Benefit impact analysis

Editor's note: Ed Cohen is president of Survey Perspectives Inc., Baldwin, N.Y.

With the advent of conjoint analysis and other sophisticated modeling techniques, considerable progress has been made in giving management the kind of information it needs to make tactical and strategic decisions about a product or service. These decisions are based on evaluating numerous and complex marketing issues such as competitive frame, brand positioning, product design, and packaging and pricing -- each with its own almost bewildering array of alternatives.

It is beyond the intent and scope of this article to discuss the many very useful techniques available today. Rather, we will outline one relatively simple technique, benefit impact analysis, for exploring a series of product elements that produces a measure analogous to conjoint's utility values in circumstances where a standard conjoint analysis may not be possible.

Application

Benefit impact analysis warrants consideration in any of the following situations:

• As a preliminary to a conjoint study to help define the range of variables, such as quantity, size, capacity, price, etc., to be included in the conjoint matrices.
• Where variables cannot be precisely quantified. For example, a discrete value can be assigned to price, quantity, certain physical attributes, interest payout levels and others. Inches, ounces, dollars, cents and primary colors are concrete and readily understood by consumers. On the other hand, many sensory variables are less clearly quantifiable in terms that respondents comprehend. These might include such elements as "degree of softness," "strength of fragrance," and "carbonation level."
• In cases where, for any reason (such as low incidence categories or market targets), personal interviews may be prohibitively costly, BIA data may be collected by telephone.
  The following case history illustrates one application of BIA in a situation involving both easily quantified and more qualitative types of variables. This particular study was done with personal (central location) interviews.

Study background

The client, a manufacturer of household paper products, was battling several strongly competitive brands, some of which were uniquely positioned and continually chipping away at the company's brand share. To thwart the erosion of brand share, management felt it necessary to modify its own brand in some way and considered four possibilities. Each of the alternatives would have some impact on the others and confusion reigned.

The attributes

Four variables relating to the category were candidates for modification: quantity per package, price per package, product absorbency, product softness.

The first two, quantity and price, are clearly definable in precise terms easily understood by consumers. Absorbency and softness are not. Think about what 10% softer means to the average respondent. We decided after discussions with the client that, although imperfect and admittedly still ambiguous, respondents would relate more easily to purely verbal descriptors, e.g., a little softer, a lot softer.

Method

The BIA technique was utilized to determine the relative appeal of hypothetical modifications in the four product benefit areas. Two levels for each benefit were considered:

Quantity

-25 more per package
-50 more per package

Price

-5¢ less per package
-10¢ less per package

Softness

-A little softer
-A lot softer

Absorbency

-A little more absorbent
-A lot more absorbent

Each benefit level or option was paired with every other in the array, except that the two options within benefits were not paired for obvious reasons, e.g., 5 cents less vs. 10 cents less. Thus, there were 24 "cross-benefit" pairs.

Note that one is limited to a relatively few variables, since the number of combinations (pairs) increases dramatically as we add benefits and/or levels. For example, the addition of a fifth benefit, maintaining two levels for each, yields a total of 40 cross-benefit pairs. Adding one level to each of the four benefits produces 54 such pairs. In both cases, respondent judgments are likely to become fuzzy long before the final few choices are made.

Respondents were presented with the series of 24 benefit/level pairs on a rotated basis, and given the following instruction:

"Please read each pair of alternatives and select the one choice you would prefer over the other, according to which you personally would rather have in your (product category)."
Respondents then made their selections on a self-administered basis. Had the study been conducted by telephone, instructions would have been modified to accommodate the reading of each pair by interviewers to elicit verbal choices.

BIA analysis

A. Share of preference. The analytic model calculates a "share of preference" for each of the eight benefit levels, along with statistical significance of the differences among the respective items.

Among the eight alternatives, the one with the greatest impact is the 10-cent reduction in price (15.12 share), while a close second position is held by 50 more per package (14.70 share). Clearly, the desire for economy is stronger than qualitative considerations, but these data suggest substantial absorbency improvements are likely to induce greater interest in the brand than more modest changes in quantity or pricing.

B. Benefit leverage. Using share of preference data we can answer the following type of question:
"What is the relative leverage value (or elasticity) of each type of benefit investigated?"
A simple calculation provides an estimate of the leverage/elasticity value for each of the benefit areas.

Exhibit B
Benefit Impact Scores

As shown in Exhibit B, leverage seems to be greatest for price, followed by quantity. This may be interpreted to mean that consumers are more sensitive to these benefits than to the others. Absorbency, while intrinsically important to the category, offers more modest leverage value, possibly because most brands in the category offer at least acceptable absorbency benefits. Softness, too, at the bottom of the benefit share hierarchy, seems to be meeting consumers' basic expectations and offers the least opportunity for marketing leverage.

Summary

BIA offers the researcher a fairly simple but useful technique which estimates the relative consumer appeal of certain changes in product attributes/benefits. It also provides a reading of the relative impact of benefit variables.

The potential applications of BIA are not limited solely to products, nor is the method limited to personal interviewing. The technique is quite versatile and warrants consideration in working towards a solution for your next configuration problem, be it for a new or established product or service.