5,878 research outputs found
Modeling toothpaste brand choice: An empirical comparison of artificial neural networks and multinomial probit model
Copyright @ 2010 Atlantis PressThe purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight
Neural nets - their use and abuse for small data sets
Neural nets can be used for non-linear classification and regression models. They have a big advantage
over conventional statistical tools in that it is not necessary to assume any mathematical form for the
functional relationship between the variables. However, they also have a few associated problems chief of
which are probably the risk of over-parametrization in the absence of P-values, the lack of appropriate
diagnostic tools and the difficulties associated with model interpretation. The first of these problems is
particularly important in the case of small data sets. These problems are investigated in the context of real
market research data involving non-linear regression and discriminant analysis. In all cases we compare
the results of the non-linear neural net models with those of conventional linear statistical methods. Our
conclusion is that the theory and software for neural networks has some way to go before the above
problems will be solved
Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks
The concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model for this two-step process, where we take into account that consideration sets usually are not observed. It turns out that our model is an artificial neural network, where the consideration set corresponds with the hidden layer. We discuss representation, parameter estimation and inference.We illustrate our model for the choice between six detergent brands and show that the model improves upon a one-step multinomial logit model, in terms of fit and out-of-sample forecasting.brand choice;consideration set;artificial neural network
Forecasting for Marketing
Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios.forecasting, marketing
Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks
The concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model for this two-step process, where we take into account that consideration sets usually are not observed. It turns out that our model is an artificial neural network, where the consideration set corresponds with the hidden layer. We discuss representation, parameter estimation and inference.
We illustrate our model for the choice between six detergent brands and show that the model improves upon a one-step multinomial logit model, in terms of fit and out-of-sample forecasting
Demand Forecasting: Evidence-Based Methods
In recent decades, much comparative testing has been conducted to determine which forecasting methods are more effective under given conditions. This evidence-based approach leads to conclusions that differ substantially from current practice. This paper summarizes the primary findings on what to do â and what not to do. When quantitative data are scarce, impose structure by using expert surveys, intentions surveys, judgmental bootstrapping, prediction markets, structured analogies, and simulated interaction. When quantitative data are abundant, use extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, use econometrics when prior knowledge is strong, data are reliable, and few variables are important. When there are many important variables and extensive knowledge, use index models. Use structured methods to incorporate prior knowledge from experiments and expertsâ domain knowledge as inputs to causal forecasts. Combine forecasts from different forecasters and methods. Avoid methods that are complex, that have not been validated, and that ignore domain knowledge; these include intuition, unstructured meetings, game theory, focus groups, neural networks, stepwise regression, and data mining
Optimal advertising campaign generation for multiple brands using MOGA
The paper proposes a new modified multiobjective
genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting
the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these
results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using
the real data sets from the Canadian advertising industry
Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007
The tourism and hospitality industries have widely adopted information
technology (IT) to reduce costs, enhance operational efficiency, and most importantly to
improve service quality and customer experience. This article offers a comprehensive review of
articles that were published in 57 tourism and hospitality research journals from 2005 to 2007.
Grouping the findings into the categories of consumers, technologies, and suppliers, the article
sheds light on the evolution of IT applications in the tourism and hospitality industries. The
article demonstrates that IT is increasingly becoming critical for the competitive operations of
the tourism and hospitality organizations as well as for managing the distribution and
marketing of organizations on a global scale
The Impact of Television and Short Message Service Advertising on Customer Behaviour and Brand Attitude
Marketing, advertising, and communications processes have changed to strategically capitalize on an increasingly digitally transformed, technologically empowered, globally interconnected consumer, or what service-dominant logic refers to as actors that are resource integrators. Customers are co-creators of value in the collaborative or sharing economy, and seek to actively reap the benefits of new knowledge growing at an exponential rate. However, developing models of customer behavior, especially the influence of a new kind of advertising based on the integrated use of television, web, and social networks, is a challenge. Our study starts from a preliminary empirical observation of the impact of television cooking shows on the variations of potential demand (queries on Google) and the purchase of branded/unbranded culinary products used on the show. Neural networks were used to determine significant correlations, which resulted in an operative Marketing 3.0 model. This model clearly explicates this impact factor on the consumer-purchasing process generated by a new mode of creating information and communications technologyâbased communication. Keywords: Customer behavior, knowledge management, online advertising, smart consumer, value co-creatio
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