In forecasting, evolutionary algorithms are often linked to existing forecasting methods to optimize their input parameters. Traditionally, the fitness function of these search heuristics is based on an accuracy measure. In this paper, however, we combine forecasting accuracy with business expertise by defining a flexible and easily interpretable profit function for sales forecasting, which is based on the profit margin of a given product, the volume of its sales and the accuracy of the forecast. ProfARIMA is a new procedure that selects the lags of a Seasonal ARIMA model according to the profit of a model's forecasts by taking advantage of search heuristics. This procedure is tested on both publicly available datasets and a real-life application with datasets of The Coca-Cola Company in order to assess its performance, both in profit and accuracy. Three different evolutionary algorithms were implemented during this testing process, i.e. Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing. The results indicate that ProfARIMA always performs at least equally to the Box-Jenkins methodology and often outperforms this traditional procedure. For The Coca-Cola Company, our new algorithm in combination with Genetic Algorithms even leads to a significantly larger profit for out-of-sample forecasts.

The success of retention campaigns in fast-moving and saturated markets, such as the telecommunication industry, often depends on accurately predicting potential churners. Being able to identify certain behavioral patterns that lead to churn is important, because it allows the organization to make arrangements for retention in a timely manner. Moreover, previous research has shown that

Generating insights and value from data has become an important asset for organizations. At the same time, the need for experts in analytics is increasing and the number of analytics applications is growing. Recently, a new trend has emerged, i.e. analytics-as-a-service platforms, that makes it easier to apply analytics both for novice and expert users. In this study, we approach these new services by conducting a full-factorial experiment where both inexperienced and experienced users take on an analytics task with an analytics-as-a-service technology. Our research proves that although experts in analytics still significantly outperform novices, these web-based platforms do offer an advantage to inexperienced users. Furthermore, we find that analytics-as-a-service does not offer the same benefits across different analytics tasks. That is, we observe better performance for supervised analytics tasks. Moreover, this study indicates that there are significant differences between novices. The most important distinction lies in the approach they take on the task. Novices who follow a more complex, although structured, workflow behave more similarly to experts and, thus, also perform better. Our findings can aid managers in their hiring and training strategy with regards to both business users and data scientists. Moreover, it can guide managers in the development of an enterprise-wide analytics culture. Finally, our results can inform vendors about the design and development of these platforms.