BRAND EQUITY
Ariel Research, a market research company, had just collected a large dataset from a random sample of
Canadian consumers. This data set contained 125,000 entries on different brands of products. The brands
were grouped into 41 separate categories. Product information was collected that related to the whole
concept of brand equity. The Ariel group knew all about brand equity and how important it was to any
company. They also knew that it was often a difficult concept to measure due to its intangible and complex
nature. Ariel Research had tried to quantify brand equity - that is, turn an intangible concept into tangible
measurements that would later help in making important marketing decisions. Now the company wanted
to access whether or not it had succeeded in this task.
What is Brand Equity?
Brand equity is commonly identified as the value added to a brand due to its name. High brand equity
levels help companies maintain their competitive advantage. Brand equity knowledge is also valuable as a
strategic asset since it helps managers know whether they can charge a premium for a brand and how much
they can leverage this equity into the sale of other products. For example, Coke has huge brand equity,
both as a company and at the product level. The high brand equity of Coca Cola's products world-wide
guarantees a certain level of sales just by virtue of the name Coca Cola.
Typically, brand equity can be divided into two areas: financial brand equity and marketing brand equity.
Financial brand equity can be described as "the value placed on a brand on the balance sheet, which
represents the value thought to reside in the brand name." Marketing equity, which is the area of brand
equity explored in the Ariel data set, can be described as "the value added to a brand due to its name as
endorsed by consumer loyalty, willingness to buy at a premium price, resistance to competitive marketing
efforts, etc." Continuing with the Coke example, it would be expected that customers are willing to pay a
little bit more for this beverage than they would pay for another brand of cola with lower brand equity.
Similarly, you might expect a customer who values Coke to buy a T-shirt with the Coca-Cola logo on it
over a plain T-shirt with no logo.
Brand equity is fairly complex in that many aspects can feed brand equity, such as the brand being relevant
to a customer's lifestyle and the brand having the type of personality that the customer loves. A company
creates equity in a brand through the proper combination of advertising of the brand, promotion of the
brand in a variety of ways, positioning the brand in the proper channels, and consistently managing the
brand identity over time so that a customer relationship is maintained. This creation of brand equity is a
complex and creative process that often involves treating a brand as if it has a certain personality and
relating the brand to the personality of the customer who is likely to buy the product. But how can brand
equity be measured?
Ariel's Model of Brand Equity
The group knew it was one thing to understand what brand equity is but quite another to measure it. Ariel
created a multi-dimensional measure of brand equity with five main variables: familiarity of the product,
perceived uniqueness of the product, popularity of the product, relevancy of the product to lifestyle, and
customer loyalty to the product.
Ariel's Brand Equity DatabaseThe Ariel database was collected through a mail-panel survey. Responses
were submitted by 5,000 respondents who filled out personal demographic information as well as several
category sections within the questionnaire. Each category section consisted of questions relating to a small
number of brands (usually four to six) that were considered category leaders. These leading brands were
chosen because, collectively, they occupied the majority of the category market share.
The respondent database was then re-oriented so that it contained 125,000 records, with each record
containing ratings for a specific brand, along with respondent information. This brand-oriented database
was intended to be used to model customer attitudes towards brands across categories, in order to hopefully
draw general conclusions about how these attitudes related to brand behaviour and brand loyalty. Ariel
believed that these key concepts had a strong influence on brand equity.
The Dataset
The following demographic variables are included in the dataset.
Demographic Variables Description
Region 1=Maritimes; 2=Quebec; 3=Ontario; 4=West
Gender 1=Female; 2=Male
Age in years
Children Children in home (1=Yes; 2=No)
Income 1= < $30k; 2= $30-$49.9k; 3= $50-$74.9k; 4=$75k+
Five questions were posed in order to measure brand equity. The respondents were instructed to answer
each of these questions on a scale of 1 to 10. The more they agreed with a question, the closer the score
was to 10; the less they agreed, the closer the score was to 1. The questions were as follows:
Famil I am familiar and understand what this brand is about.
Uniqu This brand has unique or different features or a distinct image other brands in this category
don't have.
Relev This brand is appropriate and fits my lifestyle and needs.
Loyal This brand is the only brand for me.
Popul This brand is a popular brand.
Ariel decided that a response of 8, 9, or 10 indicated high brand loyalty; otherwise, low brand loyalty was
indicated. Consequently, the group created five binary variables from the above five dimensions. These
variables were defined as follows:
Familbin 0 = not loyal (responses of 1 to 7); 1 = loyal (responses of 8-10)
Uniqubin 0 = not loyal (responses of 1 to 7); 1 = loyal (responses of 8-10)
Relevbin 0 = not loyal (responses of 1 to 7); 1 = loyal (responses of 8-10)
Loyalbin 0 = not loyal (responses of 1 to 7); 1 = loyal (responses of 8-10)
Populbin 0 = not loyal (responses of 1 to 7); 1 = loyal (responses of 8-10)
As a starting point, the group decided to focus its analysis on two categories: fast food companies
(FAST.SAV, product number 7B10E023A) and air travel (TRAVEL.SAV, product number 7B10E023B).
Questions
Use the dataset TRAVEL.SAV
1. Run a crosstabs using the variables BRAND and LOYAL_BIN. What do the results tell you?
2. Delete the brands associated with UK and AirUSA (use SELECT CASES). Rerun the crosstabs.
What do the results tell you?
Use the dataset FAST.SAV
1. What statistical analysis is suitable to measure brand equity with the collected data? Why?
2. Compare loyalty, relevance, familiarity, uniqueness and popularity for its brands using the
appropriate statistical analysis.
3. Analyze a fast food brand to determine relationships between loyalty and the respondent profiles
(e.g., age, region, income).
4. Ariel created binary variables for familiarity, uniqueness, relevance, loyalty and popularity by
splitting responses into "high" and "low." Why would they choose to do (or not do) this? In other
words, what information is gained and what information is lost?
5. Do you agree with Ariel's measure of brand equity

Answer rating: 100% (QA)

Run a crosstabs using the variables BRAND and LOYALBIN What do the results tell you import pandas as pd Load the dataset data pdreadcsvTRAVELSAV Crosstabulation of BRAND and LOYALBIN crosstab pdcrosst... View the full answer