A methodology for classification and validation of customer datasets

Purpose- The purpose of this research is to develop a method to classify customers according to their value to an organization. This process is complicated by the disconnected nature of a customer record in an industry such as insurance. With large numbers of customers, it is of significant benefit to managers and company analysts to create a broad classification for all customers. Design/Methodology/Approach- The initial step is to construct a full customer history and extract a feature set suited to Customer Lifetime Value calculations. This feature set must then be validated to determine its ability to classify customers in broad terms. Findings- Our method successfully classifies customer datasets with an accuracy of 90%. We also discovered that by examining the average value for key variables in each customer segment, an algorithm can label the group of clusters with an accuracy of 99.3%. Research limitations/implications- Working with a real-world dataset, it is always the case that some features are unavailable as they were never recorded. This can impair the algorithm’s ability to make good classifications in all cases. Originality/Value- We believe that this research makes a novel contribution as it automates the classification of customers but in addition, our approach provides a high level classification result (recall and precision identifies the best cluster configuration) and detailed insights into how each customer is classified by two validation metrics. This supports managers in terms of market spend on new and existing customers

Infotainment may increase engagement with science but It can decrease perceptions of seriousness

We presented 867 participants with one of two videos about climate change that differed only in terms of whether they had an infotainment or expository narration. They were available in either English or Spanish. The participants consisted of two distinct clusters: one in which all were over 30 with a university degree, and another dominated by younger participants without a university degree. The infotainment version produced a significantly reduced perception of the seriousness of climate change for the planet in the latter cluster. Furthermore, viewers of the English versions, who were predominantly residents in countries with low-context cultures, perceived the risk of climate change for the planet to be significantly higher after watching the video with the expository narration. Using infotainment for science communication is a two-edged sword: while it may help engagement, making light of a topic can reduce perceptions about its seriousness. We suggest that the use of infotainment should be determined by the aims of the communicators and the nature of the target audience. If the purpose is simply to convey information, then infotainment is likely to be the most effective and it has the additional benefit of engaging recipients that lack a university education. However, if the purpose is to affect attitudes and persuade an audience, then an expository narration is likely to be most effective