Table 26: It Was Selected Randomly From A Larger Database

Table 26 It Was Selected Randomly From A Larger Database To Be The T

Table 2.6; it was selected randomly from a larger database to be the training set. Personal Loan indicates whether a solicitation for a personal loan was accepted and is the response variable. A campaign is planned for a similar solicitation in the future, and the bank is looking for a model that will identify likely responders. Examine the data carefully and indicate what your next step would be. In fitting a model to classify prospects as purchasers or nonpurchasers, a certain company drew the training data from internal data that include demographic and purchase information.

Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data included. It was found that “refund issued” was a useful predictor in the training data. Why is this not an appropriate variable to include in the model?

Paper For Above instruction

The process of developing predictive models in marketing analytics requires careful consideration of the data quality, relevance, and applicability to future use cases. When a dataset is obtained from the internal records of a firm, it often contains detailed and proprietary information, including variables that directly or indirectly reflect the purchase behavior of customers. In the context of building a classification model to predict whether prospects will respond positively to a solicitation, the selection of predictor variables is critical for the model's accuracy and generalizability. One such variable, “refund issued,” has been identified in the internal dataset as a useful predictor during model training. However, its inclusion in the model when applying to future data obtained from external sources raises significant concerns regarding its appropriateness.

First, the fundamental issue pertains to the nature and origin of the “refund issued” variable. In the internal dataset, this variable likely reflects a transaction or event that occurred in connection with previous purchases or customer interactions. The internal dataset may include detailed purchase histories, including whether refunds were issued as part of customer service or product return processes. Because this information is directly linked to past purchasing behavior, it can have a high predictive validity within the internal dataset. However, the external data, sourced from purchased lists, only contain demographic information and lack detailed purchase histories or transaction-specific variables.

Utilizing "refund issued" as a predictor in the model trained on internal data would lead to a problematic disconnect when applying the model to the external data. This is because "refund issued" will not be available in the external datasets; thus, the model would rely on a variable that is missing or undefined in the new data, resulting in poor predictive performance or the inability to generate predictions at all. This is an issue of variable relevance and data compatibility, fundamentally violating the assumption that predictor variables should be available and consistent across all data used for model application.

Second, including variables that are not available or not relevant in the new data can introduce bias or distortions in the predictions. The “refund issued” variable might be correlated with other internal factors such as customer loyalty, transaction volume, or complaint resolution processes, which are not necessarily associated with future prospects when only demographic data is available. As a result, the model would be overfitted to the peculiarities of the internal dataset and less applicable to the external data, reducing the model’s predictive validity and utility in practice.

Third, from a modeling best practices perspective, predictor variables should be chosen based on their theoretical relevance, stability over time, and availability across different datasets. Variables that depend on transactional details, such as refunds, typically fluctuate over time and across different data sources, especially when comparing internal records to purchased external lists. Relying on such variables diminishes the model's robustness and hampers its ability to generalize to new, unseen data.

In closing, the primary reason why “refund issued” is not an appropriate variable for inclusion in the predictive model, given the external application context, is that it is not available in the future data sets on which predictions are to be made. Incorporating variables that are not consistently available across datasets leads to models that are not operationally feasible and can produce unreliable results. Therefore, it is crucial to select predictor variables that are both relevant and accessible in all datasets to ensure the effectiveness of the classification model.

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