Why Customer Do NOT buy drugs through the web?

December 10, 2008

Rob BrockelHuckjin Lee

Sultan AlkauzamSeunghun Seok.

Index

• Description of the marketing issue to be analyzed using the dataset

• PEW Data

• Model Statement and Hypothesis

• Description of Data

• Interpretation of the findings

• Summary and Conclusion

PROLOGUE

• Online Prescription Drugs (3:37)

• Ad – Online Pharmacy : Canada Pharmacy (0:30)

• How to save money? (1:29)

• Hazards of Buying Prescription drugs online (June 2007)

(2:11)

Description of the marketing issue

• Pew Survey Data

• Cross Sectional Data• Single period

• Disaggregated Data (individual level)

• Issue to be analyzed.

• Why Customer Do NOT buy drugs through the web.

Pew Data

• Classify Variables to categories:

• usage of computer, the internet.• Usage and perception toward search engines• Perception on buying drugs on the internet• Demographics• Restriction on using the net for children.• Opinions about the content of the media Channels (the net, TV)

Pew Data

• Categories are taken in the model:

• usage of computer, the internet.• Usage and perception toward search engines• Perception on buying drugs on the internet• Demographics

Why Buying drugs interesting ?

• Drugs market Grew 3.8 Percent in 2007 (sale $286.5 Billion)• Huge chance for Pioneer to grow through (3.8% Buy)

Why Buying drugs interesting ?

• Marketing through the net has boomed last 10 years.

Type of Model Used

• PEW Data Set

• Our dependent variable (DO4): Have you ever PURCHASED prescription drugs on the Internet, whether you placed the order yourself or someone else did it for you?

• Yes or No question – Must use Binary Logistic Model

Model Specification

• The problem we chose to discuss – why people are more likely NOT to buy prescription drugs online.

• In our regression analysis we assigned 1 for NO and 0 for YES

• No being people did not buy drugs online• Yes being people did buy drugs online

• Our independent variables were chosen based on relevance

• Used a significance level of 0.05

Choosing Independent Variables

• Originally we went through every variable in the PEW data set to choose which ones we felt were relevant to the decision making process of whether or not to buy drugs online

• Also included all demographic variables

• Ran model

• Looked at significance levels of all variables

• Continued to run regressions and purge variables until significance level was below 0.05

Independent Variables

• SE5•Thinking about your own Internet searches…How much of the information you search for online is information that you absolutely NEED TO FIND, for instance to accomplish an important task or answer an urgent question you have?

• DO1• Switching topics…Have you, personally, ever gone on the Internet to look for information about prescription drugs?

• DO16• In general, do you think purchasing prescription drugs online is as safe as purchasing them at a local pharmacy, or do you think purchasing them online is LESS safe?

• DO18• Others argue that people should not be allowed to purchase prescription drugs online because not all online pharmacies are licensed in the United States, and there’s no way to guarantee the safety of drugs that come from other countries. Do you agree or disagree with that argument?

Independent Variables

• AGE• What is your age?

• HAND

• Does any disability, handicap, or chronic illness keep you from participating fully in work,

school, housework, or other activities, or not?

• RXD• Do you, personally, take prescription drugs on a regular basis, only occasionally, or never?

• INC

• Last year, that is in 2003, what was your total family income from all sources, before taxes. Just stop me when I get to the right category… (READ 1-8)

Hypothesis

• Eight Independent Variables

• Based on intuition we felt people would be more likely NOT to buy drugs online because of:

• Safety• Lack of Infrastructure• Less Income• Confidence in Online Search• Demographic Variables

• Overall we feel that currently people will not want to purchase prescription drugs online for the reasons above

• Familiarity with the internet and computer infrastructure standardization will increase online prescription drug buying over the coming years

Description of Data

• Total number of Pew data set – 3,938• 3,930 data were included in our analysis

• Dependent Variable

DO4: Have you ever PURCHASED prescription drugs on the Internet, whether you placed

the order yourself or someone else did it for you?

Fre-

quencyPercent Valid Percent Cumulative Percent

Valid

Yes 150 3.8 3.8 3.8

No 3780 96.2 96.2 100.0

Total 3930 100.0 100.0

Description of Data (Independent Variables)

SE5: Thinking about your own Internet searches…How much of the information you search for online is information that

you absolutely NEED TO FIND, for instance to accomplish an important task or answer an urgent question you have?

Frequency Percent Valid Percent Cumulative Percent

Valid All, 255 6.5 12.3 12.3

Most, 665 16.9 32.0 44.2

Some, or 645 16.4 31.0 75.2

Very little? 500 12.7 24.0 99.2

Don’t know/Refused 16 .4 .8 100.0

Total 2081 53.0 100.0

Missing System 1849 47.0

Total 3930 100.0

DO1: Switching topics…Have you, personally, ever gone on the Internet to look for information about prescription drugs?

Frequency Percent Valid Percent Cumulative Percent

Valid

Yes 810 20.6 20.6 20.6

No 3111 79.2 79.2 99.8

Don’t know/Refused 8 .2 .2 100.0

Total 3930 100.0 100.0

Description of Data (Independent Variables)

DO16: In general, do you think purchasing prescription drugs online is as safe as purchasing them at a local pharmacy, or

do you think purchasing them online is LESS safe?

Frequency Percent Valid Percent Cumulative Percent

Valid As safe 782 19.9 19.9 19.9

Less safe 2439 62.0 62.0 81.9

(VOL) Depends 157 4.0 4.0 85.9

Don’t know/Refused 553 14.1 14.1 100.0

Total 3930 100.0 100.0

DO18: Others argue that people should not be allowed to purchase prescription drugs online because not all online phar-

macies are licensed in the United States, and there’s no way to guarantee the safety of drugs that come from other coun-

tries. Do you agree?

Frequency Percent Valid Percent Cumulative Percent

Valid Agree 2787 70.9 70.9 70.9

Disagree 800 20.4 20.4 91.3

Don’t know/Refused 343 8.7 8.7 100.0

Total 3930 100.0 100.0

Description of Data (Independent Variables)

HAND: Does any disability, handicap, or chronic illness keep you from participating fully in work, school, housework, or

other activities, or not?

Frequency Percent Valid Percent Cumulative Percent

Valid Yes 636 16.2 16.2 16.2

No 3270 83.2 83.2 99.4

Don’t know/Re-

fused25 .6 .6 100.0

Total 3930 100.0 100.0

RXD: Do you, personally, take prescription drugs on a regular basis, only occasionally, or never?

Frequency Percent Valid Percent Cumulative Percent

Valid Regular basis 1775 45.2 45.2 45.2

Occasionally 1048 26.7 26.7 71.8

Never 1068 27.2 27.2 99.0

Don’t know/Refused 39 1.0 1.0 100.0

Total 3930 100.0 100.0

Description of Data (Independent Variables)

INC: Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

INC Last year, that is in 2003, what was your total family income from all sources, before

taxes. Just stop me when I get to the right category. (READ)3938 1 9 5.34 2.609

Valid N (listwise) 3938

Table 2-8. INC Last year, that is in 2003, what was your total family income from all sources, before taxes.

Frequency Percent Valid Percent Cumulative Percent

Valid

Less than $10,000 281 7.2 7.2 7.2

$10,000 to under $20,000 446 11.3 11.3 18.5

$20,000 to under $30,000 461 11.7 11.7 30.2

$30,000 to under $40,000 427 10.9 10.9 41.1

$40,000 to under $50,000 323 8.2 8.2 49.3

$50,000 to under $75,000 584 14.9 14.9 64.2

$75,000 to under $100,000 356 9.0 9.0 73.2

$100,000 or more 347 8.8 8.8 82.1

Don't know/Refused 705 17.9 17.9 100.0

Total 3930 100.0 100.0

Description of Data (Independent Variables)

Table 2-9. AGE: Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

AGE What is your age? 3886 18 97 45.80 17.604

Valid N (listwise) 3886

The model

The model = (-1.016) + 0.289(SE5) + 1.408(DO1) + 0.878(DO16) + (-0.092)(DO18) + (-0.018)(AGE) + 0.680(HAND) + 0.667(RXD) + (-0.3)(INC) + random events

B S.E. Wald Df Sig. Exp(B)Step 1a se5 .289 .106 7.444 1 .006 1.335

do1 1.408 .230 37.593 1 .000 4.087

do16 .878 .193 20.722 1 .000 2.406

do18 -.092 .042 4.725 1 .030 .912

Age -.018 .009 4.438 1 .035 .982

Hand .680 .309 4.834 1 .028 1.974

Rxd .667 .167 16.008 1 .000 1.948

Inc -.300 .059 26.171 1 .000 .741

Constant -1.016 .841 1.460 1 .227 .362

Hypotheses validation

H1: The less critical your online searches, the less likely you are to buy drugs online. (Prescription drugs are an important purchase)

- Coefficient: + 0.289

- People who use the Internet for other purposes are more likely to purchase drugs through the Internet than people who don’t

- Supported

H2: People who have not gone on the internet to search for prescription drugs are less likely to buy them online. (Lack of confidence in internet searching)

- Coefficient: + 1.408

- Supported

- Importance of experience of going online for information about drugs in accordance with purchasing them online because

Hypotheses validation (cont.)

∙ experiences would increase the level of confidence in Internet searching

H3: People who feel that buying drugs online are not as safe are less likely to buy them online. (Less safe)

- Coefficient: + 0.878

- Supported

- The extent to which how safe people feel about purchasing prescription drugs online is considered to be one of the most influential factors in deciding purchase of prescription drugs online

Hypotheses validation (cont.)

H4: People who agree with this statement are less likely to buy prescription drugs online. (Less safe)

- Coefficient: - 0.092

- Supported

- People who disagree with allowing purchasing prescription drugs online are less likely to purchase prescription drugs online because

Hypotheses validation (cont.)

∙ they believe online drugs are not as safe as those in a local pharmacy

H5: Those who are older are less likely to buy drugs online. (Lack of confidence and skills in searching)

- Coefficient: - 0.018

- Not supported

- The older are more likely to buy drugs online because

∙ they need more drugs due to their health conditions

∙ many Internet education programs provided by government and communities help the older to learn how to use the Internet

Hypotheses validation (cont.)

H6: Those who are not handicapped are less likely to buy prescription drugs online. (More mobile)

- Coefficient: + 0.68

- Supported

- They are more likely to purchase drugs online because

Hypotheses validation (cont.)

∙ They might take drugs more often

∙ Convenience and low-cost

H7: People who rarely take prescription drugs are less likely to buy them online. (People who don’t buy regularly would want to see a doctor)

- Coefficient: + 0.667

- Supported

- People who take drugs on a regular basis are more likely to buy drugs online

∙ They need to buy drugs more often and in a large quantity

Hypotheses validation (cont.)

∙ Convenience and low-cost

H8: The higher income people earn, the less likely to purchase prescription drugs online. (Less need for low-cost)

- Coefficient: - 0.3

- Not supported

- People who earn more money are more likely to buy drugs online because

∙ Online purchase require customers to be equipped with proper infrastructure of the Internet and a PC

Hypotheses validation (cont.)

∙ not low-cost, but convenience is the primary reason

Conclusion - segmentation

◆ Online drug market can be segmented by

① whether or not people are handicapped

② the income level

① whether people take drugs on a regular basis

② the extent people rely on online information

- Demographics

- Behaviors

Conclusion - targeting

◆ Marketing managers should target

① those who rely much on online information

② those whose incomes are high

③ those who are handicapped or have disabled family members

④ those who believe that online drugs are as safe as those in a pharmacy

⑤ those who take drugs on a regular basis

⑥ the elder

⑦ those who believe that people should be allowed to purchase prescription drugs online

⑧ potential customers

Conclusion – targeting (cont.)

◆ In order to target efficiently - Liftchart

　 Online purchase of drug

# of re-sponses

Percentile of deciles saying Yes Lift Index

1 Yes 55 31.9 1978.7

2 Yes 21 11.2 693.5

3 Yes 11 5.8 356.7

4 Yes 14 7.1 438.8

5 Yes 7 3.3 207.2

6 Yes 0 .0 .0

7 Yes 0 .0 .0

8 Yes 2 .8 49.9

9 Yes 0 .0 .0

10 Yes 3 1.6 100.0

Conclusion – Targeting (cont.)

◆ Advertising

- Safety is a crucial factor

- Ads with trusting celebrities with a positive image to inform people that online drugs are as safe as those in a local pharmacy

- Place the ads on popular search engines to be exposed to those who often use the Internet for information that they need