No abstract

Purpose: Data-driven decision-making is a growing trend that lots of companies are nowadays willing to adopt. However, the organizational transformation needed is not always as simple and logical as it could seem and the comfort of the old habits can dim the change effort. The purpose of this study is to identify the potential problems that may arise in a real company’s transformation from a traditional intuition-driven decision-making model to a data-driven model. Design/methodology/approach: In order to reach this goal, a single case study method was used. Initially a literature review was conducted to analyze both the importance of the change to a data-driven culture and the process of organizational change. Thus, a case study method was adopted in a company of the automotive sector that included experimentation in the website design decision-making process. Findings: As a result of the case study, it was found that all the most cited risks for the organizational change process commented in the literature appeared in the project. However, even being warned of potential dangers the specific actions to prevent the damages were not trivial.Originality/value: The study presents in detail, the application of an organizational change model in a company. Important insights can be extracted from the specific case of a digitalization performed inside traditional industrial company.

The importance of companies' website as instrument for relationship marketing activities is well-known both in the academia and in the industry. In the last decades, there has been great interest in studying how technology can be used to influence people's attitudes and motivate behavior change. With this, web personalization has had increasing research and practitioner interest. However, the evaluation of user interaction with companies' websites and personalization effects remains an elusive goal for organizations. Online controlled experiments (A/B tests) are one of the most commonly known and used techniques for this online evaluation. And, while there is clearly value in evaluating personalized features by means of online controlled experiments, there are some pitfalls to bear in mind while testing. In this paper we present five experimentation pitfalls, firstly identified in an automotive company's website and found to be present in other sectors, that are particularly important or likely to appear when evaluating personalization features. In order to obtain the listed pitfalls, different methods have been used, including literature review, direct, and indirect observation within organizations of the automotive sector and a set of interviews to organizations form other sectors. Finally, the list of five resulting pitfalls is presented and some suggestions are made on how to avoid or mitigate each of them.