Research On The Method Of Evaluating The Feature Fatigue’s Effect On Customer Equity

Feature fatigue (FF) refers to the phenomenon that customers preferproducts with more features before use, but may also be frustrated anddiscontented with the usability problem caused by so many features. Addingfeatures to a product can really increase its initial sales, but will also make itmore complex to use, which will make customer dissatisfied with the product,and even cause sales return, thus, will damage the long term profits of thecompany, and also customer equity (CE).Nowadays, as the manufacturing technology and product design becomesmore and more effective and advanced. Many companies provide productswith more features which make the product features explosion more serious,also the FF problem. FF is now one of the important factors which can affectCE. Thus, alleviating and defeating FF becomes an important problem to solve.We propose some models to solve this problem as there are few methods andmodels till now. Our research contains mainly three parts:First, the FF-CE evaluation model based on historic data is proposed. Lossin CE is the most important reason why FF should be considered. It means that the FF-CE evaluation model should be analyzed. We analyze the evaluationmodel in two different cases——enough historic data or none historic data. Wesolve the previous one by a method based on general regression neural network(GRNN). First, a concept of Factors Vector of Product Feature Fatigue isproposed to depict the degree of feature fatigue, and its obtaining method ispresented by processing data. Then, the fruit fly algorithm optimized GRNN(FOAGRNN) is used to predict the effects of adding different feature-combinations on customer equity, thus providing decision supports for firmsto alleviate or eliminate feature fatigue. At last a case is presented and theresults show that the proposed method works well.Then another FF-CE evaluation model is proposed with none historic data.We consider three cases here—single product in the market, competition andsuccessive generations of products.1) For the first case, the evaluation modelis built based on Bass Model. First, the relationship between product featurefatigue and customer equity is discussed. Then, based on the comprehensiveanalysis of FF, customer transition and Bass Model, the parameters of themodel are ascertained to rebuild Bass Model. Thus the product diffusionprocess and the corresponding CE, which can evaluate product feature fatigue,are obtained in consideration of FF. Finally, a case study is presented and theconclusion is also given.2) For the competitive case, a competitive modelbased on previous Bass model is proposed to evaluate FF effects on CE.3) Forthe last case, a improved Norton-Bass model is proposed to solve the problem.For each model, case study is proposed to show the efficiency and effectivenessof the model.At last, the feature combinations optimization problem is also solved inthe consideration of FF. As our ultimate goal is to provide decision supports for the designers to maximize CE based on alleviating and defeating FF. Amodel integrated with genetic algorithm (GA) based on duffusion theory isproposed to maximize CE by searching for the best feature combination. Twocases are also presented to prove the efficiency the effectiveness of the model.In summary, this dissertation proposes a methodology of FF analysis andproduct feature combinations optimization, which provides decision supportsfor manufacturers in product development, thus improving manufacturers’ CE.