Software review

Software Review

Essam Mahmoud, Editor American Graduate School of International Management (Thunderbird)

With the rapid increase in the number of programs available, and with the ongoing and continuous development of new software, researchers, marketers, educators, and managers face the growing problem of selecting proper software. The choice of a marketing computer package is becoming more and more difficult as the number of choices increases. These reviews are intended to help individuals select the most appropriate package for a specific application by allowing them to reduce the number of packages to be evaluated. Reviews are to be practically based rather than an abstract assessment of the manual as a descriptive document. Publication of a review should not in any way be seen as an endorsement of a package by the Academy of Marketing Science or this journal.

P C : S O L V E . A N e w So f tware Tool f o r Marketing Analysis Reviewed by Christopher M. Miller Rice University

Shelby H. McIntyre Santa Clara University

In recent years, spreadsheet applications, statistical packages, and database management software have all increased in power and applicability to marketing analysis. However, these packages have become more complex for new users and at the same time may not have all of the flexibility needed by more advanced users. These drawbacks to cur- rent software are due to the inherent limitations of a particular approach. For example, spreadsheets become difficult to follow for advanced models because of the required use of "cell" names and "cell referencing" within the model in- stead of variable names. Rarely has software been developed that successfully introduces a new approach for the analysis of marketing problems. However, we feel that PC:SOLVE is an exception worthy of consideration.

PC:SOLVE addresses the analysis of problems by a scratchpad approach that combines the power of statistical software, spreadsheets, and database management while also supporting stand-alone application development. The program is appropriate both within the classroom as a teaching tool and for faculty research at higher levels of com- plexity.

PC:SOLVE is a continuation of Pacific Crest Software's earlier problem solving software, POINT FIVE. We review version 1.0 which was released in August of 1990.

THE APPROACH USED IN PC:SOLVE

PC:SOLVE contains three "modes" for data analysis, a scratchpad with output, data editor, and graphics. The scratchpad with output and data editor can be viewed simultaneously, but the graphics mode can only be viewed by itself.

In the scratchpad mode, the screen is split across the middle and provides a "scratchpad" in the lower half and an output area in the upper half (See Figure 1). By analogy, it might be thought of as a calculator with a paper tape coming out the top. Thus, typing 2 + 2 into the "scratchpad" results in 4 scrolling up in the output window.

The "scratchpad" has many of the characteristics of a word processor. Calculations can be re-executed by moving the cursor to the appropriate line, blocks of commands can be moved, rearranged, transferred, or deleted as in a text editor, including such features as "find and replace." This flexibility creates an environment where mistakes in the analysis are easily detected and corrected. Thus the environment encourages the user to experiment with novel approaches to analysis.

A data editor is invoked using the F2 key. The data editor is visually and functionally similar to a spreadsheet and allows for easy data entry and manipulation. One of the features of PC:SOLVE is that numbers in the data editor can

JAMS 279 SUMMER, 1991

SOFTWARE REVIEW MAHMOUD

FIGURE 1 PC:SOLVE Scratch Pad Screen

4.00 [scratch pad display area]

ool 002 003 004 005 006 007 008 009

2+2

[scratch pad]

be tagged easily to variables in the scratchpad where they can be included by name in clear equations.

Graphs are induced by typing the appropriate command in the scratchpad, such as GLINE(x,y) for a line graph. The program then switches to graphics mode to display the graph using the full screen. Striking any key returns the user to the scratchpad mode.

PC:SOLVE has over 250 built-in functions that can oper- ate on scalars, vectors, or matrices. Any function can also take arguments which are themselves functions. The built in functions include:

(1) Mathematical functions, such as matrix operations, trigonometric functions, and calculus;

(2) Statistical functions, such as descriptive statistics and regression;

(3) Database management functions, such as search, replace, and merge-by-value;

(4) Financial functions, such as IRR and NPV; and (5) Graphics, such as line graphs and exploded pie

charts.

In addition to the built-in functions, the user can easily produce his/her own LIBRARY functions�9 For example, using the built in functions as building blocks, we have developed a ridge regression function that can now be used in exactly the same manner as a built in function. Pacific Crest Software has developed a large number of library functions and continues with this development.

For the advanced user, the program can be used as a language similar to APL. Conditional operators (IF �9 . . THEN, etc.), looping, and branching are all available�9 These operations, combined with the built in functions and libraries, make application development and simulation

modeling straight forward. Additionally, because the commands are in English, it is relatively easy for an individual other than the developer of the application to understand the resulting code. For example, the basic response function in the ADBUG model (Little 1970) would be written in PC:SOLVE as:

MRKT S H A R E = MIN + ( M A X - MIN)* ADV^a / (d + ADV^a) (1)

As opposed to the same formula in a spreadsheet template:

B1 = $A$1 + ($A$2-$A$1)* (D$2^K 1 / ($ D$3 + D2^K 1) (2)

Furthermore, in PC:SOLVE the advertising variable, ADV, could be a vector which then automatically creates a MRKT SHARE vector, one element for each element in ADV. In other words, the program has dynamic data struc- tures without the need for any declaration of variables nor for the use of explicit loop constructions such as IF �9 . . THEN or FOR . . . NEXT.

PC:SOLVE IN THE CLASSROOM

PC:SOLVE is a good software package to use in a quantitative marketing class. Imagine the plight of students who face the complex cell referencing of equation (2), while still unfamiliar with the formula as it would be written on paper, as in equation (1). The English-like commands allow the student to concentrate on learning the material of the class



FIGURE 2 PC:SOLVE Data Editor Screen

BRNI Price Dist. Adv Display Promo Units

Periodl Period2 Period3 Period4 Period5 Period6 Period7 Period8 Period9 Periodl0 Periodll Periodl2 Periodl3 Periodl4 Periodl5 Periodl6 Periodl7 Periodl8 Periodl9 Period20 Period21 Period22 Period23 Period24

3.19 0.89 0.13 0.ii 0.09 1.19 3.19 0.87 0.13 0.52 0.35 1.50 3.19 0.85 0.13 0.I0 0.09 1.65 3.19 0.84 0.13 0.I0 0.08 1.62 3.13 0.82 0.33 0.i0 0.08 1.20 3.13 0.84 0.33 0.i0 0.08 1.66 2.88 0.85 0.34 0.i0 0.77 1.62 3.13 0.87 0.35 0.52 0.09 1.69 3.13 0.88 0.35 0.ii 0.09 1.90 2.88 0.88 0.35 0.ii 0.09 2.05 3.13 0.92 0.37 0.55 0.37 1.66 3.13 0.88 0.35 0.ii 0.09 1.70 2.88 0.87 0.35 0.i0 0.09 1.22 3.13 0.85 0.34 0.51 0.08 1.66 3.13 0.83 0.33 0.i0 0.08 1.72 3.13 0.83 0.33 0.i0 0.08 1.09 2.88 0.83 0.33 0.i0 0.08 1.44 3.13 0.83 0.33 0.i0 0.08 1.57 3.13 0.83 0.33 0.i0 0.66 1.66 3.13 0.83 0.33 0.i0 0.08 0.89 3.13 0.85 0.34 0.i0 0.08 1.13 2.88 0.86 0.35 0.i0 0.09 2.24 3.13 0.88 0.35 0.ii 0.09 1.35 3.13 0.90 0.36 0.ii 0.09 1.63

and conceptualizing the analysis, while spending less time translating the analysis into a specific software package.

One of the reviewers has used PC:SOLVE in a marketing course. Students were assigned to analyze A.C. Nielsen data on the spaghetti sauce category and determine the de- gree to which a promotion had affected sales. The particular promotion had been run in four of the manufacturer's eight largest markets with the other remaining markets being con- trol markets without the promotion. Figure 2 shows the data for brand 1 in the data editor mode.

Students are encouraged to think creatively about the problem and integrate the material they have learned in the class, but are not directed to solve the problem by any particular method. The students are offered the alternative of using a spreadsheet to tackle the problem, but only a few take that route and those that do seem to become frustrated with the spreadsheet fairly quickly.

A typical student's approach to the analysis might include the following scratchpad commands. First, all of the unit sales in the market are added together. Note that each brand variable is a matrix (as shown in Figure 2), but the addition of '.units' pulls out the 'units' column from the matrix. Considering each of these columns are vectors, the resulting variable, all units, is a vector:

all units = brnd l.units + brnd2, units + brnd3, units + brnd4, units (3)

Then the relative market share of brand 1 is calculated, resulting in a vector:

mktshr I = brand I/(all units/4) (4)

Next, the student may use the built in MOVINGAVG function to compute a six period moving average of the relative share vector for brand 1:

normshr I = MOVlNGAVG(mktshr 1,6) (5)

A title is selected for the graph:

gtitle='Normal Mkt Share vs. Actual Mkt Share' (6)

and a line graph of actual relative market share for brand 1 is generated along with a line graph of the moving average of that share:

gline(time,mktshrl ,normshrl) (7)



FIGURE 3 PC:SOLVE Teaching Example

K o ~ a a l H k t S h a r e v s . A c t u a l Idk~, S h a r e 1 . 6

1 . 4

1 . 2

1 . 8

. 8

6

4

13 f,., II

,r

,.l,a

X:

Io

a p B I

ID IX

B 4 8 12 16 28 24

TIME

m

MXTSHR 1

§

NORIISHR1

The resulting graph is shown in Figure 3. The difference between the two graphs in Period 19, when the promotion was run, is the supposed effect on market share.

A student may then calculate the extra bottles of spaghetti sauce that were sold due to the promotion by multiplying the total units sold in Period 19 by the "extra" market share in Period 19. This calculation might be accomplished by using the entire mktshrl and normshrl vectors and then examining the element in the 19th row, or by subscripting the vectors to pull out the data for only Period 19, as shown here:

extra = a l l u n i t s [ 19,1 ]* (mktshr 1 [ 19,1 ] -normshrl [19,1]) (8)

A student might then use the scratchpad to refine his/her approach. For instance, a student might check the relative market share "gain" in Period 19 for a non-promoted market and subtract this small share gain from the apparent share effect of the promotion. These calculations can be done by simply editing the variable names to reflect a non-promoted market, while leaving the formulas unchanged. The lines can then be re-executed without retyping the formulas and without rethinking the steps of the analysis.

By trying different approaches to the problem, students get an in-depth feel for the advantages and disadvantages of alternative approaches to the problem. Most students end up analyzing the problem with multiple methods, all within PC:SOLVE.

Aside from learning how to analyze price promotion data, students seem to benefit from learning a new way of thinking--a vector-oriented way of thinking. We believe that this orientation is very helpful for improving the analyt- ical skills of marketing students.

DOING RESEARCH WITH PC:SOLVE

PC:SOLVE has proven to be a very flexible and powerful tool for research. To date we have used the package in many research projects. One research project involved an inves- tigation of models based on interdependent demand which will serve as an example of using PC:SOLVE in research.

Interdependent demand models are models in which the actions of one consumer influence the actions of other consumers, who, in turn, influence the actions of the first (Gra- novetter and Soong 1986, Miller and Mclntyre 1990). The models are, therefore, characterized by a non-linear, dynamic process that requires the simultaneous consideration of both an individual and group level of analysis.

In our simulation work on interdependent demand models (Miller and Mclntyre 1990), the flexibility of PC:SOLVE allowed us to simulate individual-level utility functions and then to integrate them into a societal level framework for studying the dynamics of the structure.

For example, assuming a quadratic representation for each of three component utilities relevant to the fashion adoption decision at the individual level, the total utility derived by an individual consumer for adopting style xt can be expressed mathematically as

where

U ( x t ) = _ c f ( x t - x f ) 2 -~- C u ( X t - X u ) 2

- Cc(X, - x , _ 1) 2 (9)

X t

Xs

is any style within the fashion space in time period t. is the consumer's perceived 'appropriate' style.



x u is the consumer's perceived 'inappropriate' style.

x,_ 1 is the consumer's last adopted style. cf, cu, and c c are the consumer's concern parameters for ap-

propriateness, inappropriateness, and change. U(xt) is the consumer's utility for the given x,.

This expression for the individual consumer utilities is coded in PC:SOLVE as

U= - c f * ( x t - xf)^2+ cu*(xt-xu)^2 -cc*(xt- lag(xt , 1))^2 (10)

Note that the expressions are identical except for super and subscripting and the use of the built in lag function.

To examine a graph of the resulting utility function across the range of permissible values for xt, the built in index function is used. Index creates a vector of whole numbers beginning at 1 and ending with the function's argument. In this case, we construct a vector whose range is . 1 to 20 in increments o f . 1 by the expression xt=index(200)/10.

After setting the remaining parameters to specific values, and executing the scratchpad statement given in equation (10), the scratchpad command gline(xt,U) is used to examine a line graph of the resulting utility function. Examining other possible representations of the consumer's utility function (e.g., multiplicative, diminishing returns, etc.), is accomplished by editing equation (10) in the scratchpad, re- executing the modified expression, and re-executing the GLINE command. In this manner a wide variety of possible representations can be examined with minimal effort.

Considering that the individual consumer's perceptions of

appropriateness, xj~ and inappropriateness, x,, are functions of the styles adopted by other members of society, the sec- ond step in the simulation was to integrate individual consumers into a societal level of analysis characterized by interdependencies.

By mathematical manipulation, the expression for the individual consumer given in equation (9) can be integrated into a societal level expression of the form:

where

Xt = ~ X t 1 (11)

X t

X t - 1

f~

is a vector of styles, x, adopted in time t; is a vector of styles, x, adopted in time t - l ; is a summary matrix of social influence patterns whose elements are determined by the individual utility functions.

Considering that the parameter values for each consumer utility function are fixed for each simulation run, we created a library function, OMEGAGEN, which randomly gener- ates parameter values for a population of consumers and computes the resulting omega matrix. A 200 time period simulation can then be executed by a single line on the scratchpad.

F o r t = l to 200 d o X = join(X,mm(omega,X[,t- 1])) (12)

In this expression, the omega matrix is matrix multiplied, using the built-in m m function, by the t - 1 column of the X

FIGURE 4 PC:SOLVE Research Example

THE FASHION CYCLE 18

8

6

V !

8 48 88 128 16e 288

TIME

i

~ E R A G E

§

UPPER

D

LOWER



matrix and joined by column to the X matrix. As such, the X matrix will contain the styles adopted by each consumer in each of the 200 time periods. With this as the core model, the representation was refined by adding additional factors and re-executing the simulation line. To summarize a typical simulation run, we compute the mean style adopted by all consumers in each time period and two standard devia- tion limits around the mean. These calculations use built-in functions whose arguments are the entire X matrix [mean (X) and sd (X)], so that row vectors containing the appropriate values are computed by a single command. The simulation runs are then graphed, as shown in Figure 4.

Throughout this ongoing research project we have been able to develop the simulation interactively by executing and examining the results of each step. This was an advantage to our conceptual development of the simulation and in debugging the simulator. Upon completion of a given representation in the simulation, we are able to graph individual and group level results, manipulate the parameters of the system, and examine the impact of alternative representations of the utility functions with minimal additional effort.

In our ongoing empirical work on interdependent demand, we have been able to keep the structure of the simulator while replacing the simulator's parameter matrices with functions that statistically estimate these parameters from empirical data. In this manner, we have been able to directly incorporate data analysis, without having to change software packages or "re-invent the wheel."

HARDWARE AND SOFTWARE COMPATIBILITY

PC:SOLVE is designed to run on any IBM or IBM com- patible computer. A hard drive is recommended, although PC:SOLVE will run on a dual floppy drive system. As with all numerical software, a math coprocessor is also recommended.

An automatic configuration program senses the hardware available in the machine, and configures itself to the system. This eliminates the need for the user to determine which graphic card and other hardware is in the computer.

The package is capable of generating printed reports, including graphics, using the wordprocessor-like scratchpad. Printer drivers for a number of printers, including the HP Laserjet, are included. Data can be written to disk as ASCII files and read back later for use in other software packages. Additionally, the scratchpad text itself can be written out as an ASCII file and read into other scratchpads as a piece of application code. Documentation can be em- bedded in the scratchpad text at any point since comments on a line after a ";" are ignored.

From the DOS level, it is possible to invoke PC:SOLVE, execute an analysis, write out the results to an ASCII file, and exit the program. This feature allows PC:SOLVE to be linked to other software through the use of batch files or macros. For example, if the primary analysis is being con- ducted within a spreadsheet, but a portion of the data analysis is more easily done within PC:SOLVE, a spreadsheet macro can be developed to conduct that portion of the analysis within PC:SOLVE without ever leaving the spreadsheet.

DOCUMENTATION AND HELP FACILITIES

PC:SOLVE has clearly written documentation. The material is not intimidating in that it is short, concise, and direct. It is apparent that the developers have gone to great lengths to keep things simple to insure ease of use for the beginning user. Additionally, the reference material is adequate even for the more advanced user.

The on-line help system provides summaries of the built in functions, as well as summaries of any additional library functions that the user has available. The help system can be accessed using pop-down menus, or by simply typing the beginning few letters of the function for which help is needed. This allows the user to browse the help system efficiently. Also, when the appropriate help statement has been found, the statement can be held in the top right part of the screen even after the user has exited back to the scratchpad. Thus one can hold some aspects of help in view even while working.

SUGGESTIONS FOR IMPROVEMENT

As with all PC software, the greatest drawback to PC:SOLVE is memory limitations. Although the program uses overlays to maximize the amount of memory available to the user, large simulations will still exceed the memory available. As such, we would enjoy seeing a version of the program that can take advantage of the large amount of memory installed in most machines.

A few minor suggestions for improvements are as follows:

1. The ability to view graphics and the scratchpad simultaneously.

2. Variables used in user defined functions should be treated as "local" variables by default rather than requiring explicit declaration with the "local" command.

3. The ability to read ASCII files with user defined formats.

COST CONSIDERATIONS

The retail cost of the program is $495. Educational institutions can buy single copies for $295 each. Site licenses are available, beginning at $1,000 for smaller educational institutions.

CONCLUSION

In our view, PC:SOLVE provides a new approach to the analysis of marketing problems. Its approach is simple enough for a beginning user and for use in the classroom, while its flexibility and power are appropriate for research and application development. As the use of PC:SOLVE spreads through marketing and other professions, a wide variety of library functions will become available which



should further increase the program's power and applicability.

ing Fashion Theory." Working Paper. Jesse H. Jones Graduate School of Administration, Rice University, Houston, TX 77251.

APPENDIX

Information concerning PC:SOLVE can be obtained from:

Pacific Crest Software 887 NW Grant Ave. Corvallis, OR 97330

(503) 754-1067

REFERENCES

Granovetter, Mark and Roland Soong. 1986. "Threshold Models of Inter- personal Effects in Consumer Demand." Journal of Economic Behavior and Organization 7: 83-99.

Little, John D.C. 1970. "Models and Managers: The Concept of a Decision Calculus." Management Science: Applications April: 466-485.

Miller, Christopher M. and Shelby H. Mclntyre. 1990. "Toward Formaliz-

Comments from Daniel K. Apple, President, Pacific Crest Software:

...this is our plan for future PC:SOLVE development.

Pacific Crest Software plans to release PC:SOLVE libraries for Statistics, Engineering, Mathematics, Graphics, Quantitative Methods, and Utilities dur- ing the first quarter of 1991. These libraries are implemented in the PC:SOLVE language, and the functions in these libraries are usable in exactly the same way as internal functions.

A follow-on release to the university market incor- porating additional functions is planned for mid- 1991.


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