Though almost every insurer provides an integrated solution for online product research and purchase for existing and potentially new customers, there is still a significant percentage of customers turning into research-shoppers, a practice of using one channel for products search and another for purchase. This trend is visible from the channel usage statistics: according to various studies, while more than half of the customers worldwide use the insurers own website for product research, only a minor percentage of them stays there for purchase purposes. The preferred purchase channel often remains the one that enables personal contact to the sales person. This situation is mostly due to the high complexity of the insurance products. In addition, insurance products belong to the category of experience goods, where the evaluation of the product price and characteristics is difficult and can be based only upon previous experience, e.g. after experiencing a claim. While channel switch might lead to higher profit since multichannel customers were found to spend more, the change of the insurer is a serious threat. In this paper we address this issue and analyse the research-shopper phenomenon in the insurance industry. We investigate which customer and policy characteristics influence the research-shopping behaviour in terms of duration from research conducted via an online channel to purchase conducted using offline channels. Our empirical study was based on a sample of approximately 10 000 researchshopper customers of a large Swiss insurance company across the three insurance products: motor, household/liability and travel insurance. The obtained results show that there are several customer characteristics that have an effect over the duration to purchase and that these characteristics differ across different products. Our findings are relevant to academics and practitioners alike and are important for multichannel management and better understanding of the customer journey.

Purpose The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling. Design/methodology/approach For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted. Findings Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage. Research limitations/implications The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance. Practical implications This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices. Originality/value This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.

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<p>Accurate solar resource assessments are essential to project a solar photovoltaic (PV) plant’s energy production – and ultimately forecast its revenue.</p><p>Solar resource assessments are the bedrock of the ‘Revenue’ line in PV financial models. In today’s competitive financing environment, the assumptions underlying solar resource assessment often have make-or-break impact on project valuations. It’s critical that investors trust the numbers provided.</p><p>To quantify solar resource, industry typically compares different irradiation databases derived from multiple physical sources – whether measurements or satellite images. There is always some level of scatter; in Western Europe this is often around 3%, after excluding outliers.  Satellite database are never as good as accurate ground measurement.  And the rather narrow variation observed is due to past calibration of satellite derived model with data from weather stations.  The reality can be different when it comes to Ireland. </p><p>The solar sector is currently experiencing a rapid development in the Republic of Ireland, making the yield assessment and by extension the solar resource estimation key for the bankability of the projects.</p><p>The aim of our work was the validate the accuracy of different databases, available in Ireland.</p><p>The first step of this analysis will be to qualify our data sources. Everoze and Brightwind have access to measurement campaigns from multiple solar projects in Ireland. All the gathered dataset will be processed, applying state of the art quality control, to retain only trustable information.  The quality check will also include the sensors themselves, with a verification of the accuracy and calibration certificates of the different pieces of equipment.</p><p>In a second step, the qualified datasets will be used to compare satellite derived data.  We plan to use CAMS, SolarGIS and Meteonorm.  The intention is to categorise our results in regions, classified based on the difference in annual irradiation between different databases in order to reduce uncertainty – and ultimately boost investor confidence in energy yield assessments.</p>