on 17-Jan-2023 (Tue)

Because of their typical high-dimensionality, the hidden states of RNN models are usually more potent than that of hidden markov models (e.g., Netzer, Lattin, & Srinivasan, 2008), which are commonly used in marketing to capture customer dynamics. The HMM has N discrete hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001). On the other hand, the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017). RNNs combine a large number of distributed hidden states with nonlinear dynamics to update these hidden states, thereby allowing it to have a more substantial representational capacity when compared with an HMM

While an RNN can carry forward useful information from one timestep to the next, however, it is much less effective at capturing long-term dependencies (Bengio, Simard, & Frasconi, 1994; Pascanu, Mikolov, & Bengio, 2013). This limitation turns out to be a crucial problem in marketing analytics.

The effect of a direct mailing does not end after the campaign is over, and the customer has made her decision to respond or not. An advertising campaign or customer retention program can impact customers' behaviors for several weeks, even months. Customers tend to remember past events, at least partially. Hence, the effects of marketing actions tend to carry- over into numerous subsequent periods (Lilien, Rangaswamy, & De Bruyn, 2013; Schweidel & Knox, 2013; Van Diepen et al., 2009). The LSTM neural network, which we introduce next, is a kind of RNN that has been modified to effectively capture long-term dependencies in the data

The logit model performs remarkably well at high lift values (i.e., 20%), whereas the random forest model shines at lower lift values (lift at 1%). This result might suggest that the best traditional model to deploy depends on the degree of targeting the analyst seeks. Random forest models are particularly good at identifying tiny niches of super-responsive donors, and therefore are well suited for ultra-precise targeting. Logit models generalize well. Hence, they appear more appropriate to target wider portions of the donor base.

Brand Choice and Market Share Forecasting Using Scanner Data

Demand forecasting for products within a category is a critical task for retailers and brand managers alike. The multinomial logit model (MNL) is commonly used to predict brand choice and market share using marketing-mix and loyalty variables (Guadagni & Little, 1983). Artificial feedforward neural networks (ANN) have also been shown to effectively predict household brand choices, as well as brand market shares (Agrawal & Schorling, 1996). Since brand choices can be modeled as sequential choices, and data complexity increases exponentially with the number of brands (with interaction effects), LSTM neural networks offer suitable alternatives. Similar to our studies, we could encode brand choices and the decision environment as we encoded solicitations and donations: as a multidimensional vector. We conjecture that testing the performance of LSTM neural networks in the context of brand choices would constitute an exciting replication area.

From Neural Network architectures perspective, close to our work is Deep Neural Network Ensembles for Time Series Classification [8]. In this paper, authors show how an ensemble of multiple Convolutional Neural Networks can improve upon the state-of-the-art performance of individual neural networks. They use 6 deep learning classifiers in- including Multi-Layer Perceptron, Fully Convolutional Neural Network, Residual Network, Encoder [20], Multi-Channels Deep Convolutional Neural Networks [29] and Time Convolutional Neural Network [28]. The first three were originally proposed in [24]. We propose the application of such architectures in the consumer choice world and apply the concept of entity embeddings [9] along with neural network architectures like Multi-Layer Perceptron, Long Short Term Memory (LSTM), Temporal Convolutional Networks (TCN) [13] and TCN-LSTM

How to Force User to Change Password at Next Login in Linux

User name: ravi

# passwd --expire ravi

图像识别、时间序列预测、 情感分析、图像和文字生成等

In this paper, we have shown that recent neural network architectures, traditionally used in natural language processing and machine translation, could effectively do away with the complicated and time-consuming step of feature engineering, even when applied to highly structured problems such as predicting the future behaviors of a panel of customers.

The RNN processes the entire sequence of available data without having to summarize it into features.

You can specify the input shape argument that expects a tuple containing the number of time steps and the number of features

For a multiclass classification problem, the results may be in the form of an array of probabilities (assuming a one hot encoded output variable) that may need to be converted to a single class output prediction using the argmax() NumPy function.

Response models in direct marketing predict customer responses from past customer behavior and marketing activity. These models often summarize past events using features such as recency or frequency and the process of feature engineering has received significant attention

The connection between input and decision is then handled objectively by an Artificial Neural Network. This also means that the model is highly adaptive. If the goals of the agents, their input or properties of the system change, retraining the Neural Network is the only adaptation that is necessary.

The choice of activation function is most important for the output layer as it will define the format that predictions will take.

In the future, predictions on the level of products and individual tastes will be in our focus, enabling sophisticated recommendation products. This will require richer input descriptions at individual time-steps. Likewise, more sophisticated RNN architectures will be promising for future research

Demand forecasting for products within a category is a critical task for retailers and brand managers alike. The multinomial logit model (MNL) is commonly used to predict brand choice and market share using marketing-mix and loyalty variables (Guadagni & Little, 1983)

Because of their typical high-dimensionality, the hidden states of RNN models are usually more potent than that of hidden markov models (e.g., Netzer, Lattin, & Srinivasan, 2008), which are commonly used in marketing to capture customer dynamics.

The HMM has N discrete hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001). On the other hand, the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017). RNNs combine a large number of distributed hidden states with nonlinear dynamics to update these hidden states, thereby allowing it to have a more substantial representational capacity when compared with an HMM