Slide C.1 SAS MathematicalMarketing Appendix C: SAS Software Uses of SAS CRM datamining data warehousing linear programming forecasting econometrics

Slide C.Slide C.11SASSAS

MathematicalMathematicalMarketingMarketing

Appendix C: SAS Software

Uses of SAS

CRM datamining data warehousing

linear programming forecasting econometrics nonlinear parameter estimation

Data Types SAS Can Deal with

massaging sorting merging

lookups reporting

Web log data questionnaires

scanner data

Ideal When You Are …

simulation marketing models statistical analysis

panel data relational databases

transforming manipulating



Two Types of SAS Routines

DATA Steps• Read and Write Data

• Create a SAS dataset

• Manipulate and Transform Data

• Open-Ended - Procedural Language

• Presence of INPUT statement creates a Loop

PROC Steps• Analyze Data

• Canned or Preprogrammed Input and Output



A Simple Example

data my_study ;

input id gender $ green recycle ;

cards ;

001 m 4 2

002 m 3 1

003 f 3 2

••• ••• ••• •••

;

proc reg data=my_study ;

class gender ;

model recycle = green gender ;



The Sequence Depends on the Need

data step to read in scanner data;

data step to read in panel data ;

data step to merge scanner and panel records ;

data step to change the level of analysis to the household ;

proc step to create covariance matrix ;

data step to write covariance matrix in LISREL compatable format ;



The INPUT Statement - Character Data

List input

$ after a variable - character var

input last_name $ first_name $ initial $ ;

Formatted input

$w. after a variable

input last_name $22. first_name $22. initial $1.

Column input

$ start-column - end-column

input last_name $ 1 - 22 first_name $ 23 - 44 initial $ 45 ;



The INPUT Statement - Numeric Data

List input input score_1 score_2 score_3 ;

Formatted input

w.d (field width and number of digits after an implied decimal point) after a variable

input score_1 $10. score_2 $10. score_3 10.

Column input

$ start-column - end-column

input score_1 1 - 10 score_2 11 - 20 score_3 21 - 30 ;



Grouped INPUT Statements

input (var1-var3) (10. 10. 10.) ;

input (var1-var3) (3*10.) ;

input (var1-var3) (10.) ;

input (name var1-var3) ($10. 3*5.1) ;



The Column Pointer in the INPUT Statement

input @3 var1 10. ;

input more @ ;

if more then input @15 x1 x2 ;

input @12 x1 5. +3 x2 ;



Documenting INPUT Statements

input @4 green1 4. /* greeness scale first item */

@9 green2 4. /* greeness scale 2nd item */

@20 aware1 5. /* awareness scale first item */

@20 aware2 5. ; /* awareness scale 2nd item */



The Line Pointer

input x1 x2 x3 / x4 x4 x6 ;

input x1 x2 x3 #2 x4 x5 x6 ;

input x1 x2 x3

#2 x4 x5 x6 ;



The PUT Statement

put _all_ ;

put a= b= ;

put _infile_ ;

put _page_ ;

col1 = 22 ; col2 = 14 ;

put @col1 var245 @col2 var246 ;

put x1 x2 x3 @ ;input x4 ;put x4 ;

put x1 #2 x2 ;

put x1 / x2 ;



Copying Raw Data

infile in ′c:\old.data′ ;

file out ′c:\new.data′ ;

data _null_ ;

infile in ;

outfile out ;

input ;

put _infile_ ;



SAS Constants

'21Dec1981'D

'Charles F. Hofacker'

492992.1223



Assignment Statement

x = a + b ;

y = x / 2. ;

prob = 1 - exp(-z**2/2) ;



The SAS Array Statement

array y {20} y1-y20 ;

do i = 1 to 20 ;

y{i} = 11 - y{i} ;

end ;



The Sum Statement

variable+expression ;

retain variable ;

variable = variable + expression ;

n+1 ;

cumulated + x ;



IF Statement

if a >= 45 then a = 45 ;

if 0 < age < 1 then age = 1 ;

if a = 2 or b = 3 then c = 1 ;

if a = 2 and b = 3 then c = 1 ;

if major = "FIN" ;

if major = "FIN" then do ; a = 1 ; b = 2 ;end ;



More IF Statement Expressions

name ne 'smith'

name ~= 'smith'

x eq 1 or x eq 2

x=1 | x=2

a <= b | a >= c

a le b or a ge c

a1 and a2 or a3

(a1 and a2) or a3

if then etc ;



Concatenating Datasets Sequentially

data both ; set first second ;

first:

id x y1 2 32 1 23 3 1

second:

id x y4 3 25 2 16 1 1

both:

id x y1 2 32 1 23 3 14 3 25 2 16 1 1



Interleaving Two Datasets

proc sort data=store1 ;

by date ;

proc sort data=store2 ;

by date ;

data both ;

set store1 store2 ;

by date ;



Concatenating Datasets Horizontally

data both ; merge left right ;

left:

id y1 y21 2 32 1 23 3 1

right:

id x1 x21 3 22 2 13 1 1

both:

id y1 y2 x1 x21 2 3 3 22 1 2 2 13 3 1 1 1



Table LookUp

proc sort data=database out=sorted by part ; data both ; merge table sorted ; by part ;

table:

part desc0011 hammer0012 nail0013 bow

database:

id part1 00112 00113 0013

both:

id part desc1 0011 hammer2 0011 hammer3 0013 bow

The last observations is repeated if one of the input data sets is smaller



Update

master:

part desc0011 hammer0012 nail0013 bow

transaction:

Part desc0011 jackhammer

data new_master ;

update master transaction ;

by part ;

new_master:

part desc0011 jackhammer0012 nail0013 bow



Changing the Level of Analysis 1

Subject Time Score

A 1 A1

A 2 A2

A 3 A3

B 1 B1

B 2 B2

B 3 B3

Subject Score1 Score2 Score3

A A1 A2 A3

B B1 B2 B3

Before

After




data after ; keep subject score1 score2 score3 ; retain score1 score2 ; set before ;

if time=1 then score1 = score ; else if time=2 then score2 = score ; else if time=3 then do ; score3 = score ; output ; end ;




Day Score Student

1 12 A

1 11 B

1 13 C

2 14 A

2 10 B

2 9 C

Day Highest Student

1 13 C

2 14 A

Before

After



Changing the Level of Analysis 2FIRST. and LAST. Variable Modifiers

proc sort data=log ; by day ;

data find_highest ; retain hightest ; drop score ;

set log ; by day ;

if first.day then highest=. ; if score > highest then highest = score ; if lastday then output ;



The KEEP and DROP Statements

keep a b f h ;

drop x1-x99 ;

data a(keep = a1 a2) b(keep = b1 b2) ;

set x ;

if blah then output a ;

else output b ;



Changing the Level of Analysis 3Spreading Out an Observation

Subject Score1 Score2 Score3

A A1 A2 A3

B B1 B2 B3

Subject Time Score

A 1 A1

A 2 A2

A 3 A3

B 1 B1

B 2 B2

B 3 B3

Before

After



Changing the Level of Analysis 3 – SAS Code

data spread ;

drop score1 score2 score3 ;

set tight ;

time = 1 ; score = score1 ; output ;





Use of the IN= Dataset Indicator

data new ;

set old1 (in=from_old1)

old2 (in=from_old2) ;

if from_old1 then … ;

if from_old2 then … ;



Proc Summary for Aggregation

proc summary data=raw_purchases ;

by household ;

class brand ;

var x1 x2 x3 x4 x5 ;

output out=household mean=overall ;



Using SAS for Simulations

data monte_carlo ;

keep y1 - y4 ;

array y{4} y1 - y4 ;

array loading{4} l1 - l4 ;

array unique{4} u1 - u4 ;

l1 = 1 ; l2 = .5 ; l3 = .5 ; l4 = .5 ;

u1 = .2 ; u2 = .2 ; u3 = .2 ; u4 = .2 ;

do subject = 1 to 100 ;

eta = rannor(1921) ;

do j = 1 to 4 ;

y{j} = eta*loading{j} + unique{j}*rannor(2917) ;

end ;

output ;

end ;

proc calis data=monte_carlo ;

etc. ;

SimulationLoop



External Data Sets and Windows/Vista

filename trans 'C:\Documents\june\transactions.data' ;

libname clv 'C:\Documents\customer_projects\' ;

...

data clv.june ;

infile trans ;

input id 3. purch 2. day 3. month $ ;