterion Intercept Only Intercept andCovariates

AIC 560.438 508.160

SC 570.333 537.845

-2 Log L 554.438 490.160

Analysis of Maximum Likelihood Estimates

Parameter   DF Estimate StandardError

WaldChi-

Square

Pr > ChiSq

Intercept 4 1 -2.6343 0.3221 66.8808 <.0001

Intercept 3 1 -1.2671 0.2750 21.2246 <.0001

Intercept 2 1 0.0864 0.2596 0.1109 0.7391

C_R1 2 1 0.3245 0.3151 1.0606 0.3031

C_R1 3 1 1.4112 0.3850 13.4320 0.0002

C_R2 2 1 0.7879 0.3166 6.1946 0.0128

C_R2 3 1 1.2909 0.4485 8.2835 0.0040

C_R3 1 1 15.4864 916.0 0.0003 0.9865

C_R3 3 1 1.7805 0.3561 24.9955 <.0001

Page 20: MKT 798 (Zheng, Mandi, Mei)

Appendix

Score Test for the ProportionalOdds Assumption

Chi-Square DF Pr > ChiSq

6.0118 12 0.9155

Model Fit Statistics

Criterion Intercept Only Intercept andCovariates

AIC 503.857 479.182

SC 513.436 507.919

-2 Log L 497.857 461.182

Analysis of Maximum Likelihood Estimates

Parameter   DF Estimate StandardError

WaldChi-Square

Pr > ChiSq

Intercept 4 1 -2.0727 0.2862 52.4493 <.0001

Intercept 3 1 -0.9170 0.2492 13.5381 0.0002

Intercept 2 1 0.4189 0.2409 3.0251 0.0820

C_Speaker 2 1 1.0033 0.8819 1.2942 0.2553

C_Speaker 3 1 0.6000 0.3589 2.7949 0.0946

C_Sales 1 1 0.1654 0.3032 0.2977 0.5853

C_Sales 3 1 1.3904 0.6487 4.5944 0.0321

Crebatenew

1 1 15.5484 916.5 0.0003 0.9865

Crebatenew

3 1 1.3017 0.2889 20.3088 <.0001

Page 21: MKT 798 (Zheng, Mandi, Mei)

Appendix

Score Test for the ProportionalOdds Assumption

Chi-Square DF Pr > ChiSq

15.8638 12 0.1976

Model Fit Statistics

Criterion Intercept Only Intercept andCovariates

AIC 560.399 455.860

SC 570.293 485.545

-2 Log L 554.399 437.860

Analysis of Maximum Likelihood Estimates

Parameter

  DF Estimate StandardError

WaldChi-

Square

Pr > ChiSq

Intercept 4 1 -1.1714 0.4189 7.8203 0.0052

Intercept 3 1 0.6185 0.4143 2.2284 0.1355

Intercept 2 1 2.2070 0.4302 26.3207 <.0001

S_R1 2 1 2.0011 0.6181 10.4819 0.0012

S_R1 3 1 1.5160 0.3148 23.1840 <.0001

S_R2 1 1 -2.0887 0.4176 25.0210 <.0001

S_R2 2 1 12.7331 881.3 0.0002 0.9885

S_R3 2 1 16.8230 881.3 0.0004 0.9848

S_R3 3 1 1.7164 0.3998 18.4266 <.0001

Page 22: MKT 798 (Zheng, Mandi, Mei)

Appendix

Score Test for the ProportionalOdds Assumption

Chi-Square DF Pr > ChiSq

18.7908 16 0.2797

Model Fit Statistics

Criterion Intercept Only Intercept andCovariates

AIC 242.307 205.517

SC 249.773 232.892

-2 Log L 236.307 183.517

Analysis of Maximum Likelihood Estimates

Parameter   DF Estimate StandardError

WaldChi-Square

Pr > ChiSq

Intercept 4 1 -4.1860 0.6355 43.3927 <.0001

Intercept 3 1 -2.1072 0.4550 21.4508 <.0001

Intercept 2 1 -0.3101 0.3861 0.6450 0.4219

S_Voucher 1 1 0.5175 0.5249 0.9718 0.3242

S_Voucher 2 1 1.1051 0.5112 4.6736 0.0306

S_Sales 2 1 -0.3907 0.5360 0.5312 0.4661

S_Sales 3 1 -28.4081 563.2 0.0025 0.9598

S_Speaker 2 1 -0.4467 0.6025 0.5497 0.4585

S_Sample 3 1 0.9176 0.4686 3.8342 0.0502

Srebatenew

2 1 2.1749 0.5101 18.1772 <.0001

Srebatenew

3 1 15.5274 343.4 0.0020 0.9639