Key Success and Failure Paths in Fashion Marketing Strategies

Abstract

Both successful and unsuccessful design+marketing projectsin high fashion products and services represent creating andimplementing recipes or paths of key success factors (KSFs). While implementing any one KSF is not sufficient for success,creating and taking certain paths that includespartially-independent KSFs is sufficient for success; other pathslead to failure; some paths are never taken because they arenever though of or designers consider them to be totallyunrealistic options. Consequently, fashion marketing strategists need to lookbeyond research attempting to learn the net effects ofindependent influences of KSFs. Configurations (i.e., recipes)representing alternative combinations of design+marketingdimensions are indicators of sufficiency for success versusfailure for fashion marketing projects. The study of alternativedecision configurations is particularly useful for fashionmarketing strategists and researchers. The objectives of this article include (1) describing keyssuccess/failure path (KS/FP) theory and (2) illustratingconfigural thinking processes for a design+marketing firm thatfocuses on fashion household accessories. “Design+marketing”is a term used here to indicate the strategy operatingphilosophy of creating unique designs that are successful in themarketplace. This article applies propositions in a theory ofKS/FP theory to design+marketing contexts. A major objective present article is to propose a theory ofKS/FPs. The core tenants of KS/FP theory are applicable forfashion marketing strategies. The core tenants include thefollowing propositions:(1) No one KSF is sufficient nor likely necessary forsuccess(2) No one KSF is necessary for success(3) Decision paths occur in executing fashion marketingstrategies(4) Some of these decision paths are sufficient, but notnecessary, for success(5) Some of these paths result in failure for new productsor services(6) Mail surveys using 5 or 7 point Likert scales areinsufficient for explicating the nitty-gritty specifics of dimensions and configurations occurring in KS/FPs. The article reports on findings of a case study that takesthe perspective that the design+marketing strategists havingcompleted more than one hundred (or 200 to 500) new fashionmarketing projects have developed mental models representingsuccessful and unsuccessful combinations (paths) of decisionsthat occur within these projects. The case study is developedhere from a series of interviews with a chief executive officer(CEO) and leading designing for a well-known fashionmarketing firm for household accessories, Alberto Alessi. Theinterviews were completed at Alberto Alessi’s design studioand headquarters by McKinsey Corporation (a consultancyfirm). The article describes how to use configural comparativeanalysis (CCA) which includes applying Boolean algebra ratherthan matrix algebra to test combinations within antecedentconditions (e.g., recipes that include a specific level of each ofthe four dimensions in the Alessi model). Both crisp set(binary levels) and fuzzy set (0.00 to 1.00) values aresometimes used in CCA modeling. Two particularly useful operations in set theory include thecomputing the value for combinations of two or singularantecedent conditions. The lowest value among the two ormore dimensions is the amount the two dimensions shareincome. Consider the combination of the following foursingular antecedent conditions into one complex antecedentcondition expressed as Q·S·R·D=.20. The mid-level dot (·)signifies the operation, “and”; the value of .20 represents thiscomplex antecedent condition because .20 is the lowest fuzzyset values among the following four dimensions (the numbersin the parentheses represent fuzzy set scores with 0.00 indicatenon-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 newproducts (.50)• D = R&D spending on new products (as % of thebusiness’s sales) (.20). Crisp or fuzzy set scoring also applies for the outcomecondition (e.g., accepting further design+marketing developmentof the new fashion product-service, or low to highprofitability). A complex antecedent condition is found to behighly consistent in its relationship with an outcome conditionacross a number of new design+marketing case studies when the sum is totaled by taking the lowest value for eachantecedent-outcome pair and divided by the sum of all antecedent values across all the case studies. CCA provides a straightforward relatively easy to understandmethod for describing and understanding the impact ofcomplex, configural, antecedent conditions on an outcomecondition. Unlike statistical analyses using correlation methods(e.g., multiple or probit regression methods), CCA assumesasymmetrical not symmetrical relationships among antecedent(X) and outcome (Y) values. Thus, unlike correlation methods,assuming an asymmetric relationship recognizes that low valueson the antecedent condition can relate to both low and highvalues on the outcome condition. For high sufficiency, findingconsistent with a substantial relationship between an antecedentand outcome condition occurs when high values only occur forthe outcome condition when the values are high for theantecedent condition. For the same highly consistent model,values may be low and high for the outcome condition forlow vales for the antecedent condition-high values for theoutcome condition paired with low values for the antecedentcondition in such models simply indicates additional paths tohigh values in the outcome condition exist along with themodel showing that when the antecedent condition is high, theoutcome condition is always high.