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Identifying industrial new product success: Project NewProd
Identifying Industrial New Product Success: Project NewProd RobertG. Cooper What is the key to success in industriul product innovution? This question is,f~equcntly posed, und many authors und munugers huve speculuted us to which critical fuc.tors or vuriables decide thr ,futr of new industrial products. What is missing in the debate is evidox~r bused on uctuul new product su~~~~csses and jtiilures. Project NovProd is un investigutinn thut KYIIS designed to fill this void. In this urticlc we report the results of u study into a large number of successful and unsuc~~essful IIPK products, project NewProd, whose goal was to ident(fi the determinants of commercial success in industriul product irlnovution.
An important task facing any corporation developing new products is to identify new business opportunities that the firm might exploit. An equally important point is to determine what the firm can do to improve the chances of commercial success for those new product projects it decides to invest in. These two issues-how to identify the potential “winners” and how to improve the odds of success-lead to the focal issue of our research, namely, to determine which characteristics discriminate between
Address correspondence ment, McGill University. H3A IGS.
to: Dr. Robert G. Cooper, Faculty of Manage1001 Sherbrooke West. Montreal PQ. Canada
commercially successful and unsuccessful industrial new products. Relatively few investigations have dealt with the question of differentiating successful and unsuccessful new products. The most notable exception is project Sappho I1 I, a British study, which concluded that successful innovators were found to have a much better understanding of user needs, paid more attention to marketing, performed development work more efficiently, made more use of outside advice, and were persons with greater management authority. Hungarian, Finnish, and West German studies, similar to Sappho, also yielded consistent results. The only research in North America that comes close to reporting on success-failure discriminators was one conducted by Rubenstein [4], where managers suggested a long list of facilitators and barriers to product success but the researchers concluded that there was little consistency in the effect of many of these on venture outcomes. These investigations into new product outcomes, beginning with Sappho in 1972, represent pioneer work in the field. But project NewProd differs from its predecessors in several important respects: 1. NewProd utilizes a large sample of companies new products, thereby permitting meaningful
and use
El
OUTCOME SUCCESS OR FAILURE
COMMERCIAL
ENVIRONMENT
ENTITY
NEW PRODUCT PROCESS THE MARKETPLACE
THE FIRM (resource
base)
Activities undertaken
ENVIRONMENT
THENATUREOFTHEVENTURE
J
A model of new product outcomes. The new product process yields a commercial entity, whose fate is determined in the marketplace. The entire process takes place within three environments. FIGURE 1.
of statistical analysis tools and the generalization of results. 2. NewProd is based on a conceptual model of product outcomes. This model was developed to identify relevant variables for investigation.
Ft. G. COOPER is the associate dean and director of the M.B.A. program in the faculty of Management, McGill University, Montreal. He is also an associate professor of marketing in the faculty and was former head of the marketing area group at McGill. He holds Bachelor’s and Master’s degrees in engineering (chemical) and an M.B.A. and Ph.D. in business administration.
3. NewProd identifies product discriminators using statistical methods to indicate barriers and facilitators. A MODEL
OF NEW
PRODUCT
OUTCOMES
A myriad of possible factors or variables exist that could be associated with successful versus unsuccessful new products. However, to identify those variables of interest in a logical and consistent fashion and group them according to some useful categorization scheme, a conceptual model was developed. This model is essentially a model of new product outcomes, where sets of variables that could influence outcomes are identified. The model is briefly described in the following paragraphs and is shown in Figure 1. 125
The new product outcome-success or failure-is determined by the interaction of the commercial entity with the marketplace (Figure 1, top). The commercial entity we define to be that entity with which the firm enters the marketplace-the product, its price, the launch and/or marketing strategy, and the production-service facilities that back up the launch effort. The commercial entity is the result of the new product process. This process is a stagewise sequence of goaloriented activities and information-gathering functions, beginning with idea generation, proceeding through various stages of market research and product development, and culminating in production start-up and market launch [2]. How these activities are undertaken and the nature of information that they yield are two sets of variables that manifest themselves in the commercial entity. The new product is developed and launched within a particular environment that could have a marked impact on the new product process. Three environments have been identified: (1) the firm’s external environment (i.e., the nature of the marketplace at which the new product is targeted), (2) the firm (i.e., the resource base of the firm and its compatibility with the new product venture’s requirements), and (3) the nature of the new product project itself (e.g., its source and relative magnitude). The project’s characteristics help shape the process activities and hence have a bearing on the eventual outcome. The model of new product outcomes (Fig. 1) has identified two broad categories of variables that have a potential impact on new product success. These are controllable variables and environmental descriptors. The controllable variables are those that describe the new product process and its output. The environmental variables describe the setting in which a new product is developed (see Table 1). COLLECTING
THE DATA
To determine which variables or characteristics differentiate successful from unsuccessful new products, a large number of actual successes and failures was investigated. A random sample of 177 firms was selected from among a listing of industrial companies known to be active in product development. ’ The companies were initially contacted by telephone, after which the appropriate manager was mailed a questionnaire. The questionnaire requested the manager to
IThe geographic
126
sample area was Ontario and Quebec,
Canada 131.
TABLE 1 New Product Outcome
Type Controllable
Environmental
Model Variables Varrable
Example
Element5 ofthe commercial entity
Whether or not the product was priced at less than competition
Proficiencies activities
How well the marketing research was undertaken (if at all)
of pr0ces.s
Nature of information acquired
Extent to which customers’ price sensitivities were known
The product’s
Degree of competition the market
marketplace
The firm (resource
The venture
base)
in
Degree to which the firm’s R&D skills were adequate to develop the product Innovativeness of the product relative to other products on the market
select two of his company’s typical new products that had been introduced to the market-+ne a commercial success and the other a failure. Several selection guidelines were provided as well as operational definitions of success and failure.* The respondent was then presented with a list of 77 statements that could describe the new product venture and its environment (based on the model outlined in Table 1). The manager was asked to indicate whether he agreed or disagreed with the statement as it pertained to that particular new product project on a 0- 10 scale. Finally the manager had to rate the degree of commercial success of the product.2 A telephone follow-up provided assistance and encouraged responses. Of the 177 firms initially sampled, 27 were disqualified; four were no longer in business or could not be contacted, and 23 indicated they simply did not have the new product failures or successes needed for discussion. Of the remaining sample of 150, a total of 103 replied to for an effective response rate of the questionnaire, 68.7%. The eventual sample numbered 102 successes and 93 failures.
‘Success and failure were defined from the point of view of the firm and in terms of profitability (i.e.. the degree to which a product‘s profitability exceeded or fell short of the minimum acceptable profitability for this type of project or investment, however the firm measured profitability). Because of selection errors that could result from difficulties or the use of this operational definition of success and failure, managers were asked to select products that were unmistakably successes and failures.
TABLE 2 Success-Failure
Impact of Variables
Existence of a mass market (as opposed to one or a few customers) Degree of need for products in product class Existence of a potential demand only (no actual market) Market size Market growth Degree of product homogeneity in the market Degree of competition Degree of price competition Number of competitors Existence of a dominant competitor Degree of loyalty to competitors’ products Degree of satisfaction with competitors’ products Frequency of new product introductions in the market Degree to which users’ needs change quickly in the market Extent of role of government in the marketplace
Describing
the Marketplace Slgnlflcance” of ANOVA
Mean” Score for Successes
Mean Score for Failures
Correlation Coefficient, f
5.33
5.13
NO
0.008
NO
7.40
5.78
A
0.329
A
3.37
3.53
No
6.41 6.43 6.26
5.80 5.27 6.1 I
No A No
0.108 0.221 0.015
No A No
7.17 5.88 4.75 5.57 4.79
7.29 6.41 5.45 5.87 4.93
No No No No No
0.002 -0.058 -0.061 -0.051 0.003
No No No No No
4.15
5.54
C
-0.141
C
3.23
3.67
No
~0.024
No
3.24
3.87
No
-0.122
No
4.45
3.62
No
-0.098
No
-0.029
Significance” of r
No
“Means are on a O-10 scale, where 0 is low and 10 is high (same footnote applies to Tables 3-7). “Significance test of analysis of variance: A indicates significance at the 0.001 level, B at the 0.01 level, and C at the 0.05 level (same footnote applies to Tables 3-7).
yielded generally consistent results. The results both methods are presented in Tables 2 to 8.
RESULTS How each descriptor variable helps to decide the fate of a new industrial product is outlined in the following paragraphs. Results are presented first for the environmental variables, over which the firm has relatively little control, and are followed by results for the controllable variables. Two statistical techniques were used to determine the strength of relationships between success/failure and descriptor variables: 1. Analysis of variance (ANOVA), which tests for the difference of the means of each descriptor variable for successful ventures versus failures. 2. The correlation coefficient, which measures the degree of correlation between the scaled success/ failure rating and each descriptor variable.3 Both methods are simple bivariate techniques,
and both
3The square of the correlation coefficient gives the percent variance of the degree of success or failure explained by the variations in the descriptor variable.
Environmental
from
Variables
Environmental variables do not play a critical role in deciding new product success. Environmental variables describe: (I) the marketplace, (2) the firm (resource base), and (3) the nature of the venture. These variables were anticipated to have a marked impact on the outcome of a new product venture. This expectation was based largely on the importance given to environmental variables in most predictive screening models. But the results of this research proved otherwise CTables 2-4). Of the 41 environmental variables originally thought to influence outcomes, only 15 were significantly related to success or failure, and only eight variables-less than one-fifth-were strongly related. Not only are two-thirds of the environmental variables unrelated to product outcome, but a review of Tables 2-4 reveals relatively low correlation coefficients even for the significant variables, with the maximum r equal to 0.37. These weak relationships can be contrasted to the very 127
TABLE 3 Success-Failure
Impact of Product-Firm
Compatablllty of the FolIowIng Resources In the Firm for Thts Product Project (0 = Poor; IO = High)
Mean Score for Successes
Mean Score for Failures
8.07 7.93 7.82 7.12
7.59 6.81 6.92 5.26
NO A B A
0.107 0.239 0.213 0.372
NO A A A
7.78 7.48 7.36
6.53 6.18 5.68
A C A
0.316 0.142 0.374
A C A
6.99
5.4x
A
0.305
A
Financial resources R&D skills and people Engineering skills and people Marketing research skills and people Management skills Production resources and skills Sales force and/or distribution resources and skills Advertising and promotion skills and resources “Same footnotes
TABLE 4 Success-Failure
Impact of the Characteristics
Source Whether product idea was market derived Market determinateness (whether product specifications were clearly defined by the marketplace) Technical determinateness (whether the technical solution was clear at the beginning) Whether the product was a defensive (as opposed to an offensive) introduction Newness to the firm Newness of customers for product Newness of product class Newness of customer need Newness of production process Newness of technology Newness of distribution-sales force Newness of advertising-promotion Newness of competitors
128
Signkance of ANOVA
Correlation Coefflclent, r
Slgnlflcance of r
as in Table 2.
General Innovativeness of product to the market Technology level of product Per unit price (whether product was a big ticket item) MechanicalLtechnical complexity of product Whether product was a custom product or not Relative magnitude of investment (vis h vi.7other new products)
“Same footnote\
Compatabilities”
as in Table 2
of the New Product Venture”
Mean Score for Successes
Mean Score for Failures
5.92 6.67 5.37
4.80 5.74 4.50
5.76
Slgnlflcance of ANOVA
C
Correlation Coefficient, r
Slgnlficance of r
No
No
0.199 0.064 0.072
5.33
No
0.032
No
3.14
3.18
No
5.52
5.41
No
0.087
No
7.19
7.00
No
0.061
No
6.62
6.09
No
PO.009
No
5.36
5.09
No
0.014
No
3.04
3.02
No
PO.035
No
2.97 4.46 4.46 3.73 4.02 2.92 3.16 3.54
3.88 5.95 6.10 4.1 I 4.52 3.69 4.1 I 4.59
No
~0.098 -0.163 ~0.169 -0.020 -0.063 ~0.065 ~0.065 ~0.129
No
C
0
B No No No No C
~0.046
0 No
No
C
B No No No No C
strong ties between controllable variables and product outcomes later discussed. Added to this is the fact that the significant environmental variables are not evenly distributed across all categories of variables. Resourcecompatibility measures dominate the environmental descriptors. Of the 15 significant environment variables, seven, or almost half, describe adequacies of the firm’s resource base. MARKET VARIABLES Variables describing the marketplace (Table 2) are notable for their lack of impact on new product outcomes. Only three of the 15 market descriptors were related to product success, with the mean correlation for all 15 equal to 0.085 (Table 8). This result should not be interpreted to mean that marketing, market information, or market-oriented activities are unimportant to product outcomes; in fact, quite the reverse is true, as evidenced from the results of controllable variable relationships. The result merely implies that the nature of the marketplace at which the new product is targeted does not play a key role in determining the success or failure of the product. This conclusion is quite provocative, especially when one considers the important place given to market descriptors in most qualitative screening models. The only strongly related market variables are (in descending order): (1) degree of customer need for products in this product class (r = 0.329), (2) rate of growth of market (r = 0.221), and (3) degree of customer satisfaction with competitive products (negative) (r = -0.141). Perhaps the most important finding among these market relationships is a gap between the venture outcome and product life-cycle variables-product homogeneity, intensity of competition, level of price competition, number of competitors, and so on (see Table 2). Only market growth was found to be important. Although these surrogates for stage of product life cycle are highly correlated with each other (r = 0.18-0.73), individually none is significantly related to success or failure, thereby lending support to recent arguments to deemphasize the product life cycle as a strategic or predictive tool.
FIRM VARIABLES(RESOURCEBASE) How compatible the firm’s resource base is with the needs of the venture is an important factor in new product success. With one exception, all the resource compatibility measures (Table 3) are related to the product outcome. The mean correlation coefficient is 0.259, with the maxium equal to 0.372 (Table 8). Thus the notion of “product-company fit,” or degree of synergy between new business and old, appears to be a critical concept in determining new product success. The four most important measures, all highly related to product performance, are: (1) sales force and/or distribution resources and skills (r = 0.374), (2) marketing research skills and people (Y = 0.372), (3) management skills (r = 0.3 16), and (4) advertising and promotion skills and resources (r = 0.305). The key role of marketing resource compatibility in differentiating successes from failures, particularly sales force/distribution and marketing research, is clearly demonstrated. The only insignificant resource-compatibility measure was financial resources. Here the relationship is positive, but not very strong. The interpretation is not that financial resources are unimportant to a new product venture, but that compatibility of financial resources does not discriminate all that well between success and failure, and that there are other factors that are much more closely related to success or failure than financial strength. The argument that managers screen projects on the basis of their financial requirements versus the firm’s financial resources, thereby eliminating financially incompatible projects and, in the process, negating financial resources as a discriminator, is easily refuted. The mean of this variable is 7.84 (0- 10 scale), and the standard deviation 2.66. Both values are similar to those obtained in the case of other resource measures and also suggest a reasonable range of projects on this financial resource compatibility dimension. One must conclude that financial strength alone is simply not that critical a determinant of new product success. NATURE OF THE VENTURE The final set of environmental variables measured characteristics of the product
“Two broad categories of variables that have a potential impact on new product success have been identified.” 129
and/or venture itself-nature of the venture, its source, and degree of newness to the firm. The results in Table 4 reveal a relatively weak impact of these variables on success/failure. Of the 18 descriptors, only four are related to product outcomes, and none of these particularly strongly (Table 8). The mean correlation with degree of success is 0.077, but the maximum is only 0.199. The three most important characteristics of the venture are: (1) innovativeness of the product to the market (Y =
compatibility measures. But the relationships are not strong enough to permit one set of variables to be used as surrogates for the other. The conclusion is that newness measures should lent be used in screening models, either as measures of synergy or as predictors of new product success. None of the variables that describe the source of the product idea are related to success. Proponents of the marketing concept might hypothesize that market-
“There is a gap between venture outcome and product life-cycle’ variables.” 0.199), (2) newness of the type of customer need served to the firm (negative) (r = -0.169), and (3) newness of the product class to the firm (negative) (r = -0.163). All three characteristics measure newness dimensions and highlight the importance of such dimensions so often found in marketing literature. But the fact that “newness of the product” is positively related to success and the other two variables, essentially measuring newness of the product and market IO the jirm, are correlated with failure helps to explain the new product dilemma the firm often faces-specifically, the choice of the optimal level Pursuing highly innovative of product innovativeness. products (to the market) is one route to success; conversely, seeking out new types of customers and new product classes (for the firm) is more likely to lead to negative outcomes. Fortunately, the dilemma is not as great as might be thought, since the three dimensions of innovativeness are essentially independent of each other. Firms appear able to seek and develop innovative products without necessarily encountering new customers or moving into new product classes. The fact that only two “newness-to-the-firm” dimensions are important in deciding product outcomes is surprising. These newness measures are often included in new product screening models because they are thought to be valid surrogates for “company-product fit” or synergy. These research results suggest otherwise. The resource compatibility measures (Table 3) are indeed highly related to product outcomes, whereas newness measures are not. A closer scrutiny reveals that many newness measures-technology, production, and customers-are significantly correlated with corresponding 130
derived ideas and ideas with a high degree of market determinateness (i.e., product idea clearly specified by marketplace) would have a better chance of success. Not so, according to the results of our research, at least in the case of industrial new products. Technology-push ideas are as likely to succeed as market-pull products. Similarly, products that are not defined by the market at the outset have as equal a chance of success as those that are well defined. In a similar view, technical determinateness-whether the technical solution is clear at the beginning of the venture-also is unrelated to success. Finally, whether an introduction is defensive has no bearing on the success of the project. Controllable
Variables
The controllable variables have a decided and strong impact on new product success, in contrast to the environmental variables (Tables 5-7). The controllable variables measured included: (1) elements of the commercial entity, (2) proficiencies of process activities, and (3) nature of information acquired during the new product process. Of these 36 controllable variables, a total of 35 are related to new product outcomes, 30 of these in a strong way. Table 8 demonstrates clearly the dominant effect on outcomes of controllable vs. environmental variables. The fate of new products thus appears to depend far more on variables over which the firm has control during the innovation process and not so much on situational or environmental variables. This result has both positive and negative implications to product managers. On the one hand, it is indeed reassuring to learn that the firm can
TABLE 5 Success-Failure
Impact of Proficiencies
Extent” to Which the FoIlowIng New Product Activities Were Proflclently Undertaken (0 = Poor, 10 = Excellent)
of New Product Activities”
Mean Score for Successes
Mean Score for Failures
Mean Rating of Activity
Initial screening Preliminary technical
7.15 7.22
5.24 5.98
6.24 6.63
A A
0.370 0.282
A A
5 11
assessment Preliminary market
7.07
5.23
6.19
A
0.328
A
8
assessment Product development Detailed market study or
7.82 6.21
6.06 4.20
6.98 5.25
A A
0.394 0.342
A A
4 I
market research Prototype testing in-house Prototype testing with
7.83 7.62
6.22 5.30
7.06 6.51
A A
0.325 0.415
A A
9 2
customer Trial production Test marketing-trial selling Start-up of full production Market launch Financial analysis
7.12 5.22 7.07 7.33 6.75
5.61 3.28 5.38 4.71 5.19
6.42 4.29 6.25 6.08 6.01
A A A A A
0.267 0.407 0.394 0.517 0.309
A A A A A
12 3 4 1 10
“Same footnotes as in Table 2. “The zero point was defined as “done very poorly or mistakenly were used for not applicable responses. ( Importance based on correlation coefficient
Signlflcance of ANOVA
omitted altogether”.
control the outcome of its new product ventures, and perhaps to a larger extent than was otherwise assumed. The product fatalists have been proven wrong. Astute management and a well-executed new product process can do much to overcome a bad new product situation. On the other hand, the fact that the environmental variables play a lesser role in deciding the outcome of new products challenges the efficacy of current qualitative screening techniques. First, qualitative screening techniques rely on an assessment of the new product project based largely on environmental variables-nature of the venture, resource compatibility with the firm, nature of the market, and so on. Second, the controllable variables have not yet been decided at the initial screening stage. Controllable variables are largely process variables and are determined during or as part of the new product process. Thus the new product screening is not likely to be a particularly accurate evaluation since the important variables, which really determine the venture’s outcome, ure not yrt known at the point of screening. PROFICIENCIES OF PROCESS ACTIVITIES Of the three sets of controllable variables measured, activity proficiencies stand as the most important (Table 8). The 12 activity scores have a mean correlation of 0.363 with degree of new product success and a maximum of 0.5 17. Although
Correlation Coefflclent,
A response category
r
Significance of r
Rank Order’ of Importance
for not applicable was provided,
and means
every activity is positively and significantly related to success, the strength of the relationships varies across activities (Table 5). The three most important activities in differentiating successes from failures are: (1) market launch (r = 0.517), (2) prototype test with customer (r = 0.415), and (3) test marketing-trial sell (r = 0.407). Thus how well market-oriented activities are executed appears to be most critical in terms of determining the outcome of new industrial products. Overall, the five market-oriented activities in Table 6 have a mean correlation of 0.402, compared to 0.332 for the five technicalproduction activities and 0.340 for the two evaluative activities. The least important activities, although still significantly related to success or failure, are (in ascending order): (1) trial production (r = 0.267), (2) preliminary technical assessment (r = 0.282), and (3) financial analysis (r = 0.309). The fact that financial analysis is among the least important activities is not to suggest that it should be omitted from the new product process. With an r of 0.31, it is still reasonably related to the successfailure outcome. Moreover, this result merely indicates that how well the financial analysis is undertaken does not impact as strongly as other variables on whether the product is a success or failure. This research also enabled 131
TABLE 6 Success-Failure
Impact of the Elements
Characterlstlcs of the Commercial Entity (0 = No or Low; 10 = Yes or High) Product offering Product had unique features or attributes Product met customer needs better than competitors’ Product reducedcustomers’costs Product permitted customer to perform a unique task Product was of higher qualitylasted longer, more reliable, etc. Product was priced lower Launch effort First into the market Strong sales force-distribution effort Sales force-distribution effort well targeted Strong advertising-promotion effort Advertising-promotion effort targeted Production facilities were geared up and smooth Production volume was adequate
of the Commercial
Entity”
Mean Score for Successes
Mean Score for Failures
Significance of ANOVA
1.63
6.00
0.300
A
6
7.78
4.71
0.492
A
1
6.67 6.27
4.23 4.73
0.378 0.238
A A
4 9
7.25
5.00
0.416
A
2
5.42
5.80
0.053
No
5.42 6.02
3.95 4.42
0.177
0.283
B A
12 7
7.70
5.32
0.410
B
3
5.19
4.01
0.233
A
10
6.78
4.94
0.331
A
5
6.52
5.43
0.245
A
8
1.44
6.47
0.208
B
II
No
Correlation Significance or r Coefflctent. r
Rank Order” of Importance
“Same footnotes as in Table 2. “Importance based on correlation coefficients.
a ranking of the proficiencies with which activities were undertaken in new product projects. The three best and the three most poorly executed activities include:
Best In-house prototype testing Product development Preliminary technical assessment
Worst Test marketing-trial selling Detailed market study-marketing Financial analysis
research
Thus the most proficiently executed activities tend to be the production-technical activities, whereas the most poorly undertaken tasks are in the marketing field, with financial analysis also in question. It is particularly disturbing to find the worst-rated activity, namely, test marketing, to be among the three most critical activities correlated with success. The nature of the commercial enCOMMERCIAL ENTITY tity also has a marked influence on success or failure, although not as strong as activity proficiencies (Table 5). The mean correlation of commercial entity variables with degree of success is 0.290 and the maximum 0.492, 132
whereas 12 out of 13 variables are related to success and nine very strongly. The three most important elements of the commercial entity are (Table 7): (1) degree to which the product met customer needs better than competitors’ (1. = 0.492), (2) quality (reliability) level of the product (r = 0.416), and (3) degree to which the sales force-distribution effort was targeted (Y = 0.410). The fact that the two most important elements of the commercial entity both describe the product itself points to the product as the core or uiticnl strategy in industrial product innovation. The same might not be true in the case of new consumer products, where success could depend much more on effective advertising, promotion, or distribution. Another important element is the accurate targeting of the sales force and/or distribution effort. It is the dirrctiotz of effort (r = 0.410) rather than the magnituck of effort (r = 0.283) that impacts more strongly on new product results. The least important elements of the commercial entity in terms of differentiating between success and failure were: (1) extent to which product was priced lower (not significant), (2) whether firm was first into the market (r
TABLE 7 Success-Failure
Impact of Information
Information Acquired: Extent to Which the Company (0 = No-None; 10 = Yes-High) Knew customer
Acquired
during the New Product Process”
Mean Score for Successes
Mean Score for Farlures
needs, wants, and
Srgnificance of ANOVA
Correlation Coefficient, f
Srgnificance of r
Rank Order of Importance”
7.69
5.61
A
0.362
A
3
specifications for product Knew customer price sensitivity Knew competitors-products,
1.66 7.04
5.68 5.55
A A
0.394 0.331
A A
5
strategies, etc. Understood buyer behavior Knew market size Understood product’s technology Understood product design well-
7.13 6.93 7.81 6.42
5.30 5.26 6.96 5.15
A A C B
0.391 0.284 0.203 0.270
A A B A
2 7 10 8
all design “bugs” were ironed out Knew production costs Knew production process-
7.26 7.13
6.05 6.63
B B
0.291 0.250
A A
6 9
equipment Viewed project as high-risk one Were confident about product’s
3.38 7.66
4.98 5.91
B A
-0.175 0.338
B A
I1 4
1
success “Same footnotes as in Table 2. “Based on correlation coefficients.
= 0.177) (3) whether production volume was adequate (r = 0.208), and strength of advertising-promotional effort (r = 0.233). It is noteworthy that a price advantage is not only the least important of the elements of the commercial entity, but is not even significantly related to the success-failure outcome. The implication here is that a price advantage is likely to do little in the way of assuring the success of a new product; rather, product attributes
TABLE 8 Impact of Sets of Variables
appear to be the key elements. The question of whether a firm should be first, second, or last into the market with a product has been the subject of much debate. Although there are arguments both for and against being the innovative firm, the evidence from research reported here suggests that being first to market is usually advantageous. The fact that a strong advertising and promotional campaign is positively but not strongly related to new
on New Product Success
No. of Variables Marketplace descriptors (Table 2)
No Srgnrfrcant” at 0.05 Level
No. Srgnifrcant” at 0 001 Level
Mean Correlatron Coefficient
Mode of Correlation Coeffrcrent
Maximum Correlation Coefficient
15
3
2
0.085
0.058
0.329
8
7
6
0.259
0.24-0.30
0.374
Characteristics of the venture (Table 4)
18
4
0
0.077
0.064
0.199
Activities proficiencies (Table 5)
12
12
12
0.363
0.34-0.37
0.517
Elements of the commercial entity (Table 6)
13
12
9
0.290
0.283
0.492
Information acquired (Table 7)
11
11
9
0.299
0.291
0.394
Product-company
fit:
resource compatibility (Table 3)
“Based on correlation
coefficient.
133
product outcomes is not surprising to industrial marketers. This finding merely supports the contention that although desirable, a strong marketing communications effort for industrial goods is likely to have nowhere near the impact that it would have in a consumer goods setting. INFORMATIONACQUIRED The final set of controllable variables considered were those that described the nature or amount of information acquired during the new product process (Table 7). What information or knowledge is critical to the success of a new product? The set of controllable variables, knowledge or information, was found to play an important role in discriminating between success and failure. Of the 11 information variables measured, all were related to degree of success, nine in a strong fashion. The mean correlation with success was 0.299 and the maximum, 0.394 (Table 8). The most important types of information to acquire, as measured by correlation with success, include: (1) knowledge of customer price sensitivity (r = 0.394), (2) understanding of buyer behavior (r = 0.391), and (3) knowledge of customers’ needs, wants, and specifications for the product (r = 0.362). All three variables are closely related and, taken together, could be considered to be “knowledge of the customer. ” The evidence from this research points strongly to an understanding of the customer-his needs, wants, behavior, and price sensitivity-as being vital to success in industrial product innovation. A comparison of the relative impact of all of the types of information reveals market information as the major factor in new product success. The mean correlation with success of the five market-information variables (the first five listed in Table 7) is 0.352, compared to 0.254 for the technical-production information variables (next four listed in Table 7). This difference is significant (CX d .OOl) and would support the contention that good market information is absolutely essential for more success-
neither was particularly highly correlated with success, as: (1) firms were almost as confident about the outcomes of products that failed as they were about successes; and (2) firms thought products that failed to be only somewhat more risky than those that succeeded.
CONCLUSION This investigation into new product success and failure, project NewProd, has provided a glimpse into what makes the difference between a “winner” and a “loser” in the new product game. The article began with a question as to what the key to success is in industrial product innovation. The answer to this question has been provided in part by the results and the preceding discussion. More simply, the 15 most important variables in differentiating between industrial new product success and failure are: 1. Proficiently
2. 3.
4. 5. 6. 7. 8. 9. 10.
executing the launch-selling, promoting, and distributing. Having a new product that more clearly meets customers’ needs than do competitor products. Having a higher quality new product than competitors in terms of tighter specifications, greater durability and reliability, and so on. Undertaking a good prototype test of the product with the customer. Having the sales force and/or distribution effort well targeted-at the right customers. Undertaking a proficient test market or trial sell. Proficiently starting up full-scale production. Knowing customers’ price sensitivities. Executing product development well. Understanding buyer behavior and the customers’ purchase decision process.
“Technology-push ideas are as likely to succeed as market-pull products.” ful industrial 7, perceived outcome of information 134
products. The final two variables in Table project risk and degree of confidence in the the venture, were both global measures of and knowledge. What is interesting is that
11. Having a product that permits the customer to reduce his costs. 12. Having a good “company-product fit” in terms of sales force and/or distribution.
fit” in terms 13. Having a good “company-product of marketing research skills and needs. 14. Doing a good job on idea screening. 15. Understanding customers’ needs, wants, and specifications for the product. The dominant position of controllable variables in the determination of new product outcomes was aptly demonstrated by the research results. The lack of influence of environmental descriptors was a provocative finding, particularly in view of the inferred importance of these descriptors in the literature on screening and predictive models. The message from the research for new product developers is that “it matters not what situation you face; it matters more what you do about it! ” The research demonstrates that the outcome of a new industrial venture is to a large extent in the hands of the managers and implementors, and to a much less extent dependent on “outside factors” determined at the outset of the venture. A strong market orientation makes all the difference when it comes to separating successful vs unsuccessful industrial new products. The marketing variables generally dominated the important sets of variables in determining product outcomes. The manner in which the market-oriented activities were undertaken during the new product process had a major impact on success; similarly, market information was the critical factor in
the analysis of information requirements. Finally, the nature of the elements of the marketing mix were closely related to success/failure, with “product” as clearly the core or central strategy. ACKNOWLEDGMENT This research was funded by the Associates’ Workshop on Business Research, School of Business Administration, The University of Western Ontario (Canada), and by the Department of Industry, Trade and Commerce, Office of Science and Technology, Canadian Federal Government.
REFERENCES Centre for the Study of Industrial Innovation, Success cmd Failure in Industrial Innovation: Report on Project Sappho (February 1972). Cooper, Robert G., Introducing Successful Journal of Marketing, 10 (6), 24-25.
New Products,
European
Directory of’ Research and Development Establishments in Cmudiun Industry 1973, Ministry of State, Science and Technology, Information Canada, Ottawa, 1974. Rubenstein. A. H., Chakrabarti, A. K., and O’Keefe, R. D., Field Studies of the Technological Innovation Process, in Progress in Assessing Technologiml Innovation 1974, H. R. Clauser, ed., NSF, Technome Publications, Westport, Corm., 1975.
135
An important task facing any corporation developing new products is to identify new business opportunities that the firm might exploit. An equally important point is to determine what the firm can do to improve the chances of commercial success for those new product projects it decides to invest in. These two issues-how to identify the potential “winners” and how to improve the odds of success-lead to the focal issue of our research, namely, to determine which characteristics discriminate between
Address correspondence ment, McGill University. H3A IGS.
to: Dr. Robert G. Cooper, Faculty of Manage1001 Sherbrooke West. Montreal PQ. Canada
commercially successful and unsuccessful industrial new products. Relatively few investigations have dealt with the question of differentiating successful and unsuccessful new products. The most notable exception is project Sappho I1 I, a British study, which concluded that successful innovators were found to have a much better understanding of user needs, paid more attention to marketing, performed development work more efficiently, made more use of outside advice, and were persons with greater management authority. Hungarian, Finnish, and West German studies, similar to Sappho, also yielded consistent results. The only research in North America that comes close to reporting on success-failure discriminators was one conducted by Rubenstein [4], where managers suggested a long list of facilitators and barriers to product success but the researchers concluded that there was little consistency in the effect of many of these on venture outcomes. These investigations into new product outcomes, beginning with Sappho in 1972, represent pioneer work in the field. But project NewProd differs from its predecessors in several important respects: 1. NewProd utilizes a large sample of companies new products, thereby permitting meaningful
and use
El
OUTCOME SUCCESS OR FAILURE
COMMERCIAL
ENVIRONMENT
ENTITY
NEW PRODUCT PROCESS THE MARKETPLACE
THE FIRM (resource
base)
Activities undertaken
ENVIRONMENT
THENATUREOFTHEVENTURE
J
A model of new product outcomes. The new product process yields a commercial entity, whose fate is determined in the marketplace. The entire process takes place within three environments. FIGURE 1.
of statistical analysis tools and the generalization of results. 2. NewProd is based on a conceptual model of product outcomes. This model was developed to identify relevant variables for investigation.
Ft. G. COOPER is the associate dean and director of the M.B.A. program in the faculty of Management, McGill University, Montreal. He is also an associate professor of marketing in the faculty and was former head of the marketing area group at McGill. He holds Bachelor’s and Master’s degrees in engineering (chemical) and an M.B.A. and Ph.D. in business administration.
3. NewProd identifies product discriminators using statistical methods to indicate barriers and facilitators. A MODEL
OF NEW
PRODUCT
OUTCOMES
A myriad of possible factors or variables exist that could be associated with successful versus unsuccessful new products. However, to identify those variables of interest in a logical and consistent fashion and group them according to some useful categorization scheme, a conceptual model was developed. This model is essentially a model of new product outcomes, where sets of variables that could influence outcomes are identified. The model is briefly described in the following paragraphs and is shown in Figure 1. 125
The new product outcome-success or failure-is determined by the interaction of the commercial entity with the marketplace (Figure 1, top). The commercial entity we define to be that entity with which the firm enters the marketplace-the product, its price, the launch and/or marketing strategy, and the production-service facilities that back up the launch effort. The commercial entity is the result of the new product process. This process is a stagewise sequence of goaloriented activities and information-gathering functions, beginning with idea generation, proceeding through various stages of market research and product development, and culminating in production start-up and market launch [2]. How these activities are undertaken and the nature of information that they yield are two sets of variables that manifest themselves in the commercial entity. The new product is developed and launched within a particular environment that could have a marked impact on the new product process. Three environments have been identified: (1) the firm’s external environment (i.e., the nature of the marketplace at which the new product is targeted), (2) the firm (i.e., the resource base of the firm and its compatibility with the new product venture’s requirements), and (3) the nature of the new product project itself (e.g., its source and relative magnitude). The project’s characteristics help shape the process activities and hence have a bearing on the eventual outcome. The model of new product outcomes (Fig. 1) has identified two broad categories of variables that have a potential impact on new product success. These are controllable variables and environmental descriptors. The controllable variables are those that describe the new product process and its output. The environmental variables describe the setting in which a new product is developed (see Table 1). COLLECTING
THE DATA
To determine which variables or characteristics differentiate successful from unsuccessful new products, a large number of actual successes and failures was investigated. A random sample of 177 firms was selected from among a listing of industrial companies known to be active in product development. ’ The companies were initially contacted by telephone, after which the appropriate manager was mailed a questionnaire. The questionnaire requested the manager to
IThe geographic
126
sample area was Ontario and Quebec,
Canada 131.
TABLE 1 New Product Outcome
Type Controllable
Environmental
Model Variables Varrable
Example
Element5 ofthe commercial entity
Whether or not the product was priced at less than competition
Proficiencies activities
How well the marketing research was undertaken (if at all)
of pr0ces.s
Nature of information acquired
Extent to which customers’ price sensitivities were known
The product’s
Degree of competition the market
marketplace
The firm (resource
The venture
base)
in
Degree to which the firm’s R&D skills were adequate to develop the product Innovativeness of the product relative to other products on the market
select two of his company’s typical new products that had been introduced to the market-+ne a commercial success and the other a failure. Several selection guidelines were provided as well as operational definitions of success and failure.* The respondent was then presented with a list of 77 statements that could describe the new product venture and its environment (based on the model outlined in Table 1). The manager was asked to indicate whether he agreed or disagreed with the statement as it pertained to that particular new product project on a 0- 10 scale. Finally the manager had to rate the degree of commercial success of the product.2 A telephone follow-up provided assistance and encouraged responses. Of the 177 firms initially sampled, 27 were disqualified; four were no longer in business or could not be contacted, and 23 indicated they simply did not have the new product failures or successes needed for discussion. Of the remaining sample of 150, a total of 103 replied to for an effective response rate of the questionnaire, 68.7%. The eventual sample numbered 102 successes and 93 failures.
‘Success and failure were defined from the point of view of the firm and in terms of profitability (i.e.. the degree to which a product‘s profitability exceeded or fell short of the minimum acceptable profitability for this type of project or investment, however the firm measured profitability). Because of selection errors that could result from difficulties or the use of this operational definition of success and failure, managers were asked to select products that were unmistakably successes and failures.
TABLE 2 Success-Failure
Impact of Variables
Existence of a mass market (as opposed to one or a few customers) Degree of need for products in product class Existence of a potential demand only (no actual market) Market size Market growth Degree of product homogeneity in the market Degree of competition Degree of price competition Number of competitors Existence of a dominant competitor Degree of loyalty to competitors’ products Degree of satisfaction with competitors’ products Frequency of new product introductions in the market Degree to which users’ needs change quickly in the market Extent of role of government in the marketplace
Describing
the Marketplace Slgnlflcance” of ANOVA
Mean” Score for Successes
Mean Score for Failures
Correlation Coefficient, f
5.33
5.13
NO
0.008
NO
7.40
5.78
A
0.329
A
3.37
3.53
No
6.41 6.43 6.26
5.80 5.27 6.1 I
No A No
0.108 0.221 0.015
No A No
7.17 5.88 4.75 5.57 4.79
7.29 6.41 5.45 5.87 4.93
No No No No No
0.002 -0.058 -0.061 -0.051 0.003
No No No No No
4.15
5.54
C
-0.141
C
3.23
3.67
No
~0.024
No
3.24
3.87
No
-0.122
No
4.45
3.62
No
-0.098
No
-0.029
Significance” of r
No
“Means are on a O-10 scale, where 0 is low and 10 is high (same footnote applies to Tables 3-7). “Significance test of analysis of variance: A indicates significance at the 0.001 level, B at the 0.01 level, and C at the 0.05 level (same footnote applies to Tables 3-7).
yielded generally consistent results. The results both methods are presented in Tables 2 to 8.
RESULTS How each descriptor variable helps to decide the fate of a new industrial product is outlined in the following paragraphs. Results are presented first for the environmental variables, over which the firm has relatively little control, and are followed by results for the controllable variables. Two statistical techniques were used to determine the strength of relationships between success/failure and descriptor variables: 1. Analysis of variance (ANOVA), which tests for the difference of the means of each descriptor variable for successful ventures versus failures. 2. The correlation coefficient, which measures the degree of correlation between the scaled success/ failure rating and each descriptor variable.3 Both methods are simple bivariate techniques,
and both
3The square of the correlation coefficient gives the percent variance of the degree of success or failure explained by the variations in the descriptor variable.
Environmental
from
Variables
Environmental variables do not play a critical role in deciding new product success. Environmental variables describe: (I) the marketplace, (2) the firm (resource base), and (3) the nature of the venture. These variables were anticipated to have a marked impact on the outcome of a new product venture. This expectation was based largely on the importance given to environmental variables in most predictive screening models. But the results of this research proved otherwise CTables 2-4). Of the 41 environmental variables originally thought to influence outcomes, only 15 were significantly related to success or failure, and only eight variables-less than one-fifth-were strongly related. Not only are two-thirds of the environmental variables unrelated to product outcome, but a review of Tables 2-4 reveals relatively low correlation coefficients even for the significant variables, with the maximum r equal to 0.37. These weak relationships can be contrasted to the very 127
TABLE 3 Success-Failure
Impact of Product-Firm
Compatablllty of the FolIowIng Resources In the Firm for Thts Product Project (0 = Poor; IO = High)
Mean Score for Successes
Mean Score for Failures
8.07 7.93 7.82 7.12
7.59 6.81 6.92 5.26
NO A B A
0.107 0.239 0.213 0.372
NO A A A
7.78 7.48 7.36
6.53 6.18 5.68
A C A
0.316 0.142 0.374
A C A
6.99
5.4x
A
0.305
A
Financial resources R&D skills and people Engineering skills and people Marketing research skills and people Management skills Production resources and skills Sales force and/or distribution resources and skills Advertising and promotion skills and resources “Same footnotes
TABLE 4 Success-Failure
Impact of the Characteristics
Source Whether product idea was market derived Market determinateness (whether product specifications were clearly defined by the marketplace) Technical determinateness (whether the technical solution was clear at the beginning) Whether the product was a defensive (as opposed to an offensive) introduction Newness to the firm Newness of customers for product Newness of product class Newness of customer need Newness of production process Newness of technology Newness of distribution-sales force Newness of advertising-promotion Newness of competitors
128
Signkance of ANOVA
Correlation Coefflclent, r
Slgnlflcance of r
as in Table 2.
General Innovativeness of product to the market Technology level of product Per unit price (whether product was a big ticket item) MechanicalLtechnical complexity of product Whether product was a custom product or not Relative magnitude of investment (vis h vi.7other new products)
“Same footnote\
Compatabilities”
as in Table 2
of the New Product Venture”
Mean Score for Successes
Mean Score for Failures
5.92 6.67 5.37
4.80 5.74 4.50
5.76
Slgnlflcance of ANOVA
C
Correlation Coefficient, r
Slgnlficance of r
No
No
0.199 0.064 0.072
5.33
No
0.032
No
3.14
3.18
No
5.52
5.41
No
0.087
No
7.19
7.00
No
0.061
No
6.62
6.09
No
PO.009
No
5.36
5.09
No
0.014
No
3.04
3.02
No
PO.035
No
2.97 4.46 4.46 3.73 4.02 2.92 3.16 3.54
3.88 5.95 6.10 4.1 I 4.52 3.69 4.1 I 4.59
No
~0.098 -0.163 ~0.169 -0.020 -0.063 ~0.065 ~0.065 ~0.129
No
C
0
B No No No No C
~0.046
0 No
No
C
B No No No No C
strong ties between controllable variables and product outcomes later discussed. Added to this is the fact that the significant environmental variables are not evenly distributed across all categories of variables. Resourcecompatibility measures dominate the environmental descriptors. Of the 15 significant environment variables, seven, or almost half, describe adequacies of the firm’s resource base. MARKET VARIABLES Variables describing the marketplace (Table 2) are notable for their lack of impact on new product outcomes. Only three of the 15 market descriptors were related to product success, with the mean correlation for all 15 equal to 0.085 (Table 8). This result should not be interpreted to mean that marketing, market information, or market-oriented activities are unimportant to product outcomes; in fact, quite the reverse is true, as evidenced from the results of controllable variable relationships. The result merely implies that the nature of the marketplace at which the new product is targeted does not play a key role in determining the success or failure of the product. This conclusion is quite provocative, especially when one considers the important place given to market descriptors in most qualitative screening models. The only strongly related market variables are (in descending order): (1) degree of customer need for products in this product class (r = 0.329), (2) rate of growth of market (r = 0.221), and (3) degree of customer satisfaction with competitive products (negative) (r = -0.141). Perhaps the most important finding among these market relationships is a gap between the venture outcome and product life-cycle variables-product homogeneity, intensity of competition, level of price competition, number of competitors, and so on (see Table 2). Only market growth was found to be important. Although these surrogates for stage of product life cycle are highly correlated with each other (r = 0.18-0.73), individually none is significantly related to success or failure, thereby lending support to recent arguments to deemphasize the product life cycle as a strategic or predictive tool.
FIRM VARIABLES(RESOURCEBASE) How compatible the firm’s resource base is with the needs of the venture is an important factor in new product success. With one exception, all the resource compatibility measures (Table 3) are related to the product outcome. The mean correlation coefficient is 0.259, with the maxium equal to 0.372 (Table 8). Thus the notion of “product-company fit,” or degree of synergy between new business and old, appears to be a critical concept in determining new product success. The four most important measures, all highly related to product performance, are: (1) sales force and/or distribution resources and skills (r = 0.374), (2) marketing research skills and people (Y = 0.372), (3) management skills (r = 0.3 16), and (4) advertising and promotion skills and resources (r = 0.305). The key role of marketing resource compatibility in differentiating successes from failures, particularly sales force/distribution and marketing research, is clearly demonstrated. The only insignificant resource-compatibility measure was financial resources. Here the relationship is positive, but not very strong. The interpretation is not that financial resources are unimportant to a new product venture, but that compatibility of financial resources does not discriminate all that well between success and failure, and that there are other factors that are much more closely related to success or failure than financial strength. The argument that managers screen projects on the basis of their financial requirements versus the firm’s financial resources, thereby eliminating financially incompatible projects and, in the process, negating financial resources as a discriminator, is easily refuted. The mean of this variable is 7.84 (0- 10 scale), and the standard deviation 2.66. Both values are similar to those obtained in the case of other resource measures and also suggest a reasonable range of projects on this financial resource compatibility dimension. One must conclude that financial strength alone is simply not that critical a determinant of new product success. NATURE OF THE VENTURE The final set of environmental variables measured characteristics of the product
“Two broad categories of variables that have a potential impact on new product success have been identified.” 129
and/or venture itself-nature of the venture, its source, and degree of newness to the firm. The results in Table 4 reveal a relatively weak impact of these variables on success/failure. Of the 18 descriptors, only four are related to product outcomes, and none of these particularly strongly (Table 8). The mean correlation with degree of success is 0.077, but the maximum is only 0.199. The three most important characteristics of the venture are: (1) innovativeness of the product to the market (Y =
compatibility measures. But the relationships are not strong enough to permit one set of variables to be used as surrogates for the other. The conclusion is that newness measures should lent be used in screening models, either as measures of synergy or as predictors of new product success. None of the variables that describe the source of the product idea are related to success. Proponents of the marketing concept might hypothesize that market-
“There is a gap between venture outcome and product life-cycle’ variables.” 0.199), (2) newness of the type of customer need served to the firm (negative) (r = -0.169), and (3) newness of the product class to the firm (negative) (r = -0.163). All three characteristics measure newness dimensions and highlight the importance of such dimensions so often found in marketing literature. But the fact that “newness of the product” is positively related to success and the other two variables, essentially measuring newness of the product and market IO the jirm, are correlated with failure helps to explain the new product dilemma the firm often faces-specifically, the choice of the optimal level Pursuing highly innovative of product innovativeness. products (to the market) is one route to success; conversely, seeking out new types of customers and new product classes (for the firm) is more likely to lead to negative outcomes. Fortunately, the dilemma is not as great as might be thought, since the three dimensions of innovativeness are essentially independent of each other. Firms appear able to seek and develop innovative products without necessarily encountering new customers or moving into new product classes. The fact that only two “newness-to-the-firm” dimensions are important in deciding product outcomes is surprising. These newness measures are often included in new product screening models because they are thought to be valid surrogates for “company-product fit” or synergy. These research results suggest otherwise. The resource compatibility measures (Table 3) are indeed highly related to product outcomes, whereas newness measures are not. A closer scrutiny reveals that many newness measures-technology, production, and customers-are significantly correlated with corresponding 130
derived ideas and ideas with a high degree of market determinateness (i.e., product idea clearly specified by marketplace) would have a better chance of success. Not so, according to the results of our research, at least in the case of industrial new products. Technology-push ideas are as likely to succeed as market-pull products. Similarly, products that are not defined by the market at the outset have as equal a chance of success as those that are well defined. In a similar view, technical determinateness-whether the technical solution is clear at the beginning of the venture-also is unrelated to success. Finally, whether an introduction is defensive has no bearing on the success of the project. Controllable
Variables
The controllable variables have a decided and strong impact on new product success, in contrast to the environmental variables (Tables 5-7). The controllable variables measured included: (1) elements of the commercial entity, (2) proficiencies of process activities, and (3) nature of information acquired during the new product process. Of these 36 controllable variables, a total of 35 are related to new product outcomes, 30 of these in a strong way. Table 8 demonstrates clearly the dominant effect on outcomes of controllable vs. environmental variables. The fate of new products thus appears to depend far more on variables over which the firm has control during the innovation process and not so much on situational or environmental variables. This result has both positive and negative implications to product managers. On the one hand, it is indeed reassuring to learn that the firm can
TABLE 5 Success-Failure
Impact of Proficiencies
Extent” to Which the FoIlowIng New Product Activities Were Proflclently Undertaken (0 = Poor, 10 = Excellent)
of New Product Activities”
Mean Score for Successes
Mean Score for Failures
Mean Rating of Activity
Initial screening Preliminary technical
7.15 7.22
5.24 5.98
6.24 6.63
A A
0.370 0.282
A A
5 11
assessment Preliminary market
7.07
5.23
6.19
A
0.328
A
8
assessment Product development Detailed market study or
7.82 6.21
6.06 4.20
6.98 5.25
A A
0.394 0.342
A A
4 I
market research Prototype testing in-house Prototype testing with
7.83 7.62
6.22 5.30
7.06 6.51
A A
0.325 0.415
A A
9 2
customer Trial production Test marketing-trial selling Start-up of full production Market launch Financial analysis
7.12 5.22 7.07 7.33 6.75
5.61 3.28 5.38 4.71 5.19
6.42 4.29 6.25 6.08 6.01
A A A A A
0.267 0.407 0.394 0.517 0.309
A A A A A
12 3 4 1 10
“Same footnotes as in Table 2. “The zero point was defined as “done very poorly or mistakenly were used for not applicable responses. ( Importance based on correlation coefficient
Signlflcance of ANOVA
omitted altogether”.
control the outcome of its new product ventures, and perhaps to a larger extent than was otherwise assumed. The product fatalists have been proven wrong. Astute management and a well-executed new product process can do much to overcome a bad new product situation. On the other hand, the fact that the environmental variables play a lesser role in deciding the outcome of new products challenges the efficacy of current qualitative screening techniques. First, qualitative screening techniques rely on an assessment of the new product project based largely on environmental variables-nature of the venture, resource compatibility with the firm, nature of the market, and so on. Second, the controllable variables have not yet been decided at the initial screening stage. Controllable variables are largely process variables and are determined during or as part of the new product process. Thus the new product screening is not likely to be a particularly accurate evaluation since the important variables, which really determine the venture’s outcome, ure not yrt known at the point of screening. PROFICIENCIES OF PROCESS ACTIVITIES Of the three sets of controllable variables measured, activity proficiencies stand as the most important (Table 8). The 12 activity scores have a mean correlation of 0.363 with degree of new product success and a maximum of 0.5 17. Although
Correlation Coefflclent,
A response category
r
Significance of r
Rank Order’ of Importance
for not applicable was provided,
and means
every activity is positively and significantly related to success, the strength of the relationships varies across activities (Table 5). The three most important activities in differentiating successes from failures are: (1) market launch (r = 0.517), (2) prototype test with customer (r = 0.415), and (3) test marketing-trial sell (r = 0.407). Thus how well market-oriented activities are executed appears to be most critical in terms of determining the outcome of new industrial products. Overall, the five market-oriented activities in Table 6 have a mean correlation of 0.402, compared to 0.332 for the five technicalproduction activities and 0.340 for the two evaluative activities. The least important activities, although still significantly related to success or failure, are (in ascending order): (1) trial production (r = 0.267), (2) preliminary technical assessment (r = 0.282), and (3) financial analysis (r = 0.309). The fact that financial analysis is among the least important activities is not to suggest that it should be omitted from the new product process. With an r of 0.31, it is still reasonably related to the successfailure outcome. Moreover, this result merely indicates that how well the financial analysis is undertaken does not impact as strongly as other variables on whether the product is a success or failure. This research also enabled 131
TABLE 6 Success-Failure
Impact of the Elements
Characterlstlcs of the Commercial Entity (0 = No or Low; 10 = Yes or High) Product offering Product had unique features or attributes Product met customer needs better than competitors’ Product reducedcustomers’costs Product permitted customer to perform a unique task Product was of higher qualitylasted longer, more reliable, etc. Product was priced lower Launch effort First into the market Strong sales force-distribution effort Sales force-distribution effort well targeted Strong advertising-promotion effort Advertising-promotion effort targeted Production facilities were geared up and smooth Production volume was adequate
of the Commercial
Entity”
Mean Score for Successes
Mean Score for Failures
Significance of ANOVA
1.63
6.00
0.300
A
6
7.78
4.71
0.492
A
1
6.67 6.27
4.23 4.73
0.378 0.238
A A
4 9
7.25
5.00
0.416
A
2
5.42
5.80
0.053
No
5.42 6.02
3.95 4.42
0.177
0.283
B A
12 7
7.70
5.32
0.410
B
3
5.19
4.01
0.233
A
10
6.78
4.94
0.331
A
5
6.52
5.43
0.245
A
8
1.44
6.47
0.208
B
II
No
Correlation Significance or r Coefflctent. r
Rank Order” of Importance
“Same footnotes as in Table 2. “Importance based on correlation coefficients.
a ranking of the proficiencies with which activities were undertaken in new product projects. The three best and the three most poorly executed activities include:
Best In-house prototype testing Product development Preliminary technical assessment
Worst Test marketing-trial selling Detailed market study-marketing Financial analysis
research
Thus the most proficiently executed activities tend to be the production-technical activities, whereas the most poorly undertaken tasks are in the marketing field, with financial analysis also in question. It is particularly disturbing to find the worst-rated activity, namely, test marketing, to be among the three most critical activities correlated with success. The nature of the commercial enCOMMERCIAL ENTITY tity also has a marked influence on success or failure, although not as strong as activity proficiencies (Table 5). The mean correlation of commercial entity variables with degree of success is 0.290 and the maximum 0.492, 132
whereas 12 out of 13 variables are related to success and nine very strongly. The three most important elements of the commercial entity are (Table 7): (1) degree to which the product met customer needs better than competitors’ (1. = 0.492), (2) quality (reliability) level of the product (r = 0.416), and (3) degree to which the sales force-distribution effort was targeted (Y = 0.410). The fact that the two most important elements of the commercial entity both describe the product itself points to the product as the core or uiticnl strategy in industrial product innovation. The same might not be true in the case of new consumer products, where success could depend much more on effective advertising, promotion, or distribution. Another important element is the accurate targeting of the sales force and/or distribution effort. It is the dirrctiotz of effort (r = 0.410) rather than the magnituck of effort (r = 0.283) that impacts more strongly on new product results. The least important elements of the commercial entity in terms of differentiating between success and failure were: (1) extent to which product was priced lower (not significant), (2) whether firm was first into the market (r
TABLE 7 Success-Failure
Impact of Information
Information Acquired: Extent to Which the Company (0 = No-None; 10 = Yes-High) Knew customer
Acquired
during the New Product Process”
Mean Score for Successes
Mean Score for Farlures
needs, wants, and
Srgnificance of ANOVA
Correlation Coefficient, f
Srgnificance of r
Rank Order of Importance”
7.69
5.61
A
0.362
A
3
specifications for product Knew customer price sensitivity Knew competitors-products,
1.66 7.04
5.68 5.55
A A
0.394 0.331
A A
5
strategies, etc. Understood buyer behavior Knew market size Understood product’s technology Understood product design well-
7.13 6.93 7.81 6.42
5.30 5.26 6.96 5.15
A A C B
0.391 0.284 0.203 0.270
A A B A
2 7 10 8
all design “bugs” were ironed out Knew production costs Knew production process-
7.26 7.13
6.05 6.63
B B
0.291 0.250
A A
6 9
equipment Viewed project as high-risk one Were confident about product’s
3.38 7.66
4.98 5.91
B A
-0.175 0.338
B A
I1 4
1
success “Same footnotes as in Table 2. “Based on correlation coefficients.
= 0.177) (3) whether production volume was adequate (r = 0.208), and strength of advertising-promotional effort (r = 0.233). It is noteworthy that a price advantage is not only the least important of the elements of the commercial entity, but is not even significantly related to the success-failure outcome. The implication here is that a price advantage is likely to do little in the way of assuring the success of a new product; rather, product attributes
TABLE 8 Impact of Sets of Variables
appear to be the key elements. The question of whether a firm should be first, second, or last into the market with a product has been the subject of much debate. Although there are arguments both for and against being the innovative firm, the evidence from research reported here suggests that being first to market is usually advantageous. The fact that a strong advertising and promotional campaign is positively but not strongly related to new
on New Product Success
No. of Variables Marketplace descriptors (Table 2)
No Srgnrfrcant” at 0.05 Level
No. Srgnifrcant” at 0 001 Level
Mean Correlatron Coefficient
Mode of Correlation Coeffrcrent
Maximum Correlation Coefficient
15
3
2
0.085
0.058
0.329
8
7
6
0.259
0.24-0.30
0.374
Characteristics of the venture (Table 4)
18
4
0
0.077
0.064
0.199
Activities proficiencies (Table 5)
12
12
12
0.363
0.34-0.37
0.517
Elements of the commercial entity (Table 6)
13
12
9
0.290
0.283
0.492
Information acquired (Table 7)
11
11
9
0.299
0.291
0.394
Product-company
fit:
resource compatibility (Table 3)
“Based on correlation
coefficient.
133
product outcomes is not surprising to industrial marketers. This finding merely supports the contention that although desirable, a strong marketing communications effort for industrial goods is likely to have nowhere near the impact that it would have in a consumer goods setting. INFORMATIONACQUIRED The final set of controllable variables considered were those that described the nature or amount of information acquired during the new product process (Table 7). What information or knowledge is critical to the success of a new product? The set of controllable variables, knowledge or information, was found to play an important role in discriminating between success and failure. Of the 11 information variables measured, all were related to degree of success, nine in a strong fashion. The mean correlation with success was 0.299 and the maximum, 0.394 (Table 8). The most important types of information to acquire, as measured by correlation with success, include: (1) knowledge of customer price sensitivity (r = 0.394), (2) understanding of buyer behavior (r = 0.391), and (3) knowledge of customers’ needs, wants, and specifications for the product (r = 0.362). All three variables are closely related and, taken together, could be considered to be “knowledge of the customer. ” The evidence from this research points strongly to an understanding of the customer-his needs, wants, behavior, and price sensitivity-as being vital to success in industrial product innovation. A comparison of the relative impact of all of the types of information reveals market information as the major factor in new product success. The mean correlation with success of the five market-information variables (the first five listed in Table 7) is 0.352, compared to 0.254 for the technical-production information variables (next four listed in Table 7). This difference is significant (CX d .OOl) and would support the contention that good market information is absolutely essential for more success-
neither was particularly highly correlated with success, as: (1) firms were almost as confident about the outcomes of products that failed as they were about successes; and (2) firms thought products that failed to be only somewhat more risky than those that succeeded.
CONCLUSION This investigation into new product success and failure, project NewProd, has provided a glimpse into what makes the difference between a “winner” and a “loser” in the new product game. The article began with a question as to what the key to success is in industrial product innovation. The answer to this question has been provided in part by the results and the preceding discussion. More simply, the 15 most important variables in differentiating between industrial new product success and failure are: 1. Proficiently
2. 3.
4. 5. 6. 7. 8. 9. 10.
executing the launch-selling, promoting, and distributing. Having a new product that more clearly meets customers’ needs than do competitor products. Having a higher quality new product than competitors in terms of tighter specifications, greater durability and reliability, and so on. Undertaking a good prototype test of the product with the customer. Having the sales force and/or distribution effort well targeted-at the right customers. Undertaking a proficient test market or trial sell. Proficiently starting up full-scale production. Knowing customers’ price sensitivities. Executing product development well. Understanding buyer behavior and the customers’ purchase decision process.
“Technology-push ideas are as likely to succeed as market-pull products.” ful industrial 7, perceived outcome of information 134
products. The final two variables in Table project risk and degree of confidence in the the venture, were both global measures of and knowledge. What is interesting is that
11. Having a product that permits the customer to reduce his costs. 12. Having a good “company-product fit” in terms of sales force and/or distribution.
fit” in terms 13. Having a good “company-product of marketing research skills and needs. 14. Doing a good job on idea screening. 15. Understanding customers’ needs, wants, and specifications for the product. The dominant position of controllable variables in the determination of new product outcomes was aptly demonstrated by the research results. The lack of influence of environmental descriptors was a provocative finding, particularly in view of the inferred importance of these descriptors in the literature on screening and predictive models. The message from the research for new product developers is that “it matters not what situation you face; it matters more what you do about it! ” The research demonstrates that the outcome of a new industrial venture is to a large extent in the hands of the managers and implementors, and to a much less extent dependent on “outside factors” determined at the outset of the venture. A strong market orientation makes all the difference when it comes to separating successful vs unsuccessful industrial new products. The marketing variables generally dominated the important sets of variables in determining product outcomes. The manner in which the market-oriented activities were undertaken during the new product process had a major impact on success; similarly, market information was the critical factor in
the analysis of information requirements. Finally, the nature of the elements of the marketing mix were closely related to success/failure, with “product” as clearly the core or central strategy. ACKNOWLEDGMENT This research was funded by the Associates’ Workshop on Business Research, School of Business Administration, The University of Western Ontario (Canada), and by the Department of Industry, Trade and Commerce, Office of Science and Technology, Canadian Federal Government.
REFERENCES Centre for the Study of Industrial Innovation, Success cmd Failure in Industrial Innovation: Report on Project Sappho (February 1972). Cooper, Robert G., Introducing Successful Journal of Marketing, 10 (6), 24-25.
New Products,
European
Directory of’ Research and Development Establishments in Cmudiun Industry 1973, Ministry of State, Science and Technology, Information Canada, Ottawa, 1974. Rubenstein. A. H., Chakrabarti, A. K., and O’Keefe, R. D., Field Studies of the Technological Innovation Process, in Progress in Assessing Technologiml Innovation 1974, H. R. Clauser, ed., NSF, Technome Publications, Westport, Corm., 1975.
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