Market Segmentation: Dr Pepper

Author

Joaquin Ramirez

Market Segmentation Analysis Report: Dr Pepper

Introduction

This report presents a comprehensive market segmentation analysis aimed at identifying consumer segments that align with Dr Pepper’s marketing strategies. By leveraging variables related to consumer behavior, preferences, and attitudes, actionable insights have been extracted to inform targeted marketing efforts.

The objective of this analysis is to uncover distinct consumer segments by analyzing key drivers and descriptors derived from the NCS data dictionary. These insights aim to guide targeted marketing strategies, enhance customer engagement, and increase market share for Dr Pepper.

Data Sources: Data were obtained from a personal survey booklet, and the segmentation analysis was conducted using SAS software.

Methodology

In this section, I will outline the key analytical steps taken to perform the segmentation analysis.

The Target variable for segmentation is Dr Pepper, representing consumer preferences for the brand.

The Driver Variables were utilized to effectively segment the market, four key driver variables were selected, representing consumer behaviors that are likely to influence their choices regarding beverages:

   drink_between_meals: Reflects whether the consumer often drinks between meals.

   like_to_try_new_drinks: Captures curiosity and willingness to try new beverages.

   when_on_tv_go_online_get_more: Measures how likely the consumer is to go online after seeing an ad on TV.

   buy_online_or_in_store: Indicates whether the consumer prefers to shop online or in-store.

For the Abstract Constructor Factor Variables: I selected two key factors that were derived from multiple related variables using Principal Component Analysis (PCA):

Indulgence Behavior and Attitude: How likely is the consumer indulging in various indulgent behaviors or habits?

     DRINKING_GET_DRUNK: "The point of drinking is to get drunk."

     OFTEN_DRINK_ALCOHOL: "Often drink alcoholic beverages at restaurants."

     TRY_NEW_FOOD_PRDCT: "I'm usually first to try new food products."

     FEEL_GUILTY_SWEETS: "I feel guilty when I eat sweets."

Environmental Behavior and Attitude: How important does the consumer believe in taking responsibility for the environment?

     PEOPLE_NEED_TO_RECYCLE: "People have a duty to recycle."

     MAKE_EFFORT_TO_RECYCLE: "I make an effort to recycle."

     PEOPLE_RESPONS_TO_RECYCLD_PRDCTS: "People have a responsibility to use recycled products."

     PERSONAL_ENVRNMNT_RESPONSIBLE: "Personal obligation and environmental responsibility."

Next, the Descriptor Variables: provide additional insights into consumer characteristics:

   Competitive Preferences:

      dr_pepper: "I prefer Dr Pepper."

      Coca_Cola: "I prefer Coca-Cola."

      Sprite: "I prefer Sprite."

   Demographic Factors:

      Gender: Male, Female

      Region: Northeast, Midwest, South, West

      Age Groups**: 18-24, 25-49, 50+

   Media Preferences**:

      Spotify**: "Spotify was used during the last 7 days."

      YouTube**: "YouTube was used during the last 7 days."
 
      ESPN**: "ESPN was viewed during the last 7 days."

Factor Analysis

To reduce dimensionality and identify underlying factors, PCA was conducted. Two key factors were derived, reflecting consumer behaviors and attitudes.

Principal Component Analysis (PCA)

The PCA was utilized to extract abstract constructs from multiple variables. The Kaiser Criterion was applied to retain factors with eigenvalues greater than or equal to 1. The analysis resulted in two factors accounting for 52.34% of the total variance (Factor 1: 35.79%, Factor 2: 16.55%).

________________________________________________________________________________________

To further refine our factor selection, we examined the scree plot and applied the elbow criterion. By focusing on the point where the curve begins to flatten, we confirmed the decision to retain two factors. This approach ensures that we capture the most meaningful patterns in the data without overcomplicating the model.

________________________________________________________________________________________

After identifying the two key factors through PCA, the Rotated Factor Pattern was applied to enhance interpretability. Interestingly, the rotation revealed that Factor 1, initially labeled as reflecting Indulgent Behaviors, actually exhibited stronger correlations with variables related to Environmental Responsibility. Conversely, Factor 2, originally linked to Environmental Responsibility, displayed significant associations with Indulgent Behaviors. This shift in factor interpretation underscores the nuanced interplay between consumer behaviors and attitudes, paving the way for a deeper exploration of these patterns.

________________________________________________________________________________________

Building on the insights from factor analysis, K-Means Clustering was employed to further segment the consumer groups. The number of clusters was evaluated using the CCC Plot and Pseudo F Plot, which suggested viable cluster counts of 3, 5, and 9. Through detailed analysis of cluster means and interpretability, a three-cluster solution emerged as the most parsimonious and insightful segmentation.

Number of Clusters R square CCC Pseudo F
3 0.27176 39.855 5622.32
4 0.36802 9.71 5076.37
5 0.42914 21.246 5104.69
6 0.46407 11.616 4523.63
7 0.49418 2.269 4118.01
8 0.51624 34.21 4258.66
9 0.53490 53.675 4263.33

CCC Plot: The CCC plot reveals the first local maximum at k=3, with subsequent notable peaks at k=5 and k=9. These observations suggest that 3, 5, and 9 could be viable cluster counts.

The Pseudo F plot corroborates the findings from the CCC plot, showing the highest value at k=3, with a secondary peak at k=5. Beyond k=5, the values begin to decline, reinforcing the idea that both 3 and 5 clusters are reasonable solutions.

________________________________________________________________________________________

However, determining the best solution requires deeper analysis. To that end, we proceed by examining the Cluster Means to understand the variation within clusters and assess the interpretability of each model.

After reviewing the Cluster Means for both 3 and 5 clusters, clear differences emerge. Cluster 3 exhibits significant variation across most variables, indicating that a simpler 3-cluster model may offer better interpretability. In contrast, while Cluster 5 also shows variation, it introduces additional complexity, which may hinder the clarity of the solution. Based on these observations, a 3-cluster solution appears to be more practical and interpretable.

________________________________________________________________________________________

To ensure that this solution is robust, we conduct a Gap Analysis to confirm the cluster validity and further evaluate the chosen model.

A Gap Analysis was conducted to validate the cluster solutions. While the First Peak and Global Peak initially suggested a 2-cluster solution, the K-Means clustering with 3 clusters was favored for its clarity and ease of interpretation.

By examining the Gap Analysis cluster means, we found that the clusters are well-defined, with clear distinctions between them. This supports the decision to proceed with the 3-cluster solution.

Examining the GAP Analysis cluster means further supports the notion that the clusters are well-defined, demonstrating adequate discrimination between them.

Moreover, a comparison between the K-means and HPCLUS approaches provides additional insights into the strengths of each method. With this confirmation, we can now delve into the detailed findings and descriptions of each cluster, which will provide a clearer understanding of the consumer segments identified through the analysis.

________________________________________________________________________________________

Findings and Cluster Descriptions

Cluster 1 (Seasonal Customers): This cluster is characterized by older individuals, predominantly women, who display a lower preference for beverages compared to other clusters. They tend to be more conservative in trying new drinks and show moderate engagement in online shopping.

Cluster 2 (Media Enthusiasts): Members of this cluster are inclined to consume beverages between meals and demonstrate a strong interest in trying new drinks. They are highly responsive to media prompts and exhibit the highest usage of media platforms such as YouTube and Spotify. The demographic distribution is more balanced in terms of age and gender, and this group shows a higher preference for beverages.

Cluster 3 (Beverage Aficionados): Individuals in this cluster have the highest propensity for drinking between meals and the greatest enthusiasm for trying new beverages. They are particularly responsive to media advertisements and exhibit a preference for online shopping over in-store purchases. This cluster also shows the highest preference for beverages and ranks second in terms of media engagement.

Recommendations

My recommendation is for Dr Pepper to direct their marketing efforts towards Clusters 2 and 3.

  • Cluster 2 comprises individuals who exhibit a positive attitude towards trying new drinks and demonstrate high online activity, including making purchases online.

  • Cluster 3 consists of individuals who are environmentally conscious, enthusiastic about trying new beverages, and favor online shopping. They also show a strong preference for drinks.

Targeting these two clusters would allow Dr Pepper to effectively leverage its advertising and marketing strategies, potentially acquiring new customers, particularly in the southern region of the country.

Conclusion

This market segmentation analysis offers valuable insights for Dr Pepper to refine its marketing strategies by targeting distinct consumer segments. Concentrating on Clusters 2 and 3 enables Dr Pepper to engage with segments that demonstrate a higher propensity for digital media consumption and a willingness to experiment with new beverages. This strategic focus is expected to enhance market presence and foster stronger customer retention.

SAS CODE:

libname mylib "P:\";
filename bigrec "P:\fa15_data.txt"  lrecl = 65576;
data mytemp;
infile bigrec;
input 
myid 1-7

/*Driver Variables */
snack_between_meals_aglo  4280
snack_between_meals_agli  4327                                                  
snack_between_meals_anya  4374
snack_between_meals_neit  4421                                                  
snack_between_meals_dgli  4468                                                  
snack_between_meals_dglo  4515                                                                                                
snack_between_meals_anyd  4562                                                  
try_new_drinks_aglo  4305
try_new_drinks_agli  4352                                                  
try_new_drinks_anya  4399
try_new_drinks_neit  4446                                                  
try_new_drinks_dgli  4493                                                  
try_new_drinks_dglo  4540                                                                                                
try_new_drinks_anyd  4587                                                  
see_on_tv_go_online_more_aglo  5515
see_on_tv_go_online_more_agli  5553                                                  
see_on_tv_go_online_more_anya  5591
see_on_tv_go_online_more_neit  5629                                                  
see_on_tv_go_online_more_dgli  5667                                                  
see_on_tv_go_online_more_dglo  5705                                                                                                
see_on_tv_go_online_more_anyd  5743   
buy_online_or_store_aglo  5518
buy_online_or_store_agli  5556                                                  
buy_online_or_store_anya  5594
buy_online_or_store_neit  5632                                                  
buy_online_or_store_dgli  5670                                                  
buy_online_or_store_dglo  5708                                                                                                
buy_online_or_store_anyd  5746  

/*First Abstract - indulgence */
DRINKING_TO_GET_DRUNK_aglo  4298
DRINKING_TO_GET_DRUNK_agli  4345
DRINKING_TO_GET_DRUNK_anya  4392
DRINKING_TO_GET_DRUNK_neit  4439
DRINKING_TO_GET_DRUNK_dgli  4486
DRINKING_TO_GET_DRUNK_dglo  4533
DRINKING_TO_GET_DRUNK_anyd  4580
OFTEN_DRINK_ALCOHOLIC_aglo  4291
OFTEN_DRINK_ALCOHOLIC_agli  4338
OFTEN_DRINK_ALCOHOLIC_anya  4385
OFTEN_DRINK_ALCOHOLIC_neit  4432
OFTEN_DRINK_ALCOHOLIC_dgli  4479
OFTEN_DRINK_ALCOHOLIC_dglo  4526
OFTEN_DRINK_ALCOHOLIC_anyd  4573
FIRST_TRY_NEW_FOOD_aglo 4310
FIRST_TRY_NEW_FOOD_agli 4357
FIRST_TRY_NEW_FOOD_anya 4404
FIRST_TRY_NEW_FOOD_neit 4451
FIRST_TRY_NEW_FOOD_dgli 4498
FIRST_TRY_NEW_FOOD_dglo 4545
FIRST_TRY_NEW_FOOD_anyd 4592
GUILTY_TO_EAT_SWEETS_aglo 4282
GUILTY_TO_EAT_SWEETS_agli   4329
GUILTY_TO_EAT_SWEETS_anya   4376
GUILTY_TO_EAT_SWEETS_neit   4423
GUILTY_TO_EAT_SWEETS_dgli   4470
GUILTY_TO_EAT_SWEETS_dglo   4517
GUILTY_TO_EAT_SWEETS_anyd   4564

/*SECOND Abstract - Enviromental */
PEOPLE_DUTY_RECYCLE_aglo    4192
PEOPLE_DUTY_RECYCLE_agli    4206
PEOPLE_DUTY_RECYCLE_anya    4220
PEOPLE_DUTY_RECYCLE_neit    4234
PEOPLE_DUTY_RECYCLE_dgli    4248
PEOPLE_DUTY_RECYCLE_dglo    4262
PEOPLE_DUTY_RECYCLE_anyd    4276
MAKE_EFFORT_RECYCLE_aglo    4189
MAKE_EFFORT_RECYCLE_agli    4203
MAKE_EFFORT_RECYCLE_anya    4217
MAKE_EFFORT_RECYCLE_neit    4231
MAKE_EFFORT_RECYCLE_dgli    4245
MAKE_EFFORT_RECYCLE_dglo    4259
MAKE_EFFORT_RECYCLE_anyd    4273
RESPONS_RECYCLD_PRD_aglo    4191
RESPONS_RECYCLD_PRD_agli    4205
RESPONS_RECYCLD_PRD_anya    4219
RESPONS_RECYCLD_PRD_neit    4233
RESPONS_RECYCLD_PRD_dgli    4247
RESPONS_RECYCLD_PRD_dglo    4261
RESPONS_RECYCLD_PRD_anyd    4275
ENVRNMNT_RESPONSIBLE_aglo   4184
ENVRNMNT_RESPONSIBLE_agli   4198
ENVRNMNT_RESPONSIBLE_anya   4212
ENVRNMNT_RESPONSIBLE_neit   4226
ENVRNMNT_RESPONSIBLE_dgli   4240
ENVRNMNT_RESPONSIBLE_dglo   4254
ENVRNMNT_RESPONSIBLE_anyd   4268

/*Descriptor Variables */ 

/*Target Variable */
Dr_Pepper 39807

/* Major Competitor */
Coca_Cola   40127
Sprite      39830


/*Demographics*/

MALE    2383
FEMALE  2384

NORTHEAST   3075
MIDWEST 3076
SOUTH   3077
WEST    3078

Age_18_24   2401
Age_25_49   2407
Age_50  2415

/*Media Variables*/

SPOTIFY 8184
YOUTUBE 8978
ESPN    9625







;

/* the above reads in the raw data from the data file -  now create five point scale variables */
/* now before we create variables lets create formats so we know what each value will mean */

proc format;
value myscale
     1 = "disagree a lot"
     2 = "disagree a little"
     3 = "neither agree nor disagree"
     4 = "agree a little"
     5 = "agree a lot";
value yesno
     0 = "no"
     1 = "yes";




/* do that by creating a new temp sas data set myvars by starting with the temp sas data set mytemp */
data myvars;
set mytemp;

/*Driver Variables */
if snack_between_meals_dglo = 1 then drink_between_meals = 1;                                                  
if snack_between_meals_dgli = 1 then drink_between_meals = 2;                                             
if snack_between_meals_neit = 1 then drink_between_meals = 3;                                                  
if snack_between_meals_agli = 1 then drink_between_meals = 4;                                                  
if snack_between_meals_aglo = 1 then drink_between_meals = 5;     
if try_new_drinks_dglo = 1 then like_to_try_new_drinks = 1;                                                 
if try_new_drinks_dgli = 1 then like_to_try_new_drinks = 2;                                                
if try_new_drinks_neit = 1 then like_to_try_new_drinks = 3;    
if try_new_drinks_agli = 1 then like_to_try_new_drinks = 4;                                                 
if try_new_drinks_aglo = 1 then like_to_try_new_drinks = 5;   
if see_on_tv_go_online_more_dglo = 1 then when_on_tv_go_online_get_more = 1;                                                 
if see_on_tv_go_online_more_dgli = 1 then when_on_tv_go_online_get_more = 2;                                                
if see_on_tv_go_online_more_neit = 1 then when_on_tv_go_online_get_more = 3;    
if see_on_tv_go_online_more_agli = 1 then when_on_tv_go_online_get_more = 4;                                                 
if see_on_tv_go_online_more_aglo = 1 then when_on_tv_go_online_get_more = 5;  
if buy_online_or_store_dglo = 1 then buy_online_or_in_store = 1;                                                 
if buy_online_or_store_dgli = 1 then buy_online_or_in_store = 2;                                                
if buy_online_or_store_neit = 1 then buy_online_or_in_store = 3;    
if buy_online_or_store_agli = 1 then buy_online_or_in_store = 4;                                                 
if buy_online_or_store_aglo = 1 then buy_online_or_in_store = 5;  
/*First Abstract - indulgence */
if DRINKING_TO_GET_DRUNK_dglo = 1   then  DRINKING_GET_DRUNK = 1;
if DRINKING_TO_GET_DRUNK_dgli = 1   then  DRINKING_GET_DRUNK = 2;
if DRINKING_TO_GET_DRUNK_neit = 1   then  DRINKING_GET_DRUNK = 3;
if DRINKING_TO_GET_DRUNK_agli = 1   then  DRINKING_GET_DRUNK = 4;
if DRINKING_TO_GET_DRUNK_aglo = 1   then  DRINKING_GET_DRUNK = 5;
if OFTEN_DRINK_ALCOHOLIC_dglo = 1   then  OFTEN_DRINK_ALCOHOL = 1;
if OFTEN_DRINK_ALCOHOLIC_dgli = 1   then  OFTEN_DRINK_ALCOHOL = 2;
if OFTEN_DRINK_ALCOHOLIC_neit = 1   then  OFTEN_DRINK_ALCOHOL = 3;
if OFTEN_DRINK_ALCOHOLIC_agli = 1   then  OFTEN_DRINK_ALCOHOL = 4;
if OFTEN_DRINK_ALCOHOLIC_aglo = 1   then  OFTEN_DRINK_ALCOHOL = 5;
if FIRST_TRY_NEW_FOOD_dglo = 1  then  TRY_NEW_FOOD_PRDCT = 1;
if FIRST_TRY_NEW_FOOD_dgli = 1  then  TRY_NEW_FOOD_PRDCT = 2;
if FIRST_TRY_NEW_FOOD_neit = 1  then  TRY_NEW_FOOD_PRDCT = 3;
if FIRST_TRY_NEW_FOOD_agli = 1  then  TRY_NEW_FOOD_PRDCT = 4;
if FIRST_TRY_NEW_FOOD_aglo = 1  then  TRY_NEW_FOOD_PRDCT = 5;
if GUILTY_TO_EAT_SWEETS_dglo = 1    then  FEEL_GUILTY_SWEETS = 1;
if GUILTY_TO_EAT_SWEETS_dgli = 1    then  FEEL_GUILTY_SWEETS = 2;
if GUILTY_TO_EAT_SWEETS_neit = 1    then  FEEL_GUILTY_SWEETS = 3;
if GUILTY_TO_EAT_SWEETS_agli = 1    then  FEEL_GUILTY_SWEETS = 4;
if GUILTY_TO_EAT_SWEETS_aglo = 1    then  FEEL_GUILTY_SWEETS = 5;
/*SECOND Abstract - Enviromental */
if PEOPLE_DUTY_RECYCLE_dglo = 1 then PEOPLE_NEED_TO_RECYCLE = 1;                                                 
if PEOPLE_DUTY_RECYCLE_dgli = 1 then PEOPLE_NEED_TO_RECYCLE = 2;                                                
if PEOPLE_DUTY_RECYCLE_neit = 1 then PEOPLE_NEED_TO_RECYCLE = 3;    
if PEOPLE_DUTY_RECYCLE_agli = 1 then PEOPLE_NEED_TO_RECYCLE = 4;                                                 
if PEOPLE_DUTY_RECYCLE_aglo = 1 then PEOPLE_NEED_TO_RECYCLE = 5; 
if MAKE_EFFORT_RECYCLE_dglo = 1 then  MAKE_EFFORT_TO_RECYCLE = 1;
if MAKE_EFFORT_RECYCLE_dgli = 1 then  MAKE_EFFORT_TO_RECYCLE = 2;
if MAKE_EFFORT_RECYCLE_neit = 1 then  MAKE_EFFORT_TO_RECYCLE = 3;
if MAKE_EFFORT_RECYCLE_agli = 1 then  MAKE_EFFORT_TO_RECYCLE = 4;
if MAKE_EFFORT_RECYCLE_aglo = 1 then  MAKE_EFFORT_TO_RECYCLE = 5;
if RESPONS_RECYCLD_PRD_dglo = 1 then  PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 1;
if RESPONS_RECYCLD_PRD_dgli = 1 then  PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 2;
if RESPONS_RECYCLD_PRD_neit = 1 then  PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 3;
if RESPONS_RECYCLD_PRD_agli = 1 then  PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 4;
if RESPONS_RECYCLD_PRD_aglo = 1 then  PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 5;
if ENVRNMNT_RESPONSIBLE_dglo = 1    then  PERSONAL_ENVRNMNT_RESPONSIBLE = 1;
if ENVRNMNT_RESPONSIBLE_dgli = 1    then  PERSONAL_ENVRNMNT_RESPONSIBLE = 2;
if ENVRNMNT_RESPONSIBLE_neit = 1    then  PERSONAL_ENVRNMNT_RESPONSIBLE = 3;
if ENVRNMNT_RESPONSIBLE_agli = 1    then  PERSONAL_ENVRNMNT_RESPONSIBLE = 4;
if ENVRNMNT_RESPONSIBLE_aglo = 1    then  PERSONAL_ENVRNMNT_RESPONSIBLE = 5;


/* now set up binary yes   no variables knowing that missing values get a zero and a 1 gets a 1 */

/*Descriptor Variables*/

/*Target Variable*/
if Dr_Pepper = .  then Dr_Pepper = 0;
if Dr_Pepper = 1 then Dr_Pepper = 1;
/*Competitor*/
if Coca_Cola = .  then Coca_Cola = 0;
if Coca_Cola = 1 then Coca_Cola = 1;
if Sprite = .  then Sprite = 0;
if Sprite = 1 then Sprite = 1;
/*Demographic*/
if MALE  = .  then Male = 0;
if MALE  = 1 then Male = 1;
if FEMALE = .  then Female = 0;
if FEMALE = 1 then Female = 1;
if NORTHEAST = .  then NorthEast = 0;
if NORTHEAST = 1 then NorthEast = 1;
if MIDWEST = .  then MidWest = 0;
if MIDWEST = 1 then MidWest = 1;
if SOUTH = .  then South = 0;
if SOUTH = 1 then South = 1;
if WEST = .  then West = 0;
if WEST = 1 then West = 1;
if Age_18_24 = .  then Age_18_24 = 0;
if Age_18_24 = 1 then Age_18_24 = 1;
if Age_25_49 = .  then Age_25_49 = 0;
if Age_25_49 = 1 then Age_25_49 = 1;
if Age_50 = .  then Age_50 = 0;
if Age_50 = 1 then Age_50 = 1;
/*Media*/
if SPOTIFY = .  then Spotify = 0;
if SPOTIFY = 1 then Spotify = 1;
if YOUTUBE   = .  then YouTube = 0;
if YOUTUBE   = 1 then YouTube = 1;
if ESPN = .  then ESPN = 0;
if ESPN = 1 then ESPN = 1;



/* Assign labels to variables */

label drink_between_meals = 'I often drink between meals';
label like_to_try_new_drinks ='I like to try new drinks';
label when_on_tv_go_online_get_more = 'When I see on tv I go online and find more';
label buy_online_or_in_store ='More likely to buy online than in store';


label DRINKING_GET_DRUNK = 'The point of drinking is to get drunk';
label OFTEN_DRINK_ALCOHOL ='Often drink alcoholic beverages at resturants';
label TRY_NEW_FOOD_PRDCT = 'Im usually first to try new food products';
label FEEL_GUILTY_SWEETS ='I feel guilty when I eat sweets';


label PEOPLE_NEED_TO_RECYCLE = 'People have a duty to recycle';
label MAKE_EFFORT_TO_RECYCLE ='I make an effort to recycle';
label PEOPLE_RESPONS_TO_RECYCLD_PRDCTS = 'People have a response to use recycle products';
label PERSONAL_ENVRNMNT_RESPONSIBLE ='Personal obligation and enviroment responsibility';


label Dr_Pepper = 'I prefer DrPepper';

label Coca_Cola ='I prefer CocaCola';
label Sprite ='I prefer Sprite';

label Male = 'Response by Male';
label Female ='Response by Female';

label NorthEast = 'Region: NorthEast';
label MidWest ='Region: Mid-West';
label South = 'Region: South';
label West ='Region: West';
label Age_18_24 = 'Age: 18-24';
label Age_25_49 ='Age: 25-49';
label Age_50 = 'Age: 50+';

label Spotify ='Spotify was used during last 7 days';
label YouTube = 'YouTube was used during last 7 days';
label ESPN ='ESPN was viewed during last 7 days';






/* now attach the values for each of the variables using the proc format labels */

format
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store

DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS

PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE
myscale.

Dr_Pepper
Coca_Cola
Sprite 
MALE
FEMALE 
NORTHEAST
MIDWEST 
SOUTH 
WEST
Age_18_24
Age_25_49 
Age_50
SPOTIFY
YOUTUBE
ESPN
yesno. 


run;

/* now run freqs to check your work */
proc freq data = myvars;
tables
snack_between_meals_dglo                                                  
snack_between_meals_dgli                                             
snack_between_meals_neit                                                  
snack_between_meals_agli                                                  
snack_between_meals_aglo     
try_new_drinks_dglo                                                
try_new_drinks_dgli                                              
try_new_drinks_neit   
try_new_drinks_agli                                                
try_new_drinks_aglo   
see_on_tv_go_online_more_dglo                                                 
see_on_tv_go_online_more_dgli                                                
see_on_tv_go_online_more_neit    
see_on_tv_go_online_more_agli                                                 
see_on_tv_go_online_more_aglo  
buy_online_or_store_dglo                                            
buy_online_or_store_dgli                                                
buy_online_or_store_neit    
buy_online_or_store_agli                                                 
buy_online_or_store_aglo

DRINKING_TO_GET_DRUNK_aglo  
DRINKING_TO_GET_DRUNK_agli  
DRINKING_TO_GET_DRUNK_anya  
DRINKING_TO_GET_DRUNK_neit  
DRINKING_TO_GET_DRUNK_dgli  
DRINKING_TO_GET_DRUNK_dglo  
DRINKING_TO_GET_DRUNK_anyd  
OFTEN_DRINK_ALCOHOLIC_aglo  
OFTEN_DRINK_ALCOHOLIC_agli  
OFTEN_DRINK_ALCOHOLIC_anya  
OFTEN_DRINK_ALCOHOLIC_neit  
OFTEN_DRINK_ALCOHOLIC_dgli  
OFTEN_DRINK_ALCOHOLIC_dglo  
OFTEN_DRINK_ALCOHOLIC_anyd  
FIRST_TRY_NEW_FOOD_aglo 
FIRST_TRY_NEW_FOOD_agli 
FIRST_TRY_NEW_FOOD_anya 
FIRST_TRY_NEW_FOOD_neit 
FIRST_TRY_NEW_FOOD_dgli 
FIRST_TRY_NEW_FOOD_dglo 
FIRST_TRY_NEW_FOOD_anyd 
GUILTY_TO_EAT_SWEETS_aglo 
GUILTY_TO_EAT_SWEETS_agli   
GUILTY_TO_EAT_SWEETS_anya   
GUILTY_TO_EAT_SWEETS_neit   
GUILTY_TO_EAT_SWEETS_dgli   
GUILTY_TO_EAT_SWEETS_dglo   
GUILTY_TO_EAT_SWEETS_anyd

PEOPLE_DUTY_RECYCLE_dglo                                                 
PEOPLE_DUTY_RECYCLE_dgli                                              
PEOPLE_DUTY_RECYCLE_neit  
PEOPLE_DUTY_RECYCLE_agli                                                 
PEOPLE_DUTY_RECYCLE_aglo
MAKE_EFFORT_RECYCLE_dglo
MAKE_EFFORT_RECYCLE_dgli 
MAKE_EFFORT_RECYCLE_neit
MAKE_EFFORT_RECYCLE_agli
MAKE_EFFORT_RECYCLE_aglo
RESPONS_RECYCLD_PRD_dglo
RESPONS_RECYCLD_PRD_dgli
RESPONS_RECYCLD_PRD_neit
RESPONS_RECYCLD_PRD_agli
RESPONS_RECYCLD_PRD_aglo
ENVRNMNT_RESPONSIBLE_dglo
ENVRNMNT_RESPONSIBLE_dgli
ENVRNMNT_RESPONSIBLE_neit
ENVRNMNT_RESPONSIBLE_agli
ENVRNMNT_RESPONSIBLE_aglo 

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store

DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS

PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE

Dr_Pepper
Coca_Cola
Sprite
Male
Female 
NorthEast
MidWest 
South 
West 
Age_18_24 
Age_25_49 
Age_50
Spotify 
YouTube 
ESPN;

/* K-MEANS STARTS */
 
PROC FACTOR DATA = myvars 
MAXITER=100
METHOD=principal
MINEIGEN=1
ROTATE=varimax
MSA
SCREE
SCORE
PRINT
NFACTORS=2
OUT=myscores;
var
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS

PEOPLE_NEED_TO_RECYCLE 
MAKE_EFFORT_TO_RECYCLE 
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS 
PERSONAL_ENVRNMNT_RESPONSIBLE;
run;


DATA myscores1;
SET myscores;
RENAME factor1 = IndulgenceFactors;
RENAME factor2 = GreenAttitudeFactor;
run;


PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=3 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;
PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=4 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;
PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=5 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;
PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=6 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;
PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=7 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;
PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=8 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;

PROC FASTCLUS DATA=myscores1 MAXITER=100 MAXCLUSTERS=9 OUT=finalclus;
VAR
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
RUN;     /* K-MEANS ENDS */



/* GAP ANALYSIS STARTS */


/*proc hpclus data=myscores1 MAXITER=100 maxclusters=6
noc=abc(b=20 minclusters=2 align=pca criterion=globalpeak);
        /* score puts CLUSTER variable in dataset and OUT= outputs the data set */
/*score out=mycluster;
        /* here are the drivers for the HPCLUS cluster solution */
/*input 
IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store / level=interval;
run;


        /* only the variables listed in the ID statement will be kept in the MYCLUSTER data set*/
        /* so be sure to put your id variable, drivers and descritpor variables in the ID statement */
        /* the drivers are earlyadopt socialphone loser ad_receptivity and the descriptors are coca_cola_classic 
pepsi_classic 
espn_sports  
ikea_furniture  
kfc_chicken  
nike_trainers  */


/*id myid

IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store

        /*IndulgenceFactors
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS

GreenAttitudeFactor
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE*/

/*dr_pepper
Sprite
MALE
FEMALE 
NORTHEAST
SOUTH 
WEST
Age_18_24
Age_25_49 
Age_50
YOUTUBE
ESPN;
run;                                        

proc contents data=mycluster;
run; 
proc sort data=mycluster out=mysort;
by _CLUSTER_ID_ ;
proc means data=mysort;
by _CLUSTER_ID_;
var

IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store

        /*IndulgenceFactors
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS

GreenAttitudeFactor
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE*/

/*Sprite
MALE
FEMALE 
NORTHEAST
SOUTH 
WEST
Age_18_24
Age_25_49 
Age_50
YOUTUBE
ESPN;
run;   /* GAP ANALYSIS ENDS */













/* I found k=3 as my first local maximum */
/* so lets run it again and save the cluster file */
proc fastclus data=myscores1 out=cluster_results maxiter=100 maxclusters=3;
var 

IndulgenceFactors
GreenAttitudeFactor

drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store;
run;                             
                                   
/* now we need to sort the data set by cluster to use the BY option in proc means */
proc sort data=cluster_results  out=cluster_sorted;
By cluster;
run;

/* now we can produce means for some descriptor variables */
proc means data=cluster_sorted;
By cluster;
var
Dr_Pepper
Coca_Cola
Sprite
MALE
FEMALE 
NORTHEAST
SOUTH 
Age_18_24
Age_50
YOUTUBE
Spotify;
run;

SAS Code Output:

The SAS System

The FREQ Procedure

snack_between_meals_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1919 100.00 1919 100.00
Frequency Missing = 23520
snack_between_meals_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2526 100.00 2526 100.00
Frequency Missing = 22913
snack_between_meals_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3409 100.00 3409 100.00
Frequency Missing = 22030
snack_between_meals_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 9817 100.00 9817 100.00
Frequency Missing = 15622
snack_between_meals_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6582 100.00 6582 100.00
Frequency Missing = 18857
try_new_drinks_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5504 100.00 5504 100.00
Frequency Missing = 19935
try_new_drinks_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3428 100.00 3428 100.00
Frequency Missing = 22011
try_new_drinks_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6830 100.00 6830 100.00
Frequency Missing = 18609
try_new_drinks_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5040 100.00 5040 100.00
Frequency Missing = 20399
try_new_drinks_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3259 100.00 3259 100.00
Frequency Missing = 22180
see_on_tv_go_online_more_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4997 100.00 4997 100.00
Frequency Missing = 20442
see_on_tv_go_online_more_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2420 100.00 2420 100.00
Frequency Missing = 23019
see_on_tv_go_online_more_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6244 100.00 6244 100.00
Frequency Missing = 19195
see_on_tv_go_online_more_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6783 100.00 6783 100.00
Frequency Missing = 18656
see_on_tv_go_online_more_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3502 100.00 3502 100.00
Frequency Missing = 21937
buy_online_or_store_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6591 100.00 6591 100.00
Frequency Missing = 18848
buy_online_or_store_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4045 100.00 4045 100.00
Frequency Missing = 21394
buy_online_or_store_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6541 100.00 6541 100.00
Frequency Missing = 18898
buy_online_or_store_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4251 100.00 4251 100.00
Frequency Missing = 21188
buy_online_or_store_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2705 100.00 2705 100.00
Frequency Missing = 22734
DRINKING_TO_GET_DRUNK_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1060 100.00 1060 100.00
Frequency Missing = 24379
DRINKING_TO_GET_DRUNK_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1443 100.00 1443 100.00
Frequency Missing = 23996
DRINKING_TO_GET_DRUNK_anya Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2503 100.00 2503 100.00
Frequency Missing = 22936
DRINKING_TO_GET_DRUNK_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4201 100.00 4201 100.00
Frequency Missing = 21238
DRINKING_TO_GET_DRUNK_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2622 100.00 2622 100.00
Frequency Missing = 22817
DRINKING_TO_GET_DRUNK_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 14730 100.00 14730 100.00
Frequency Missing = 10709
DRINKING_TO_GET_DRUNK_anyd Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 17352 100.00 17352 100.00
Frequency Missing = 8087
OFTEN_DRINK_ALCOHOLIC_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2635 100.00 2635 100.00
Frequency Missing = 22804
OFTEN_DRINK_ALCOHOLIC_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3944 100.00 3944 100.00
Frequency Missing = 21495
OFTEN_DRINK_ALCOHOLIC_anya Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6579 100.00 6579 100.00
Frequency Missing = 18860
OFTEN_DRINK_ALCOHOLIC_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3844 100.00 3844 100.00
Frequency Missing = 21595
OFTEN_DRINK_ALCOHOLIC_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 2923 100.00 2923 100.00
Frequency Missing = 22516
OFTEN_DRINK_ALCOHOLIC_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 10655 100.00 10655 100.00
Frequency Missing = 14784
OFTEN_DRINK_ALCOHOLIC_anyd Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 13578 100.00 13578 100.00
Frequency Missing = 11861
FIRST_TRY_NEW_FOOD_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1981 100.00 1981 100.00
Frequency Missing = 23458
FIRST_TRY_NEW_FOOD_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3335 100.00 3335 100.00
Frequency Missing = 22104
FIRST_TRY_NEW_FOOD_anya Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5316 100.00 5316 100.00
Frequency Missing = 20123
FIRST_TRY_NEW_FOOD_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 8873 100.00 8873 100.00
Frequency Missing = 16566
FIRST_TRY_NEW_FOOD_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4584 100.00 4584 100.00
Frequency Missing = 20855
FIRST_TRY_NEW_FOOD_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5257 100.00 5257 100.00
Frequency Missing = 20182
FIRST_TRY_NEW_FOOD_anyd Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 9841 100.00 9841 100.00
Frequency Missing = 15598
GUILTY_TO_EAT_SWEETS_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3990 100.00 3990 100.00
Frequency Missing = 21449
GUILTY_TO_EAT_SWEETS_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6231 100.00 6231 100.00
Frequency Missing = 19208
GUILTY_TO_EAT_SWEETS_anya Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 10221 100.00 10221 100.00
Frequency Missing = 15218
GUILTY_TO_EAT_SWEETS_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6081 100.00 6081 100.00
Frequency Missing = 19358
GUILTY_TO_EAT_SWEETS_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3700 100.00 3700 100.00
Frequency Missing = 21739
GUILTY_TO_EAT_SWEETS_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3897 100.00 3897 100.00
Frequency Missing = 21542
GUILTY_TO_EAT_SWEETS_anyd Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 7597 100.00 7597 100.00
Frequency Missing = 17842
PEOPLE_DUTY_RECYCLE_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 658 100.00 658 100.00
Frequency Missing = 24781
PEOPLE_DUTY_RECYCLE_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 910 100.00 910 100.00
Frequency Missing = 24529
PEOPLE_DUTY_RECYCLE_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5955 100.00 5955 100.00
Frequency Missing = 19484
PEOPLE_DUTY_RECYCLE_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6201 100.00 6201 100.00
Frequency Missing = 19238
PEOPLE_DUTY_RECYCLE_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 10597 100.00 10597 100.00
Frequency Missing = 14842
MAKE_EFFORT_RECYCLE_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1082 100.00 1082 100.00
Frequency Missing = 24357
MAKE_EFFORT_RECYCLE_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 1195 100.00 1195 100.00
Frequency Missing = 24244
MAKE_EFFORT_RECYCLE_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 3756 100.00 3756 100.00
Frequency Missing = 21683
MAKE_EFFORT_RECYCLE_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 5406 100.00 5406 100.00
Frequency Missing = 20033
MAKE_EFFORT_RECYCLE_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 13114 100.00 13114 100.00
Frequency Missing = 12325
RESPONS_RECYCLD_PRD_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 507 100.00 507 100.00
Frequency Missing = 24932
RESPONS_RECYCLD_PRD_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 791 100.00 791 100.00
Frequency Missing = 24648
RESPONS_RECYCLD_PRD_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6176 100.00 6176 100.00
Frequency Missing = 19263
RESPONS_RECYCLD_PRD_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 6971 100.00 6971 100.00
Frequency Missing = 18468
RESPONS_RECYCLD_PRD_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 9884 100.00 9884 100.00
Frequency Missing = 15555
ENVRNMNT_RESPONSIBLE_dglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 442 100.00 442 100.00
Frequency Missing = 24997
ENVRNMNT_RESPONSIBLE_dgli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 550 100.00 550 100.00
Frequency Missing = 24889
ENVRNMNT_RESPONSIBLE_neit Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 4847 100.00 4847 100.00
Frequency Missing = 20592
ENVRNMNT_RESPONSIBLE_agli Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 8332 100.00 8332 100.00
Frequency Missing = 17107
ENVRNMNT_RESPONSIBLE_aglo Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1 10152 100.00 10152 100.00
Frequency Missing = 15287
I often drink between meals
drink_between_meals
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1186
I like to try new drinks
like_to_try_new_drinks
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1378
When I see on tv I go online and find more
when_on_tv_go_online_get_more
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1493
More likely to buy online than in store
buy_online_or_in_store
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1306
The point of drinking is to get drunk
DRINKING_GET_DRUNK
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1383
Often drink alcoholic beverages at resturants
OFTEN_DRINK_ALCOHOL
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1438
Im usually first to try new food products
TRY_NEW_FOOD_PRDCT
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1409
I feel guilty when I eat sweets
FEEL_GUILTY_SWEETS
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1540
People have a duty to recycle
PEOPLE_NEED_TO_RECYCLE
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1118
I make an effort to recycle
MAKE_EFFORT_TO_RECYCLE
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 886
People have a response to use recycle products
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1110
Personal obligation and enviroment responsibility
PERSONAL_ENVRNMNT_RESPONSIBLE
disagree a lot
disagree a little
neither agree nor disagree
agree a little
agree a lot
Frequency Missing = 1116
I prefer DrPepper
Dr_Pepper
no
yes
I prefer CocaCola
Coca_Cola
no
yes
I prefer Sprite
Sprite
no
yes
Response by Male
MALE
no
yes
Response by Female
FEMALE
no
yes
Region: NorthEast
NORTHEAST
no
yes
Region: Mid-West
MIDWEST
no
yes
Region: South
SOUTH
no
yes
Region: West
WEST
no
yes
Age: 18-24
Age_18_24
no
yes
Age: 25-49
Age_25_49
no
yes
Age: 50+
Age_50
no
yes
Spotify was used during last 7 days
SPOTIFY
no
yes
YouTube was used during last 7 days
YOUTUBE
no
yes
ESPN was viewed during last 7 days
ESPN
no
yes

________________________________________________________________________________________

The SAS System

The FACTOR Procedure

Input Data Type Raw Data
Number of Records Read 25439
Number of Records Used 21942
N for Significance Tests 21942

________________________________________________________________________________________

The SAS System

The FACTOR Procedure

Initial Factor Method: Principal Components

Partial Correlations Controlling all other Variables
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE
Kaiser’s Measure of Sampling Adequacy: Overall MSA = 0.78651538
DRINKING_GET_DRUNK
0.68962461

________________________________________________________________________________________

The SAS System

The FACTOR Procedure

Initial Factor Method: Principal Components

Prior Communality Estimates: ONE
Eigenvalues of the Correlation Matrix: Total
= 8 Average = 1
1
2
3
4
5
6
7
8

Factor Pattern
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE
Variance Explained by Each
Factor
Factor1
2.8634159
Final Communality Estimates: Total = 4.187467
DRINKING_GET_DRUNK
0.37758061

________________________________________________________________________________________

The SAS System

The FACTOR Procedure

Rotation Method: Varimax

Orthogonal Transformation Matrix
1
2
Rotated Factor Pattern
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE
Variance Explained by Each
Factor
Factor1
2.8568718
Final Communality Estimates: Total = 4.187467
DRINKING_GET_DRUNK
0.37758061

________________________________________________________________________________________

The SAS System

The FACTOR Procedure

Rotation Method: Varimax

Scoring Coefficients Estimated by Regression
Squared Multiple Correlations
of the Variables with Each
Factor
Factor1
1.0000000
Standardized Scoring Coefficients
DRINKING_GET_DRUNK
OFTEN_DRINK_ALCOHOL
TRY_NEW_FOOD_PRDCT
FEEL_GUILTY_SWEETS
PEOPLE_NEED_TO_RECYCLE
MAKE_EFFORT_TO_RECYCLE
PEOPLE_RESPONS_TO_RECYCLD_PRDCTS
PERSONAL_ENVRNMNT_RESPONSIBLE

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=3 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
Minimum Distance Between Initial Seeds = 8.408203
Iteration History
Iteration
1
1
2
3
4
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 1.0133
Cluster Summary
Cluster
1
2
3
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 5622.32
Approximate Expected Over-All R-Squared = 0.27176
Cubic Clustering Criterion = 39.855
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
Cluster Standard Deviations
Cluster
1
2
3

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=4 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
Minimum Distance Between Initial Seeds = 7.360341
Iteration History
Iteration
1
1
2
3
4
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.9619
Cluster Summary
Cluster
1
2
3
4
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 5076.37
Approximate Expected Over-All R-Squared = 0.36802
Cubic Clustering Criterion = 9.710
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
Cluster Standard Deviations
Cluster
1
2
3
4

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=5 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
5
Minimum Distance Between Initial Seeds = 7.104607
Iteration History
Iteration
1
1
2
3
4
5
6
7
8
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.9051
Cluster Summary
Cluster
1
2
3
4
5
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 5104.69
Approximate Expected Over-All R-Squared = 0.42914
Cubic Clustering Criterion = 21.246
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
5
Cluster Standard Deviations
Cluster
1
2
3
4
5

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=6 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
5
6
Minimum Distance Between Initial Seeds = 6.589243
Iteration History
Iteration
1
1
2
3
4
5
6
7
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.8842
Cluster Summary
Cluster
1
2
3
4
5
6
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 4523.63
Approximate Expected Over-All R-Squared = 0.46407
Cubic Clustering Criterion = 11.616
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
5
6
Cluster Standard Deviations
Cluster
1
2
3
4
5
6

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=7 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
5
6
7
Minimum Distance Between Initial Seeds = 6.513331
Iteration History
Iteration
1
1
2
3
4
5
6
7
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.8658
Cluster Summary
Cluster
1
2
3
4
5
6
7
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 4118.01
Approximate Expected Over-All R-Squared = 0.49418
Cubic Clustering Criterion = 2.269
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
5
6
7
Cluster Standard Deviations
Cluster
1
2
3
4
5
6
7

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=8 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
5
6
7
8
Minimum Distance Between Initial Seeds = 6.213953
Iteration History
Iteration
1
1
2
3
4
5
6
7
8
9
10
11
12
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.8240
Cluster Summary
Cluster
1
2
3
4
5
6
7
8
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 4258.66
Approximate Expected Over-All R-Squared = 0.51624
Cubic Clustering Criterion = 34.210
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
5
6
7
8
Cluster Standard Deviations
Cluster
1
2
3
4
5
6
7
8

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=9 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
4
5
6
7
8
9
Minimum Distance Between Initial Seeds = 5.902531
Iteration History
Iteration
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.7934
Cluster Summary
Cluster
1
2
3
4
5
6
7
8
9
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 4263.33
Approximate Expected Over-All R-Squared = 0.53490
Cubic Clustering Criterion = 53.675
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
4
5
6
7
8
9
Cluster Standard Deviations
Cluster
1
2
3
4
5
6
7
8
9

________________________________________________________________________________________

The SAS System

The FASTCLUS Procedure

Replace=FULL Radius=0 Maxclusters=3 Maxiter=100 Converge=0.02

Initial Seeds
Cluster
1
2
3
Minimum Distance Between Initial Seeds = 8.408203
Iteration History
Iteration
1
1
2
3
4
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 1.0133
Cluster Summary
Cluster
1
2
3
323 Observation(s) were omitted due to missing values.
Statistics for Variables
Variable
IndulgenceFactors
GreenAttitudeFactor
drink_between_meals
like_to_try_new_drinks
when_on_tv_go_online_get_more
buy_online_or_in_store
OVER-ALL
Pseudo F Statistic = 5622.32
Approximate Expected Over-All R-Squared = 0.27176
Cubic Clustering Criterion = 39.855
WARNING: The two values above are invalid for correlated variables.
Cluster Means
Cluster
1
2
3
Cluster Standard Deviations
Cluster
1
2
3

________________________________________________________________________________________

The SAS System

The MEANS Procedure

Cluster=.

Variable N Mean Std Dev Minimum Maximum
Dr_Pepper 323 0.0773994 0.2676387 0.0000000 1.0000000
Coca_Cola 323 0.3188854 0.4667677 0.0000000 1.0000000
Sprite 323 0.0650155 0.2469357 0.0000000 1.0000000
MALE 323 0.4551084 0.4987533 0.0000000 1.0000000
FEMALE 323 0.5448916 0.4987533 0.0000000 1.0000000
NORTHEAST 323 0.1919505 0.3944454 0.0000000 1.0000000
SOUTH 323 0.4520124 0.4984641 0.0000000 1.0000000
Age_18_24 323 0.0990712 0.2992211 0.0000000 1.0000000
Age_50 323 0.6377709 0.4813903 0.0000000 1.0000000
YOUTUBE 323 0.1052632 0.3073684 0.0000000 1.0000000
SPOTIFY 323 0.0216718 0.1458355 0.0000000 1.0000000
I prefer DrPepper 323 0.0000000 0.0000000 0.0000000 1.0000000
I prefer CocaCola 323 0.0000000 0.0000000 0.0000000 1.0000000
I prefer Sprite 323 0.0000000 0.0000000 0.0000000 1.0000000
Response by Male 323 0.0000000 0.0000000 0.0000000 1.0000000
Response by Female 323 0.0000000 0.0000000 0.0000000 1.0000000
Region: NorthEast 323 0.0000000 0.0000000 0.0000000 1.0000000
Region: South 323 0.0000000 0.0000000 0.0000000 1.0000000
Age: 18-24 323 0.0000000 0.0000000 0.0000000 1.0000000
Age: 50+ 323 0.0000000 0.0000000 0.0000000 1.0000000
YouTube was used during last 7 days 323 0.0000000 0.0000000 0.0000000 1.0000000
Spotify was used during last 7 days 323 0.0000000 0.0000000 0.0000000 1.0000000

Cluster = 1

Variable N Mean Std Dev Minimum Maximum
Dr_Pepper 7538 0.0862298 0.2807217 0.0000000 1.0000000
Coca_Cola 7538 0.3357655 0.4722887 0.0000000 1.0000000
Sprite 7538 0.0874237 0.2824737 0.0000000 1.0000000
MALE 7538 0.4292916 0.4950079 0.0000000 1.0000000
FEMALE 7538 0.5707084 0.4950079 0.0000000 1.0000000
NORTHEAST 7538 0.1768374 0.3815563 0.0000000 1.0000000
SOUTH 7538 0.3857787 0.4868110 0.0000000 1.0000000
Age_18_24 7538 0.0416556 0.1998142 0.0000000 1.0000000
Age_50 7538 0.7512603 0.4323113 0.0000000 1.0000000
YOUTUBE 7538 0.1771027 0.3817809 0.0000000 1.0000000
SPOTIFY 7538 0.0222871 0.1476254 0.0000000 1.0000000
I prefer DrPepper 7538 0.0000000 0.0000000 0.0000000 1.0000000
I prefer CocaCola 7538 0.0000000 0.0000000 0.0000000 1.0000000
I prefer Sprite 7538 0.0000000 0.0000000 0.0000000 1.0000000
Response by Male 7538 0.0000000 0.0000000 0.0000000 1.0000000
Response by Female 7538 0.0000000 0.0000000 0.0000000 1.0000000
Region: NorthEast 7538 0.0000000 0.0000000 0.0000000 1.0000000
Region: South 7538 0.0000000 0.0000000 0.0000000 1.0000000
Age: 18-24 7538 0.0000000 0.0000000 0.0000000 1.0000000
Age: 50+ 7538 0.0000000 0.0000000 0.0000000 1.0000000
YouTube was used during last 7 days 7538 0.0000000 0.0000000 0.0000000 1.0000000
Spotify was used during last 7 days 7538 0.0000000 0.0000000 0.0000000 1.0000000

Cluster = 2

Variable N Mean Std Dev Minimum Maximum
Dr_Pepper 10503 0.1126345 0.3161605 0.0000000 1.0000000
Coca_Cola 10503 0.3767495 0.4845943 0.0000000 1.0000000
Sprite 10503 0.1139674 0.3177868 0.0000000 1.0000000
MALE 10503 0.4606303 0.4984713 0.0000000 1.0000000
FEMALE 10503 0.5393697 0.4984713 0.0000000 1.0000000
NORTHEAST 10503 0.1939446 0.3954048 0.0000000 1.0000000
SOUTH 10503 0.3812244 0.4857106 0.0000000 1.0000000
Age_18_24 10503 0.1168238 0.3212255 0.0000000 1.0000000
Age_50 10503 0.4356850 0.4958699 0.0000000 1.0000000
YOUTUBE 10503 0.3752261 0.4842043 0.0000000 1.0000000
SPOTIFY 10503 0.0910216 0.2876535 0.0000000 1.0000000
I prefer DrPepper 10503 0.0000000 0.0000000 0.0000000 1.0000000
I prefer CocaCola 10503 0.0000000 0.0000000 0.0000000 1.0000000
I prefer Sprite 10503 0.0000000 0.0000000 0.0000000 1.0000000
Response by Male 10503 0.0000000 0.0000000 0.0000000 1.0000000
Response by Female 10503 0.0000000 0.0000000 0.0000000 1.0000000
Region: NorthEast 10503 0.0000000 0.0000000 0.0000000 1.0000000
Region: South 10503 0.0000000 0.0000000 0.0000000 1.0000000
Age: 18-24 10503 0.0000000 0.0000000 0.0000000 1.0000000
Age: 50+ 10503 0.0000000 0.0000000 0.0000000 1.0000000
YouTube was used during last 7 days 10503 0.0000000 0.0000000 0.0000000 1.0000000
Spotify was used during last 7 days 10503 0.0000000 0.0000000 0.0000000 1.0000000

Cluster 3

Variable N Mean Std Dev Minimum Maximum
Dr_Pepper 7075 0.1390813 0.3460558 0.0000000 1.0000000
Coca_Cola 7075 0.4171025 0.4931150 0.0000000 1.0000000
Sprite 7075 0.1301767 0.3365215 0.0000000 1.0000000
MALE 7075 0.4187986 0.4933971 0.0000000 1.0000000
FEMALE 7075 0.5812014 0.4933971 0.0000000 1.0000000
NORTHEAST 7075 0.1831802 0.3868415 0.0000000 1.0000000
SOUTH 7075 0.3817668 0.4858542 0.0000000 1.0000000
Age_18_24 7075 0.1139223 0.3177393 0.0000000 1.0000000
Age_50 7075 0.4705300 0.4991660 0.0000000 1.0000000
YOUTUBE 7075 0.3188693 0.4660712 0.0000000 1.0000000
SPOTIFY 7075 0.0809894 0.2728381 0.0000000 1.0000000
I prefer DrPepper 7075 0.0000000 0.0000000 0.0000000 1.0000000
I prefer CocaCola 7075 0.0000000 0.0000000 0.0000000 1.0000000
I prefer Sprite 7075 0.0000000 0.0000000 0.0000000 1.0000000
Response by Male 7075 0.0000000 0.0000000 0.0000000 1.0000000
Response by Female 7075 0.0000000 0.0000000 0.0000000 1.0000000
Region: NorthEast 7075 0.0000000 0.0000000 0.0000000 1.0000000
Region: South 7075 0.0000000 0.0000000 0.0000000 1.0000000
Age: 18-24 7075 0.0000000 0.0000000 0.0000000 1.0000000
Age: 50+ 7075 0.0000000 0.0000000 0.0000000 1.0000000
YouTube was used during last 7 days 7075 0.0000000 0.0000000 0.0000000 1.0000000
Spotify was used during last 7 days 7075 0.0000000 0.0000000 0.0000000 1.0000000