PurposeThe purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.Design/methodology/approachStarting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).FindingsAfter comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.Research limitations/implicationsThe results of this methodology are strictly applicable to the retailer which provided the data.Practical implicationsEven though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.Social implicationsCustomer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.Originality/valueThis paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.

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Modern cities face the challenge of providing citizens with an appropriate level of services to maintain the growing population. Thanks to the support of open data policymakers are capable of ensuring administrative transparency and participation in decisions, enabling citizens and employees to effectively use services and tools and integrating physical and intangible infrastructures (systems, data and processes) in a service-oriented perspective. This study investigates open data about car accidents in the metropolitan city of Rome between 2014 and 2019 through the service science lens. It is pointed out how the city roads maintenance (for example, road surfaces, road signs and traffic extent) can significantly affect the number of people involved in accidents. From these results, possible improvements in diminishing the number of people involved in car accidents are explored through a prescriptive analysis. This study represents a powerful tool to improve services in the public sphere and an example of the shared value generated by open data initiatives. It contributes in improving the understanding of a data-oriented culture and of building a network of people in all public administrations to increase the shareable information assets of the metropolitan city of Rome.