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In test marketing a new food product, low price and high advertising led to greatest sales, but high price and low advertising gained as much immediate profit. A Case for Test Market Experiments Gerald J. Eskin Test markets of new products typically use a single marketing plan which does not provide information on how marketing variables, such as pricing and advertising, affect performance of the new product. The single plan approach implicitly assumes that the new product decision-maker knows how marketing variables affect sales and that he has already taken this into account. The knowledge to make such decisions is clearly not forthcoming from published sources. It is in neither textbooks nor journals. A review of the Journal of Marketing and the Journal of Advertising Research for the five-year period ending December 1973 revealed no marketplace studies for new products in which the tools of the marketer were experimentally manipulated in order to learn their effects. Knowledge from experiments is, of course, not the only possible source of information, but it is one of the most defmitive ones. The basis for new product marketing decisions must then rest either on proprietary information or on the judgment and qualitative experience of decision-makers. Informal discussions with research suppliers and market research directors lead me to believe that although a number of companies have done some quantitative work on the role of marketing variables in new product introduction, most of it does not involve well-controlled experimentation. Only a small number of companies have tried experimental methods in a test market situation to learn about major components of the marketing plan. Of those companies that have done "variable tests" {i.e., experiments), most have done so only once or twice and have tested only one variable in each such test. Given this apparent lack of information about these relationships and the obvious fact that such information is needed for proper decision-making, one may question the popularity of the predetermined single plan mode of research and wish to assess the desirability of an alternative strategy, such as the con- trolled variable test, to obtain information about various marketing alternatives. The controlled variable test appears to be an established alternative. Procedures for experimenting with different marketing variables have been well developed for years. Banks wrote an applied book on the subject in 1965. Becknell and Mclsaac (1963) produced a sound study of Teflon's response to advertising pressure in the early Sixties, and the agricultural economists have been systematically measuring the effects of such variables for at least 15 years {see Hoofnagle, 1963, for a review of this early work). Lipstein (1965) even suggested prototype designs appropriate for the new product situation. What then are some of the reasons for non-use? The following have been suggested: 1. All aspects of the marketing plan to be utilized in subsequent national introduction have been preselected by 27 Journal ofAdvertising Research the time the test market starts. Therefore alternatives need not be tested. vertising and price, affected product performance. 2. High quality information is required concerning the one plan to be used nationally. Introduction of major variables, like advertising expenditure level, into the test will only dilute sample size on each variable, hence reducing the quality of the reading. The Newfood Test Market 3. Variable testing constitutes an unreal situation. What is desired is a real world reading, not an artificial one that cannot be translated. A new convenience food suitable for use as a snack or a nutritionally balanced meal substitute was being contemplated for national introduction. Test marketing was undertaken with several objectives in mind. Sales data were sought to allow extrapolation through time and over markets in order to obtain first-year national performance 4. Such tests have not worked in the past because they have often gone out of control due to uncooperative retailers, distribution problems, or unforseen changes in the competitive environment. 5. Results ofsuch tests are unreadable. Either they show no differences between treatments, are statistically not significant, or are counter-intuitive. Clearly, many of these reasons have merit. Experiments do cost extra money, they don't always go as planned, and results are sometimes hard to interpret. But just as clearly, the extra cost may be well worth it, many problems can be anticipated and dealt with in advance, and imperfect data often are still informative. In a practical sense the issues remain: Is it true that our facts or judgments specify enough knowledge for marketing new products? Are the five objections to experimentation sound? Are alternative experiences possible? What follows is a report on a controlled-variable test conducted during the test marketing of a new product in which attempts were made to determine how two major marketing tools, ad28 Gerald J. Eskin is an associate professor of marketing at the University of Iowa. Formerly he taught at the Graduate School of Business, Stanford University, and, prior to that, he was manager of marketing methods and of mathematical and behavioral research at the Pillsbur; Company in Minneapolis. He received his Ph.D. in economics and econometrics from the University of Minnesota. Dr. Eskin has published various articles on new product test marketing, forecasting, and pianning in the Journal of Marketing Research and eisewhere. He is a co-author of two books on Cases in Computer and Model Assisted Marketing. He is currentiy on the editorial board of the Journal of Marketing Research. projections. Information was also sought concerning possible differential responses to several aspects of the introductory marketing plan, the most important of which were variations in price and level of advertising expenditure. Design and Preliminary Analysis A controlled store test was conducted for six months in four test markets. Three levels of price were tested: a base price below 50 cents, a price 10 cents above the base, and a price 20 cents above it. A sample of 30 stores within each of the test cities was split into three matched panels of 10 stores. Each of these matched panels received one of the three price treatments. Variations in advertising expenditures were tested across the four cities, with two cities receiving a low weight plan and two receiving a high weight plan. The high weight treatment was to involve double the estimated gross impressions per 1,000 households as did the cities which received the low weight treatment. (However, later analysis revealed that the actual impressions generated differed by only 70 per cent.) The proportion of total expenditures allocated to each media class was the same in ail four test markets. All schedules were dominated by television. Advertising expenditure allocations over time were also matched, all involving front loading of expenditures in time with one-half of all test expenditures occurring within the first two months of the test market period. Figure 1 shows the overall research design. The principal dependent measure of interest was unit sales per store per month. This measure was selected in preference to market share because the boundaries of the market for this new product were not known. Various measures on extraneous factors which could conceivably affect test out-comes were also taken. These included several Volume 15, Number 2. April 1975 Figure 1 Low Weight Adwrtising: Vfarftcf 2 \iarket t •*rice: Base N= (10) +10c. (10) High weight +20c. (10) Base (10) +lOe. (10) Market _ +20c. (10) Base (10) +IOc. (10) Market +20c. (10) Base (10) +10c. (10) +20c. (10) Month 1 2 3 4 5 b Total No. of observations = 720 dep measure = toial sales/per store/by month store size measures, measures characterizing the trading area each store served, and monitoring of various instore conditions. These control measures and procedures indicated that all factors were well balanced and, hence, not included in the subsequent analysis, except the following: all commodity volume (ACV—the dollar sales for all items sold in each store); average family income of the trading areas served by each store (I); and number of facings (F), a measure of the amount of shelf space given to the test product. These variables were all significantly correlated with the dependent variable or with the experimental treatment; thus they were potentially important covariates. Table I presents the correlations among the experimental treatments, the covariates, and the dependent variable. Note first that the correlation between actual price and advertising is very small (.012). This suggests that the original design specification requiring orthogonal (independent) treatments was largely achieved—i.e., out of design conditions were minimal. Row 1 of the table displays the correlations between the sales of the new product, the experimental treatments, and the covariates. In the subsequent analyses, the effects of the covariates on the dependent variable are adjusted out using standard regression procedures. Inspection of Row 3 of the table reveals an important and unexpected problem. The experimental advertising treatment is strongly correlated with two of the covariates. Because of this, it is impossible to interpret its relationship to sales. It is not clear whether advertising has no effect on sales (as suggested by the —.005 correlation in Row 1) or whether it does have an effect, but it is being hidden by the influence of the covariates. Subsequent analysis suggested that an imbalance in sample selection had occurred. Large stores in high income neighborhoods tended to be located in the geographic areas receiving the lower advertising treatment while the small stores in the lower income neigh- borhood tended to be located in areas which received the high advertising treatment. The fact that large stores (high ACV) tended to be found in high income areas (r = .35) compounded the problem. The effects of price, on the other hand, are not greatly confounded by the effects of the covariates (see Row 2). Considering these covariates, in this case, may improve the estimates of the effects of price, but it will not change the interpretation of how price is related to sales. Before analysis can proceed, a technique is needed which eliminates the dependence between the advertising treatment and the covariates without disturbing the independence of the price treatment. Recall that, in constructing the initial design procedures, it was not possible to control, a priori, for the differential effects of market share because of the difficulty in defining the relevant market. In this study, it turned out that All Commodity Volume provided an acceptable substitute for this variable, given that the primary dependent variable was total unit sales per store per month. When this original variable is adjusted for the appropriate ACV, a new dependent variable (Sales/ACV) is obtained. This variable seems to allow for a reasonable interpretation of the effects of the advertising treatment since it is not affected by differences in store size Table 1 Correlations Between Treatments, Covariatra* and Dependent Measures Sales Price Treatments Price Adv -.397 -.005 .012 Adv Covariates Facings .029 .006 -.157 Facing ACV .586 -.043 -.271 -.005 ACV Sales/AVC -.419 .234 -.014 -.070 Income .262 -.059 -.462 .057 .351 .043 29 Journal ofAdvertising Research (ACV) which exist between the advertising treatments. It is adjusted in sueh a way that the effects of these differences are eliminated. The last row of Table 1 indirates the correlation of the treatments and covariates with this new dependent variable, Sales/ACV. Note that the correlation with price is changed very little but that the correlation with advertising is now significant and positive (.234). Sales/ACV also shows relatively low intercorrelation with the various covariates. In the analysis which follows, Sales/ACV is the dependent variable to be explained by the two experimental variables and the two remaining covariates. Principal Regression Results In the regression work each of the 720 store-period combinations is taken as an observation point. Sales divided by the store size measure (ACV) is treated as the dependent variable. This variable is regressed against a set of measures of the independent variables, each of which is treated as a continuous variable. The regression procedures allow estimation and statistical testing of (1) the main effects of the price and advertising treatments, (2) interactions between these treatments, (3) Interactions between the treatments and time, (4) the effects of the covariates, and (5) seasonal and trend factors. Table 2 displays the average Sales/ AVC values corresponding to the various treatments averaged over the entire six months, adjusted for the control factors (income and number of facings). Entries in the table are obtained by first averaging the raw data by cell, adjusting for control factor imbalance by use of the regression coefficients, and then normalizing by dividing all entries by the average for the base price-low advertising plan. This final normalization has the dual effect of protecting the proprietary nature of the raw data and 30 Table 2 Average Sales/ACV by Experimental Condition Adjusted for Income and Facing Differences Across Trading Areas ^""•^..^^/l dvertising ^ \ Price Base Price +10<t. +204. Average over aii prices Low-weight plan High-weight plan Average over both advertising plans 100 79 64 150 106 71 125 93 81 109 ^^--^ allowing users to easily determine the per cent deviations from the low price-low advertising base plan. The marginal totals indicate the main effects of the treatments. The high advertising weight plan produces sales of 109 compared to a low weight plan of 81. Literally interpreted, a highly significant (t = 9.1) 35 per cent increase is attributable to the advertising weight differential. Price increase, on the other hand, tends to substantially reduce Sales/ACV. The dependent measure falls by 32 points when the price changes from the base to 10 cents higher, and by an additional 25 points from this price to the highest price. Again the linear effect is significant (t = 3.25). Although significant main effects are present, they do not adequately describe the structure of the situation. Examination of the individual cells in Table 2 shows a pattern where the effectiveness of advertising to attract buyers depends on the price charged. At the base price, the high advertising plan produces sales 50 per cent above the low plan. At the middle price this percentage slips to 34 per cent and at the highest price the high weight plan is ahead by only 11 per cent. Conversely, looking at the same data in terms of responsiveness to price, we see substantially more sensitivity to price under the high advertising plan than under the low one. The structure of this interaction is 68 illustrated in Figure 2, "*" represents the means from Table 2 and the solid line the regression fits. As can be seen, the linear model fits the data well although the deviations suggest a slight tendency toward the type of curvilinear line that would be associated with a constant elasticity model. In the terms of a constant elasticity model (i.e., per cent change in sales/per cent change in price) the price elasticity at the low weight advertising plan is -1.0, while under the high weight plan the price elasticity is -2.5. This interaction effect is statistically significant (t = 6.65). Table 3 shows the impact of pricing and advertising over time. Part A of the table displays the ratio of Sales/A VC under the high advertising weight plan to Sales/ACV under the low weight plan for each time period (months). Again the data have been adjusted by the covariates. This affects the levels of the ratios but not the trends. Here we see a clear tendency for the ratios to be larger in the early months of the test and substantially smaller in later months. The linear component of this trend, as measured by the regression model, is statistically significant (t = 4.19). Ratios of Sales/ACV at the two higher prices to Sales/ACV at the base price are exhibited in Part B of Table 3. Again, covariate adjustments have been made that affect levels but not trends. Although there is a slight tendency for Volume 15, Number 2. April 1975 the later ratios to be smaller than the earlier ones, indicating growing price sensitivity, the linear component of this trend is not statistically significant (t = .29). Discussion Conclusion for the Product Tested. Based on the data analysis, both advertising and price were important determinants of the sales levels achieved for the new food product, at least for their first six months in markets. However, sales differentials due to price sustained themselves over time while the advertising-induced differentials did not. Finally, the effectiveness of advertising as a sales-inducing factor depended on the price charged, and conversely. Although the preceding statements all follow from the analysis, they vary in the degree of credence that should be associated with each. Interpretive ambiguity also varies. The results concerning price are by far the strongest being based on a design in which all prices were tested within the same city and replicated over four cities. The various store characteristic imbalances are not large with respect to the price variable, and the statistical errors (standard error of coefficients) are small compared to coefficient sizes. These facts lead us to place high credence in the assertion of large stable differentials due to price. Increasing price from the lowest to the highest was estimated to reduce sales by some 36 per cent under the low weight advertising plan, and by 53 per cent under the high weight plan. The validity of the conclusions concerning advertising are closely tied to the appropriateness of sample imbalance adjustment procedures via the (Sales/AVC) normalization and its relations to the covariance analysis procedure. Although adjusting for store size differences seems appropriate, it must be noted that raw sales data did not Table 3 Part A Ratios of Sales Response Under High Advertising Weight to Sales Response Under Low Weight Plan by Month (Adjusted for ACV, Income and Facing Differences) 1 2 3 4 5 1.43 1.48 1.48 1.27 1.17 Month Ratio 6 1.19 PartB Ratio of Sales Response at 10c. and 20c. Price Inereases to Base Price by Month (Adjusted for ACV, Income and Facing Differences) Month 1 2 3 4 5 Ratio at+10<t. .80 .75 .81 .76 .76 Ratio at +2(H. .59 .53 .61 .54 .52 exhibit an advertising effect. Nevertheless, there seems to be support for the assertion that the high weight advertising program resulted in sales increases of about 35 per cent over the entire six-month period and that this differential diminished with time. Under the highest price conditions, the differential has entirely disappeared by the end of the test. However, the con- 6 .69 .47 servative reader may legitimately object to this conclusion. Interpretations of the advertisingtime interaction is especially difficult because the distribution of spending for advertising over the test period was not uniform. The time effect could be attributable either to wearout—that is, advertising becoming less effective over time—or to the decrease in advertising Figure 2 Demand at Various Combinations of Advertising Spending and Price Levels 1.5 1.4 Mean Response Regression Line 1.3 1.2 1.1 1.0 .9 With low ADV .7 .6 Base Price +204. Prices 31 Journal of Advertising Research expenditures during the later periods. These alternatives appear to be observationally equivalent, hence all that can be said is that a front-loaded program of the type used here has the effect of producing large sales differentials in the early portion of the test markets and smaller ones later on. With respect to the evidence for a negative interaction between price and advertising, it is strong if one grants the appropriateness of the Sales/ACV measure. TTiis result is, of course, inconsistent with the conventional wisdom of classical economics in which the role of advertising is to reduce price elasticity, hence allowing higher prices to be charged. On the other hand, many marketers have always implicitly assumed the market works in the manner specified here. Grocery retailers who advertise only when there is a special price must believe in a negative priceadvertising interaction. For the new food product, this interaction has particular importance when the sales results are translated into profits, as shown in Figure 3. Here we see that about the same peak profits are possible with high advertising as with low. In the case of the low spending plan, a high price is required to generate profits through margin effects. In the case of high advertising expenditures, a low price will interact with the advertising to produce larger volumes that are profitable even at the lower unit margins. It should be noted in passing that before this test was conducted the best candidates for a national plan were based on the belief that the role of high prices is to create margins to allow for advertising. Thus, the promising alternatives were thought to be either high price-high advertising, or low pricelow advertising. These research results suggest quite different alternatives, namely: low price-high advertising to build a market or high price-low advertising to build margin. Figure 3 ProHt Curves as a Function of Price Conditional on Various Advertising Weight Plans Profit Index 1.0 Base Price Implications for Test-Market Planning. The objectors to experimental manipulation of marketing variables in test markets claim that (a) the technique is not needed, or (b) it does not work. In the case of Newfood, the technique was clearly needed. It was not needed as viewed before the test when there was a feeling that the best marketing plans were known, but as viewed after the test when empirical results established that new alternatives were superior to those originally contemplated. For example, if the conventional test market had been conducted using the high price-high advertising plan, expected sales results would have been about 53 per cent below the sales maximizing results obtainable under the low price-high advertising strategy. The conventional test alternative would not have generated expected revenues sufficient to warrant national introduction of the product. Without knowledge of how marketing variables work, test market failure would probably have been attributed to lack of product appeal or poor copy. in planning for the subsequent introduction of Newfood, the possibility of utilization of a price-advertising plan different from any of those tested arose, ln the eventual introduction, such a plan was, in fact, utilized. The evaluation of these non-tested alternatives constituted a problem in extrapolation requiring utilization of such curves as those in Figure 2 and 3. Such procedures are risky, but at least possible, given a properly executed experimental variable test. In the conventional single plan test, no information is forthcoming on this issue at all. Turning to the question. Do such variable tests work?, in the Newfood case, the test worked. It worked in the sense that no fatal flaw was apparent in the design or execution of the test and subsequent analysis produced plausible and statistically significant results. It also worked in the sense that a management team accepted the outcome of the project and utilized it, along with other information, in making a "go/nogo" decision concerning product introduction and in developing the eventual marketing program. Having produced a successful example, one is tempted to conjecture about why this was so and the conditions under which the experience can be replicated. Volume 15. Number 2. April 1975 In my view, the answer is pedantic and obvious. The successfulness of such experiments is most closely related to the degree to which the tests satisfy the wellknown basic principles of experimental design. These include: * Selection of measurably different treatments. Here advertising programs are being tested that are similar in all respects but one—the differences in planned weight is two to one, not a realistic 10 or 20 per cent. Such large differences are necessary to produce noticeably different results and to offset any convergence which might occur when the research is actually executed in the field. (In the case of Newfood, the actual difference in advertising levels, was more like 1.7 to 1 rather than the planned two to one.) * Replication is used. Each treatment condition is tested in many stores and in at least two cities. This allows both measurement of error and reduction of error. Still more cities would be desirable. * Measurement of treatments and extraneous factors. In test marketing it Services • Focused group moderating • Individual depth interviewing • Executive interviewing • One-way mirror facility rental • Respondent recruiting • Testimonial commercial recruiting • Custom studies • Analysis • Local/nationwide coverage • Complete package or any component Areas of Research Specialization • Basic motivational, behavioral, life-style and attitudinal research • Product category idea generation— problems, benefits, attributes, associations—for creative insight or to provide consumer language for quantitative studies • Determining the problems of target consumers and defming opportunities must be assumed that something will go wrong. Tests go "out of design" and outside factors (like competition) create imbalances; this test was no exception. In the present instance a measurement system existed to identify and quantify such problems. (See discussion of the preliminary analysis.) This made adjustment at the analysis stage possible. • Use of designs that fit the problem. If advertising performance depends on price, then the design must allow measurement of this, such as through the orthogonal design described here. If time matters, tests must be run long enough for all trends to develop or special designs built for this purpose must be used. • Appropriate analysis methods. Perfectly executed designs are amenable to simple analysis but imperfect tests are in fact the rule. In such cases, advanced analysis procedures are required to remove those extraneous effects. Finally, the role of the experience factor must be noted. As in the marketing process, experience counts in the successful utilization of research methodology. The Newfood test market was not the first variable test conducted by the makers of Newfood. It is a reasonable expectation that the probability of obtaining useful results increases with the number of such tests previously completed, and with one's willingness to inquire aboDt and take into account the experience of others. References Banks, Seymour. Experimentation in Marketing. New York: McGraw-Hill, 1965. Becknell, James C. and Robert W. Mclsaac. Testing Marketing Cookware Coated with Teflon. Journal of Advertising Research. Vol. 3, No. 3, pp. 2-8. Hoofnagle, William S. The Effectiveness of Advertising for Farm Products. Journal of Advertising Research. Vol. 3, No. 4, pp. 2-6. Lipstein, Benjamin. The Design of Test Marketing Experiments. Journal of Advertising Research, Vol. 5, No. 4, pp. 2-7. • Quiet central air conditioning • Experienced staff and operators to meet your research needs Respondent Recruiting Service • Low or high incidence/income • Blue collar/professionals Unique Group Session Facility • Ethnic groups • Convenient mid-Manhattan location • Fresh or highly verbal respondents • Relaxed in-home atmosphere • Urban/suburban—local/nationwide • Inconspicuous one-way mirrors Real People Search • Conducive surroundings for • Locating, screening and interviewing post-session discussions for testimonial commercials • Full kitchen facilities for taste tests and • Complete service nationwide/ refreshments international/local • Custom engineered sound and recording equipment SID SHAPIRO • Videotaping and all audio/visual TOM DALE equipment available I(.OEa>l 4Stli Street • Observer-moderator intercom New York. 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