Implementing Marketing Control with the Web-Based Profitability Analysis Package


 

Page 64 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

IMPLEMENTING MARKETING CONTROL WITH THE  

WEB-BASED PROFITABILITY ANALYSIS PACKAGE 
 

Aspy P. Palia 

University of Hawaii at Manoa 

aspy@hawaii.edu 

 

Jan De Ryck 

University of Hawaii at Manoa 

ryck@hawaii.edu 

ABSTRACT 

 
The Web-based Profitability Analysis Package enables 

competing participant teams to learn, identify and assess 

the underlying reasons for profitability or loss of each stra-

tegic business unit (SBU) within their brand portfolio dur-

ing each decision period.  This decision support package 

(a) extracts and presents the earnings per share of each 

competing firm as well as the main components of revenues 

and expenses for each SBU of each of the competing firms 

from the simulation results, and (b) identifies and flags the 

antecedents of each determinant of revenues and/or ex-

penditures for each SBU.  Competing participant teams use 

this package to exercise marketing control. The package 

enables users to monitor performance, identify deviations, 

understand the underlying reasons, take corrective action 

and thereby exercise marketing control. 

 
INTRODUCTION 

 
The Profitability Analysis Package is a decision sup-

port system that enables competing participant teams in the 

marketing simulation COMPETE (Faria, 1994, 2006) to 

learn, identify and assess the underlying reasons for profita-

bility or loss of each strategic business unit (SBU) within 

their brand portfolio during each decision period.  SBUs 

are specific product offerings in specific regions that have 

specific target markets with specific needs and purchase 

motivations, a specific set of strategies, facing a specific set 

of competitors with specific competing strategies. 

This Excel-based Profitability Analysis Package auto-

matically extracts relevant profitability performance data 

via external links from the Excel-version of the COMPETE 

simulation results.  The Excel-version of the simulation 

results are generated by the instructor/administrator from 

the original dos-text based COMPETE simulation results.  

Later, the Excel-version of the simulation results are up-

loaded to the COMPETE Online Decision Entry System 

(CODES) repository for subsequent access by competing 

participant teams.  Only relevant data on the determinants 

of sales revenue and expenses are extracted from the simu-

lation results.  This decision support package saves sub-

stantial time needed to identify and enter the relevant data 

and reduces the potential for data entry error. 

 
DECISION SUPPORT SYSTEMS 

 
Several scholars have commented on the value of in-

cluding decision support software/systems in computer 

simulations (Keys and Biggs, 1990; Teach, 1990; Gold and 

Pray, 1990; Wolfe and Gregg, 1989).  In addition, the liter-

ature is replete with references to the use and impact of 

decision support systems with computer simulations 

(Affisco and Chanin, 1989, 1990;  Burns and Bush, 1991; 

Cannon et al., 1993; Fritzsche et al., 1987; Grove et al., 

1986; Halpin, 2006; Honaiser and Sauaia, 2006; Markulis 

and Strang, 1985; Mitri et al., 1998; Muhs and Callen, 

1984; Nulsen et al., 1993, 1994; Palia, 1989, 1991, 2006; 

Peach, 1996; Schellenberger, 1983; Shane and Bailes, 

1986; Sherrell et al., 1986; Wingender and Wurster, 1987; 

Woodruff, 1992). 

Decision support systems (DSSs) are defined as …a 

collection of data, systems, tools, and techniques with sup-

porting software and hardware by which an organization 

gathers and interprets relevant information from business 

and environment and turns it into a basis for…action 

(Little, 1979; Burns and Bush, 1991).  In addition, they are 

defined as computer-based information systems that sup-

port the process of structuring problems, evaluating alterna-

tives, and selecting actions for more effective management 

(Forgionne, 1988).  Further, they are described as the hard-

ware and software that permit decision-makers to deal with 

a specific set of related problems by providing tools that 

amplify a manager’s judgment (Sprague, 1980). 

DSSs used with business simulations yield several 

benefits.  These include greater depth of understanding of 

simulation activity with resulting increase in planning 

(Keys et al., 1986), in-depth understanding of quantitative 

techniques as students visualize the results of their applica-

tions, sensitivity to weaknesses in techniques used, and 

experience in capitalizing on their strengths (Fritzche et al., 

mailto:aspy@hawaii.edu
mailto:ryck@hawaii.edu


 

Page 65 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

 1987).  Other benefits include minimization of paperwork 

and errors, error-free graphical representation of output, a 

competitive tool with increasing value as simulation pro-

gresses, and potential for participants to create their own 

DSSs (Burns and Bush, 1991).  In addition, DSSs enhance 

understanding of complex business relationships and pro-

vide additional value over time (Halpin, 2006).  Further, 

DSSs provide realism, relevance, literacy, flexibility and 

opportunity for refinement (Sherrell et al., 1986). 

Some authors contend that combining an active student 

generated database in the form of a simulation game with a 

DSS will result in improved decision making, lead to im-

proved pro-active rather than re-active strategic planning, 

and result in improved simulation game performance and 

enhanced learning (Muhs and Callen, 1984).  Others have 

reported no support for the premise that DSS usage im-

proves small group decision making effectiveness (Affisco 

and Chanin, 1989), and that DSS usage to support manu-

facturing function decisions resulted in decreased manufac-

turing costs and increased “earnings/cost of goods sold” 

ratio in the second year of play (Affisco and Chanin, 1990). 

Given the inconsistent findings with regard to the effi-

cacy of DSSs reported in the literature, does DSS usage 

increase decision effectiveness and/or enhance learning?  

One scholar notes that while the DSS assists the decision 

maker, it does not make decisions, nor can it substitute for 

intelligent analysis and synthesis (Schellenberger, 1983).  

In addition, as with other computer-based or experiential 

learning techniques, the effectiveness of DSSs or the deci-

sions made are less important than the insights they gener-

ate.  The level of insight generated depends heavily on the 

clear explanation of the purpose, significance, assumptions, 

usage, and limitations of the DSS and underlying concepts 

applied, by the instructor.  In addition, the level of insight 

generated depends heavily on the debriefing process used 

by the instructor to crystallize student learning (Cannon et 

al., 1993).  

 
SIMULATION PERFORMANCE 

& PROFIT ANALYSIS 

 
Several authors have investigated the relationship be-

tween game performance and use of DSSs (Keys & Wolfe, 

1990) as well as other predictor variables such as (a) past 

academic performance (GPA) and academic ability of par-

ticipants, and degree of planning and formal decision mak-

ing by teams (Faria, 2000), (b) GPA and the use of DSSs 

(Keys and Wolfe, 1990), (c) age, gender, GPA and ex-

pected course grade (Badgett, Brenenstuhl & Marshall, 

1978), (d) university GPA and academic major (Gosenpud 

& Washbush, 1991), (e) gender, GPA and course grade 

(Hornaday, 2001; Hornaday & Wheatley, 1986), (f) gender 

(Johnson, Johnson & Golden, 1997; Wood, 1987), (g) 

GPA, previous course grades, and course grade (Lynch and 

Michael, 1989), with conflicting results.  These conflicting 

results led to the conclusion that no predictor variable con-

sistently predicts simulation performance (Gosenpud, 

1987). 

Other authors have discussed the use of simulation 

profit analysis in advertising (Motes and Woodside, 1979), 

accounting (Bonczkowski, Gentry & Caldwell, 1979; Brad-

ley & Murtuza, 1988; Goosen, 1974, 1990; Leftwich, 1974; 

Lord, 1975), business ethics (Schumann, Scott and Ander-

son, 1994; business management (Millers, 1986), finance 

(Leftwich, 1974), and production operations and manage-

ment (Mukherjee & Wheatley, 1999) courses.  

The primary purpose of this paper is to present a new 

user-centered learning tool that provides participant teams 

the opportunity to assess the profitability of each SBU in 

their brand portfolio and thereby apply the Iceberg Princi-

ple in exercising Marketing Control. 
 

MARKETING CONTROL 

 
Marketing managers are charged with the responsibil-

ity of planning, organizing, implementing, and controlling 

marketing plans and programs that are designed to achieve 

a specific set of objectives (Bagozzi et al., 1998; Churchill 

and Peter, 1995; Kotler, 2003, 1988; Lehman and Winer, 

1988; Lilien, 1993; Lilien and Rangaswamy, 2003; McCar-

thy and Perreault, 1984, 1987; Perreault and McCarthy, 

1996).  In performing their responsibilities, marketing man-

agers are faced with scarce resources (discretionary market-

ing dollars) and unlimited wants to deploy these limited 

resources (sales force and advertising expenditures) in or-

der to achieve their objectives.  Consequently, they need to 

allocate the scarce resources at their disposal both effec-

tively and efficiently.  The efficient allocation of scarce 

marketing resources is facilitated through marketing con-

trol in order to keep performance in line with objectives. 

Marketing control involves setting standards, monitor-

ing performance, identifying deviations from standards, 

understanding the underlying reasons for the deviations, 

and taking corrective action when necessary (Bagozzi, et 

al., 1998; Churchill and Peter, 1995; Cravens, 2000; Cra-

vens et al., 1987; Czinkota and Kotabe, 2001; Dalrymple 

and Parsons, 1995; Kotler and Keller, 2007; Lamb et al., 

2004; Peter and Donnelly, 1994).  First, marketing manag-

ers decide which aspects of marketing strategy (such as 

price, salesforce, advertising, quality) to monitor.  Next, 

marketing managers set standards based on objectives in 

order to monitor and gauge performance.  These standards 

may include sales targets, market share, profit contribution, 

as well as behavioral standards such as level of customer 

awareness.  Then, marketing managers design feedback 

mechanisms where useful, relevant and timely information 

are used to evaluate the effectiveness of marketing activi-

ties.  They use these feedback mechanisms to interpret the 

results of marketing programs, identify gaps between ob-

jectives and performance, understand the underlying rea-

sons for the deviations in performance, and change strategy 

or tactics to eliminate or reduce the performance gaps.   



 

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Marketing managers identify which products’ sales are 

highest and why, which products are profitable, what is 

selling where, and how much the marketing process costs.  

They need to know what’s happening in detail in order to 

improve the bottom line.  Traditional accounting reports 

such as income statements and balance sheets are too gen-

eral to be of much help to marketing managers.  For in-

stance, a company may be profitable while 80 percent of its 

business comes from 20 percent of its customers or prod-

ucts.  The other relatively less profitable 80 percent may 

remain undetected unless each product, region, or customer 

segment is analyzed in order to determine its profitability.  

This 80/20 relationship is fairly common and is often re-

ferred to as the 80/20 rule or principle (McCarthy and Per-

reault, 1984, 1987; Perreault and McCarthy 1996). 

Marketing control consists of sales analysis, perfor-

mance analysis and marketing cost analysis.  Sales analysis 

involves a detailed breakdown of the company’s sales rec-

ords by geographic region, product, package size, customer 

size, type or class of trade, price or discount class, method 

of sale (mail, telephone, or direct sales), terms of payment 

(cash or charge), size of order, and or commission class.  

The purpose of sales analysis is to keep marketing manag-

ers in touch with their markets and to enable them to check 

their assumptions and hypotheses.  Ignoring sales analysis 

can lead to poor forecasting and consequent poor decisions. 

Performance analysis identifies exceptions or varia-

tions in planned performance.  Marketing managers can 

compare one territory against another, against the same 

territory’s performance in the previous year, or against ex-

pected performance.  The purpose of performance analysis 

is to improve operations by (a) monitoring performance, (b) 

comparing actual performance with projected performance, 

(c) identifying deviations (Actual – Projected) in perfor-

mance, (d) calculating performance indices (Actual / Pro-

jected x 100), (e) understanding the underlying reasons for 

sub-par performance, and (f) taking corrective action.  The 

salesperson, territory or other factors exhibiting poor per-

formance can be identified, analyzed and corrective action 

taken.  Outstanding performance can be analyzed, reasons 

for success identified, and extrapolated to other salesper-

sons, territories or other factors.  In addition to sales, other 

data such as miles traveled, number of calls made, number 

of orders, or cost of various tasks can be analyzed. 

Marketing cost analysis (Kerin and Peterson, 2004; 

McCarthy and Perreault, 1984, 1987; Perreault and McCar-

thy, 1996) enables the marketing manager to calculate the 

profitability of individual profit centers rather than total 

company profit.  Marketing cost analysis involves the con-

version of natural accounts based on how the money was 

actually spent into marketing functional accounts which 

indicate the function performed through the expenditure of 

funds (McCarthy and Perreault, 1984, 1987; Perreault and 

McCarthy, 1996; Pride and Ferrell, 1995).  First, natural 

accounts (such as salaries, depreciation, taxes, advertising 

and other expenses) in the financial statements (such as 

income statement and regional income contribution state-

ments) are converted to functional accounts which show 

the purpose for which expenditures are made.  Then, the 

functional accounts are reallocated to customers, market 

segments, regions or products for which the amounts were 

spent.  This reallocation of functional accounts enables 

marketing managers to assess the profitability of custom-

ers, market segments, territories or products. 

Marketing cost analysis deals with three broad catego-

ries of costs.  Direct costs such as salesforce salaries are 

directly attributable to the performance of marketing func-

tions such as selling (a) of a specific product, (b) in a spe-

cific region, or (c) to a specific customer.  Traceable com-

mon costs such as space rental costs for production, storage 

and selling, can be allocated indirectly, using one or several 

criteria (such as cost per square foot used for storage) to the 

functions that they support.  Non-traceable common costs 

such as interest, taxes, and top management salaries, cannot 

be assigned according to any logical criteria.  Hence, they 

are assignable only on an arbitrary basis (McCarthy and 

Perreault, 1984, 1987; Perreault and McCarthy, 1996; Pride 

and Ferrell, 1995). 

Marketing cost analysis employs either the full-cost 

approach or the direct-cost approach.  The full-cost ap-

proach includes direct costs, traceable common costs, and 

nontraceable common costs.  All costs are included to pro-

vide an accurate profit picture.  Since nontraceable com-

mon costs are allocated using arbitrary criteria, different 

criteria used can yield different results that affect profitabil-

ity, promotion potential, and bonuses received.  A cost-

conscious unit can be adversely affected and discouraged if 

numerous costs are assigned to it arbitrarily.  In order to 

eliminate such problems, the direct-cost approach, which 

includes direct costs and traceable common costs but not 

nontraceable common costs, is used.  Yet, critics say that 

the direct-cost approach is not accurate as it does not in-

clude nontraceable common costs (McCarthy and Perreault 

1984, 1987; Perreault and McCarthy, 1996; Pride and Fer-

rell, 1995). 

Marketing managers use sales analysis, performance 

analysis and marketing cost analysis in order to exercise 

marketing control.  They assess the sales, profitability and 

marketing costs of each SBU in order to improve the bot-

tom line.  In this regard, they are aware of the significance 

of both the 80/20 Principle and the Iceberg Principle. 

 
THE ICEBERG PRINCIPLE 

 
The Iceberg Principle or the 90/10 Principle states that 

much good information is hidden in summary data 

(McCarthy and Perreault 1984, 1987; Palia 2007; Perreault 

and McCarthy, 1996; Pride and Ferrell, 1995).  Icebergs 

reveal only about 10 percent of their mass above water lev-

el.  The remaining 90 percent is concealed and non-

uniformly distributed below water level, and can sink ships 

such as the Titanic that venture too near. 

Much business and marketing data exhibit the same 



 

Page 67 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

characteristics.  While the Income Statement may reflect 

substantial sales revenue and profits, and/or the Balance 

Sheet may indicate substantial amounts of cash, invest-

ments and retained income, these financial statements may 

conceal problems in specific SBUs.  Based on a review of 

these financial statements, everything may appear to be 

calm and peaceful on the surface.  Yet, closer analysis may 

reveal jagged edges in one or more SBUs that can sink the 

business.  While summary data and averages simplify and 

facilitate understanding, managers need to ensure that data 

summaries don’t conceal more than they reveal. 

A seemingly healthy person may suffer from a hidden 

cancer in the cardiac, circulatory, digestive, lymphatic, 

nervous or other system that could seriously impair overall 

long-term health.  Similarly, a seemingly healthy business 

with adequate sales, assets, profits, and cash flow, may 

suffer from hidden losses or other problems in one or more 

SBUs that could seriously impair overall long-term perfor-

mance. 

Effective health maintenance requires periodic screen-

ing tests in order to determine whether there are any indica-

tors of malfunctioning systems.  Effective marketing man-

agers monitor their results, identify SBUs that exhibit sub-

par performance, understand the underlying reasons for sub

-par performance, and take corrective action. 

The Profitability Analysis package Version 2.0 builds 

on the Proforma Analysis Package (Palia, 2007) and the 

SBU Analysis Package (Palia, 2009) used in marketing 

control.  Both the Proforma Analysis and SBU Analysis 

packages focus primarily on intra-firm data on antecedents 

of sales revenue and expenses to understand the underlying 

reasons for deviant performance (low overall profit or SBU 

contribution to margin).  The Profitability Analysis pack-

age extracts and presents both intra-firm and inter-firm data 

on each of the antecedents of sales revenue and expenses to 

help understand the underlying reasons for deviant perfor-

mance. 

 
THE MARKETING  

SIMULATION COMPETE 

 
COMPETE (Faria, 2006) is a marketing simulation 

designed to provide students with marketing strategy devel-

opment and decision-making experience.  Competing stu-

dent teams are placed in a complex, dynamic, and uncertain 

environment.  The participants experience the excitement 

and uncertainty of competitive events and are motivated to 

be active seekers of knowledge.  They learn the need for 

and usefulness of mastering an underlying set of decision-

making principles. 

Competing student teams plan, implement, and control 

a marketing program for three high-tech products in three 

regions Region 1 (R1), Region 2 (R2) and Region 3 (R3) 

within the United States.  These three products are a Total 

Spectrum Television (TST), a Computerized DVD/Video 

Editor (CVE) and a Safe Shot Laser (SSL).  The features 

and benefits of each product and the characteristics of con-

sumers in each region are described in the student manual.  

Based on a marketing opportunity analysis, a mission state-

ment is generated, specific and measurable company goals 

are set, and marketing strategies are formulated to achieve 

these goals.  Constant monitoring and analysis of their own 

and competitive performance helps the teams better under-

stand their markets and improve their decisions. 

Each decision period (quarter), the competing teams 

make a total of 74 marketing decisions with regard to mar-

keting their three brands in the three regional markets.  

These decisions include nine pricing decisions, nine ship-

ment decisions, three sales force size decisions, nine sales 

force time allocation decisions, one sales force salary deci-

sion, one sales force commission decision, twenty-seven 

advertising media decisions, nine advertising content deci-

sions, three quality-improvement R&D decisions, and three 

cost-reduction R&D decisions.  Successful planning, im-

plementation, and control of their respective marketing 

programs require that each company constantly monitor 

trends in its own and competitive decision variables and 

resulting performance. The teams use the COMPETE 

Online Decision Entry System (CODES) (Palia & Mak, 

2001; Palia et al., 2000) to enter their decisions, retrieve 

their results, and download and use a wide array of market-

ing dss packages. 

 
THE PROFITABILITY  

ANALYSIS PACKAGE 

 
The Web-based Profitability Analysis Package Version 

2.0 is accessible online to competing participant teams in 

the marketing simulation COMPETE.  It enables compet-

ing participant teams to learn, identify and assess the un-

derlying reasons for profitability or loss of each strategic 

business unit (SBU) within their brand portfolio during 

each decision period.  The competing teams can select the 

“Show Concerns” option to identify and flag the determi-

nants of revenues and/or expenditures for each SBU which 

are of potential concern.  Competing participant teams can 

use this package to monitor performance, identify devia-

tions, understand the underlying reasons, take corrective 

action and thereby exercise marketing control. 

The Profitability Analysis package (a) extracts relevant 

antecedents of the price and quantity components of sales 

revenue as well as relevant antecedents of the cost of goods 

sold and operating expense components of total expenses 

for all five competing teams from the COMPETE results 

for a specific period, (b) uses the competitor with the high-

est earnings per share as the benchmark when comparing 

each of the relevant antecedents, and (c) and provides the 

user with the option of flagging each of the antecedents of 

sales revenue and total expenses that contributed to low 

profit.  The user may show or hide the flagged antecedents 

that contributed to low profit when compared with the firm 

with the leading earnings per share, and use other bench-



 

Page 68 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

marks such as average values for each antecedent when 

analyzing the reasons for low profitability. 

The Profitability Analysis Package (Workbook) Ver-

sion 2.0 is a zipped folder “Profitability Analysis.zip” 

which consists of an Excel workbook “Profitability Analy-

sis.xlsx” (with external links to the COMPETE results 

(output) file Period.xls) and Period.xls Excel version of 

sample COMPETE output for a specified period.  This 

Profitability Analysis.xlsx workbook consists of ten work-

sheets.  The Profit worksheet focuses on Company profit of 

loss.  The other nine worksheets TST Region 1, TST Re-

gion 2, TST Regions 3, CVE Region 1, CVE Region 2, 

EXHIBIT 1 
 Company Profitability Analysis Worksheet 



 

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CVE Region 3,  SSL Region 1, SSL Region 2, and SSL 

Region 3 focus on the specified SBU profit or loss. 

The Profit worksheet consists of external links to the 

Excel version of the quarterly COMPETE output file 

“Period.xls”.  This Profit worksheet extracts and displays 

the company name, company number, and decision period 

(quarter) number from the Excel version of the COMPETE 

results file “Period.xls” (see exhibit 1).   

In order to analyze the price component of total reve-

nue, the Profit worksheet extracts, calculates and displays 

the average Price (for each of the three products TST, 

CVE, and SSL) for each of the competing firms (see exhib-

it 1).  Further, in order to calculate and display the Total 

Revenue of each of the competing firms (at the top of the 

worksheet), this worksheet extracts and displays, (a) price 

and (b) quantity sold for each of the nine SBUs for each of 

the competing firms (see exhibit 2). 

Next, in order to analyze the antecedents of the quanti-

ty sold component of total revenue, the Profit worksheet  

(see exhibit 1) extracts and displays (a) the average price 

for each of the three products), (b) total advertising $s, (c) 

advertising awareness index for each product (available 

only for company investigated), (d) total salesforce size, (e) 

salesforce salary, (f) salesforce commission, and (g) quality 

index for each product, for each of the competing firms. 

Total expenses consist of cost of goods sold item and 

operating expenses (see exhibit 1).  In order to analyze the 

antecedents of cost of goods sold, the Profit worksheet ex-

tracts and displays (a) unit cost of production for each 

product, (b) ending inventory for each product, and (c) 

overtime production for each product only for the company 

analyzed.  Further, in order to analyze the antecedents of 

operating expenses, this worksheet extracts and displays (a) 

total advertising $s , and (b) total salesforce size, for each 

of the competing firms. 

Information on (a) advertising awareness indices for 

EXHIBIT 2 

Company Profitability Analysis Worksheet 



 

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each product, (b) unit cost of production for each product, 

(c) ending inventory for each product, (d) overtime produc-

tion for each product, and (e) total R&D spending are con-

sidered confidential to each competing firm, and available 

only for the company under analysis in the Excel version of 

the company’s COMPETE results “Period.xls”.  These 

variables are extracted and displayed on the right side and 

compared with the NAEM (Industry) averages for (a) Ad-

vertising Awareness Index by product, and (b) Unit Cost of 

Production by product, as well as (c) total Industry R&D 

spending (see exhibit 1) provided in the Excel version of 

the simulation results “Period.xls”.  In addition, the Profit 

worksheet extracts the price and quantity sold for each of 

the nine SBUs by company and calculates (a) the sales rev-

enue (price x quantity sold) for each SBU by company, (b) 

the total revenue for all nine SBUs by company, and (c) the 

average industry price and quantity sold by SBU (see ex-

hibit 2). 

Each of the remaining nine SBU-specific Profit work-

sheets, such as TST-Region 1 worksheet consists of exter-

nal links to the quarterly COMPETE output file Period.xls.  

Each of these SBU-specific worksheets extracts and dis-

plays the specific SBU antecedents of the price and quanti-

ty components of SBU total revenue, and the specific SBU 

antecedents of the cost of goods sold and operating expense 

components of total expenses.  The layout of the SBU-

specific worksheet is similar to the Profit worksheet used to 

analyze the overall company performance.  The industry 

averages for the SBU-specific (a) price, (b) total advertis-

ing, (c) broadcast advertising, (d) print advertising, (e) sales 

promotion, (f) regional salesforce size, (g) salesforce sala-

ry, (h) salesforce commission, and (i) product quality index 

are calculated and displayed on the right side (see exhibit 

3).   

As with the Profit worksheet, the confidential infor-

mation on  (a) SBU-specific advertising awareness index, 

(b) product-specific unit cost of production, (c) SBU-

specific ending inventory, (d) SBU-specific overtime pro-

duction, and (e) product-specific R&D investment are ex-

tracted from “Period.xls” and displayed for the company 

under analysis on the right side (see exhibit 3). These varia-

bles are compared to the NAEM (Industry) averages for (a) 

Advertising Awareness Index by SBU, (b) unit cost of pro-

duction by product, as well as (c) product-specific total 

EXHIBIT 3 

TST – Region 1 Profitability Analysis Worksheet 



 

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EXHIBIT 4 

Data Extraction Table – Company Profit Worksheet (Revenue Determinants) 

Account Cell Worksheet (Tab) Page # Account Cell Ref.

Company Name A4 from ==> Tltle Title Pg. C15

Company Number A5 from ==> Tltle Title Pg. C14

Period A6 from ==> Tltle Title Pg. C16

TST Price - Company 1 C12 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (D32+G32+J32)/3

TST Price - Company 2 D12 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (D33+G33+J33)/3

TST Price - Company 3 E12 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (D34+G34+J34)/3

TST Price - Company 4 F12 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (D35+G35+J35)/3

TST Price - Company 5 G12 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (D36+G36+J36)/3

CVE Price - Company 1 C13 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (E32+H32+K32)/3

CVE Price - Company 2 D13 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (E33+H33+K32)/3

CVE Price - Company 3 E13 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (E34+H34+K34)/3

CVE Price - Company 4 F13 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (E35+H35+K35)/3

CVE Price - Company 5 G13 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (E36+H36+K36)/3

SSL Price - Company 1 C14 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (F32+I32+L32)/3

SSL Price - Company 2 D14 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (F33+I33+L33)/3

SSL Price - Company 3 E14 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (F34+I34+L34)/3

SSL Price - Company 4 F14 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (F35+I35+L35)/3

SSL Price - Company 5 G14 from ==> Forecast, Prices 9 Avg. Price = (Reg1+Reg2+Reg3)/3 (F36+I36+L36)/3

Advertisinig $s - Company 1 C20 from ==> Ave Comp., Ad 11 Company 1 Advertising E14

Advertisinig $s - Company 2 D20 from ==> Ave Comp., Ad 11 Company 2 Advertising E15

Advertisinig $s - Company 3 E20 from ==> Ave Comp., Ad 11 Company 3 Advertising E16

Advertisinig $s - Company 4 F20 from ==> Ave Comp., Ad 11 Company 4 Advertising E17

Advertisinig $s - Company 5 G20 from ==> Ave Comp., Ad 11 Company 5 Advertising E18

Ad Awareness - TST - NAEM Indy. Avg. H22 from ==> NAEM Bulletin 1 16 Indy Ad Awareness Index-TST avg. (D20+D21+D22)/3

Ad Awareness - TST - Company Avg. I22 from ==> NAEM Bulletin 1 16 Co. Ad Awareness Index-TST avg. (D12+D13+D14)/3

Ad Awareness - CVE - NAEM Indy. Avg. H23 from ==> NAEM Bulletin 1 16 Indy Ad Awareness Index-CVE avg. (E20+E21+E22)/3

Ad Awareness - CVE - Company Avg. I23 from ==> NAEM Bulletin 1 16 Co. Ad Awareness Index-CVE avg. (E12+E13+E14)/3

Ad Awareness - SSL - NAEM Indy. Avg. H24 from ==> NAEM Bulletin 1 16 Indy Ad Awareness Index-SSL avg. (F20+F21+F22)/3

Ad Awareness - SSL - Company Avg. I24 from ==> NAEM Bulletin 1 16 Co. Ad Awareness Index-SSL avg. (F12+F13+F14)/3

Salesforce # - Company 1 C25 from ==> Salesforce, Salaries 10 Company 1 Combined Salesforce G19

Salesforce # - Company 2 D25 from ==> Salesforce, Salaries 10 Company 2 Combined Salesforce G20

Salesforce # - Company 3 E25 from ==> Salesforce, Salaries 10 Company 3 Combined Salesforce G21

Salesforce # - Company 4 F25 from ==> Salesforce, Salaries 10 Company 4 Combined Salesforce G22

Salesforce # - Company 5 G25 from ==> Salesforce, Salaries 10 Company 5 Combined Salesforce G23

Salesforce Salary - Company 1 C26 from ==> Salesforce, Salaries 10 Company 1 Salesforce Salary F35

Salesforce Salary - Company 2 D26 from ==> Salesforce, Salaries 10 Company 2 Salesforce Salary F36

Salesforce Salary - Company 3 E26 from ==> Salesforce, Salaries 10 Company 3 Salesforce Salary F37

Salesforce Salary - Company 4 F26 from ==> Salesforce, Salaries 10 Company 4 Salesforce Salary F38

Salesforce Salary - Company 5 G26 from ==> Salesforce, Salaries 10 Company 5 Salesforce Salary F39

Salesforce Commission - Company 1 C27 from ==> Salesforce, Salaries 10 Company 1 Commission Rate E35

Salesforce Commission - Company 2 D27 from ==> Salesforce, Salaries 10 Company 2 Commission Rate E36

Salesforce Commission - Company 3 E27 from ==> Salesforce, Salaries 10 Company 3 Commission Rate E37

Salesforce Commission - Company 4 F27 from ==> Salesforce, Salaries 10 Company 4 Commission Rate E38

Salesforce Commission - Company 5 G27 from ==> Salesforce, Salaries 10 Company 5 Commission Rate E39

Quality TST - Company 1 C29 from ==> Quality, Dollar Sales 14 Company 1 TST Quality Index F9

Quality TST - Company 2 D29 from ==> Quality, Dollar Sales 14 Company 2 TST Quality Index F10

Quality TST - Company 3 E29 from ==> Quality, Dollar Sales 14 Company 3 TST Quality Index F11

Quality TST - Company 4 F29 from ==> Quality, Dollar Sales 14 Company 4 TST Quality Index F12

Quality TST - Company 5 G29 from ==> Quality, Dollar Sales 14 Company 5 TST Quality Index F13

Quality CVE - Company 1 C30 from ==> Quality, Dollar Sales 14 Company 1 CVE Quality Index F14

Quality CVE - Company 2 D30 from ==> Quality, Dollar Sales 14 Company 2 CVE Quality Index F15

Quality CVE - Company 3 E30 from ==> Quality, Dollar Sales 14 Company 3 CVE Quality Index F16

Quality CVE - Company 4 F30 from ==> Quality, Dollar Sales 14 Company 4 CVE Quality Index F17

Quality CVE - Company 5 G30 from ==> Quality, Dollar Sales 14 Company 5 CVE Quality Index F18

Quality SSL - Company 1 C31 from ==> Quality, Dollar Sales 14 Company 1 SSL Quality Index F19

Quality SSL - Company 2 D31 from ==> Quality, Dollar Sales 14 Company 2 SSL Quality Index F20

Quality SSL - Company 3 E31 from ==> Quality, Dollar Sales 14 Company 3 SSL Quality Index F21

Quality SSL - Company 4 F31 from ==> Quality, Dollar Sales 14 Company 4 SSL Quality Index F22

Quality SSL - Company 5 G31 from ==> Quality, Dollar Sales 14 Company 5 SSL Quality Index F23

Data Extraction from COMPETE Results Workbook Period.xls To Profitability Analysis Worksheet (Revenue Determinants 1)

COMPETE Profitability Analysis Worksheet COMPETE Results Workbook Period.xls



 

Page 72 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

R&D spending (see exhibit 3). 

The relevant data are extracted from the COMPETE 

Results Excel workbook Period.xls to the Profitability 

Analysis workbook as indicated in the Data Extraction Ta-

bles for the Company Profit Worksheet (see exhibits 4 and 

5), and each of the SBU-specific Worksheets (see exhibits 

6 and 7).  In each of the Data Extraction Tables, the Excel 

worksheet (tab), page number in the Excel-version of the 

COMPETE results printout, and cell references for each 

account are shown in the COMPETE Results Workbook 

table (on the right).  The corresponding cell references for 

each account are shown in the Profit Analysis worksheet 

table (on the left) in the Data Extraction Tables. 

For instance, in the Data Extraction Table for the Com-

pany Profit Analysis worksheet – Revenue determinants 

(see exhibit 4), the Advertising $s - Company 1 in a specif-

ic period in cell C20 on the Company Profit worksheet in 

exhibit 1 is extracted from cell E14 in the “Advertising 

Expenditures By Company (In Millions)” table on the “Ave 

Comp., Ad” worksheet of the COMPETE results workbook 

Period.xls.  Similarly, the Salesforce # – Company 1 in cell 

C25 on the Company Profit worksheet in exhibit 1 is ex-

tracted from cell G19 in the “Salesforce Size By Region By 

Company” table on the “Salesforce Salaries” worksheet of 

the COMPETE results workbook. 

In addition, in the Data Extraction Table for the Com-

pany Profit Analysis worksheet – Expense determinants 

(see exhibit 5), the TST Cost of Production for the compa-

ny in a specific period in cell I22 on the Company Profit 

worksheet in exhibit 1 is extracted from cell I10 in the 

“Product Cost Report” table on the “Quality, Cost, OT, 

Shipments” worksheet of the COMPETE results workbook 

Period.xls.  Similarly, the Company R&D ($000s) – in cell 

I49 on the Company Profit worksheet in exhibit 1 is ex-

tracted from cell G35 on the “USA Income Statement” 

worksheet of the COMPETE results workbook. 

Further, in the Data Extraction Table for the TST Re-

gion 1 Profit Analysis worksheet – Revenue determinants 

(see exhibit 6), the TST Region 1 Broadcast $s  for Compa-

ny 1 in cell C16 on the TST Region 1 Profit worksheet in 

exhibit 3 is extracted from cell E10 in the “Advertising 

Expenditures By Medium By Product By Region By Com-

pany (in Millions)” table on the “Full Ad., Content” work-

sheet of the COMPETE results workbook Period.xls.  Simi-

larly, the TST Region 1 Print $s for Company 2 in cell D17 

on the Company Profit worksheet in exhibit 1 is extracted 

from cell F13 in the “Advertising Expenditures By Medium 

By Product By Region By Company (in Millions)” table on 

EXHIBIT 5 

Data Extraction Table – Company Profit Worksheet (Expense Determinants) 

Cost of Production - TST Company I22 from ==> Quality,Cost,OT Ship 7

Cost of Production - CVE Company I23 from ==> Quality,Cost,OT Ship 7

Cost of Production - SSL Company I24 from ==> Quality,Cost,OT Ship 7

Ending Inventory - TST I39 from ==> Quality,Cost,OT Ship 7

Ending Inventory - CVE I40 from ==> Quality,Cost,OT Ship 7

Ending Inventory - SSL I41 from ==> Quality,Cost,OT Ship 7

Overtime Production - TST I43 from ==> Quality,Cost,OT Ship 7

Overtime Production - CVE I44 from ==> Quality,Cost,OT Ship 7

Overtime Production - SSL I45 from ==> Quality,Cost,OT Ship 7

Operating Expenses

Advertising ($mil) - Company 1 C47 from ==> Forecast, Prices 9

Advertising ($mil) - Company 2 D47 from ==> Forecast, Prices 9

Advertising ($mil) - Company 3 E47 from ==> Forecast, Prices 9

Advertising ($mil) - Company 4 F47 from ==> Forecast, Prices 9

Advertising ($mil) - Company 5 G47 from ==> Forecast, Prices 9

Salesforce # - Company 1 C25 from ==> Salesforce, Salaries 10

Salesforce # - Company 2 D25 from ==> Salesforce, Salaries 10

Salesforce # - Company 3 E25 from ==> Salesforce, Salaries 10

Salesforce # - Company 4 F25 from ==> Salesforce, Salaries 10

Salesforce # - Company 5 G25 from ==> Salesforce, Salaries 10

R&D ($'000s) - Industry Total H49 from ==> NAEM Bulletin 2 17

R&D ($'000s) - Company I49 from ==> USA Income Statement 2

Overall Measure of Profitability

Earnings per Share - Company 1 C51 from ==> EPS, Mkt%, SF Activity 8

Earnings per Share - Company 2 D51 from ==> EPS, Mkt%, SF Activity 16

Earnings per Share - Company 3 E51 from ==> EPS, Mkt%, SF Activity 16

Earnings per Share - Company 4 F51 from ==> EPS, Mkt%, SF Activity 16



 

Page 73 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

EXHIBIT 6 

Data Extraction Table – TST Region 1 Profit Worksheet (Revenue Determinants) 

TST Region 1 Sales Prom. $s - Company 1 C18 from ==> Full Ad., Content 12

TST Region 1 Sales Prom. $s - Company 2 D18 from ==> Full Ad., Content 12

TST Region 1 Sales Prom. $s - Company 3 E18 from ==> Full Ad., Content 12

TST Region 1 Sales Prom. $s - Company 4 F18 from ==> Full Ad., Content 12

TST Region 1 Sales Prom. $s - Company 5 G18 from ==> Full Ad., Content 12

TST Region 1 Quantity Antecedents

TST R1 Ad Awareness NAEM Indy Avg H19 from ==> NAEM Bulletin 1 16

TST R1 Ad Awareness Index - Company I19 from ==> NAEM Bulletin 1 16

Region 1 Salesforce # - Company 1 C20 from ==> Salesforce, Salaries 10

Region 1 Salesforce # - Company 2 D20 from ==> Salesforce, Salaries 10

Region 1 Salesforce # - Company 3 E20 from ==> Salesforce, Salaries 10

Region 1 Salesforce # - Company 4 F20 from ==> Salesforce, Salaries 10

Region 1 Salesforce # - Company 5 G20 from ==> Salesforce, Salaries 10

Salesforce Salary - Company 1 C21 from ==> Salesforce, Salaries 10

Salesforce Salary - Company 2 D21 from ==> Salesforce, Salaries 10

Salesforce Salary - Company 3 E21 from ==> Salesforce, Salaries 10

Salesforce Salary - Company 4 F21 from ==> Salesforce, Salaries 10

Salesforce Salary - Company 5 G21 from ==> Salesforce, Salaries 10

Salesforce Commission - Company 1 C22 from ==> Salesforce, Salaries 10

Salesforce Commission - Company 2 D22 from ==> Salesforce, Salaries 10

Salesforce Commission - Company 3 E22 from ==> Salesforce, Salaries 10

Salesforce Commission - Company 4 F22 from ==> Salesforce, Salaries 10

Salesforce Commission - Company 5 G22 from ==> Salesforce, Salaries 10

TST Quality Index - Company 1 C23 from ==> Quality, Dollar Sales 14

TST Quality Index - Company 1 D23 from ==> Quality, Dollar Sales 14

TST Quality Index - Company 1 E23 from ==> Quality, Dollar Sales 14

TST Quality Index - Company 1 F23 from ==> Quality, Dollar Sales 14

TST Quality Index - Company 1 G23 from ==> Quality, Dollar Sales 14

Data Extraction from COMPETE Results Workbook Period.xls To Profitability Analysis Workbook (Expense Determinants 1)

TST Region 1 Profitability Analysis Worksheet COMPETE Results Workbook Period.xls



 

Page 74 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

the “Full Ad., Content” worksheet of the COMPETE re-

sults workbook. 

Lastly, in the Data Extraction Table for the TST Re-

gion 1 Profit Analysis worksheet – Expense determinants 

(see exhibit 7), the TST Region 1 Ending Inventory in cell 

I26 on the TST Region 1 Profit worksheet in exhibit 3 is 

extracted from cell I10 in the “Shipments and Inventory By 

Region By Product” table on the “Quality, Cost, OT, Ship” 

worksheet of the COMPETE results workbook Period.xls.  

Similarly, the TST Region 1 Overtime Production in cell 

I28 on the Company Profit worksheet in exhibit 1 is ex-

tracted from cell G18 in the “Overtime Production / Ship-

ments” table on the “Quality, Cost, OT, Ship” worksheet of 

the COMPETE results workbook. 

In summary, the Company Profit Analysis worksheet 

(see exhibit 1) (a) extracts and presents for all competing 

firms the Earnings per Share, sales revenue determinants 

(price, advertising $s and awareness indices, salesforce 

size, salary, commission, and quality for all companies, (c) 

calculates, and presents the average Price and Advertising 

Awareness Index of each of the three products across all 

three regions for each of the competing firms, and (d) ex-

tracts the total Ending Inventory and Overtime Production 

for each of the three products across all three regions of the 

company being analyzed.  In addition, the Company Profit 

Analysis Worksheet (a) extracts the price charged and 

quantity sold for each of the nine SBUs for all competing 

firms, and (b) calculates and presents the Sales Revenue by 

SBU by company as well as the total sales revenue for each 

company (see exhibit 2). 

Each of the nine SBU-specific profitability worksheets 

(see exhibit 3) (a) extracts and presents for all competing 

firms the SBU-specific sales revenue determinants (price, 

advertising $s and awareness indices, salesforce size, sala-

ry, commission, and quality for all companies, (c) calcu-

lates, and presents the average Price and Advertising 

Awareness Index of each of the three products for each of 

the competing firms, and (d) extracts the SBU-specific 

Ending Inventory and Overtime Production of the SBU 

being analyzed. The use of external links ensures relevant 

data are extracted from relevant sources (statements) in the 

simulation results and precludes data entry error. 

The Profitability Analysis Package Version 2.0 enables 

the competing teams to (a) monitor and identify company 

and SBU-specific performance, and (b) uncover potential 

reasons for sub-par performance.  The package enables the 

teams to compare determinants of each of the (a) revenue 

components (price and quantity sold) and (b) expense com-

ponents (cost of goods sold and operating expenses) with 

selected benchmarks.  Each of the determinants can be 

compared with either (a) the market leader (highest earn-

ings per share), (b) the industry average, (c) a direct com-

petitor, or (d) other designated benchmarks. 

A tab labeled “Compete” at the top of each of the Prof-

itability Analysis worksheets when selected, enables the 

user to either highlight or hide concerns (potential causes of 

poor profitability). This tab was developed using the Cus-

tom UI Editor (http://openxmldeveloper.org/blog/b/

openxmldeveloper/archive/2009/08/07/7293.aspx).  When 

the “Show Concerns” button is selected, each of the team’s 

variables is compared with the market leader based on a set 

of pre-specified rules (see exhibit 8) and highlighted 

(shown on a pink background with bold red lettering) when 

appropriate.  The “Hide Concerns” button enables the user 

to remove the warning flags (highlighted cells) in order to 

use alternative benchmarks for analysis of sub-par perfor-

mance. 

The concerns (cells) are highlighted when the outcome 

of a comparison between two values is true.  The compari-

sons are always done between two values in the same row.  

EXHIBIT 7 

Data Extraction Table – TST Region 1 Profit Worksheet (Expense Determinants) 

Account Cell Worksheet (Tab) Page # Account Cell Ref.

TST Region 1 Cost of Goods Sold

TST Cost of Production NAEM Indy Avg H26 from ==> Quality, Cost, OT, Ship 7 NAEM Avg. TST Unit Cost of Production J10

TST Cost of Production - Company I26 from ==> Quality, Cost, OT, Ship 7 Company TST Unit Cost of Production I10

TST Region 1 Ending Inventory I27 from ==> Quality, Cost, OT, Ship 7 Company TST Region 1 Ending Inventory F27

TST Region 1 Overtime Production I28 from ==> Quality, Cost, OT, Ship 7 Company TST Region 1 Overtime Prodn. G18

TST Region 1 Operating Expense

TST Region 1 Advertising - Company 1 C30 from ==> Full Ad., Content 12 Co. 1 TST R1 (Broadcast + Print + SP) (E10+F10+G10) x !000000

TST Region 1 Advertising - Company 2 D30 from ==> Full Ad., Content 12 Co. 2 TST R1 (Broadcast + Print + SP) (E13+F13+G13) x !000000

TST Region 1 Advertising - Company 3 E30 from ==> Full Ad., Content 12 Co. 3 TST R1 (Broadcast + Print + SP) (E16+F16+G16) x !000000

TST Region 1 Advertising - Company 4 F30 from ==> Full Ad., Content 12 Co. 4 TST R1 (Broadcast + Print + SP) (E19+F19+G19) x !000000

TST Region 1 Advertising - Company 5 G30 from ==> Full Ad., Content 12 Co. 5 TST R1 (Broadcast + Print + SP) (E22+F22+G22) x !000000

Region 1 Salesforce - Company 1 C31 from ==> Salesforce, Salaries 10 Company 1 Regional Salesforce - Reg. 1 D19

Region 1 Salesforce - Company 2 D31 from ==> Salesforce, Salaries 10 Company 2 Regional Salesforce - Reg. 1 D20

Region 1 Salesforce - Company 3 E31 from ==> Salesforce, Salaries 10 Company 3 Regional Salesforce - Reg. 1 D21

Region 1 Salesforce - Company 4 F31 from ==> Salesforce, Salaries 10 Company 4 Regional Salesforce - Reg. 1 D22

Region 1 Salesforce - Company 5 G31 from ==> Salesforce, Salaries 10 Company 5 Regional Salesforce - Reg. 1 D23

TST R&D ($'000s) - NAEM Industry Average H32 from ==> NAEM Bulletin 2 17 NAEM Total Indy R&D ($'000s) - TST D17

TST R&D ($'000s) - Company I32 from ==> USA Income Statement 2 Company R&D ($'000s) - TST G35

Data Extraction from COMPETE Results Workbook Period.xls To Profitability Analysis Workbook (Expense Determinants 1)

TST Region 1 Profitability Analysis Worksheet COMPETE Results Workbook Period.xls

http://openxmldeveloper.org/blog/b/openxmldeveloper/archive/2009/08/07/7293.aspx
http://openxmldeveloper.org/blog/b/openxmldeveloper/archive/2009/08/07/7293.aspx


 

Page 75 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

Three types of comparisons are used.  The company under 

scrutiny is compared against either (a) the strongest com-

petitor with the highest earnings per share shown at the 

bottom of the Profit worksheet, (b) the NAEM industry 

average, or (c) a fixed value (e.g. overtime > 0). 

A VBA (Visual Basic for Applications) module was 

developed to encapsulate the logic that implements these 

row-wise comparisons.  Logic that ensures the correct test 

is applied to each row has been added to each of the ten 

worksheets (Profit worksheet and nine SBU-specific work-

sheets). There is also some logic for each worksheet (Profit 

and the 9 SBU’s) that ensures the correct test is applied to 

each row.  

 

PROFITABILITY ANALYSIS  

PACKAGE USE 

 
The web-based Profitability Analysis Package Version 

2.0 is accessible online to competing participant teams in 

the marketing simulation COMPETE.  The Profitability 

Analysis Package Version 2.0 is a zipped folder Profitabil-

ity Analysis.zip that consists of an Excel workbook file 

Profitability.xlsx with external links to the Excel version of 

sample COMPETE results (output) Period.xls for a specific 

period. 

The updated Profitability Analysis workbook consists 

of a Company Profit Analysis worksheet and nine SBU-

specific Profit Analysis worksheets.  The Company Profit 

Analysis worksheet (see exhibit 1) is used to monitor and 

assess company profitability performance relative to com-

petitors and to understand the primary reasons for profit or 

loss during a specific decision period (quarter).  The user 

can compare each of the primary determinants of company 

sales revenues (such as price, advertising, salesforce size, 

salary, and commission, and quality relative to the leading 

firm with the highest earnings per share.  In addition, the 

user can compare each of the primary determinants of com-

pany expenses (such as unit cost of production, ending in-

ventory, overtime production relative to the industry aver-

age extracted from the industry trade association (NAEM) 

newsletter bulletin in the COMPETE results.  Further, the 

user can compare each of the primary operating expenses 

(such as advertising expense, salesforce expense, R&D 

expense) relative to the leading firm with the highest earn-

ings per share.  The user can use the “Show Concerns” op-

tion to highlight (flag) those determinants of company sales 

revenues and expenses with sub-par performance relative to 

the leading firm.  Alternatively, the user can use the “Hide 

Concerns” option to revert to the original display in order 

to compare each of the determinants of company sales rev-

enue and expenses with sub-par performance relative to the 

industry average or a specific competitor. 

Each of the nine SBU-specific Profit Analysis work-

sheets such as TST Region 1 Profit Analysis worksheet 

(see exhibit 2) can be used in a similar manner to monitor 

and assess the specific SBU profitability relative to com-

petitors and to understand the primary reasons for adequate 

or inadequate contribution to margin during a specific deci-

sion period (quarter).  The user can compare each of the 

primary determinants of SBU-specific sales revenues (such 

as price, advertising, salesforce size, salary, and commis-

sion, and quality relative to the leading firm with the high-

est earnings per share.  In addition, the user can compare 

each of the primary determinants of SBU-specific expenses 

(such as unit cost of production, ending inventory, overtime 

production relative to the industry average extracted from 

the industry trade association (NAEM) newsletter bulletin 

in the COMPETE results.  Further, the user can compare 

each of the primary operating expenses (such as advertising 

expense by SBU, salesforce expense by region, R&D ex-

pense by product) relative to the leading firm with the high-

est earnings per share.  The user can use the “Show Con-

cerns” option to highlight (flag) those determinants of SBU

-specific sales revenues and expenses with sub-par perfor-

mance relative to the leading firm.  Alternatively, the user 

can use the “Hide Concerns” option to revert to the original 

display in order to compare each of the determinants of 

SBU-specific sales revenue and expenses with sub-par per-

formance relative to the industry average or a specific com-

petitor. 

 

PROFITABILTY ANALYSIS  

PACKAGE PROCESS 

 
First, the user downloads and unzips the Profitability 

Analysis.zip folder for a specific period.  Next, the user 

logs in to CODES and downloads, renames and saves the 

Excel version of results for a specific decision period 

(quarter) as Period.xls in the unzipped “C:\Profitability 

Analysis” directory.  Then, the user opens and updates the 

Profitability.xlsx workbook. 

Next, the user selects the Profit worksheet to com-

mence analysis of the overall company performance.  The 

market leader (company with highest earnings per share) 

can be identified at the bottom of the Profit worksheet.  

Then, the user selects the Compete tab at the top of the 

worksheet.  This reveals two buttons “Show Concerns” and 

“Hide Concerns” at the top left of the worksheet.  When the 

“Show Concerns” button is selected, the user can immedi-

ately identify the highlighted cells with potential causes of 

sub-par profitability relative to the market leader (see ex-

hibit 8).  These cells are identified and highlighted based on 

a set of pre-specified rules relative to the market leader 

with the highest earnings per share (see exhibit 9).   When 

the “Hide Concerns” button is selected, the highlighted 

cells disappear, enabling the user to analyze sub-par com-

pany profit performance using other benchmarks such as a 

more direct competitor instead of the market leader. 

For example, the executives of one of the competing 

participant teams TriniTech (Company 2) have used the 

Profitability Analysis package to analyze the operations of 



 

Page 76 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

their firm during period 6.  The Company Profit Analysis 

worksheet (see exhibit 8) indicates (at the bottom) that their 

earnings per share of $0.56 are lower than the leading 

$1.00 earnings per share of the market leader Company 5.  

When they use the Compete tab to select “Show Con-

cerns,” the highlighted cells in red on a pink background 

indicate potential reasons for their company performance.   

For instance, when they analyze the determinants of 

revenue (price and quantity sold) they find that their TST 

price of $4,490 and CVE price of $443 are lower than the 

corresponding prices of $4,493 and $444 respectively of 

the market leader company 2 (see exhibit 8).  These lower 

EXHIBIT 8  

Company Profitability Analysis Worksheet with Warning Flags 



 

Page 77 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

prices could have yielded lower margins and lower profits, 

especially if demand for these products is price inelastic.  

Their advertising awareness index of 101.3 for the CVE is 

less than the industry average of 102.7.  This indicates that 

there is room for improvement in media choice (broadcast 

v print v sales promotion) and copy choice (price, quality, 

features, benefits, warranty/service/repairs) when compared 

to the industry average.  In addition, there salesforce size of 

101 is lower than the 112 salesforce size of the market 

leader.  Furthermore, their TST quality index of 102 and 

CVE quality index of 101 are both lower than the corre-

sponding 103 and 102 quality indices of the market leader.  

In a brief period, they have uncovered the primary reasons 

for weak sales revenues relative to the leader. 

On the expense side they find that their unit cost of 

production for the TST of $3,558.32 and for the CVE of 

$358.81 both exceed the NAEM Industry Average of 

$3,556.73 and $358.71 respectively, making them less 

competitive and reducing their margins.  In addition, they 

find that they have relatively high ending inventories for 

the CVE of 619 units and for the SSL of 7703 units leading 

to spoilage, high inventory carrying costs, storage costs and 

possibly clearance sales at marked down prices.  Finally, 

they have overtime production of 106 units for the CVE 

and 3,179 units for the SSL as a consequence of poor fore-

casting leading  to stockouts, lost sales and most important-

ly lost customers. 

The pre-specified rules for the Company Profit Analy-

sis worksheet are indicated on the right side (see exhibit 9). 

The benchmark price (BM) used when the Compete tab is 

used to select “Show Concerns” is the industry leader Com-

pany 5 with the highest earnings per share.   

Sales revenues are the product of price charge and 

quantity sold.  Assuming an inelastic demand (not neces-

sarily true in all instances) for these luxury products, when 

prices are lower than the benchmark (P < BM), the cells are 

highlighted to indicate concerns.  The antecedents of quan-

tity sold (market share) are price, advertising budget, ad-

vertising awareness index, salesforce size, salary, commis-

sion, and quality.  All these determinants have a direct rela-

tionship with quantity sold.  Accordingly, the cells are 

highlighted if the values for each of the above variables for 

the company are less than the corresponding values for the 

market leader Company 5.   The rules are specified on the 

right. 

Total expenses consist of cost of goods sold items and 

operating expenses.  The primary determinants of cost of 

goods sold are the unit cost of production for each of the 

three products, ending inventory & storage charge, and 

overtime production.  The major operating expenses in-

clude advertising, salesforce expense, and R&D expense 

which can be used to improve quality and lower cost of 

production.  All these determinants have a direct relation-

ship either with costs of goods sold or with operating ex-

penses.  Accordingly, the cells are highlighted if the values 

for each of the above variables for the company are more 

than the corresponding values for the market leader Com-

pany 5 or the NAEM industry average in the case of unit 

cost of production.  Excessive inventory is flagged when 

TST inventory exceeds 100 units, CVE inventory exceeds 

500 units, and SSL inventory exceeds 1000 units as indicat-

ed on the right.  Any overtime is considered undesirable as 

indicated. 

Later, the user can select one or more of the nine SBU-

specific worksheets in order to analyze sub-par profitability 

(contribution to margin) performance of specific SBUs.  

The nine SBU-specific worksheets extract SBU-specific 

data on such variables as SBU price, SBU advertising with 

breakdowns of broadcast advertising, print advertising, and 

sales promotion, and regional instead of total salesforce 

size.  In addition, each of the SBU worksheets calculates 

and displays industry averages for each line item where 

industry averages are not available from the Excel version 

of the COMPETE results Period.xls.  Once again, the selec-

tion of the “Compete” tab followed by the “Show Con-

cerns” button enables the user to immediately identify the 

highlighted cells with potential causes of sub-par profitabil-

ity relative to the market leader (see exhibit 10).  These 

cells are identified and highlighted based on a similar set of 

pre-specified rules relative to the market leader with the 

highest earnings per share (see exhibit 11).  When the 

“Hide Concerns” button is selected, the user can analyze 

sub-par SBU profit (contribution to margin) performance 

using other benchmarks such as a more direct SBU-specific 

competitor instead of the overall market leader or the calcu-

lated and displayed SBU-specific industry averages. 

For example, the executives of TriniTech (Company 2) 

have used the Profitability Analysis workbook to analyze 

the operations of their SBU TST – Region 1 during period 

6.  The TST – Region 1 Analysis worksheet (see exhibit 

10) uses the same benchmark industry leader Company 5 

with the leading $1.00 earnings per share identified in the 

Company Profit worksheet.  When they use the Compete 

tab to select “Show Concerns,” the highlighted cells in red 

on a pink background indicate potential reasons for their 

TST – Region 1 performance.   

For instance, when they analyze the determinants of 

revenue (price and quantity sold) they find that their TST -  

Region 1 price of $4,530 is higher than the corresponding 

TST – Region 1 price of $4,500 of the market leader com-

pany 2 (see exhibit 10).  This higher price yielded higher 

margins and profits, especially if demand for these products 

is price inelastic and hence is not flagged as a determinant 

of sales revenue.  Yet, when analyzing the determinants of 

quantity sold, this same higher price of $4,530 could have 

resulted in lower unit sales (all other variables held con-

stant) as the COMPETE simulation is based on the rational 

man model.  Their total TST – Region 1 advertising budget 

of $270,000, Broadcast advertising budget of 110,000, 

Print advertising budget of $80,000, Sales Promotion budg-

et of $80,000 are all less than the corresponding budgets of 

$330,000, $130,000, $100,000, and $100,000 respectively 



 

Page 78 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

EXHIBIT 9 

Company Profitability Analysis Worksheet Rules 



 

Page 79 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

of the market leader Company 5 (see exhibit 10).  Their 

TST – Region 1 advertising awareness index of 105 is 

higher than the NAEM TST – Region 1 industry average of 

103 and is accordingly not flagged.  Their Region 1 

salesforce size of 37 is lower than the 45 Region 1 

salesforce size of the market leader.  Their salesforce salary 

and commission of $4,000 and 2.8% are the same as or 

better than the $4,000 salary and 0.3% commission of the 

market leader company 5 and are not flagged.  However, 

their TST quality index of 102 is lower than the corre-

sponding 103 TST quality index of the market leader.  

Again, they have uncovered the primary reasons for weak 

TST – Region 1 sales revenues relative to the leader. 

On the Expense side they find that their unit cost of 

production for the TST of $3,558.32 exceeds the NAEM 

Industry Average of $3,556.73, making them less competi-

tive and reducing their TST – Region 1 contribution-to-

margin.  They have no TST – Region 1 ending inventory or 

stockout concerns, but their company R&D budget exceeds 

that of the NAEM industry average.  While this may result 

in higher quality, it contributes to total operating expenses 

and lower profits. 

The pre-specified rules for the TST – Region 1 Profit 

Analysis worksheet are indicated on the right side (see ex-

hibit 11). The benchmark price (BM) used when the Com-

pete tab is used to select “Show Concerns” is once again 

the industry leader Company 5 with the highest earnings 

per share.  As before all determinants of the quantity sold 

component of total revenues have a direct relationship with 

sales revenue except for price.  This is due to the rational 

man model assumption used in the COMPETE simulation.  

In addition, all the determinants of Total Expenses have a 

direct relationship either with costs of goods sold or with 

operating expenses.  Accordingly, the cells are highlighted 

if the values for each of the above variables for the compa-

ny are more than the corresponding values for the market 

leader Company 5 or the NAEM industry average in the 

case of unit cost of production.  Excessive inventory is 

flagged when TST inventory exceeds 100 units as indicated 

on the right.  Any overtime is considered undesirable and 

highlighted. 

Once the reasons for sub-par overall company and 

SBU-specific performance are recognized, appropriate cor-

rective action can be taken to improve performance, there-

by operationalizing the Iceberg Principle and exercising 

marketing control.   

 

 

EXHIBIT 10 

TST-Region 1 Profitability Analysis Worksheet with Warning Flags 



 

Page 80 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

STRENGTHS AND LIMITATIONS 

 
Company and SBU-specific profitability analysis can 

help management identify (a) the degree of profitability of 

the company and each SBU within the brand portfolio, (b) 

which SBUs within the brand portfolio are not contributing 

to overall profit, (c) the primary reasons for lack of overall 

profitability, and (d) the primary reasons for lack of contri-

bution to margin of poorly performing SBUs.  After they 

identify relatively unprofitable SBUs, and understand the 

primary reasons for lack of profitability, marketing manag-

ers can use the insight derived to take appropriate correc-

tive action.  

Positive anecdotal student feedback was received from 

undergraduate students at the end of the Spring 2013 se-

mester.  Some undergraduate students reported that the 

decision support packages were very useful and helpful in 

understanding the determinants of profitability.  They indi-

cated that the automatic extraction feature saved a “LOT” 

of time instead of having to type in all the numbers.  They 

hoped it would continue to be used in the future as it defi-

nitely made a difference. 

The Online Profitability Analysis Package has some 

limitations.  First, some of the variables extracted from the 

COMPETE results are broken down by product (Quality, 

Cost of Production, R&D expense), other variables are bro-

ken down by region (salesforce size), and a few other varia-

bles such as salesforce salary and commission are only 

available company-wide.  These data limitations may not 

accurately reflect the emphasis that management decides to 

give each of the nine SBUs in their marketing program.  In 

addition, if the firm does not order the necessary market 

research reports, the required information will be missing 

and not available for extraction from the Excel version of 

the COMPETE results Period.xls file.  Further, the Profita-

bility Analysis Package flags the determinants of sales rev-

enue and expenses for both the company and each of the 

nine SBUs based on comparison with the leading competi-

tor with the highest overall earnings per share.  However, it 

is possible for the user to turn off the flags by selecting the 

“Hide Concerns” option, and compare each antecedent of 

sales revenue and expense with average company values or 

EXHIBIT 11  

TST-Region 1 Profitability Analysis Worksheet Rules 



 

Page 81 - Developments in Business Simulation and Experiential Learning, volume 41, 2014 

with a specific selected competitor.  In future, it may be 

possible to provide the user with benchmark options.  The 

user can then select relevant or alternative benchmarks to 

analyze their own profitability. 

Despite these limitations, the Profitability Analysis 

Package is a simple yet powerful web-based user-centered 

learning tool that extracts relevant data from the simulation 

results, precludes data entry error, and saves considerable 

time involved in identifying and entering relevant data.  

Yet, in order to maximize learning about the Iceberg Prin-

ciple and Marketing Control, and actualize the learning 

potential of the Profitability Analysis Package, the instruc-

tor needs to (a) explain the purpose, significance, assump-

tions, usage, and limitations of this dss package, (b) require 

inclusion of a sample analysis in a team report or presenta-

tion, and (c) test students on their understanding of the un-

derlying concepts at the end of the semester. 

 
CONCLUSION 

 
The Web-based Profitability Analysis Package is a 

user-centered learning tool that helps to prepare students 

for marketing decision-making responsibilities in their fu-

ture careers.  The package enables users to apply the Ice-

berg Principle in Marketing Control and determine whether 

each SBU in their brand portfolio is contributing to the 

overall company profit or loss.  Participants use the Profita-

bility Analysis Package to determine if each SBU is profit-

able or not, and to compare the primary antecedents of 

sales revenue and expenses with those of the leading com-

petitor with the highest earnings per share.  This Web-

based Profitability Analysis Package facilitates the integra-

tion of computers, the Internet and the World Wide Web 

into the marketing curriculum. 

 

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