Key Success and Failure Paths in Fashion Marketing Strategies

Both successful and unsuccessful design+marketing projects in high fashion products and services represent creating and implementing recipes or paths of key success factors (KSFs). While implementing any one KSF is not sufficient for success, creating and taking certain paths that includes partially-independent KSFs is sufficient for success; other paths lead to failure; some paths are never taken because they are never though of or designers consider them to be totally unrealistic options. Consequently, fashion marketing strategists need to look beyond research attempting to learn the net effects of independent influences of KSFs. Configurations (i.e., recipes) representing alternative combinations of design+marketing dimensions are indicators of sufficiency for success versus failure for fashion marketing projects. The study of alternative decision configurations is particularly useful for fashion marketing strategists and researchers. The objectives of this article include (1) describing keys success/failure path (KS/FP) theory and (2) illustrating configural thinking processes for a design+marketing firm that focuses on fashion household accessories. “Design+marketing” is a term used here to indicate the strategy operating philosophy of creating unique designs that are successful in the marketplace. This article applies propositions in a theory of KS/FP theory to design+marketing contexts. A major objective present article is to propose a theory of KS/FPs. The core tenants of KS/FP theory are applicable for fashion marketing strategies. The core tenants include the following propositions: (1) No one KSF is sufficient nor likely necessary for success(2) No one KSF is necessary for success(3) Decision paths occur in executing fashion marketing strategies(4) Some of these decision paths are sufficient, but not necessary, for success(5) Some of these paths result in failure for new products or services(6) Mail surveys using 5 or 7 point Likert scales are insufficient for explicating the nitty-gritty specifics of dimensions and configurations occurring in KS/FPs. The article reports on findings of a case study that takes the perspective that the design+marketing strategists having completed more than one hundred (or 200 to 500) new fashion marketing projects have developed mental models representing successful and unsuccessful combinations (paths) of decisions that occur within these projects. The case study is developed here from a series of interviews with a chief executive officer (CEO) and leading designing for a well-known fashion marketing firm for household accessories, Alberto Alessi. The interviews were completed at Alberto Alessi’s design studio and headquarters by McKinsey Corporation (a consultancy firm). The article describes how to use configural comparative analysis (CCA) which includes applying Boolean algebra rather than matrix algebra to test combinations within antecedent conditions (e.g., recipes that include a specific level of each of the four dimensions in the Alessi model). Both crisp set (binary levels) and fuzzy set (0.00 to 1.00) values are sometimes used in CCA modeling. Two particularly useful operations in set theory include the computing the value for combinations of two or singular antecedent conditions. The lowest value among the two or more dimensions is the amount the two dimensions share income. Consider the combination of the following four singular antecedent conditions into one complex antecedent condition expressed as Q·S·R·D=.20. The mid-level dot (·) signifies the operation, “and”; the value of .20 represents this complex antecedent condition because .20 is the lowest fuzzy set values among the following four dimensions (the numbers in the parentheses represent fuzzy set scores with 0.00 indicate non-membership and 1.00 full membership in the dimension); Q = A high-quality new product process (.90)S = A defined new product strategy for the business unit (.85)R = Adequate resources-people and money-for new products (.50)D = R&D spending on new products (as % of the business’s sales) (.20). Crisp or fuzzy set scoring also applies for the outcome condition (e.g., accepting further design+marketing development of the new fashion product-service, or low to high profitability). A complex antecedent condition is found to be highly consistent in its relationship with an outcome condition across a number of new design+marketing case studies when the sum is totaled by taking the lowest value for each antecedent-outcome pair and divided by the sum of all antecedent values across all the case studies. CCA provides a straightforward relatively easy to understand method for describing and understanding the impact of complex, configural, antecedent conditions on an outcome condition. Unlike statistical analyses using correlation methods (e.g., multiple or probit regression methods), CCA assumes asymmetrical not symmetrical relationships among antecedent (X) and outcome (Y) values. Thus, unlike correlation methods, assuming an asymmetric relationship recognizes that low values on the antecedent condition can relate to both low and high values on the outcome condition. For high sufficiency, finding consistent with a substantial relationship between an antecedent and outcome condition occurs when high values only occur for the outcome condition when the values are high for the antecedent condition. For the same highly consistent model, values may be low and high for the outcome condition for low vales for the antecedent condition-high values for the outcome condition paired with low values for the antecedent condition in such models simply indicates additional paths to high values in the outcome condition exist along with the model showing that when the antecedent condition is high, the outcome condition is always high.