Data and behavioral contracts for web services

The recent trend in Web services is fostering a computing scenario where loosely coupled parties interact in a distributed and dynamic environment. Such interactions are sequences of xml messages and in order to assemble parties – either statically or dynamically – it is important to verify that the “contracts” of the parties are “compatible”. The Web Service Description Language (wsdl) is a standard used for describing one-way (asynchronous) and request/response (synchronous) interactions. Web Service Conversation Language extends wscl contracts by allowing the description of arbitrary, possibly cyclic sequences of exchanged messages between communicating parties. Unfortunately, neither wsdl nor wscl can effectively define a notion of compatibility, for the very simple reason that they do not provide any formal characterization of their contract languages. We define two contract languages for Web services. The first one is a data contract language and allow us to describe a Web service in terms of messages (xml documents) that can be sent or received. The second one is a behavioral contract language and allow us to give an abstract definition of the Web service conversation protocol. Both these languages are equipped with a sort of “sub-typing” relation and, therefore, they are suitable to be used for querying Web services repositories. In particular a query for a service compatible with a given contract may safely return services with “greater” contract.

Customer Evolution in Sales Channel Migration

This research has been triggered by an emergent trend in customer behavior: customers have rapidly expanded their channel experiences and preferences beyond traditional channels (such as stores) and they expect the company with which they do business to have a presence on all these channels. This evidence has produced an increasing interest in multichannel customer behavior and it has motivated several researchers to study the customers’ channel choices dynamics in multichannel environment. We study how the consumer decision process for channel choice and response to marketing communications evolves for a cohort of new customers. We assume a newly acquired customer’s decisions are described by a “trial” model, but the customer’s choice process evolves to a “post-trial” model as the customer learns his or her preferences and becomes familiar with the firm’s marketing efforts. The trial and post-trial decision processes are each described by different multinomial logit choice models, and the evolution from the trial to post-trial model is determined by a customer-level geometric distribution that captures the time it takes for the customer to make the transition. We utilize data for a major retailer who sells in three channels – retail store, the Internet, and via catalog. The model is estimated using Bayesian methods that allow for cross-customer heterogeneity. This allows us to have distinct parameters estimates for a trial and an after trial stages and to estimate the quickness of this transit at the individual level. The results show for example that the customer decision process indeed does evolve over time. Customers differ in the duration of the trial period and marketing has a different impact on channel choice in the trial and post-trial stages. Furthermore, we show that some people switch channel decision processes while others don’t and we found that several factors have an impact on the probability to switch decision process. Insights from this study can help managers tailor their marketing communication strategy as customers gain channel choice experience. Managers may also have insights on the timing of the direct marketing communications. They can predict the duration of the trial phase at individual level detecting the customers with a quick, long or even absent trial phase. They can even predict if the customer will change or not his decision process over time, and they can influence the switching process using specific marketing tools