Analysis of Markets

While we have spent considerable time thinking about the nature of resources, product categories, and

competition. Throughout the discussions, a point of emphasis has been the role of benefits. Benefits

help us define our businesses. Benefits help us identify our competitors. Benefits help us seek new

competitive opportunities. While the benefits our products offer are of critical importance to us, they

are of even greater importance to our customers because these customers seek benefits from their

purchases. Thus, to truly understand any of the concepts we’ve discussed thus far, we must understand

our customers. While much of this understanding should have come from your MKT 450 class, the

process is not complete. Here, we look at how to analyze and quantify this understanding, and in some

cases, how to act on it strategically.

REFRESHER ON MARKETS AND MARKET SEGMENTATION

The Meaning of “Markets”

When marketing students come from their economics, accounting, or finance classes, they may have

learned that markets are places where exchanges take place. Sometimes these are even referred to as

markets such as “stock markets,” “super markets,” “flea markets,” or “antique markets.” Technically,

these are all correct when considered from the economics, accounting, or finance traditions. To

marketers, however, markets are not places; markets are people who have similar wants and needs and

the ability to satisfy those needs. When we speak of the automobile market or the athletic shoe market,

or the cell phone market we do not refer to Interstate Ford, Foot Locker, or the Verizon kiosk at the

mall. We refer to people who want cars, athletic shoes, or cell phones, and who have the means to

satisfy their needs.

Segmentation of Markets

For many products, particularly those that offer a variety of benefits, markets are heterogeneous. That

is, the people who want the product and who have the ability to buy it may not all be the same.

Therefore it makes sense for marketers to identify the different types of people in markets for their

products, learn about them, and then decide which types of people to target with their marketing

efforts. We refer to this process as market segmentation and it is among the most important activities

that marketing managers can engage in. Market segmentation occurs as a three step process where

customer benefits again play a key role.

Market Analysis 2

Step One: Identify the Product Market. If markets are people with similar wants and needs, then the

term product market simply applies this definition to some product category. That is, a product market

is all of the people with wants or needs for the benefits of a particular product category and the ability

to satisfy those needs. Note that the concept of product markets simply takes the notion of product

categories and expresses it from a customer point of view. If a product category is a group of products

that offer products with some similarity of benefits, then product markets are simply those who desire

that set of benefits. They are basically all of the people who want or need the benefits that are offered

by your product and your competition’s products. You may not know who these people are, but if they

want your product or a competitive product, then they’re in the product market.

Step Two: Divide the Product Market into Segments. The second step in the produces is to divide the

large group of people represented by the product market into smaller groups called market segments,

which are subgroups of the product market with shared characteristics. The characteristics they share,

beyond their need or want for particular benefits, will be based on four sets of variables called the bases

of market segmentation. These are demographic variables (age, sex, income, education, occupation,

etc.), geographics (actual location, location characteristics), psychographic or lifestyle variables

(activities, interests, and opinions), and desired benefits (resource, sensory, psychological).

Exhibit 1. Segmentation Bases Relationship and Process

Desired Benefits

Geographics

Demographics

Psychographics

Geographics

Demographics

Psychographics Desired Benefits

List sets of related benefits

Describe lifestyle of person wanting benefits.

Add demographic and geographic descriptors.

Exhibit 1A. Theoretical Relationships Among Segmentation Bases

Exhibit 1B. Process for Describing Subgroups

Market Analysis 3

A useful way for dividing people according to these benefits is to start with the theoretical relationships

between them, which are illustrated in Exhibit 1A. The exhibit shows that where a consumer is in life in

terms of their physical and social characteristics (i.e., demographics) and where the consumer lives (i.e.,

geographics) largely determines his or her lifestyle (i.e., psychographics). The lifestyle, which is defined

by their activities, interests, and opinions, determines the set of benefits the consumer desires from the

product. These theoretical relationships between the segmentation bases help lay out a process for

actually thinking of and describing segments, as shown in Exhibit 7B.

Notice that Exhibit 7B shows the theoretical relationships in reverse. That is, to segment the product

market into understandable subgroups, begin by listing a small set of related benefits to using the

product. Then, think of the lifestyle (activities, interests, and opinions) of a person who would desire

those benefits. Then describe the demographic and geographic characteristics of the person living that

lifestyle.

Consider a very brief example. Suppose you marketed toothpaste. To segment this product market,

you would think of the many benefits of toothpaste: fresh breath, no cavities, bright teeth, lower dental

bills, improved self-confidence, sex-appeal, less gum disease, etc. Then you might group together a few

that seem to fit with each other, say, fresh breath, bright teeth, confidence, and sex appeal. Then you

describe the activities, interests and opinions of a hypothetical person who would value that set of

benefits the most in a toothpaste. The person may be very physically fit, outgoing, and appearance

conscious, socially active, with a large network of friends. Finally, you take this lifestyle, and think -of

the demographics of a person who would be like this: an 18-24 year old single female college student.

Or you could think of a 35-44 year old divorced male who works as a professional salesperson or other

occupation that brings him into contact with many people. One lifestyle can produce many

demographic profiles. And bear in mind we did not use all of the benefits.

Step Three: Select a Target Market from the Identified and Described Segments. The output of step two

should be several profiles of segments within the product market. However, not all market segments

are worth pursuing. Some may be small; some may be difficult to reach; some may be highly brand loyal

to a competitor. Others, however, may be excellent to target. A target market is simply a segment that

is selected to pursue. Firms should certainly be open to having multiple targets at any one time.

Note: The segmentation system works well if used analytically and systematically. However, we do not have time to completely cover it here or in class. If you had me in MKT 450, you’ve seen this before, but may benefit from a refresher. If you did not take MKT 450 with me, this may be new material to you. Using web notes from the MKT 450 class, I prepared an extended example using this system that goes into much more detail.

ANALYZING THE CHARACTERISTICS OF MARKET SEGMENTS

The focus of this material is not the market segmentation process. Instead, we’ll consider the metrics

and strategies associated with learning about the segments you may be considering as target markets.

In other words, the assumption at this point is that you’ve identified and described one or more market

segments in the product market. Now you wish to analyze and evaluate them. In this section, we’ll look

at a few of the questions you should ask about the market segments and some analytical tools to help

answer those questions. Some of these analytical tools require the collection of survey data and use

Market Analysis 4

questionnaire items you should already know and understand; others use secondary or syndicated data

sources.

Size, Demographic, and Geographic Characteristics of the Market Segment

The place to turn for any demographic information about the United States is the Census Bureau, who

publishes amazingly detailed population estimates online. The data tools that the Census Bureau

incorporates allow users to customize search processes and reports so that tables for virtually every

level of every demographic variable is compared to every other level of any other variable. Once you

learn to navigate the online app, which admittedly takes a little while, you can quickly get a feel for the

breadth of data and depth with which it is offered.

As an example, suppose I wanted to gather information about a target group that is demographically

defined only as being women, aged 25 to 34 living in the Dayton, Ohio, metropolitan area. Exhibit 2

shows counts of random characteristics about them that I gleaned in only a few minutes of haphazard

looking around using the American Factfinder tool on the Census Bureau website.

Category Description Number

Total Number of 25-34 year old women in Dayton, Ohio, metro 51,949 Number of African 25-34 year old African American women in Dayton metro 8,459 Number of 25-34 year old women with a hearing difficulty 684 Number of 25-34 year old women with a bachelor’s degree or higher 15,795 Number of 25-34 year old women with health insurance coverage 42,952 Number of 25-34 year old women living in skilled nursing facilities 17

Exhibit 2. Selected Counts of Characteristics of 25-34 Year Old Women in Dayton, Ohio, metro

The amount of data available to count the members of very specific groups of people is amazing for a

resource that is available anytime at no cost. While we may wish to include psychographic variables in

our counts, which could require using survey data to estimate, the Census Bureau provides an

outstanding starting point for measuring the size of virtually any demographically defined group of

people. Importantly, the Census Bureau permits researchers to aggregate the data at any level, from

national counts to counts within individual census tracts, which are often only a few blocks large.

Moreover, census data are archived, meaning that you can compare to past counts to current counts

and calculate growth rates.

Brand Awareness Knowledge, and Attitudes

Pursuing new market segments or defending existing target markets may require that marketers learn

more about how the market segments regard your brand or competitive brands. Depending on the

results, some segments may be more attractive than others, more expensive than others, or both. For

example, a potentially valuable segment may be very loyal to a competitive brand; Windows is very

unlikely to make inroads against Macs among professional designers or other artistic types, where Apple

holds a dominant market share. Some segments may not know of your brand or may not know what

you want them to know, and the cost of familiarizing and educating them may be prohibitive.

Market Analysis 5

For these and other reasons, it’s important to understand what segments know, how they feel, and

what they value. In these instances, analysis will likely be based on survey research. Here, we’ll focus

on some common measures for important variables and then look at how to use them.

Brand Awareness. Logically, a customer must be aware of a product or brand and then have at least

some minimal knowledge of the product or brand before the customer will be willing to consider buying

the brand. Many measures exist for brand awareness but tend to take one of three approaches.

Unaided Recall Approach: Often called top of mind awareness, unaided recall measures ask

respondents to name the first brand that comes to mind when given the product category

name. Often respondents are asked to name the first few brands that come to mind, however,

the first listed brand is usually the focus. Recall that the brands that come to mind when

searching for purchase options to solve a problem are collectively called the evoked set.

Research has shown that brands recalled earlier are generally preferred, so many marketers are

keenly interested in whether their brands are remembered top of mind, or even close. The

problem with unaided recall tests is that for brands not enjoying top of mind awareness, they

may have to interview many respondents before finding any who name these brands first. The

net result is that getting top of mind information about low share brands requires many

interviews, which can be enormously expensive. For all but the market leaders, unaided recall

tests are not often used.

Aided Recall Approach: Sometimes used in conjunction with the top of mind approach, the

aided recall approach permits the research to offer hints to respondents. For example, a

question might ask, “Write down the name of the men’s underwear brand that Michael Jordan

endorses?” While this approach has the advantage of linking recall with specific marketing

activities, it is does not measure true top of mind awareness. Therefore the link between

awareness and preference is not well established, as it is with the unaided method. Moreover,

the rate of successfully naming the brand in aided recall tests may have as much to do with the

quality of the hint as it does with the brand itself. In other words, knowing that Hanes is the

brand that Michael Jordan endorses may have as much or more to do with awareness of

Michael Jordan than it does awareness of Hanes.

Recognition Approach: Recognition is an easier task than recall, both for respondents and for

researchers. From a cognitive point of view, recognizing something is easier than recalling it,

which is why brand awareness measures that use recognition are often criticized for overstating

brand awareness. With the simplest brand recognition measures, respondents are asked if they

have heard of a particular brand, and answer yes or no. (Given my feelings about yes or no

items generally, I think this is a terrible approach to awareness measurement.) A better

recognition based approach to measuring awareness is to scale the item. For example, a

questionnaire item could ask, “Which of the following best describes your knowledge of Hanes

brand underwear?” The item could then be scaled:

□ I’ve never heard of this brand. □ I’ve heard of this brand but know nothing about it. □ I’ve heard of the brand and am a little familiar with it. □ I’ve heard of the brand often and am very familiar with it.

Market Analysis 6

The advantage to scaling recognition based awareness measures this way is that they offer a

range of responses that adds some dimension or context to awareness; that is, awareness

becomes something more than a binary variable. A problem with all recognition approaches is

that they may benignly mislead respondents and researchers. Respondents may see a brand

name and think to themselves that they must have seen it somewhere, leading them to avoid

checking the first box. Or, the measure may encourage social desirability bias on the parts of

respondents, who may indicate at least a passing familiarity with a brand because they do not

wish to appear to be naïve or uninformed.

Brand Knowledge. The recognition based measure of awareness shown above crosses over to the next

level of cognitions about a brand, brand knowledge. Being knowledgeable about a brand implies that a

consumer has more information about the brand than merely having heard the name. Brand knowledge

implies that the consumer has retained certain information about the brand’s attributes in memory.

There are two ways of assessing the degree of that knowledge, both of which require collection of

survey data.

Objective Knowledge Measurement. Objective knowledge measurement works similarly to a

multiple choice test, but most frequently uses a checklist type scale. Respondents are asked to

select statements that they know to be true about the brand in question or to select product

claims that they have heard attributed to the brand. Optimally, the statement will reflect

unique attributes about the brand that cannot be confused with attributes about competitive

brands. In addition, the checklist item may contain “distractor” facts about the brand that are

not true. Scoring is generally done as a simple count of correct selections minus the number of

incorrect selections. For example, returning to the Hanes example, an objective measure might

ask respondents to “Check the statements below that you believe to be true about Hanes

underwear.” The respondent would then check the appropriate statements.

□ Claims to be the best selling men’s underwear in America. □ Is endorsed by Michael Jordan. □ Is the official men’s underwear of NCAA football. □ Makes tagless briefs, boxers, and t-shirts. □ Is introducing a line of men’s casual wear.

As alluded to above, successfully constructing these measures can be difficult and depends on

the types of attributes included in the statements. Distractors may be needed in case some

respondents decide to simply check all of the statements.

Subjective Knowledge Measurement. More commonly, researchers will simply ask respondents

to indicate how knowledgeable they believe they are about a particular brand. Some consumer

psychologists believe that perceived expertise about a brand will may affect marketplace

behavior as much as actual expertise, though possibly not in the same ways. In one study of

potentially harmful products, researchers found that those with objective knowledge of the

product demonstrated more risk aversion than people who subjectively claimed expertise about

the product (CITE). However, in most cases, subjective knowledge claims are probably relatively

accurate, though perhaps a bit overestimated.

Market Analysis 7

Measuring subjective knowledge is relatively straightforward and can be done with any of

several different types of scale items. For example, a researcher ask respondents how they

would “describe their knowledge of power tools,” and then use the average of responses to a

few semantic differential items, as shown below.

limited □ □ □ □ □ extensive narrow □ □ □ □ □ broad

little □ □ □ □ □ great

Alternatively, subjective knowledge could be measured using rating scales, asking people to

“rate their knowledge of power tools” and scale the question as follows.

Have Little Knowledge □ □ □ □ □ Have Great Knowledge

There are other valid approaches that would satisfactorily capture a person’s beliefs in how

much they know about a particular subject. Because of their ease and general reliability, these

types of scales are used frequently.

Brand Attitude. Before we explore how to capture brand attitudes, let’s keep in mind that our goal here

is to assess the suitability of existing or new market segments and that when marketers select segments

to be targets, the segments’ awareness, knowledge and attitudes toward the brand can certainly

influence the attractiveness of the segments. Simply put, attitudes capture the degree to which

someone feels positively or negatively about something, in this case, a brand. As you probably learned

in MKT 450, there are many satisfactory ways of measuring attitudes. These parallel the subjective

measures of brand knowledge shown above. For example, a commonly multi-item semantic differential

scale for measuring brand attitude is shown below. Attitudes are typically measured as the average

score.

bad □ □ □ □ □ good ineffective □ □ □ □ □ effective low quality □ □ □ □ □ high quality

negative □ □ □ □ □ positive unsatisfactory □ □ □ □ □ satisfactory

Benefits and Brand Attributes

Analyzing markets also means assessing the degree to which your brand provides the benefits

considered important by market segments. It may be that the market segment being analyzed do not

find value in certain product attributes that your brand emphasizes. This was the case with American

car makers in the 1970s, when Japanese models began taking market share from U.S. manufacturers.

Rather than focus on issues of efficiency, quality, and reliability, American manufacturers responded to

the Japanese competition by making cars available in many bright colors, splashy striping, and a broad

array of interior trim options. Japanese models offered few colors and few options as a way keeping

prices low and quality high. This is what American buyers cared about. In the end, the Japanese auto

makers knew the American market better than the Americans did.

Market Analysis 8

The story makes a couple of useful points about understanding the wants and needs of market

segments. First, understanding your customers not only involves giving your customers what they want;

it involves giving your customers what they want in the ways that matter most to them. It’s not that the

U.S. automakers picked car colors and trim options that Americans did not like. What the Americans did

not understand was that color and trim were not that important to American buyers compared to

quality, reliability, and efficiency. This suggests that marketers need to know not only the attributes

that customers like, but also how important the attributes are to the buying decision.

In this section, we extend the previous discussion of brand attitudes to using attitudinal information in a

way that accounts for both how segments feel about a brand’s attributes and about how important the

attributes are to the people in the market segment. Understanding these two things help marketers

make several decisions. One is whether they should modify their brand’s attributes in some way. A

second is whether they should keep the product the same but emphasize different priorities. A third is

whether the segments are worth pursuing as targets. No matter the decision to be made, an

appreciation of the attributes that people use to drive purchase decisions is critical for effective

marketing strategy.

The Theory of Reasoned Action. To do this analysis, we first rely on an important model in consumer

psychology called the Theory of Reasoned Action, first proposed by Martin Fishbein and Icek Ajzen

(1975). Often called simply the “Fishbein model,” the theory of reasoned action tries to account for

both how well brands do in providing certain attributes (“beliefs”) and the importance of the attributes

(“evaluations”). Fishbein and Azjen found that a weighted sum of attribute delivery and importance was

predictive of brand attitudes. The simple formula for their model is shown below,

𝐴𝐵 = ∑ 𝑏𝑖𝑒𝑖

𝑛

𝑖=1

where: AB = attitude toward some branded product,

bi = the respondent’s belief about how well the brand delivers the ith attribute,

ei = the ith evaluation of how important the ith attribute is to the respondent, and

n = the number of attributes being considered.

Mathematically, the theory is quite simple. The theory posits that attitudes can be represented

mathematically by summing the products of beliefs and evaluations. To perform the calculations,

obviously you will need data, which you can collect via survey questionnaire.

To demonstrate how the theory works, consider attitudes toward brands of interior paint. Consider

consumer attitudes toward a brand of paint, say Sherwin Williams paint. To apply Fishbein and Ajzen’s

theory, we would first need to list all of the attributes that consumers think of when evaluating interior

paint. Suppose these attributes are color selection, durability, price, ease of application, single coat

coverage, paint odor, and container design. Two measures are needed for each of these attributes. First

is a measure of how well the brand delivers the attribute. Such a measure could look like the one shown

in Exhibit 3A on the next page. Exhibit 3B shows importance measures for all of the same attributes.

Market Analysis 9

Exhibit 3A. Beliefs Measure Please indicate how well each of the following statements describes Sherwin Williams paint.

Does Not Describe At All

Describes Very Well

The paint is durable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint has many attractive color options. . . . . . . . . . . . . . The paint is priced fairly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint is easy to apply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint covers well in one coat. . . . . . . . . . . . . . . . . . . . . . . The paint does not smell bad. . . . . . . . . . . . . . . . . . . . . . . . . . The paint’s container is easy to handle. . . . . . . . . . . . . . . . . .

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Exhibit 3B. Evaluation Measure Now please indicate how important to you each of the following paint characteristics are when you buy paint.

Not At All Important

Very Important

The paint is durable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint has many attractive color options. . . . . . . . . . . . . . The paint is priced fairly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint is easy to apply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . The paint covers well in one coat. . . . . . . . . . . . . . . . . . . . . . . The paint does not smell bad. . . . . . . . . . . . . . . . . . . . . . . . . . The paint’s container is easy to handle. . . . . . . . . . . . . . . . . .

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Exhibit 3. Belief and Evaluation Measures for Sherwin Williams Paint

Suppose a respondent answered the questions as shown by the bolded and underlined numbers in

Exhibit 3. According to the Fishbein model, the person’s attitude toward Sherwin Williams interior

paints would be calculated by summing the products of the beliefs and evaluations measure for each

attribute, to create essentially a weighted sum. The calculation is shown in Exhibit 4, below.

i Belief

Response (bi) Evaluation Response (ei)

Product (biei)

1 2 3 4 5 6 7

The paint is durable. The paint has many attractive color options. The paint is priced fairly. The paint is easy to apply. The paint covers well in one coat. The paint does not smell bad. The paint’s container is easy to handle.

4 5 2 4 2 5 5

5 5 4 3 4 2 1

20 25 8 12 8 10 5

∑ 𝑏𝑖𝑒𝑖 = 88

Exhibit 4. Theory of Reasoned Action Attitude Calculation

Market Analysis 10

Now that you see how the calculations are performed, two points are in order regarding the Fishbein

model. First is to emphasize why the beliefs and evaluations are combined. It’s nice to say that

consumers believe a brand performs well on certain attributes. However, what if the brand is

performing well on the attributes consumers don’t care about, and not performing well on the

attributes they do care about? That’s why incorporating the importance measure into the calculations is

so useful. It allows the beliefs to be compensated by evaluations, which is why the Fishbein model

belongs to a class of attitude models called compensatory models.

Second, the attitude score calculated by the Fishbein model in the example above is 88. What does that

mean? There are really two ways of looking at it. One is in an absolute sense. The maximum possible

score (5 on all beliefs and 5 on all evaluations) would be 25 × 7 = 175. So an 88 out of 175 helps put the

score in some perspective, but not much. What is the expectation that the paint would score perfectly

on seven very important attributes? Pretty low in all likelihood. Another is in a relative sense. That is,

looking at the one score relative to scores from other respondents. Remember, the score calculated

above is for a single respondent. A sample of respondents, perhaps in the hundreds, would give

perspective to any individual score. For data of this type, looking at scores from a relative perspective is

a better approach.

Regression Based Compensatory Model. One question that may have come to mind as you read

through the calculations of the Fishbein model is, “How do they know that the calculations really stand

for attitude?” Another question that may have occurred to you is, “So what?” Let’s deal with the first

question. In consumer psychology circles, the Fishbein model is widely accepted often tested. An

important test of the model’s validity (i.e., truth) has been the degree to which the scores calculated by

multiplying beliefs and evaluations and then summing the products actually correlate well with other

attitude measures. For example, look at the semantic differential attitude measure below, which is the

same one from the earlier discussion on attitude.

bad □ □ □ □ □ good ineffective □ □ □ □ □ effective low quality □ □ □ □ □ high quality

negative □ □ □ □ □ positive unsatisfactory □ □ □ □ □ satisfactory

Suppose we had respondents fill out this scale along with the beliefs and evaluations scales for Sherwin

Williams paints in Exhibit 3. If the Fishbein model is correct that the sum of the belief × evaluation

product represents people’s attitudes toward brands, then it stands to reason that the sum of the belief

× evaluation product would correlate strongly with a separate measure of attitude such as the one

shown above. As it turns out, many studies have consistently shown this to be correct. The two

measures of attitude to correlate strongly, suggesting that the two are actually measuring the same

thing.

Having answered the first question, let’s turn to the second, “So what?” By itself, the simple summative

formula of the Fishbein model does not provide much useful information. However, if we use

regression analysis to not only test the relationship between the belief × evaluation products with

attitude, but to also test the strength of the relationships between attitude and the attributes, we can

get an appreciation of which attributes and related benefits contribute most to positive feelings toward

Market Analysis 11

the brand. If so, then we would have information that we could use to help formulate marketing

strategy.

Recall that regression analysis seeks to build models of dependent variables using collections of

independent variables. So instead of simply using the simple formula of the Fishbein model, we will use

regression analysis to estimate a statistical model that will tell us which product attributes predict

attitude. To continue with the Sherwin Williams example, suppose that we have data from a sample of

582 respondents who rate their overall attitudes to the brand by using their average score to the

attitude scale shown on the previous page. We also have their responses to belief and evaluation

questions for the seven product attributes as shown in Exhibit 3, and that we have multiplied the beliefs

and evaluations together.

We can now use the data to estimate a regression model that uses overall attitude as its dependent

variable and the seven attribute belief × evaluation products as independent variables. The model that

the data will estimate is given by the equation below. The equation states that Attitude, the dependent

variable denoted by Y, is a function of the seven attributes. The regression coefficients, denoted by b1

through b7, express the strength of the relationships between their respective independent variables

and the dependent, attitude.

𝑌 = 𝑏0 + 𝑏1(𝑑𝑢𝑟𝑎𝑏𝑙𝑒) + 𝑏2(𝑐𝑜𝑙𝑜𝑟𝑠) + 𝑏3(𝑝𝑟𝑖𝑐𝑒) + 𝑏4(𝑒𝑎𝑠𝑒) + 𝑏5(𝑐𝑜𝑎𝑡) + 𝑏6(𝑠𝑚𝑒𝑙𝑙) + 𝑏7(𝑐𝑎𝑛) + 𝑒

The model is estimated using SPSS statistical software; Exhibit 4 on the following page shows the results.

By this point, you should know the basics of hypothesis testing and interpreting SPSS regression output,

so these results will only be highlighted. First look at Table 4B, which shows the partitioning of the

variance in the dependent variable attitude. (Remember, the word ANOVA here means only that this is

an ANOVA table that shows how variance is divided. It should not be confused with the statistical

procedure we call ANOVA.) Variance is quantified here by the sums of squares. The amount of variance

in attitude captured by the model is given in the row labeled “Regression.” The amount of variance left

to error is given in the row labeled “Residual.” The two add up to the total amount of variance, shown

as sum of squares total.

If you divide the variance captured by the model (Sum of Squares Regression = 422.395) by the total

variance (Sum of Squares Total = 893.354), you express the variance in Y captured by the model as a

percentage. Thus, 422.395 ÷ 893.354 = .472. Now look at Exhibit 4A and find the column labeled “R

Square.” As you know, R2, also called the coefficient of determination, expresses the percentage of

variable in the dependent variable captured or explained by the regression model. This figure gives

some indication of how accurately the model represents the dependent variable. Generally, the higher

the R2, the better the model, though what’s “high” is often highly context dependent. For example,

econometric models that use financial data often produce R2 figures of .80 or higher. A model that

explained only 47% of the variance in a dependent variable would be unacceptable.

With psychological data collected from scaled responses by questionnaire, however, a figure of 47%

explained variance is generally quite satisfactory. When using scale responses to measure people’s

feelings and impressions, analysts should expect that much variance would necessarily go unexplained.

Market Analysis 12

Exhibit 4A. Summary of Model Performance

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .688a .473 .466 .906

a. Predictors: (Constant), One Coat Coverage, Does not Smell Bad, Easy to Apply, Fair Price, Variety of Colors, Paint is Durable, Can is Easy to Handle

Exhibit 4B. Partitioning of Model Variance

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 422.395 7 60.342 73.544 .000b

Residual 470.959 574 .820

Total 893.354 581

a. Dependent Variable: Attitude to Brand b. Predictors: (Constant), Can is Easy to Handle, Does not Smell Bad, Fair Price, Easy to Apply,

One Coat Coverage, Variety of Colors, Paint is Durable

Exhibit 4C. Testing of Independent Variable Significance

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta

1

(Constant) .660 .169 3.894 .000

Paint is Durable .217 .045 .221 4.840 .000

Variety of Colors .296 .037 .320 7.924 .000

Fair Price .154 .045 .158 3.394 .001

Easy to Apply -.010 .037 -.008 -.265 .791

One Coat Coverage .110 .033 .123 3.331 .001

Does not Smell Bad -.006 .034 -.006 -.189 .850

Can is Easy to Handle .020 .029 .022 .688 .492

a. Dependent Variable: Attitude to Brand

Exhibit 4. Regression Output for Brand Attitude and Brand Attributes

Returning to Exhibit 4B, note that the model is statistically significant, as shown by the p-value in the

right most column labeled “Sig.” All p-values in SPSS are labeled “Sig.” This p-value is less than 0.05,

which indicates that the model as a whole explains more variance in the data than you could expect by

chance. In other words, the results are worth investigating further. With that conclusion, we look at the

results of individual hypotheses tests, shown in Exhibit 4C.

Market Analysis 13

For this type of analysis, we do not need to pay particular attention to any of the regression coefficients.

They are estimated as a matter of mathematical necessity, but their meanings with scale data are very

limited and difficult to interpret. Instead, we will look at the p-values in the “Sig.” column on the right

side of Exhibit 4C. Here, the results are more straightforward. These results show whether the seven

independent variables are statistically significant predictors of attitude. The conventional decision rule

in much statistical analysis is that if p < 0.05, the results are statistically significant. Looking at Exhibit

4C, the independent variables that reach statistical significance are durability, color variety, price, and

one coat coverage. The nonsignificant variables are ease of application, smell, and container.

Remember, this analysis was intended to help us address the question, “So what?” What can marketing

managers take away from the results? What they say is that of the seven attributes driving attitude

among the sample, four matter and three do not. Of the four that matter, variety of colors matters a bit

more than the others (Note that the t-value is higher than any of the others, indirectly suggesting a

stronger relationship between this variable and attitude.). Not only do these results help guide ongoing

marketing efforts to target markets we’ve already selected, they help us select new target markets that

may value the attributes that match our strengths. They keep us from investing in things that

consumers don’t care that much about, and we have good statistical evidence about their priorities.

Surprisingly, ease of application did not relate to attitude to the brand. This may be because consumers

see all interior paint as relatively similar in ease of application. You brush or roll it on walls. That

interpretation is purely speculation, but in total, the results seem to make sense.

THE LIFTIME VALUE OF CUSTOMERS

A fairly recent trend in marketing analysis is to project the value of customers over time. The idea is to

consider what customers can be expected to project in terms of revenue relative to the cost of obtaining

the revenue. With the data available about customers and the records companies can keep about their

interactions with customers, it is possible to pretty accurately project what a customer is worth to a

company over the life of the customer. This differs from forecasting sales, which we will discuss later in

the semester. The customer value calculations help marketers determine how much effort to put into

acquiring or retaining customers or considering the worth to the company of pursuing particular market

segments.

Simple Calculation

The basic calculation we’ll examine in this section is called customer lifetime value or CLV. The view you

should take of a regular, brand loyal customer is that these customers are like annuities. You should

know from your finance classes that annuities are simply series of regular payments. Annuities are

popular investments because the regularity of the payments makes them predictable, and predictability

reduces risk. CLV treats regular customers as if they are financial annuities. Thus, the definition of

customer lifetime value is simply the present value of future cash flows attributed to a customer

relationship.

To understand the concept of CLV, you should also know what is meant by present value. Present value

is simply what something in the future is worth today. Perhaps you’re familiar with those commercials

Market Analysis 14

for services like J.D. Wentworth or Peachtree Financial. These companies appeal to people who receive

annuities or structured settlements from trusts or lawsuits. Suppose a woman inherits a million dollars,

but must receive the inheritance in payments of $100,000 per year for ten years (i.e., an annuity). Then

suppose she wants to start a business and needs all of her inheritance now. If she called J.D.

Wentworth, they would offer to buy the right to her inheritance for say 80% of its value. The woman

must be willing to accept $800,000 now rather than a million dollars over ten years. So to her, the value

of having the money now is worth $200,000 and to her the present value of her inheritance is $800,000.

The point here is that need or uncertainty reduces the value of money in the future and makes money

received now worth more. So to calculate CLV, as with any present value calculation, we basically take

what we expect the customer to pay us over the expected life of the relationship and then discount the

value of the money received later in the relationship relative to money received early in the relationship.

There are many ways of varying complexity available to calculate CLV. Our approach begins with only

three variables.

𝐶𝐿𝑉($) = 𝑀𝑎𝑟𝑔𝑖𝑛($) × 𝑅𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛 (%)

1 + 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒(%) − 𝑅𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛(%)

One quick point is in order about the formula. The three variables are listed as percent values, which is

to remind you that they are rates. In calculations using this formula, you should concert these to

decimals. Now let’s look at the three variables beginning with margin. Margin is simply the revenue a

customer generates after the cost of generating that revenue is deducted. Therefore, margin can be

calculated as:

𝑀𝑎𝑟𝑔𝑖𝑛($) = 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 − (𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐶𝑜𝑠𝑡𝑠 + 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐹𝑖𝑥𝑒𝑑 𝐶𝑜𝑠𝑡𝑠)

That is, margin equals the revenue in a period minus the sum of variable costs for the period and fixed

costs attributable to that period. So if the margin were being calculated for a month, then the sum of

that month’s variable costs and the year’s total costs divided by twelve would be subtracted from the

month’s revenues.

The retention rate refers to the percentage of a customer’s business the company can expect to keep

year over year. If the company does well, the retention rate may actually represent a growth rate,

which means that it would be greater than 1.0. However, if the customer gradually reduces the amount

of business, the retention rate will be less than 1.0.

The discount rate is a bit more complicated to explain. It’s not a number like the prime rate that just

exists and is looked up from some source. The discount rate is assigned to the calculations based on

intuition or a substitute value is used as the discount rate. The discount rate is the rate at which future

dollars are reduced in value relative to current dollars. The discounting of future dollars is done to

account for uncertainty, risk, and currency devaluing factors such as inflation. Most applications of the

CLV formula simply use the weighted average cost of capital as the discount rate, which is a concept you

may remember from your finance classes.

Some of you may be wondering where time is in this equation. The term “lifetime” certainly seems to

imply that time is a factor, and mathematically it is. While we will not go into those mathematics here,

CLV equations that incorporate time variables into them place the time variable as an exponent in the

Market Analysis 15

rate and discount fraction and then added to the previous time period. When time extends to infinity,

the sums actually simplify to the equation for CLV given above. And for practical purposes, by the time

the calculations extend out more than ten or fifteen years, the effects of discounting make the

incremental present value of revenues so small that they are essentially meaningless.

Bear in mind that this formulation of CLV can be made more complex and probably more accurate.

However, the accuracy of the formula all depends on the confidence you have in the figures you put into

the formula. How good is your estimate of retention rate? Will it remain constant? Is the weighted

average cost of capital a good indicator of the discount rate in your case? Will margin remain constant?

The assumptions necessary to address these questions are never completely accurate. However,

despite their flaws, they do provide a useful means of considering what a customer is worth to a

company.

Using the Information from CLV

CLV helps marketers make several two important decisions. One is whether a particular customer, or a

particular market segment, is worth pursuing and the other is how much to invest in that pursuit. In the

case of business to business markets, CLV may be applied to single customers. In the case of consumer

markets, CLV can be applied to market segments by simply estimating average costs and average

retention rates. In either case, the analyses allows marketers to pose “what if” questions. What would

happen to the value of the segment if the retention rate is lower than expected? What would the effect

on margins be if investments were made to improve the retention rate? These questions, of course,

cannot be answered in a vacuum. CLV makes most sense when it is used to compare the value of

alternate investments. No marketers have unlimited funds; therefore, choices must be made. Inherent

in those choices are opportunity costs, which are the revenues forgone by selecting one option and not

another. Those choices should be made with supporting information.

Market Analysis 16

REFERENCES

Some of the material on aided and unaided recall drew from

Feinberg, Fred, Thomas Kinnear, and James Taylor (2013), Modern Marketing Research: Concepts,

Methods, and Cases. Mason, OH: Cengage.

You can read about the Theory of Reasoned Action at

Fishbein, Martin and Icek Ajzen (1975), Belief, Attitude, Intention, and Behavior: An Introduction to

Theory and Research, Reading, MA: Addison-Wesley.

The formula for CLV was adapted from

Lehmann, Donald R. and Russell S. Winer (2008), Analysis for Marketing Planning (7th ed.). New York: McGraw Hill.