Purpose: To understand volume projections.

When projecting sales for relatively new products, marketers typically use a system of trial and repeat calculations to anticipate sales in future periods. This works on the principle that everyone buying the product will either be a new customer (a “trier”) or a repeat customer. By adding new and repeat customers in any period, we can establish the penetration of a product in the marketplace.

It is challenging, however, to project sales to a large population on the basis of simulated test markets or even full-fledged regional rollouts. Marketers have developed various solutions to increase the speed and reduce the cost of test marketing, such as stocking a store with products (or mockups of new products) or giving customers money to buy the products of their choice. These simulate real shopping conditions but require specific models to estimate full-market volume on the basis of test results. To illustrate the conceptual underpinnings of this process, we offer a general model for making volume projections on the basis of test market results.

Construction

The penetration of a product in a future period can be estimated on the basis of population size, trial rates, and repeat rates.

Trial Rate (%): The percentage of a defined population that purchases or uses a product for the first time in a given period.

First-Time Triers in Period t (#): The number of customers who purchase or use a product or brand for the first time in a given period.

Penetrationt⁢(#)=[Penetration int−1⁢(#)*Repeat Rate Periodt⁢(%)]+First-Time Triers in Periodt⁢(#)

From penetration, it is a short step to projections of sales.

Projection of Sales(#)=Penetration(#)*Frequency of Purchase* Units per Purchase(#)

Simulated Test Market Results and Volume Projections

Trial Volume

Trial rates are often estimated on the basis of surveys of potential customers. Typically, these surveys ask respondents whether they will “definitely” or “probably” buy a product. As these are the strongest of several possible responses to questions of purchase intentions, they are sometimes referred to as the “top two boxes.” The less favorable responses in a standard five-choice survey include “may or may not buy,” “probably won’t buy,” and “definitely won’t buy.” (Refer to Section 2.7 for more on intention to purchase.)

Because not all respondents follow through on their declared purchase intentions, firms often make adjustments to the percentages in the top two boxes in developing sales projections. For example, some marketers estimate that 80% of respondents who say they’ll “definitely buy” and 30% of those who say that they’ll “probably buy” will in fact purchase a product when given the opportunity.¹ (The adjustment for customers following through is used in the following model.) Although some respondents in the bottom three boxes might buy a product, their number is assumed to be insignificant. By reducing the score for the top two boxes, marketers derive a more realistic estimate of the number of potential customers who will try a product, given the right circumstances. Those circumstances are often shaped by product awareness and availability.

Awareness Sales projection models include an adjustment for lack of awareness of a product within the target market (see Figure 4.1). Lack of awareness reduces the trial rate because it excludes some potential customers who might try the product but don’t know about it. By contrast, if awareness is 100%, then all potential customers know about the product, and no potential sales are lost due to lack of awareness.

Data Sources, Complications, and Cautions

Sales projections based on test markets will always require the inclusion of key assumptions. In setting these assumptions, marketers face tempting opportunities to make the assumptions fit the desired outcome. Marketers must guard against that temptation and perform sensitivity analysis to establish a range of predictions.

Relatively simple metrics such as trial and repeat rates can be difficult to capture in practice. Although strides have been made in gaining customer data—through customer loyalty cards, for example—it is often difficult to determine whether customers are new or repeat buyers.

When considering awareness and distribution, keep in mind that assumptions concerning the level of public awareness to be generated by launch advertising are fraught with uncertainty. Marketers are advised to think about what sort of awareness the product needs and what complementary promotions can aid the launch.

Trial and repeat rates are both important. Some products generate strong results in the trial stage but fail to maintain ongoing sales. Consider the following example.

Related Metrics and Concepts

Ever-Tried: This is slightly different from trial in that here it measures the percentage of the target population that has “ever” (in any previous period) purchased or consumed the product under study. Ever-Tried is a cumulative measure and can never add up to more than 100%. Trial, by contrast, is an incremental measure. It indicates the percentage of the population that tries the product for the first time in a given period. Even here, however, there is potential for confusion. If a customer stops buying a product but tries it again six months later, some marketers will categorize that individual as a returning purchaser, and others will categorize that individual as a new customer. By the latter definition, if individuals can “try” a product more than once, then the sum of all “triers” could equal more than the total population. To avoid confusion, when reviewing a set of data, it’s best to clarify the definitions behind it.

Variations on Trial: Certain scenarios reduce the barriers to trial but entail a lower commitment by the customer than a standard purchase.

• Forced Trial: No other similar product is available. For example, many people who prefer Pepsi-Cola have “tried” Coca-Cola in restaurants that only serve the latter and vice versa.

• Discounted Trial: Consumers buy a new product but at a substantially reduced price.

Forced and discounted trials are usually associated with lower repeat rates than trials made through volitional purchase.

Evoked Set: The set of brands that consumers name in response to questions about which brands they consider (or might consider) when making a purchase in a specific category. The Evoked Set for breakfast cereals, for example, is often quite large, while for coffee it may be smaller.

Number of New Products: The number of products introduced for the first time in a specific time period.

Revenue from New Products: Usually expressed as the percentage of sales generated by products introduced in the current period or, at times, in the most recent three to five periods.

Margin on New Products: The dollar or percentage profit margin on new products. This can be measured separately but does not differ mathematically from margin calculations.

Company Profit from New Products: The percentage of company profits derived from new products. In working with this figure, it is important to understand how “new product” is defined.

Target Market Fit: Of customers purchasing a product, target market fit represents the percentage who belong in the demographic, psychographic, or other descriptor set for that item. Target market fit is useful in evaluating marketing strategies. If a large percentage of customers for a product belongs to groups that have not previously been targeted, marketers may reconsider their targets—and their allocation of marketing spending.

Purpose: To measure growth.

Growth is the aim of virtually all businesses. Indeed, perceptions of the success or failure of many enterprises are based on assessments of their growth. Measures of year-on-year growth, however, are complicated by two factors:

• Changes over time in the base from which growth is measured: Such changes might include increases in the number of stores, markets, or salespeople generating sales. This issue is addressed by using “same store” measures (or corollary measures for markets, sales personnel, and so on).

• Compounding of growth over multiple periods: For example, if a company achieves 30% growth in one year, but its results remain unchanged over the two subsequent years, this would not be the same as 10% growth in each of three years. CAGR is a metric that addresses this issue.

Construction

Percentage growth is the central plank of year-on-year analysis. It addresses the question: What has the company achieved this year, compared to last year? Dividing the results for the current period by the results for the prior period will yield a comparative figure. Subtracting one from the other will highlight the increase or decrease between periods. When evaluating comparatives, one might say that results in Year 2 were, for example, 110% of those in Year 1. To convert this figure to a growth rate, one need only subtract 100%.

The periods considered are often years, but any time frame can be chosen.

Year-on-Year Growth(%)=Value($,#,%)t−Value($,#,%)t−1Value($,#,%)t−1

Same Stores Growth: This metric is at the heart of retail analysis. It enables marketers to analyze results from stores that have been in operation for the entire period under consideration. The logic is to eliminate the stores that have not been open for the full period to ensure comparability. Thus, this metric sheds light on the effectiveness with which equivalent resources were used in the period under study versus the prior period. In retail, modest same stores growth and high general growth rates would indicate a rapidly expanding organization, in which growth is driven by investment. When both same stores growth and general growth are strong, a company can be viewed as effectively using its existing base of stores.

Compounding Growth, Value at Future Period: By compounding, managers adjust growth figures to account for the iterative effect of improvement. For example, 10% growth in each of two successive years would not be the same as a total of 20% growth over the two-year period. The reason: Growth in the second year is built upon the elevated base achieved in the first. Thus, if sales run $100,000 in Year 0 and rise by 10% in Year 1, then Year 1 sales come to $110,000. If sales rise by a further 10% in Year 2, however, then Year 2 sales do not total $120,000. Rather, they total $110,000 + (10% * $110,000) = $121,000.

The compounding effect can be easily modeled in spreadsheet packages, which enable you to work through the compounding calculations one year at a time. To calculate a value in Year 1, multiply the corresponding Year 0 value by one plus the growth rate. Then use the value in Year 1 as a new base and multiply it by one plus the growth rate to determine the corresponding value for Year 2. Repeat this process through the required number of years.

There is a mathematical formula that generates this effect. It multiplies the value at the beginning—that is, in Year 0—by one plus the growth rate to the power of the number of years over which that growth rate applies.

Value in Future Period($,#,%)=Current Value($,#,%)*[(1+CAGR(%))^ Numberof Periods(#)]

Compound Annual Growth Rate (CAGR): A constant year-on-year growth rate applied over a period of time. Given starting and ending values and the length of the period involved, it can be calculated as follows:

CAGR (%) = {[Ending Value ($, #)/Starting Value ($, #)] ^ 1/Number of Periods (#)} − 1

Data Sources, Complications, and Cautions

Percentage growth is a useful measure as part of a package of metrics. It can be deceiving, however, if not adjusted for the addition of such factors as stores, salespeople, or products or for expansion into new markets. “Same store” sales and similar adjustments for other factors tell us how effectively a company uses comparable resources. These adjustments, however, are limited by their deliberate omission of factors that weren’t in operation for the full period under study. Adjusted figures must be reviewed in tandem with measures of total growth.

Be careful about the difference between percentage changes and changes in percentage points. Consider a firm that grew 10% last year. This year, a marketer might say that the rate of growth increased by 10%. This means that the growth was 10% more than last year, so 10% * (1 + 10%) means growth was 11%. If growth increased by 10 percentage points, growth would now be 20%. It had increased from 10% and added 10 more percentage points. This is dramatically different.

You may also hear about basis points, a related term. Basis points are simply percentage points divided by 100, so 50 basis points equal 0.5%. Basis points are often used in finance because fractional percentage point differences can make very significant differences in outcomes, and it is easier to speak of basis points than fractions of a percentage point.

Related Metrics and Concepts

Life Cycle: Marketers view products as passing through four stages of development:

• Introductory: Small markets not yet growing fast

• Growth: Larger markets with faster growth rates

• Mature: Largest markets but little or no growth

• Decline: Variable size markets with negative growth rates

This is a rough classification. No generally accepted rules exist for making these classifications.

Data Sources, Complications, and Cautions

As noted previously, with cannibalization, one of a firm’s products takes sales from one or more of that firm’s other products. Sales taken from the products of competitors are not “cannibalized” sales, although some managers label them as such.

Cannibalization rates depend on how the features, pricing, promotion, and distribution of the new product compare to those of a firm’s existing products. The greater the similarity of their respective marketing strategies, the higher the cannibalization rate is likely to be.

Although cannibalization is always an issue when a firm launches a new product that competes with its established line, this dynamic is particularly damaging to the firm’s profitability when a low-margin entrant captures sales from the firm’s higher-margin offerings. In such cases, the new product’s weighted contribution margin can be negative. Even when cannibalization rates are significant, however, and even if the net effect on the bottom line is negative, it may be wise for a firm to proceed with a new product if management believes that the original line is losing its competitive strength. The following example is illustrative.

Purpose: To measure the value of a brand.

A brand encompasses the name, logo, image, and perceptions that identify a product, service, or provider in the minds of customers. It takes shape in advertising, packaging, and other marketing communications and becomes a focus of the relationship with consumers. In time, a brand comes to embody a promise about the goods it identifies—a promise about quality, performance, or other dimensions of value that can influence consumers’ choices among competing products. When consumers trust a brand and find it relevant, they may select the offerings associated with that brand over those of competitors, even at a premium price. Often people refer to brand equity as the incremental unit sales and/or price a product commands. Be careful not to confuse a brand’s equity with only the premium price that it can command in a market. It should be noted that a complete measure of brand equity should also include the additional volume that is generated because of the product’s name. Some firms elect to capture most of this value in price, such as for luxury goods, and sometimes in volume, such as for discounters. Most of the time, it is some of both.

When a brand’s promise extends beyond a particular product, its owner may leverage it to enter new markets. This is another component of the brand’s equity. As when measuring brand equity in its traditional market, the brand’s equity in the new market is the incremental volume and/or price the product is able to attain in this new market attributable to the brand itself. As such, a brand can hold tremendous value.

Brand equity can be remarkably difficult to measure. At a corporate level, when one company buys another, analysts might apportion some of the excess of the purchase price over the value of recorded assets purchased to shed light on the value of the brands acquired. Beyond the value of its assets on the firm’s balance sheet, a company’s brands typically constitute important, unrecorded, intangible assets. Of course, a company’s brands are rarely the only unrecorded items acquired in such a transaction. The excess of purchase price over recorded value frequently encompasses intellectual property, distribution systems, customer lists, and other intangibles in addition to brand. Firm valuations (sales or share prices) are also subject to economic cycles, investor “exuberance,” and other influences that are difficult to separate from the intrinsic value. One often wants to consider the discounted cash flow from the incremental volume and price into current dollars. This requires forecasting sales and the influence of the brand into the future, which adds another degree of complexity to this measure.

From a consumer’s perspective, the value of a brand might be the amount she would be willing to pay for merchandise that carries the brand’s name, over and above the price she’d pay for identical unbranded goods.² Marketers strive to estimate this premium in order to gain insight into brand equity. Here again, however, they encounter daunting complexities, as individuals vary not only in their awareness of different brands but in the criteria by which they judge them, the evaluations they make, and the degree to which those opinions guide their purchase behavior. In a similar vein, the value of the brand might be the increased propensity to buy the product because it carries the brand name. This heterogeneity across the population reflects that the brand’s value to one individual might be very different from another’s. For example, the Starbucks brand might be very appealing to an individual, and it might make the person more likely to buy coffee at a Starbucks and willing to pay extra for it because it is Starbucks coffee. For another individual who may not like the taste of Starbucks coffee or the atmosphere of Starbucks shops, the value of the Starbucks brand might be zero and perhaps even negative.

Theoretically, a marketer might aggregate these preferences across an entire population to estimate the total premium its members would pay for goods of a certain brand. Even that, however, wouldn’t fully capture brand equity. Kusum Ailawadi and her colleagues contend that the equity of a brand is better captured by its overall revenue premium (relative to generic goods) than by its price per unit alone. The revenue figure incorporates both price and quantity and so reflects a jump from one demand curve to another rather than a movement along a single curve.³

A successful brand shifts the demand curve for its goods or services outward; that is, it not only enables a provider to charge a higher price (P' rather than P, as shown in Figure 4.3), but it also sells an increased quantity (Q' rather than Q). Thus, brand equity in this example can be viewed as the difference between the revenue with the brand (P' * Q') and the revenue without the brand (P * Q)—depicted as the shaded area in Figure 4.3. (Of course, this example focuses on revenue, when, in fact, it is profit or present value of profits that matters more. But, given that the increase in price associated with the brand comes with no incremental variable costs, this lift represents profits as well.)

In practice, of course, it’s difficult to measure a demand curve, and few marketers do so. Because brands are crucial assets, however, both marketers and academic researchers have devised means to contemplate their value. David Aaker, for example, tracks ten attributes of a brand to assess its strength, VSLY&R, a marketing consultancy, has developed a tool called the BrandAsset^® Valuator, which measures a brand’s power on the basis of differentiation, relevance, esteem, and knowledge. An even more theoretical conceptualization of brand equity is the difference of the firm value with and without the brand. If you find it difficult to imagine a firm without its brand, then you can appreciate how difficult it is to quantify brand equity. Interbrand, a brand strategy agency, draws upon its own model to separate tangible product value from intangible brand value and uses the latter to rank the top 100 global brands each year. Finally, conjoint analysis can shed light on a brand’s value because it enables marketers to measure the impact of that brand on customer preference, treating it as one among many attributes that consumers trade off in making purchase decisions (see Section 4.5).

Construction

The International Organization for Standardization (ISO) has standards for brand valuation (10668) and brand evaluation (20671). (Valuation is usually used to refer to supplying dollar values, whereas evaluation is a wider term related to general brand health.) MASB sees ISO 20671, which was adopted in 2018, as a crucial step in helping accountable marketers build and manage their brands. A key requirement of the recent ISO standard is that there be a regular brand evaluation. Marketers should actively manage and monitor brands, which are a key asset for many firms.

Brand Equity Ten (Aaker) David Aaker, a marketing professor and brand consultant, highlights ten attributes of a brand that can be used to assess its strength: differentiation, satisfaction or loyalty, perceived quality, leadership or popularity, perceived value, brand personality, organizational associations, brand awareness, market share, and market price and distribution coverage. Aaker doesn’t weight the attributes or combine them in an overall score, as he believes any weighting would be arbitrary and would vary among brands and categories. Rather, he recommends tracking each attribute separately.

Brandasset Valuator (BAV Group/VSLY&R)

BrandAsset^® Valuator is a tool for diagnosing the power and value of a brand by surveying consumers’ perspectives along four dimensions:

• Differentiation: The defining characteristics of the brand and its distinctiveness relative to competitors

• Relevance: The appropriateness and connection of the brand to a given consumer

• Esteem: Consumers’ respect for and attraction to the brand

• Knowledge: Consumers’ awareness of the brand and understanding of what it represents

BAV Group/VSLY&R maintains that these criteria reveal important factors behind brand strength and market dynamics. For example, although powerful brands score high on all four dimensions, growing brands may earn higher grades for differentiation and relevance than for knowledge and esteem. Fading brands often show the reverse pattern, as they’re widely known and respected but may be declining toward commoditization or irrelevance (see Figure 4.4).

The BrandAsset^® Valuator is a proprietary tool, but the concepts behind it have broad appeal. Many marketers apply these concepts by conducting independent research and exercising judgment about their own brands relative to the competition. Leon Ramsellar of Philips Consumer Electronics, for example, has reported using four key measures in evaluating brand equity and offers sample questions for assessing them:⁴

• Uniqueness: Does this product offer something new to me?

• Relevance: Is this product relevant for me?

• Attractiveness: Do I want this product?

• Credibility: Do I believe in the product?

Clearly, Ramsellar’s list is not the same as the BrandAsset^® Valuator, but the similarity of the first two factors is hard to miss.

Data Sources, Complications, and Cautions

The methods described previously represent experts’ best attempts to place a value on a complex and intangible entity. Almost all of the metrics in this book are relevant to brand equity along one dimension or another.

Related Metrics and Concepts

Brand strategy is a broad field and includes several concepts that at first may appear to be measurable. Strictly speaking, however, brand strategy is not a metric.

Brand Identity: This is the marketer’s vision of an ideal brand—the company’s goal for perception of that brand by its target market. All physical, emotional, visual, and verbal messages should be directed toward realization of that goal, including name, logo, signature, and other marketing communications. Brand Identity, however, is not stated in quantifiable terms.

Brand Position and Brand Image: These metrics refer to consumers’ actual perceptions of a brand, often relative to its competition. Brand Position is frequently measured along product dimensions that can be mapped in multidimensional space. If measured consistently over time, these dimensions may be viewed as metrics—as coordinates on a perceptual map. (See Section 2.7 for a discussion of attitude, usage measures, and the hierarchy of effects.)

Product Differentiation: This is one of the most frequently used terms in marketing, but it has no universally agreed-upon definition. More than mere “difference,” it generally refers to distinctive attributes of a product that generate increased customer preference or demand. These are often difficult to view quantitatively because they may be actual or perceived—as well as non-monotonic. In other words, although certain attributes such as price can be quantified and may follow a linear preference model (that is, either more or less is always better), others can’t be analyzed numerically or may fall into a sweet spot, outside of which neither more nor less would be preferred (the spiciness of a food, for example). For all these reasons, Product Differentiation is hard to analyze as a metric and has been criticized as a “meaningless term.”⁵

Purpose: To understand the relative preferences of consumers for product attributes.

Conjoint analysis is a method used to estimate customers’ preferences, based on how customers weight the attributes on which a choice is made. The premise of conjoint analysis is that a customer’s preference between product options can be broken into a set of attributes that are weighted to form an overall evaluation. Rather than ask people directly what they want and why, in conjoint analysis, marketers ask people about their overall preferences for a set of choices described on their attributes and then decompose those into the component dimensions and weights underlying them. A model can be developed to compare sets of attributes to determine which represents the most appealing bundle of attributes for customers.

Conjoint analysis is a technique commonly used to assess the attributes of a product or service that are important to targeted customers and to assist in the following:

• Product design

• Advertising copy

• Pricing

• Segmentation

• Forecasting

Construction

Conjoint Analysis/Choice Modeling: A method of estimating customers’ preference by assessing the overall preferences or choices customers assign to alternative options.

An individual’s preference or choice can be expressed as the total of his or her baseline preferences for any option, plus the partworths (relative values) for that choice expressed by the individual.

In a linear form, this can be represented by the following formula:

Conjoint Preference Linear Form(I)=[Partworth of Attribute 1 to Individual(I)*Attribute Level(1)]+[Partworth of Attribute2 toIndividual(I)*Attribute Level(2)]+[Partworth ofAttribute3 to Individual(I)*Attribute Level(3)]and so on

Ideally, if the partworths are measured correctly, by assessing the partworth of a feature relative to the corresponding partworth for price, it would be possible to measure a willingness to pay (WTP) for a feature or level of a feature.⁶

Data Sources, Complications, and Cautions

Prior to conducting a conjoint study, it is necessary to identify the attributes of importance to a customer. Focus groups are commonly used for this purpose. After attributes and levels are determined, a typical approach to conjoint analysis is to use a fractional factorial orthogonal design, which is a partial sample of all possible combinations of attributes. This reduces the total number of choice evaluations required by the respondent. With an orthogonal design, the attributes remain independent of one another, and the test doesn’t weigh one attribute disproportionately to another.

There are multiple ways to gather data, but a straightforward approach would be to present respondents with choices and to ask them to rate those choices according to their preferences. Those preferences then become the dependent variable in a regression, in which attribute levels serve as the independent variables, as in the previous equation. Conjoint utilities constitute the weights determined to best capture the preference ratings provided by the respondent.

Often, certain attributes work in tandem to influence customer choice. For example, a fast and sleek sports car may provide greater value to a customer than would be suggested by the sum of the fast and sleek attributes. Such relationships between attributes are not captured by a simple conjoint model unless one accounts for interactions.

Ideally, conjoint analysis is performed on an individual level because attributes can be weighted differently across individuals. Marketers can also create a more balanced view by performing analysis across a sample of individuals. It is appropriate to perform the analysis within consumer segments that have similar weights. Conjoint analysis can be viewed as a snapshot in time of a customer’s desires. It will not necessarily translate indefinitely into the future.

It is vital to use the correct attributes in any conjoint study. People can only tell you their preferences within the parameters you set. If the correct attributes are not included in a study, while it may be possible to determine the relative importance of those attributes that are included, and it may technically be possible to form segments on the basis of the resulting data, the analytic results may not be valid for forming useful segments. For example, in a conjoint analysis of consumer preferences regarding colors and styles of cars, one may correctly group customers as to their feelings about these attributes. But if consumers really care most about engine size, then those segmentations will be of little value.

Purpose: To identify segments based on conjoint utilities.

As described in the previous section, conjoint analysis is used to determine customers’ preferences on the basis of the attribute weightings that they reveal in their decision-making processes. These weights, or utilities, are generally evaluated on an individual level.

Segmentation entails the grouping of customers who demonstrate similar patterns of preference and weighting with regard to certain product attributes, distinct from the patterns exhibited by other groups. Using segmentation, a company can decide which group(s) to target and can determine an approach to appeal to the segment’s members. After segments have been formed, a company can set strategy based on their attractiveness (size, growth, purchase rate, diversity) and on the company’s capability to serve these segments, relative to competitors.

Construction

To complete a segmentation based on conjoint utilities, one must first determine utility scores at an individual customer level. Next, one must cluster these customers into segments of like-minded individuals. This is generally done through a methodology known as cluster analysis.

Data Sources, Complications, and Cautions

As noted previously, a customer’s utilities may not be stable, and the segment to which a customer belongs can shift over time or across occasions. An individual might belong to one segment for personal air travel, in which price might be a major factor, and another for business travel, in which convenience might become more important. Such a customer’s conjoint weights (utilities) would differ depending on the purchase occasion.

Determining the appropriate number of segments for an analysis can be somewhat arbitrary. There is no generally accepted statistical means for determining the “correct” number of segments. Ideally, marketers look for a segment structure that fulfills the following qualifications:

• Each segment constitutes a homogeneous group, within which there is relatively little variance between attribute utilities of different individuals.

• Groupings are heterogeneous across segments; that is, there is a wide variance of attribute utilities between segments.

Purpose: To use conjoint analysis to project the market share and the sales volume that will be achieved by a product or service.

Conjoint analysis is used to measure the utilities for a product. The combination of these utilities, generally additive, represents a scoring of sorts for the expected popularity of that product. These scores can be used to rank products. However, further information is needed to estimate market share. One can anticipate that the top-ranked product in a selection set will have a greater probability of being chosen by an individual than products ranked lower for that individual. (This is one method to estimate market share.) Adding the number of customers who rank the brand first should allow the calculation of customer share.

Data Sources, Complications, and Cautions

To complete a sales volume projection, it is necessary to conduct a full conjoint analysis. This analysis must include all the important features according to which consumers make their choice. Defining the “market” is clearly crucial to a meaningful result.

To define a market, it is important to identify all the choices in that market. Calculating the percentage of “first choice” selections for each alternative merely provides a “share of preferences.” To extend this to market share, one must estimate (1) the volume of sales per customer, (2) the level of distribution or availability for each choice, and (3) the percentage of customers who will defer their purchase until they can find their first choice.

The greatest potential error in this process would be to exclude meaningful attributes from the conjoint analysis.

Network effects can also distort a conjoint analysis. In some instances, customers do not make purchase decisions purely on the basis of a product’s attributes but are also affected by its level of acceptance in the marketplace. Such network effects, and the importance of harnessing or overcoming them, are especially evident during shifts in technology industries.

Further Reading

Aaker, D. A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name, Free Press.

Aaker, D. A. (1996). Building Strong Brands, Free Press.

Aaker, D. A., and J. M. Carman. (1982). “Are You Overadvertising?” Journal of Advertising Research, 22(4), 57–70.

Aaker, D. A., and K. L. Keller. (1990). “Consumer Evaluations of Brand Extensions,” Journal of Marketing, 54(1), 27–41.

Ailawadi, Kusum, and Kevin Keller. (2004). “Understanding Retail Branding: Conceptual Insights and Research Priorities,” Journal of Retailing, 80(4), 331–342.

Ailawadi, Kusum, Donald Lehman, and Scott Neslin. (2003). “Revenue Premium as an Outcome Measure of Brand Equity,” Journal of Marketing, 67(4), 1–17.

Bruno, Hernan A., Unmish Parthasarathi, and Nisha Singh, eds. (2005). “The Changing Face of Measurement Tools Across the Product Lifecycle,” Does Marketing Measure Up? Performance Metrics: Practices and Impact, Marketing Science Institute, No. 05-301.

Rangan, V. Kasturi, and Marie Bell. (1995). Nestle Refrigerated Foods: Contadina Pasta & Pizza (A). Harvard Business School.