The Sawtooth Software Market Simulator. (A Supplement to the CBC v2.6 Manual)

Transcription

1 The Sawtooth Software Market Simulator (A Supplement to the CBC v2.6 Manual) (Updated 26 January 06) Sawtooth Software, Inc. Sequim, WA

2 In this manual, we refer to product names that are trademarked. Windows, Windows 95, Windows 98, Windows 2000, Windows XP, Windows NT, Excel, PowerPoint, and Word are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Bryan Orme, Editor Copyright Sawtooth Software

3 About Technical Support We ve designed this manual to teach you how to use our software and to serve as a reference to answer your questions. If you still have questions after consulting the manual, we offer telephone support. When you call us, please be at your computer and have at hand any instructions or files associated with your problem, or a description of the sequence of keystrokes or events that led to your problem. This way, we can attempt to duplicate your problem and quickly arrive at a solution. For customer support, contact our Sequim, Washington office at , support@sawtoothsoftware.com, (fax: ). Outside of the U.S., contact your Sawtooth Software representative for support.

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5 Table of Contents The Sawtooth Software Market Simulator Introduction... 1 Chapter... 1: Data and Utility Run Management 2 Importing... Utility Runs 2 Merging... Segmentation Data 5... Custom Segmentation Variables 7... Weighting Respondents 8 Data... Management Tools 9 Tables... Program 12 Chapter... 2: Using Simulators to Answer Strategic Questions 16 Overview What... Is a Market Simulation? 17 Why... Conduct Market Simulations? 18 Typical... Questions for Conjoint Simulators 20 Chapter... 3: Market Simulation Theory and Models 21 Base... Case Scenario 21 The... Exponent (Scale Factor) 22 The... Red-Bus/Blue-Bus Problem 23 Market... Simulator Models 24 External... Effects 27 Interpolating... between Levels 29 The... "None" Weight 30 Chapter... 4: Using the Market Simulator 31 The... Market Simulator Dialog 31 Adding... a Simulation Scenario 33 Other... Controls in Simulation Scenarios 35 Running... the Simulation 37 Chapter... 5: Practical Simulation Examples 38 Introduction New... Product Introductions 39 Estimating... Demand Curves and Elasticities 41 Designing... Products to Appeal to Unique Market Segments 44 Chapter... 6: Interpreting Conjoint Analysis Data 47 Chapter... 7: Technical Details for Simulations 50 First... Choice Method 50 Share... of Preference Options 51 Purchase... Likelihood Option 54 Randomized... First Choice 55 Chapter... 8: CBC Analysis: Counts and Logit 59 Counting... Analysis for CBC 59 Logit... Analysis for CBC 63 Appendices... for Simulation Topics 71 A:... Importing Conjoint Data from ASCII Files 71 B:... How the Simulator Interpolates between Levels 75

6 C:... How the Simulator Uses Latent Class Data 77 Index 78

7 1 The Sawtooth Software Market Simulator 1.1 Introduction The Sawtooth Software Market Simulator 1 This manual is a supplement to the hardcopy CBC System v2.6 manual under the SMRT platform. This version of CBC is also called "CBC for Windows." There is some overlap in topics between the hardcopy CBC manual and this electronic (.PDF format) manual. However, this supplement contains a number of new sections: Importing utility runs from CBC/HB and Latent Class Data management tools Using conjoint simulators to answer strategic questions Using the market simulator Practical simulation examples Interpreting conjoint results Importing conjoint data from ASCII (text-only) files How the simulator uses Latent Class data

8 2 CBC v Chapter 1: Data and Utility Run Management Importing Utility Runs Overview SMRT can automatically import conjoint part worth data generated by any of Sawtooth Software's current conjoint software systems by clicking Analysis Run Manager Import. (The number of attributes and levels per attribute to be imported cannot exceed the limitations of your system). It can also import part worths that you have generated in most any other standard way, as long as you format those data as an.hbu file and optionally create a file describing the coded linear terms (.VAL) if they apply. The formats automatically imported by the Market Simulator are: HB Utilities (*.hbu, *.hbc) ICE Utilities (*.utl) Latent Class Utilities (*.pxx) Generic Conjoint File (*.hbu) ACA/Web or ACA v4.x (*.utl) CVA (Traditional Conjoint) Utilities (*.utl) Latent Class, HB Utilities, and the Generic Conjoint *.hbu formats are the most complex, allowing for a great deal of flexibility in the inputs, including first-order interactions and linear term specifications of utilities. Importing Utilities into the Market Simulator Many users of CBC estimate individual-level part worth utilities using the CBC/HB program, which runs separately from SMRT. After estimating utilities under CBC/HB, you'll need to import those results into SMRT for use in the market simulator. If you are using the logit or Latent Class routines integrated within the SMRT menus, you do not need to import part worth utilities; the utilities will have already been saved within the SMRT project files. To open an existing SMRT study, select File Open, and browse to the directory containing the studyname.smt file. Double-click the studyname.smt file. If attributes and levels definitions exist in the study, they must match the attributes and levels for the conjoint data to be imported. After you have opened the study, choose Analysis Run Manager. The Import option on the Run Manager dialog lets you import conjoint utility (part worth) data from ASCII (text-only) files for use within the Market Simulator. You can import multiple sets of conjoint utilities which become separate selectable runs. The utility data can come from a number of formats or sources: HB Utilities (*.hbu) ACA/Web (*.utl) CVA Utilities (*.utl) Hierarchical Bayes utilities generated by CBC/HB or ACA/HB (see the HB manuals for data layout). If importing an.hbu file, the attribute labels are read from the header section of the.hbu file. Part worth utilities from the ACA/Web system (see the ACA/Web manual for data layout) or ACA v4.x. If you import a studyname.utl file, labels for the attribute levels are imported from the studyname.aca file. If a studyname.aca file does not exist, default attribute and level labels are assigned. Part worth utilities from the CVA v2.x system. If you import a studyname.utl file, labels for the attribute levels are imported from

9 The Sawtooth Software Market Simulator 3 the studyname.cva file. If a studyname.cva file does not exist, default attribute and level labels are assigned. ICE Utilities (*.utl) Latent Class (*.pxx) ICE utilities (see the ICE manual for data layout). If you import a studyname.utl file for ICE data, labels for the attribute levels are imported from the studyname.att file. If a studyname.att file does not exist, default attribute and level labels are assigned. Latent Class utilities generated by the CBC Latent Class v2 Module (see the Latent Class manual for data layout). When importing Latent Class utilities, the data are read from two files: *.Pxx (e.g. studyname.p05 for a 5-group solution), which includes information about respondents' probabilities of membership in each group; and the *.LCU file which contains the average utility estimates for each group. Note that the.pxx file is not generated by default, but must be requested by a parameter in the.par file prior to generating the Latent Class run. During the import process, individual-level utility data are generated by multiplying the probabilities of membership by the vector of utilities for each class. If importing Latent Class data, the attribute levels are read from the header section of the.lcu file. If linear specification of quantitative attributes has been used, there must be an accompanying.val file in the same directory. You can also merge the latent class membership for use as banner points and/or filters. To merge group (class) membership, select File Merge Variables Add... and then choose Latent Class 2.x Group Membership (*.Pxx). SMRT assigns each respondent to the group for which he/she has the highest probability of membership (ties broken randomly). To use group membership from latent class as a banner point, you must first specify the merged variable as a Custom Segment. Generic Conjoint (*.hbu) With the Generic Conjoint.HBU format, you can import conjoint utility data generated in any other way or software program, including main effects, first-order interactions or linear term specifications, as long as the attributes and levels do not exceed the capacity of your system and the data are formatted as an.hbu file. The generic.hbu format assumes one case per respondent (not "Draws" as can be an option for Hierarchical Bayes estimation). Data from ASCII files are read with 15 significant digits of precision. When utility run data are imported, four files are modified or created: Studyname.UCS (a binary data file containing respondent numbers and conjoint utilities) Studyname.QNR (a binary data file containing attribute level labels and questionnaire settings) Studyname.DAT (a binary data file containing respondent numbers and any segmentation information) Studyname.IDX (a binary index file describing the layout of the Studyname.DAT file). If your study had no respondent data prior to importing a utility run, a list of respondent numbers is created in a Studyname.DAT file. If your study already included respondent numbers and data, then the conjoint part worths are written to the.ucs file and it is assumed that corresponding respondent records are available in the.dat file (if applying banner points, filters or weights). If no attribute labels are provided, SMRT provides default labels. If conjoint attributes and level labels exist in your study prior to importing conjoint utility data, those definitions are preserved and the utilities to be imported must match that layout. If there is a mis-match between the number of imported attribute levels and

10 4 CBC v2.6 the number of existing levels in the study, SMRT will issue a warning message. If your ASCII conjoint data file included segmentation information you want to import, you must merge those data separately using File Merge Variables. Exporting Utilities into ASCII Format The Export option lets you export conjoint utility data from the binary.ucs file into an ASCII (text-only).hbu format. Data are written with up to 5 decimal places of precision. ASCII conjoint data in an.hbu format can be imported for use in the Market Simulator.

11 The Sawtooth Software Market Simulator Merging Segmentation Data Introduction SMRT includes a Merge Wizard to lead you through the process of merging additional descriptive or segmentation variables into that same data file. These variables might be demographics, such as gender, household size or age, or information about a firm such as company size, volume purchased, or industry classification. You can also merge continuous variables to be used as respondent weights. The type of file the typical user will merge will probably be in ASCII (DOS Text) format as created by another interviewing system or data processing software. The data file to be merged must include a respondent classification (Case ID) number. All merged data must be numeric only. After you have merged additional variables into your data set, these are automatically available to you as "stub variables" for use in the Tables program or as respondent weights in either the Tables or the Market Simulator. If you undertake the additional step of defining Custom Segmentation Variables based on these merged variables, you can also use the merged information as filters or "banner points" during simulations if using individual-level utilities. The next few sections include basic information about merging segmentation information and defining Custom Segmentation Variables. Additional help and details are available in the on-line help within SMRT, accessed by pressing F1 at any time during the merge process. Merging Segmentation Data from ASCII Files Often users merge segmentation information (demographics, usage data, firmographics, etc.) into their market simulators for use as segmentation, filters or weighting variables. These data can come from any source, as long as the data have been formatted as an ASCII, text-only file (either fixed-column or delimited). The respondent numbers in the file(s) to be merged must be numeric and must match the respondent numbers in the conjoint data file. The cases do not necessarily need to be in the same sort order. The Merge program in the Market Simulator has a "Merge Wizard" that leads you through the entire process. For the Merge program to work properly, you must have unique respondent numbers that match in both the source and destination data files (numeric data only). You should not have duplicate respondent numbers in either file. Any respondent in the file of conjoint part worths not found in the file to be merged will receive a "missing" value for the selected merge variables. Delimited ASCII Files When you specify to merge information from a Delimited ASCII file into your data file (*.dat), you need to instruct the software on how to interpret the data file. You do so by specifying the delimiter (the character that separates data fields, such as a space, tab or comma) and specifying what value (if any) is used to designate missing values. This value may be any string of alphanumeric characters or symbols. The Merge Wizard asks you to specify whether respondent data is on one line (example below, with three respondents numbered 1001 to 1003) or otherwise In the example above, each respondent record occupies a single line, and the data are separated by

12 6 CBC v2.6 blank spaces. This layout is an example of "one line per respondent record." Below is an example in which each respondent's data span more than one line: In the example above, each respondent record is spread across two lines. In order to understand how to read this layout, the Merge Wizard asks you how many fields per respondent record there are. In this case, there are five (note that this number includes the respondent number field). The Merge Wizard asks you to indicate in which field the respondent number is located. Note that this is not the "column number" where each column is a single character, but the field in which fields are separated by the delimiter. In the examples above, respondent number is located in field #1. To avoid misinterpretation of text strings (which may contain delimiters), text in the data file may be enclosed in quotation marks. Single (') or double (") quotation marks are supported. The following is an example of how Merge reads data depending on the user-specified parameters for handling string variables: Text in Delimiter Quotes? Data Read as String read as Comma Yes 5,6,"B K O",32 4 fields B K O (1 string) Comma No 5,6,"B K O",32 4 fields "B K O" (1 string) Space Yes 5 6 "B K O" 32 4 fields B K O (1 string) Space No 5 6 "B K O" 32 6 fields "B K O" (3 strings) Fixed ASCII Files When you are merging data from a fixed column ASCII file, the Merge Wizard asks you to specify in which columns the data are located and the length of each field. A column is the width of a single character. Many text editors (such as Microsoft's DOS editor, which you can access from the DOS prompt by typing EDIT and pressing the ENTER key) provide a column counter at the top or bottom of the edit window. With fixed column ASCII files, the data are aligned in columns when viewed with a non-proportional font (such as Courier) such as follows (the first two lines below are a reference for counting columns, they would not appear in the data file): Assume that the first variable is the respondent number (though it is not necessary that it be the first variable in the file). It is right-justified within an area that begins in column 1 and has a length of 6. The next variable starts in column 15 and has a length of 2. The third variable starts in column 28 and has a length of two (note that the second case has a missing value). The final variable starts in column 34 and has a length of 1.

13 The Sawtooth Software Market Simulator Custom Segmentation Variables The variables you merge into your data set (i.e. from another data processing program an ASCII source) though valid automatically as weights are not available as banner points and respondent filters until you first create new variables based on those data called Custom Segmentation Variables. A Custom Segmentation Variable is one you create based on the values of one or more other merged variables. Custom Segmentation Variables can be used as banner points and respondent filters in the Tables and the Market Simulator programs. You can create Custom Segmentation Variables by selecting Analysis Custom Segments. The details for specifying these variables is available in the on-line help by pressing F1 when you are at the Custom Segments dialog. As an example, we could create a Custom Segmentation Variable based on gender and income. Suppose we merged a variable called Q31 from an outside questionnaire that classified a respondent's gender (Male=1, Female=2), and Q32 that reported a respondent's income (continuous variable). These variables would not be available for choosing as banner points or respondent filters in the simulator until we had created a custom segmentation variable. We might call this custom variable Gender_Income, and indicate the following definitions: Definitions for Custom Segmentation Variable: Gender_Income Segment Label Logical Definition Female Low Income (Q31 = 2) & (Q32 <= 25000) Female High Income (Q31 = 2) & (Q32 > 25000) Male Low Income (Q31 = 1) & (Q32 <= 25000) Male High Income (Q31 = 1) & (Q32 > 25000) After creating a new Custom Segmentation Variable called Gender_Income, these segments are available for selection as banner points and respondent filters. Though our example above used two variables to define a Custom Segmentation Variable, you could use only one variable, or many more variables. The Market Simulator supports sophisticated ways for defining Custom Segmentation Variables, including the following operators: =, >, >=, <, <=,!= (not equal to), & (and), (or),! (not), $MISSING (missing value). You can include complex statements that include parentheses for controlling the flow of logical operations.

14 8 CBC v Weighting Respondents Weighting lets some respondents have more impact on summary statistics than others. For example, a respondent with a weight of 2.0 is counted twice as much as another respondent with a weight of 1.0. Weighting is useful for adjusting a sample to reflect known population characteristics. Weights can be applied during all stages of analysis. There are two ways to define weights: 1. Use a merged variable value as a weight. You may have calculated a weight for each respondent and placed those weights along with the respondent numbers in an ASCII file for merging with your part worth data. 2. Assign weights to segmentation categories. In this case, the weighting variable must be defined as a Custom Segmentation Variable. For example, assume we wanted to weight our sample based on Gender. The table below shows the weight that needs to be given to respondents to achieve the target gender representation. Actual Proportion Target Proportion Weight Male /0.35 = Female /0.65 = If we apply these weights, the average weight will be 1.0, and the unweighted number of respondents will be equal to the weighted number of respondents. Whether you use a merged variable value as the weight or assign weights to categories of a segmentation variable, we strongly suggest your weights have this property. If not, some statistics in Tables will be incorrect.

15 The Sawtooth Software Market Simulator Data Management Tools Introduction The Market Simulator includes some data management tools that can be useful for some situations. These tools are accessed from the Tools menu, and include: View/Edit Data Index Data Doctor Optimize Study files This section describes the purpose and use for these data management tools. View/Edit Data The Market Simulator lets you view the respondent numbers and any segmentation variables that have been merged into the conjoint part worth utility data. (You cannot view the part worths themselves through this option.) When you click Tools View/Edit Data, a dialog is displayed with three separate tabs: Respondents, View Respondent Data and Edit Respondent Data. Respondents Tab When you click the Respondents tab, SMRT reads the.idx file located in the study directory, which provides an index of the.dat file (pointers to the location of data segments within a.dat file) including a list of the respondent numbers. Respondent numbers are initially displayed in sorted order (by default) and an asterisk following a number indicates a gap in the sequence. Note that SMRT does not read the studyname.dat file to generate the report on this tab. Therefore, if your.idx file for some reason has become corrupted or for some reason does not match (pertain to) the.dat file, it is possible to see information on this tab that is really not in the.dat file. To ensure that the information in the.idx file matches the.dat file, you should run either Tools Index, or Tools Data Doctor. Close Print New Disk Filter Sort Disposition Closes the current dialog. Prints the information displayed on the screen. Lets you view or edit data in another directory or floppy disk. (Drop down list). This drop-down list lets you filter the respondents to be displayed by complete/incompletes or a particular disposition. It also lets you view summary counts of the number of respondents by disposition or status (these disposition and status codes are a carryover from Sawtooth Software's Ci3 system, and are not relevant to the Market Simulator). By default, the respondent numbers in this window have been sorted in ascending order. Note that the actual order of the records in the file will likely be different. If you uncheck the Sort box, the actual order of the respondent records is displayed. Note that this check box only changes the order in which the records are displayed on the screen, it doesn't actually change their order in the data file. Displays the disposition codes (applies to Ci3 data only see the Ci3 manual for more details) for each respondent record. View Respondent Data Tab This dialog reads the information from the studyname.dat file and displays any segmentation data

16 10 CBC v2.6 that you may have merged with the conjoint part worths. If not all of the merged information can be displayed within the window, you can use the scroll bar at the right of the dialog box to see values for later variables. You can scroll through data for different respondents by clicking on the double left-or-right arrows at the bottom of the dialog box. Alternatively, you can select a different respondent by clicking on that respondent's number at the left of the screen. Close Closes the current dialog. Print Prints the information displayed on the screen. << and >> These buttons let you move between adjacent records in your data file. New Disk Lets you view or edit data in another directory or floppy disk. Edit Respondent Data This dialog shows detailed information on each respondent, permitting editing the values for merged information or changing the respondent number. The Answer: field lets you change the value of any response. You can make tentative editing changes to answers that will not become permanent until you say that you want to retain them. If you click the Change Respondent Number button in the upper right hand corner, a box appears in which you can provide a different respondent number. You change a respondent number by typing in a different respondent number, and then clicking on OK. Unlike segmentation variable values that you change, a respondent number change is made immediately, and you will not have the opportunity to decide that this change should be ignored. You can also delete this respondent if you like. You do that by clicking the box labeled Delete This Respondent. A check mark appears in the box, indicating that the respondent will be deleted. If individual-level utility runs have been saved, the utility data will also be deleted for this respondent. If you change your mind you can click that box a second time and remove the check mark. You can move from one merged segmentation variable to the next by scrolling with the double arrows at the bottom of the dialog box, or scrolling in the box in which the question (variable) name is displayed. When you have finished with this respondent, click the Respondents Tab at the top of the dialog box to again see the list of respondents with data in this file, and select another respondent number. Then click again on the Edit Respondent Data tab, and you will be able to edit that respondent's data. When you make changes to the data file, they are recorded in a file named questionnaire.log. That file is in ASCII format and can be viewed by any editor or word processor. Close When you click the Close button, any changes you made to the data are made permanent. You can make modifications to multiple records before clicking the Close button. << and >> These buttons let you move between adjacent records in your data file. New Disk Lets you view or edit data in another directory or floppy disk. Delete This Respondent Deletes the current respondent (when you click the Close button). Change Respondent Number Lets you change the case identification number. This change is made immediately rather than when you click the Close button.

17 The Sawtooth Software Market Simulator 11 Index The data file (studyname.dat) used by SMRT is always accompanied by an Index file (studyname.idx). This file contains information about where each respondent's data are located within the file. If your studyname.idx file gets lost or corrupted, you can easily regenerate it by clicking this option. By default, the software will re-index the file within your study directory. You can also regenerate lost or corrupted.idx files in other directories (or your floppy drive) by specifying a different path. Data Doctor The Data Doctor is a utility for repairing data files (studyname.dat) that have become corrupted. Occasionally, sectors on disks fail or hardware doesn't properly record the data. With very large data sets and tens of thousands of respondents, data corruption or loss becomes increasingly likely. Sometimes a single corrupted record in the middle of a data file makes it impossible for the Market Simulator to understand how to deal with the subsequent respondents. Other times, the information at the top of the data file is corrupted so that the Market Simulator doesn't even know how to begin. The Data Doctor analyzes a.dat file and checks it for validity, skipping non-critical sections of the file if it finds errors in them. The user is prompted as to what action to take for each error encountered. This process can be slow with large data files. The Data Doctor can operate on the accumulated data file in the study directory, or on any other drive/directory. Optimize Study Files This tool is used to optimize the study files (.SMT,.QNR and.ucs), removing information no longer needed. For example, when you delete a utility run (from the.ucs), the section is only marked for deletion until you choose Optimize Study Files. Optimizing study files will help speed access to information in those files.

18 12 CBC v Tables Program Overview The functionality and output of the Tables program resembles the cross-tab capabilities found in many statistical and cross-tab packages today. However, the SMRT Market Simulator is primarily concerned with conjoint analysis; the Tables program is basic and relatively inflexible. If you need to analyze the results with a more sophisticated package, SMRT can export data for use in other software systems. Analyzing Nominal/Ordinal Data Nominal data are numeric values, where the numbers refer to categories or classifications. For example, one might code gender as: Male = 1 Female = 2 An example of a type of survey question that might be analyzed is shown below: Which of the following best describes the highest level of education you achieved? o Some high school o Graduated high school o Some college o Graduated college o Some post-graduate studies o Post-graduate degree o Doctoral degree If we assign the following codes: Some high school = 1 Graduated high school = 2 Some college = 3 Graduated college = 4 Some post-graduate studies = 5 Post-graduate degree = 6 Doctoral degree = 7 it is true that larger numbers reflect greater education (most would agree on this point). In contrast to the previous example where the coded value really had no quantitative meaning other than nominal classification, these data reflect ordinal scaling. However, as with nominal data, it still doesn't make sense to apply mathematical operations such as addition, subtraction, multiplication or division to ordinal data. For example, a person with a "post-graduate degree" (code 6) doesn't necessarily have twice as much education as a person who completed "some college" (code 3).

19 The Sawtooth Software Market Simulator 13 The Table Display When you analyze survey responses using Tables, a table of results (in this case, frequencies) is generated and displayed in the report window: Total Education Some high school 3 Graduated high school 35 Some college 68 Graduated college 73 Some post-graduate studies 18 Post-graduate degree 11 Doctoral degree 4 Total 212 Missing - This simple table displays summary frequencies for the entire sample. Frequencies represent the number of times respondents answered (or were classified) in a particular way. Every time you click Compute!, a new table (or set of tables if you have selected multiple variables) is appended to the report window. You clear the window by clicking Clear. In addition to frequencies, there are other statistics you can include (by clicking Statistics) in the table appropriate for nominal or ordinal variables: Column percent Row percent Table percent Chi-Square These statistics are described in any good statistics textbook. You can also analyze the results by another variable, such as Gender. In the example below, Gender is the banner (column) variable, and Education the stub (row) variable. This display is often referred to as a cross-tabulation. Education by Gender Gender Male Female Total Education Some high school 3-3 Graduated high school Some college Graduated college Some post-graduate studies Post-graduate degree Doctoral degree Total Missing - - -

20 14 CBC v2.6 This display lets us compare educational achievement by gender. For example, 33 males went as far as graduating college (but no farther) vs. 40 females. If we include column percentages, we make better sense of the results, since there are an unequal number of males and females in the sample. Education by Gender Gender Male Female Total Education Some high school 3-3 3% - 1% Graduated high school % 17% 17% Some college % 29% 32% Graduated college % 39% 34% Some post-graduate studies % 10% 8% Post-graduate degree % 5% 5% Doctoral degree % 1% 2% Total % 100% 100% Missing Analyzing Continuous Variables You can also analyze continuous variables with the Tables program. The following statistics are available in Tables and appropriate for analyzing continuous data: Mean Standard deviation Variance Minimum Maximum Standard Error of Mean These statistics are described in any good statistics textbook. Continuous variables can only be specified as stub variables; they cannot be used as a banner variable unless you recode them to discrete categories by creating a custom segment. If a continuous variable you are analyzing has many unique values, the table can become extremely large. A simple remedy is to turn off frequencies and only request summary statistics, such as the mean, minimum, maximum, standard deviation and standard error.

21 The Sawtooth Software Market Simulator 15 Some Notes on Respondent Weighting The Tables program lets you weight respondents differentially. Weighting lets some respondents have more impact on the statistical summaries than others. For example, a respondent with a weight of 2.0 is counted twice as much as another respondent with a weight of 1.0. Weighting is useful for adjusting a sample to reflect known population characteristics. Weights affect every statistic mentioned earlier in this section, except for Minimum and Maximum. When you request weighted tables, both weighted and unweighted totals are reported. We strongly suggest you assign weights so that the average weight is equal to 1.0. If the weights do not average 1.0, the total unweighted number of respondents will not equal the total weighted respondents. More critically, many statistics are incorrect if the average weight is not equal to 1.0, including: Standard error Chi Square You will also see some inaccuracy in the following statistics because of the division by (n-1), especially if you have small sample sizes: Standard deviation Variance

22 16 CBC v Chapter 2: Using Simulators to Answer Strategic Questions Overview The Market Simulator is usually considered the most important tool resulting from a conjoint project. The simulator is used to convert raw conjoint (part worth utility) data into something much more managerially useful: simulated market choices. Products can be introduced within a simulated market scenario and the simulator reports the percent of respondents projected to choose each. A market simulator lets an analyst or manager conduct what-if games to investigate issues such as new product design, product positioning, and pricing strategy. A Warning about Interpreting the Output of Market Simulators Under very controlled conditions (such as markets with equal information and distribution), market simulators often report results that closely match long-range equilibrium market shares. However, conjoint part worth utilities cannot account for many real-world factors that shape market shares, such as length of time on the market, distribution, out-of-stock conditions, advertising, effectiveness of sales force, and awareness. Conjoint analysis predictions also assume that all relevant attributes that influence share have been measured. Therefore, the share of preference predictions usually should not be interpreted as market shares, but as relative indications of preference. Divorcing oneself from the idea that conjoint simulations predict market shares is one of the most important steps to getting value from a conjoint analysis study and the resulting simulator. While "external effect" factors can be built into the simulation model to tune conjoint shares of preference to match market shares, we suggest avoiding this temptation if at all possible. No matter how carefully conjoint predictions are calibrated to the market, the researcher may one day be embarrassed by differences that remain.

23 The Sawtooth Software Market Simulator What Is a Market Simulation? A conjoint study leads to a set of utilities (part worths) that quantify respondents' preferences for each level of each attribute. These part worths can be analyzed in a number of ways. You can examine each respondent's part worths (but this task could become overwhelming). You might summarize the average part worth utilities, or compute average importances. You could create graphs and charts to display that information, but to many it might seem somewhat abstract and difficult to grasp. Examining average responses could also fail to detect important segments of the market that have unique and targetable preferences. A good market simulator is like having all of your respondents gathered in one room for the sole purpose of voting on product concepts and competitive scenarios (defined in terms of the attribute levels you measured) you show them. You walk into the room, show them a market scenario (i.e. products A, B and C), and they vote for the one(s) they prefer. Millions of potential products and market situations could be evaluated, and your captive audience would never get tired, ask for lunch breaks, or require you to pay them by the hour. How does a market simulator work? Let's suppose we had a way (such as through conjoint or choice analysis) to quantify how much people liked the different qualities of ice cream cones. Let's refer to those preferences as part worth utilities, and assume the following values for a given respondent: Utility Chocolate 0 Vanilla 30 Strawberry 40 $ $ $ Using those utility values, we could predict how he would choose between a vanilla cone for $0.80 or a strawberry cone for $1.00. Vanilla (30 utiles) + $0.80 (25 utiles) = 55 utiles Strawberry (40 utiles) + $1.00 (0 utiles) = 40 utiles We'd predict he would prefer the vanilla cone. If we had data for 500 respondents, we could count the number of times each of the two cones was preferred, and compute a "Share of Preference," also referred to as a "Share of Choice": Share of Choice $ /500 = 0.60 $ /500 = 0.40 In our hypothetical market simulation, 60% of the respondents preferred the vanilla, and 40% the strawberry cone. This illustrates the most simple simulation approach, referred to as the First Choice model.

24 18 CBC v Why Conduct Market Simulations? Looking only at average preferences (part worth utilities) can mask important market forces caused by patterns of preference at the segment or individual level. Marketers are often not interested in averages, but in the targetable, idiosyncratic behavior of segments or individuals. It is no surprise, then that most users of our CBC products choose to use CBC/HB to estimate individual-level part worth utilities (Source: 2005 Sawtooth Software Customer Feedback Survey). For example, consider the following three respondents, and their preferences (utilities) for color: Utilities for Color Blue Red Yellow Respondent A Respondent B Respondent C Average: Looking only at average preferences, we would pronounce that red is the most preferred color, followed by yellow. However, if one of each color was offered to each respondent, red would never be chosen under the First Choice model, yellow would be chosen once, and blue twice the exact opposite of what aggregate part worth utilities suggest. While this is a hypothetical example, it demonstrates that average part worth utilities do not always tell the whole story. Many similar, complex effects can be discovered only through conducting simulations. Some reasons for conducting conjoint simulations include: 1. Conjoint simulations transform raw utility data into a managerially useful and appealing model: that of predicting market choice (Share of Preference) for different products. Under the proper conditions, shares of preference quite closely track with the idea of market share something most every marketer cares about. 2. As demonstrated earlier, conjoint simulations can capture idiosyncratic preferences occurring at the individual or group level. These "hidden" effects can have a significant impact on preference for products in market scenarios. When multiple product offerings have been designed to appeal to unique segments of the market, capturing such effects is especially important for accurately predicting preference. 3. Conjoint simulations can reveal differential substitutability (cannibalism/cross-elasticity effects) between different brands or product features. If two brands are valued highly by the same respondents (have correlated preferences), these brands will tend to compete more closely. Product enhancements by one of these brands will result in more relative share being lost by the correlated brand than by other less similar brands within the same simulation. Examining aggregate part worth utilities cannot reveal these important relationships. 4. Conjoint simulations can reveal interaction effects between attributes. If the same respondents that strongly prefer the premium brand are also less price sensitive than those who are more likely to gravitate toward a discount brand, sensitivity simulations will reflect a lower price elasticity for the premium relative to the discount brand. A similar interaction effect can occur between many other types of attributes: such as model style and color. Note when using CBC data: It is important to note that complex effects other than two-way interactions such as cross-effects cannot be reflected using the model of aggregate-level logit offered by our CBC system. Latent Class is a technique for estimating part worth utilities and reflecting respondent differences at the group/segment level, and CBC/HB (Hierarchical Bayes) and ICE (Individual Choice Estimation) are ways to estimate utilities at the individual level for CBC data. Because they are built on individual-level preferences, simulators based on these models are able to reflect the important and complex behaviors mentioned earlier. It is not surprising that Latent Class and CBC/HB have been shown to outperform similarly-defined aggregate level logit models in terms of predictive validity.

25 The Sawtooth Software Market Simulator 19 ACA (Adaptive Conjoint Analysis) and CVA (Full-Profile Conjoint Analysis) capture respondent-byrespondent preferences and are thus very useful inputs to this and other market simulation models.

26 20 CBC v Typical Questions for Conjoint Simulators There are many marketing strategies that can be investigated with the conjoint simulator. Here are three of the most common: 1. Given a current competitive environment, what product should I offer to maximize interest in my offering? How can I modify an existing product to capture more relative demand? A Market Simulator lets you input multiple products and place them in simulated competition one with another. Each product is defined using the attribute levels measured in the conjoint study (brands, colors, prices, speeds, warrantees, etc.). Therefore, if you have measured the relevant brands and features offered in the market, you can simulate a realistic market scenario within the Market Simulator. Within that market scenario, you can add a new product and see how well it competes. If the goal is to maximize share, offering the best features at the lowest price is often the trivial solution. The Market Simulator focuses on the demand side of the marketing equation; but it is also important to pay attention to the supply side and take the costs of producing different products/services into consideration. If you have cost information available to you, the Market Simulator permits you to investigate the incremental benefits of different features of a product relative to the cost of offering them. (SMRT does not provide an automatic way to input cost information, but you can divide the incremental gain in share of preference by the incremental cost to produce a profitability index.) 2. What is the relative price sensitivity of different brands? If I raise my price by 10%, how will it affect my brand? How will it affect competitors' brands? You can conduct "sensitivity analysis" for attributes such as price using the Market Simulator to generate relative demand curves. The approach involves holding all other brands at a constant price and changing the price of a single brand, recording the relative share at each point for that brand along the price continuum. If your conjoint data reflect respondent differences (all models offered by Sawtooth Software except for aggregate CBC logit), differential cross-elasticities can be investigated through this approach. 3. What portfolio of products can I offer to appeal to different market segments and maximize overall share? The market simulator lets you merge additional segmentation variables (such as demographics or firmographics) with your conjoint part worths. If you have segmentation information, you can investigate product formulations that appeal to different groups of respondents. It is likely that by designing products that appeal uniquely to targetable segments that you can increase overall share for your product line or occupy a niche that is not currently being served. These three strategic questions and the simulation strategies for responding to them are illustrated later within this documentation.

27 The Sawtooth Software Market Simulator Chapter 3: Market Simulation Theory and Models Base Case Scenario Introduction Market Simulations provide a very useful way to use conjoint/choice data. Simulations provide an intuitive tool to move from the esoteric realm of part worth estimates/effects toward the practical world of predicting buyer behavior for specific market situations. Before using the Market Simulator within SMRT, you should become familiar with some terminology, issues and theory. This section will discuss setting up a base case scenario, the scaling of simulation results, the Red- Bus/Blue-Bus problem, and the five simulation models offered in the Market Simulator. Finally, External Effects and the use of the None weight are introduced. Base Case Scenario Usually the first step in using the market simulator is to define a "Base Case" scenario. A base case typically reflects a current (or future) market scenario: your brand vs. the relevant competition. If there is no relevant competition, or your conjoint study was designed to model only your product, the base case may be a single product, reflecting a likely configuration. The Market Simulator lets you input the market scenario in a grid format, where the products are rows (you can have up to 100), and the attributes are columns (you can have up to 30). Product Attribute 1 (Brand) Attribute 2 (Package) Attribute 3 (Color) Product A Product B Product C You provide text labels in the first column to identify the products. In the attribute cells, you type the numeric value associated with different attributes. For example, Product B is defined by level 2 of Brand, level 1 of Package and level 2 of Color. (The Market Simulator displays the list of attributes and the codes associated with each level as you enter values in the grid.) After defining the market scenario, you should decide which simulation method is appropriate for your data and the types of strategic questions you intend to answer. Later, we'll describe all five available models in the Sawtooth Software market simulator and provide some notes and recommendations for each. After you have chosen the appropriate simulation technique, you can begin conducting simulations. Typically, one first examines the shares of preference (or choice) given to the products in the base case. Then, modifications to the base case are investigated by altering the base case itself and rerunning the analysis, or by adding additional "scenarios." A scenario is just another name for a defined set of competitive products, and setting up each subsequent scenario feels just like defining the first base case scenario. The market simulator lets you input many simulation scenarios, and stores these for your convenience. Prior to introducing the different models of choice offered by the Market Simulator, it is instructive to cover two topics: the Exponent and the Red-Bus/Blue-Bus problem.

28 22 CBC v The Exponent (Scale Factor) Assume that you set up a simulation as defined in the previous section with three products: A, B and C. Also assume that you are simulating the projected choice for just one individual under a Share of Preference model (described in greater detail later). After clicking Compute!, simulation results for this individual might come back as follows: Product Share of Choice A 10.8% B 24.0% C 65.2% Total 100.0% Note that in conjoint simulations, the resulting shares are normalized to sum to 100%. We interpret these results to mean that if this respondent was faced with the choice of A, B, or C, he would have a 10.8% probability of choosing A, a 24.0% probability of choosing B, and a 65.2% probability of choosing C. Note that B is more than twice as likely to be selected as A, and C is more than twice as likely to be chosen as B. Let's suppose, however, that the differences in share seen in this simulation are really greater than what we would observe in the real world. Suppose that random forces come to bear in the actual market (e.g. out-of-stock conditions, buyer confusion or apathy) and the shares (probabilities of choice) are really flatter. We can often tune the results of market simulations using an adjustment factor called the Exponent. The table below shows results for the previous simulation under different settings for the Exponent: Share of Choice under Different Exponent Values Product A 33.0% 20.2% 10.8% 2.4% 0.0% Product B 33.3% 30.1% 24.0% 11.6% 0.7% Product C 33.7% 49.7% 65.2% 86.0% 99.3% Total 100% 100% 100% 100% 100% The exponent is applied as a multiplicative factor to all of the utility part worths prior to computing shares. As the exponent approaches 0, the differences in share are minimized, and preference is divided equally among the various product offerings. As the exponent becomes large, the differences in share are maximized, with nearly all the share allocated to the single best product. (Given a large enough multiplier, the approach is identical to the First Choice model, with all of the share given to a single product.) If you have solid external information (such as existing market share data) and have reason to expect that conjoint shares should resemble market shares (see earlier assumptions), you may want to tune the exponent within simulations. Or perhaps you have choice data from a holdout choice scenario included in your conjoint survey. You may decide to tune the exponent so that simulated shares resemble these holdout shares. If you do not have solid external information, you probably should not change the exponent from the default value of 1. Once the exponent is "set" for a data set, one typically does not change it from one simulation to the next.

29 The Sawtooth Software Market Simulator The Red-Bus/Blue-Bus Problem While market simulators have proven eminently useful for simulating buyer behavior, one of the most common simulation models (the Logit or Share of Preference model) has displayed a problematic result as characterized by the oft-cited Red-Bus/Blue-Bus problem. The underlying property leading to this problem is termed IIA, which is shorthand for "Independence from Irrelevant Alternatives." The basic idea of IIA is that the ratio of any two products' shares should be independent of all other products. This sounds like a good thing, and at first, IIA was regarded as a beneficial property. However, another way to say the same thing is that an improved product gains share from all other products in proportion to their shares; and when a product loses share, it loses to others in proportion to their shares. Stated that way, it is easy to see that IIA implies an unrealistically simple model. In the real world, products compete unequally with one another, and when an existing product is improved, it usually gains most from a subset of products with which it competes most directly. Imagine a transportation market with two products, cars and red busses, each having a market share of 50%. Suppose we add a second bus, colored blue. An IIA simulator would predict that the blue bus would take share equally from the car and red bus, so that the total bus share would become 67%. But it's clearly more reasonable to expect that the blue bus would take share mostly from the red bus, and that total bus share would remain close to 50%. It is important to note that some degree of IIA is appropriate and useful within market simulations. In many markets, there is some degree of randomness to buyer behavior. It is not that people are irrational, but that buyers must balance the costs of making a utility maximizing decision against the costs of taking the time to make perfect decisions. It is quite reasonable for rational buyers to make what on the surface may seem as haphazard decisions especially for low-involvement purchases. A similar or even duplicate offering could thus be expected to capture more share in the real world than a rational simulation model might suggest. In general, market simulation models based on disaggregate models of preference (utilities estimated at the individual level) are more immune to IIA difficulties than aggregate models of preference (aggregate logit, as offered by our CBC System). In addition to modeling respondent preferences at the individual level, there are market simulation methods that help deal with IIA. These are described in the next sections.

30 24 CBC v Market Simulator Models The Market Simulator offers five models: 1. First Choice 2. Share of Preference 3. Share of Preference with Correction for Similarity 4. Purchase Likelihood 5. Randomized First Choice This chapter provides a brief introduction to the models used in SMRT's market simulator. More detail is provided in Chapter 7 entitled: "Technical Details for Simulations." First Choice This option is the simplest and is sometimes referred to as the "Maximum Utility Rule." It assumes the respondent chooses the product with the highest overall utility. The results for this option are invariant over many kinds of rescalings of the utilities. In particular, one could add any constant to all the levels for an attribute and/or multiply all part worth utilities by any positive constant without affecting the shares for the simulated products. The First Choice model requires individual-level utilities, such as those generated by ACA, CVA, ICE or CBC/HB. The First Choice model cannot be used with Latent Class or Logit runs for CBC. The First Choice model is very intuitive and simple to implement. Its principal strength is its immunity to IIA difficulties (red-bus/blue-bus problem). In other words, the First Choice rule does not artificially inflate share for similar (or identical products). This property is especially important for product line simulations or situations in which some product offerings are quite similar to others in the competitive set. Its principal weakness is that the share of preference results are generally more extreme than the other simulation models and one cannot adjust the steepness of the model using the exponent multiplier. We have seen evidence that the First Choice model's predictions can often be more extreme (especially when using CVA or ACA utilities) than market shares in the real world especially for low involvement purchases. Another weakness is that it reflects information only about the respondent's first choice. Information about the relative preference for the remaining products in the simulation is lost. As a result, standard errors for the First Choice model are generally higher than with the other models offered in the Market Simulator. Sample sizes need to be larger for First Choice modeling than the other approaches to achieve equal precision of estimates. We recommend using the First Choice model with ACA or CVA utilities if you have large sample sizes and have determined through holdout choice validation or, preferably, through validation versus actual market choices that the First Choice model accurately predicts shares better than the other approaches. Share of Preference Models The Share of Preference models (both with and without correction for product similarity) use the logit rule for estimating shares. The product utilities are exponentiated and shares are normalized to sum to 100%. The Share of Preference models result in "flatter" scaling of share predictions than the First Choice

31 The Sawtooth Software Market Simulator 25 model. In general, we expect that this flatter scaling more closely matches what occurs in the real world. The Share of Preference models capture more information about each respondent's preferences for products than the First Choice method. Not only do we learn what product is preferred, but we learn the relative desirability of the remaining products. This means that standard errors of share predictions are lower than the First Choice shares. The Share of Preference model (without correction for product similarity) is subject to IIA, and can perform poorly when very similar products are placed in competitive scenarios (e.g. line extension simulations) relative to other less similar items within the same set. If using CBC under aggregate logit simulations, the IIA problem is intensified. Under Latent class, the problem is somewhat reduced. With individual-level utility models (ACA, CVA, ICE or CBC/HB), the problem is greatly reduced, but nonetheless can still be an issue. The Share of Preference with Correction for Product Similarity model can result in more valid predictions when the competitive set includes products that have significant differences in similarities. However, this model is not as theoretically complete as the Randomized First Choice method and can give unexpected results, especially when conducting sensitivity simulations. The Randomized First Choice method has been shown to handle product similarity issues in conjoint simulations better. For this reason, we generally don't suggest using the Share of Preference with Correction for Product Similarity. It remains an option in the Sawtooth Software simulator mainly for historical purposes. Purchase Likelihood Model The purchase likelihood model estimates the stated purchase likelihood for products you specify in the simulator, where each product is considered independently. The likelihood of purchase projection is given on a 0 to 100 scale. If you intend to use the Likelihood of Purchase option in the Market Simulator, your data must be appropriately scaled. The following estimation methods result in data appropriate for the purchase likelihood option: 1. ACA, if calibration concepts have been asked and used in utility estimation. 2. CVA, if single-concept presentation was used, and the logit rescaling option used with OLS regression. 3. ICE or CBC/HB, if calibration concepts have been asked and the CALIB program used to rescale the utilities. Any other procedure will result in simulations that are not an accurate prediction of stated purchase likelihood. Also keep in mind that the results from the Purchase Likelihood model are only as accurate as respondents' ability to predict their own purchase likelihoods for conjoint profiles. Experience has shown that respondents on average exaggerate their own purchase likelihood. You may use the Purchase Likelihood model even if you didn't scale the data using calibration concepts, but the results must only be interpreted as a relative desirability index. Meaning: a value of "80" is higher (more desirable) than a value of "60," but it doesn't mean that respondents on average would have provided an 80% self-reported likelihood of purchase for that particular product. The purchase likelihoods that the model produces are not to be interpreted literally: They are meant to serve as a gauge or "barometer" for purchase intent. Under the appropriate conditions and discount adjustments (calibration), stated intentions often translate into reasonable estimates of market acceptance for new products.

32 26 CBC v2.6 Randomized First Choice The Randomized First Choice (RFC) method combines many of the desirable elements of the First Choice and Share of Preference models. As the name implies, the method is based on the First Choice rule, and can be made to be immune to IIA difficulties. As with the Share of Preference model, the overall scaling (flatness or steepness) of the shares of preference can be tuned with the Exponent. Most of the theory and mathematics behind the RFC model are nothing new. However, to the best of our knowledge, those principles have never been synthesized into a generalized conjoint/choice market simulation model. RFC, suggested by Orme (1998) and later refined by Huber, Orme and Miller (1999), was shown to outperform all other Sawtooth Software simulation models in predicting holdout choice shares for a data set they examined. The holdout choice sets for that study were designed specifically to include product concepts that differed greatly in terms of similarity within each set. Rather than use the part worth utilities as point estimates of preference, RFC recognizes that there is some degree of error around these points. The RFC model adds unique random error (variation) to the part worth utilities and computes shares of choice in the same manner as the First Choice method. Each respondent is sampled many times to stabilize the share estimates. The RFC model results in a correction for product similarity due to correlated sums of errors among products defined on many of the same attributes. The RFC model is very computationally intensive, but with today's fast computers speed is not much of an issue. With the suggested minimum of 100,000 total sampling iterations for a conjoint data set, it takes only a few moments longer than the faster methods to perform a single simulation. According to the evidence gathered so far on this model, we think it is worth the wait. The RFC model is appropriate for all types of conjoint simulations, based on either aggregate- or individual-level utilities. The most complete use of the RFC model requires tuning the appropriate amount of attribute- and product-level error. By default, only attribute-level error is used in the simulator. This setting assumes no product share inflation for identical offerings. If you have questions regarding tuning the RFC model read the section covering the details of RFC or read the technical paper entitled "Dealing with Product Similarity in Choice Simulations," available for downloading from our home page:

33 The Sawtooth Software Market Simulator External Effects The factors evaluated in a conjoint analysis study usually focus on product attributes, ignoring other factors that affect market share. Consequently, the Market Simulator calculates a "preference share" rather than a "market share," emphasizing that important factors for calculating market share are missing from the model. Such missing factors include: the level and effectiveness of advertising, the size and effectiveness of the sales force, the number of outlets where the product is sold, the length of time the product has been on the market, and whether the product was first on the market. The principal value of conjoint simulators is to indicate the kinds of changes that would make the most favorable differences in customer preferences. There is no real need for them to have the appearance of market shares. All the same, it is sometimes disconcerting for those viewing results to see shares of choice that are vastly different from known market shares. The External Effect option of the Market Simulator helps to account for factors outside the model. When used properly, External Effects can lead to more realistic predictions. Even so, we recognize that the method used in our simulator to adjust for external effects is a simple approach with certain weaknesses. External Effects are introduced into the Market Simulator by applying a multiplicative External Effects factor to each product's preference share. The factor ranges from 0 to A value of 1 introduces no effect, a value greater than 1 increases a product's preference share above what the model would otherwise predict, a value less than 1 decreases the predicted preference below this value, and a value of 0 eliminates the product from the model. You set External Effect factors from the Scenario Specification dialog, by checking Apply External Effects. When the Apply External Effects box is checked, an additional column in the product specification grid appears (at the far right) in which you can type external effects. Setting the factor for each product is subjective. The following procedure minimizes the level of subjectivity: 1. Start by running a simulation on products currently on the market for which market shares are known or can be estimated. Set the External Effects factor to 1 for all products in this simulation. 2. Divide the actual market share for each product by the preference share predicted by the model. This number becomes the External Effects factor for that product. 3. Re-run the Market Simulator with these factors applied. Check that the results now reproduce the actual market shares for the products. 4. Set the External Effects factor for new products, using the External Effects factor for the current products as benchmarks. A new product should have an External Effects factor close to the ones for similar existing products (similar with respect to the "missing" factors listed above). 5. The Market Simulator produces shares that new products are expected to achieve when they have fully penetrated the market. The External Effects factor can be used to estimate shares before full penetration is achieved. One way to do this is to estimate, from past experience, the extent to which new products have reached full penetration at a time t after their introduction. When you want a simulation for time t, multiply this penetration factor by the one you have calculated using the steps above, and use this result as the External Effects factor.

34 28 CBC v2.6 For example, suppose you are a car manufacturer and you are introducing a new sedan. And suppose from your past sales you know the following penetration rate (fraction of expected monthly sales) for a sedan: Months After Introduction Time Market Penetration Then, if the External Effects factor at full market penetration is expected to be 1.2, the External Effects factor used as a function of time t would be: Months After Introduction Time Market Penetration As a final note, if you do need to apply external effect factors, we suggest you first investigate tuning the Exponent to best fit target shares prior to invoking external effect adjustments.

35 The Sawtooth Software Market Simulator Interpolating between Levels Sometimes you may want to simulate a product that has a level in between two levels you measured. This is called interpolation, and it is usually a reasonable thing to do when dealing with attributes that are quantitative in nature (price, speed, etc.). The market simulator uses a straight-line interpolation between adjacent part worths. Consider the following attribute: Level # Level Text 1 5 pages per minute 2 10 pages per minute 3 15 pages per minute 4 20 pages per minute The simulator lets you specify a level code between two levels you measured. For instance, level 2.5 represents the point half-way between 10 and 15 pages per minute, or 12.5 pages per minute. Twelve pages per minute represents a value 40% of the way between 10 and 15 pages per minute. Therefore, level 2.4 corresponds to 12 pages per minute. But there is a much easier way to interpolate between levels. You may find it more convenient to refer to levels of quantitative attributes in the Market Simulator using values other than the original level numbers. When you click Assign Level Values, you can assign values to represent levels, such as: Level # Level Value Level Text pages per minute pages per minute pages per minute pages per minute It is much easier to specify products in the market simulator after recoding level numbers to values that match the pages per minute. To simulate a product at 12 pages per minute, we would now specify "12" for speed. Three additional points are worth mentioning regarding recoding attribute levels for use in the simulator: 1. Interpolation only works correctly if all values are either ascending or descending for an attribute. 2. If you want to omit an attribute from simulations, you must omit it for all products in the simulation. You omit a level by specifying "N/A" (you type these characters) instead of a numeric code. 3. If linear terms (coefficients) were estimated instead of part worths for a quantitative function such as price, you should make sure not to change the level values from those that your consultant provided. For example, if the coded values for a linear price term were -2, 0 and +2 (corresponding to zero-coded values representing $2, $4, and $6), it would not be appropriate to change those to 200, 400, 600, as a utility effect 100 times the appropriate effect would be applied in simulations. The Market Simulator zero-centers (but doesn't standardize) all coded values for linear terms before multiplying by the utility coefficients. Thus, values under Assign Level Values... of 2, 4, 6 would be applied the same as 100, 102, 104 or 52, 54, 56. After zero-centering, these three different coding schemes would be applied as -2, 0, +2 in simulations. For more information about how the Market Simulator interpolates between levels when interaction terms are involved, please see the Appendix entitled: "How the Simulator Interpolates between Levels."

36 30 CBC v The "None" Weight If you are conducting simulations for a CBC study and the questionnaire contains choice tasks including a "None" option, then the option of None can be included in simulations. However, the share of respondents predicted to choose None will only be correct if the number of products in the simulation is the same as the number of products in the CBC questionnaire. This is another problematic outcome of the IIA rule and especially affects simulators built upon aggregate logit runs. With individual-level modeling under CBC/HB, we have seen that the share of "None" is approximately correct irrespective of the number of products used in the simulator (within a reasonable range of product alternatives). The market simulator lets you specify a "None" weight from Scenario Specification dialog by clicking the Advanced Settings... button. By default, the None weight is set to "0," which means that we do not report a None percentage and we assume that all respondents are in the market and must "buy" a product. If you are using an aggregate logit run in your market simulations and are using a different number of products than were reflected in your CBC questionnaire, you may need to consider the suggestions below to deal with the problems of IIA and the "None" weight. If you are using individual-level utilities from CBC/HB, then you these considerations are less an issue for you. None Calibration for Aggregate Logit Models Logit (Share of Preference) models tend to give too much share to products that are similar to one another, and to penalize products that are unique (this is especially the case with aggregate logit solutions). The None option does not have specified levels on any of the conjoint attributes, and is therefore unique as compared to the products in the simulation. If you do a simulation with a few products plus None, and then try another that includes those same products plus others, you will find that the share predicted for None will be smaller when there are more products. That may not be an accurate reflection of reality. If the respondents who chose None did so because they would never buy a product in that category, then it would clearly be incorrect to assume that fewer would choose none just because they are offered an array of more products. On the other hand, if respondents are really candidates for those products, then one would expect the share choosing None to decrease when respondents are offered a richer set of choices. We know of no way to tell how the share choosing None should vary as the number of products in the simulation changes, although we think some allowance should be made in those cases where more products are in the simulation than in the original choice tasks. For that reason, we provide the user with the capability of adjusting the percentage choosing None, through multiplication by a constant supplied by the user. In general, we think the None weight should be somewhere between unity and the fraction n/t, where n is the number of products in the simulation and t is the number of products in the average choice task. With individual-level utilities, less (or no) adjustment for the None may be needed. With aggregate utilities, more adjustment may be appropriate.

37 1.5 Chapter 4: Using the Market Simulator The Market Simulator Dialog Introduction The Sawtooth Software Market Simulator 31 The previous chapter dealt mainly with theory. This chapter discusses how to use the software to simulate market choices after you have computed part worth utilities or have imported utilities from another system. We should note that context-sensitive on-line help for all screens and dialogs within the software is available by pressing F1. You can access general help within SMRT by clicking Help Help Topics. The Market Simulator Dialog To start SMRT, click Start Programs Sawtooth Software Sawtooth Software SMRT. The first step in running SMRT's Market Simulator is to open a study. To open an existing study, select File Open, and browse to the directory containing the studyname.smt file. Double-click the studyname.smt file. The main program menu is displayed. To open the market simulator, click Analysis Market Simulator. The Market Simulator dialog is displayed: This dialog contains two main lists: the list of available Utility Runs (which you cannot modify) and the

38 32 CBC v2.6 list of Simulation Scenarios (which you can add to and modify). A description of each of the settings and buttons is provided below: Banner Utility Runs If you are using part worth utilities from a method other than logit, you can specify the banner variable to use in reporting the results of the simulation. For example, if you want to see the results split out between large and small business, you can choose that variable as the banner point. A utility run contains a set of part worths utilities estimated in a particular way for a given number of respondents. For example, perhaps you imported part worth data from two separate HB runs for use in the Market Simulator. The first set involved main effects only and the second set additionally involved some interaction effects. When you imported these two separate data sets, you provided a label for each utility run by which it is referenced and selected during simulation analysis. For example, you may have referred to the first run as "Main Effects Only" and the second run as "With Interactions." By default, the first utility run on the list is highlighted. Highlight the utility run you want to use during simulations by clicking with the mouse. Only one utility run can be selected at a time. Simulation Scenarios When you first set up a study, the list of Simulation Scenarios is blank. To specify a new scenario, click Add. To choose an existing scenario, check the scenario box with the mouse. You can check more than one simulation scenario at a time. If you choose multiple scenarios, all of the simulations will be conducted at the same time (in batch mode). To edit an existing scenario, highlight it on the list and click Edit... To delete an existing scenario, highlight it on the list and click Delete. Output Precision Assign Level Values This setting controls how many decimal places of precision are used when reporting results. This button lets you define the numeric code to be applied to different levels, such as for levels of price, speed or quantity. Assigning level codes can make it easier to define products in simulation scenarios and interpolate between measured levels. Compute! Clear Print Save As Close Click this to perform a simulation for the selected Utility run and Simulation Scenarios. The simulation results are displayed in the report window. If no products have been specified in a Simulation Scenario, average part worth utilities and importances are displayed. Click this to clear the results from the report window. Prints the results displayed in the report window. Saves the results displayed in the report window to a text file. Exits the Market Simulator dialog.

39 The Sawtooth Software Market Simulator Adding a Simulation Scenario Usually the first step in using the market simulator is to define a "Base Case" scenario. A base case typically reflects a current (or future) market scenario: your brand vs. the relevant competition. If there is no relevant competition, or your conjoint study was designed to model only your product, the base case may be a single product. If you have defined a base case scenario or other scenarios, when you open the Market Simulator dialog, these will appear in the list of Simulation Scenarios. You can view or edit an existing simulation scenario by highlighting that scenario and clicking Edit. To add a new scenario, click Add. If a scenario or scenarios have already been defined, you are next asked whether the new scenario should be a copy of an existing scenario (used as a starting point, so that you don't have to re-enter common information again) or whether you want to start with a blank template. The Scenario Specification dialog is then displayed: The cursor is initially active in the Name: field. You must provide a name to identify the scenario. This name will appear in the Simulation Scenarios list in the Market Simulator dialog. The Market Simulator lets you input the market scenario in a grid format, where the products are rows (you can have up to 100), and the attributes are columns (you can have up to 30). For example, you may have in mind to specify the following competitive scenario for a study that has three total attributes with three levels each: