PROBLEM The business and marketing environments of today are rapidly changing due to several trends in society and in the market (Kotler & Keller, 2016). To maintain their position, companies must continually renew their knowledge management systems. The emergence of information technology (IT)-based intelligence systems suggests more possibilities for gathering knowledge than ever before. These possibilities can help to better meet the information needs of marketers in corporate setting. Sparked by implementation of such technologies in the business-to-consumer (B2C) marketing environment, business-to-business (B2B) marketers of the case firm (an IT consulting company) were interested in the possibilities that implementation of state-of-the-art IT based intelligence systems would offer for meeting their information needs. The objective of this research was, therefore, to explore the possibilities of intelligence systems that could add to the marketing information system of B2B marketers. This resulted in the following research question: How should an intelligence system look like to effectively add to the B2B marketing information system of an IT solution consulting company? The literature suggests that differences exist between B2B and B2C marketing. These differences point to different intelligence needs in B2B, which should be met with a different marketing information system. However, literature on the specific intelligence needs of B2B marketers and their marketing information system was not available. Furthermore, the existing literature provides enough knowledge on intelligence systems, but lacked on specific knowledge on IT based marketing intelligence systems, let alone marketing intelligence systems designed specifically for B2B marketing. Based on the available literature on overall intelligence systems, a framework was made that combined several methods and possibilities for intelligence systems. By adding challenges for implementation, this framework could help in determining how an intelligence system for specific purposes would look like. The identified gaps of the literature, combined with the challenges for implementation for intelligence system provide an overview of what information is needed about the B2B marketing environment to answer the research question. METHODOLOGY The information needed for answering the research question was extracted by means of an embedded case study on an IT consulting company. Several units of analysis were distinguished, based on general approaches to B2B marketing. In-depth semi structured interviews with the marketers within the case company were used to gather data. The topics were based on the information gaps in the literature, combined with knowledge from several resources about the marketing environment such as observation, available documentary and introductory interviews. The interviews were transcribed and coded using specialized software (Atlas TI). The analysis focused on finding relationships between key concepts found in the interviews. FINDINGS By using this methodology, three phenomena were found. First, by linking the available marketing information system with the marketing needs that were suggested by the marketers, gaps could be identified, which could be used in the development of an intelligence system tailored to the needs of the case company. Converging evidence was found between the identified units of analysis suggesting the existence of an intelligence gap for knowledge on the people working at their customers. Specifically, the B2B marketing information system does provide information on customers, however does so on (too) high (read: general) an abstraction level. The B2B marketers indicated a need for information on a lower (read: more personal and precise) abstraction level, specifically: interest, hobbies, roles, activities, how they are linked together and whether they are part of a decision making unit. Second, the interviews also produced a list of potential data sources for an intelligence system. Scoping these sources towards the needed intelligence suggest that an intelligence system should make use of user-generated content (i.e. social media, blogs and forums), online published interviews and surveys. These sources might contain relevant information, but they are in textual unstructured format. Finally, the preferences for presenting the intelligence were extracted from the marketers. The marketers would prefer intelligence presentation to be implemented in other systems. They would like to have a search option, and they preferred easy to use display of the intelligence. PRACTICAL IMPLICATIONS Based on the found phenomena, choices could be made using the framework for intelligence system implementation based of the literature. The proposed intelligence system design was built from the following layers: (1) the intelligence system will use user-generated content (UGC) (i.e. social media, blogs and forums), online published interviews, and surveys as data source. (2), Data will be extracted from these sources using natural language processing methods such as named entity recognition, relationship extraction and sentiment analysis. (3) The extracted data will be stored in databases. (4) OLAP servers will be used for slicing and dicing of the data. (5) The data will be presented as intelligence by use of searchable relational graphs. This form of presentation could be implemented in dashboards of other systems such as the marketers’ CRM system. The intelligence system can add to the marketing information system by providing valuable insight on customers on a lower (read: more personal and precise) abstraction level. This form of intelligence is useful to the marketers, but is not currently provided decently by existing B2B marketing information systems.