Personalized Communications : A Cross Media tool for the future

Personalized communication is when the marketing message is adapted to each individual by using information from a databaseand utilizing it in the various, different media channels available today. That gives the marketer the possibility to create a campaign that cuts through today’s clutter of marketing messages and gets the recipients attention. PODi is a non-profit organization that was started with the aim of contributing knowledge in the field of digital printingtechnologies. They have created a database of case studies showing companies that have successfully implemented personalizedcommunication in their marketing campaigns. The purpose of the project was therefore to analyze PODi case studies with the main objective of finding out if/how successfully the PODi-cases have been and what made them so successful. To collect the data found in the PODi cases the authors did a content analysis with a sample size of 140 PODi cases from the year 2008 to 2010. The study was carried out by analyzing the cases' measurable ways of success: response rate, conversion rate, visited PURL (personalized URL:s) and ROI (Return On Investment). In order to find out if there were any relationships to be found between the measurable result and what type of industry, campaign objective and media vehicle that was used in the campaign, the authors put up different research uestions to explore that. After clustering and merging the collected data the results were found to be quite spread but shows that the averages of response rates, visited PURL and conversion rates were consistently very high. In the study the authors also collected and summarized what the companies themselves claim to be the reasons for success with their marketing campaigns. The resultshows that the creation of a personalized campaign is complex and dependent on many different variables. It is for instance ofgreat importance to have a well thought-out plan with the campaign and to have good data and insights about the customer in order to perform creative personalization. It is also important to make it easy for the recipient to reply, to use several media vehicles for multiple touch points and to have an attractive and clever design.

A Microdata Analysis Approach to Transport Infrastructure Maintenance

Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy. This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance. Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies. In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.