1. Slide C.1 SAS MathematicalMarketing 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

2. Slide C.2 SAS MathematicalMarketing 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

3. Slide C.3 SAS MathematicalMarketing A Simple Example data my_study ; input id gender $ green recycle ; cards ; 001m42 002m31 003f32 ; proc reg data=my_study ; class gender ; model recycle = green gender ;

4. Slide C.4 SAS MathematicalMarketing 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 ;

5. Slide C.5 SAS MathematicalMarketing 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 ;

6. Slide C.6 SAS MathematicalMarketing 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 ;

7. Slide C.7 SAS MathematicalMarketing 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) ;

8. Slide C.8 SAS MathematicalMarketing 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 ;

9. Slide C.9 SAS MathematicalMarketing 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 */

10. Slide C.10 SAS MathematicalMarketing 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 ;

11. Slide C.11 SAS MathematicalMarketing 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 ;

12. Slide C.12 SAS MathematicalMarketing Copying Raw Data infile in ′c:\old.data′ ; file out ′c:\new.data′ ; data _null_ ; infile in ; outfile out ; input ; put _infile_ ;

13. Slide C.13 SAS MathematicalMarketing SAS Constants '21Dec1981'D 'Charles F. Hofacker' 492992.1223

14. Slide C.14 SAS MathematicalMarketing Assignment Statement x = a + b ; y = x / 2. ; prob = 1 - exp(-z**2/2) ;

15. Slide C.15 SAS MathematicalMarketing The SAS Array Statement array y {20} y1-y20 ; do i = 1 to 20 ; y{i} = 11 - y{i} ; end ;

16. Slide C.16 SAS MathematicalMarketing The Sum Statement variable+expression ; retain variable ; variable = variable + expression ; n+1 ; cumulated + x ;

17. Slide C.17 SAS MathematicalMarketing 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 ;

18. Slide C.18 SAS MathematicalMarketing More IF Statement Expressions name ne 'smith' name ~= 'smith' x eq 1 or x eq 2 x=1 | x=2 a = c a le b or a ge c a1 and a2 or a3 (a1 and a2) or a3 if then etc ;

19. Slide C.19 SAS MathematicalMarketing Concatenating Datasets Sequentially data both ; set first second ; first: id x y 1 2 3 2 1 2 3 3 1 second: id x y 4 3 2 5 2 1 6 1 1 both: id x y 1 2 3 2 1 2 3 3 1 4 3 2 5 2 1 6 1 1

20. Slide C.20 SAS MathematicalMarketing Interleaving Two Datasets proc sort data=store1 ; by date ; proc sort data=store2 ; by date ; data both ; set store1 store2 ; by date ;

21. Slide C.21 SAS MathematicalMarketing Concatenating Datasets Horizontally data both ; merge left right ; left: id y1 y2 1 2 3 2 1 2 3 3 1 right: id x1 x2 1 3 2 2 2 1 3 1 1 both: id y1 y2 x1 x2 1 2 3 3 2 2 1 2 2 1 3 3 1 1 1

22. Slide C.22 SAS MathematicalMarketing Table LookUp proc sort data=database out=sorted by part ; data both ; merge table sorted ; by part ; table: part desc 0011 hammer 0012 nail 0013 bow database: id part 1 0011 2 0011 3 0013 both: id part desc 1 0011 hammer 2 0011 hammer 3 0013 bow The last observations is repeated if one of the input data sets is smaller

23. Slide C.23 SAS MathematicalMarketing Update master: part desc 0011 hammer 0012 nail 0013 bow transaction: Part desc 0011 jackhammer data new_master ; update master transaction ; by part ; new_master: part desc 0011 jackhammer 0012 nail 0013 bow

24. Slide C.24 SAS MathematicalMarketing Changing the Level of Analysis 1 Subject Time Score A 1 A 1 A 2 A 2 A 3 A 3 B 1 B 1 B 2 B 2 B 3 B 3 Subject Score1 Score2 Score3 A A 1 A 2 A 3 B B 1 B 2 B 3 Before After

25. Slide C.25 SAS MathematicalMarketing Changing the Level of Analysis 1 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 ;

26. Slide C.26 SAS MathematicalMarketing Changing the Level of Analysis 2 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

27. Slide C.27 SAS MathematicalMarketing Changing the Level of Analysis 2 FIRST. 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 ;

28. Slide C.28 SAS MathematicalMarketing 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 ;

29. Slide C.29 SAS MathematicalMarketing Changing the Level of Analysis 3 Spreading Out an Observation Subject Score1 Score2 Score3 A A 1 A 2 A 3 B B 1 B 2 B 3 Subject Time Score A 1 A 1 A 2 A 2 A 3 A 3 B 1 B 1 B 2 B 2 B 3 B 3 Before After

30. Slide C.30 SAS MathematicalMarketing Changing the Level of Analysis 3 – SAS Code data spread ; drop score1 score2 score3 ; set tight ; time = 1 ; score = score1 ; output ; time = 2 ; score = score2 ; output ; time = 3 ; score = score3 ; output ;

31. Slide C.31 SAS MathematicalMarketing 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 … ;

32. Slide C.32 SAS MathematicalMarketing Proc Summary for Aggregation proc summary data=raw_purchases ; by household ; class brand ; var x1 x2 x3 x4 x5 ; output out=household mean=overall ;

33. Slide C.33 SAS MathematicalMarketing 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. ; Simulation Loop

34. Slide C.34 SAS MathematicalMarketing 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 $ ;