Needmining - Analyzing Social Networks to Guide New Product Development Activities; first funding period: Analyzing Networks to Guide Targeted Marketing Communication

在第一个资助期，项目A1分析了社交网络在产品发布后的早期阶段用于有针对性的营销传播的各种方式。最初，重点是网络结构和标准的推导，描述其适合有针对性的广告。网络分类的正式标准已经制定，并在模拟范围内成功测试。此外，通信网络中的互惠性方面进行了分析，没有充分考虑到实证研究到现在。最后，在最后一步，开发了第一个复杂的模拟模型，这有助于显著改善具体的营销决策（例如，广告费的奖励或新产品的停/走决策）。第一个融资期的重点是在产品发布后使用社交网络，而即将到来的项目将调查产品发布前的时间。研究如何在产品发布之前使用社交网络中获得的信息进行构思和产品开发。为此，文本挖掘方法的现有方法将被扩展和改进以实现自动化需求挖掘，即，对潜在客户需求的定量、客观表示。这种自动化的需求挖掘是基于用户生成的内容，随着参与式网络和社交网络的兴起，现在可以广泛用于评估。通过这一方法，将建立构思和产品开发阶段的“专业化”，以便从潜在客户的沟通中过滤出定量收集、定性编辑的愿望，而不是有偏见的本能和创造力，作为决策支持。（1）哪些文本挖掘方法适合于需求挖掘概念？(2)在哪一个细节层次上，需求挖掘是有用的-在宏观层次上，收集一般趋势，和/或在微观层次上，推导出具体的产品特性和功能？(3)在产品开发的哪个阶段应用需求挖掘是有用的？(4)如何为需求挖掘设计一个反馈回路，以测试所收集的需求的实现是否也满足了潜在用户的期望？

In the first funding period, project A1 analyzed the various ways in which social networks can be used for targeted marketing communication in the early phases after product launch. Initially, the focus was on network structures and the derivation of criteria that describe their suitability for targeted advertising. Formal criteria for network classification were developed and successfully tested within the scope of simulations. In addition, aspects of reciprocity in communication networks were analyzed that have not been adequately considered in empirical research up to now. Finally, in the last step, first complex simulation models were developed, which help to significantly improve concrete marketing decisions (e.g. the award of advertising premiums or stop / go decisions for new products).Whereas the focus in the first funding period was to use social networks after a product launch, the upcoming project will investigate the time before the product launch. It is to be investigated how information obtained in social networks can be used even before product launch for ideation and product development. To this end, existing methods of text mining approaches are to be extended and refined to enable an automated needmining, i.e., a quantitative, objective representation of potential customer needs. This automated needmining is based on user-generated content that with the rise of the participative web and social networks is nowadays widely available for evaluation. Through this approach a "datafication" of the ideation- and product development phase is to be established, so that quantitatively collected, qualitatively edited wishes, filtered out of the communication of the potential customers can be used as decision support instead of biased instinct and creativity.Thus in the context of the second funding phase the following research questions will be examined: (1) Which methodical approaches of text mining are appropriate for the needmining concept? (2) On which level of detail is needmining useful - on the macro level for the collection of general trends and / or on the micro level for the derivation of specific product characteristics and functionalities? (3) At which stage of product development is the application of needmining useful? (4) How can a feedback loop be designed for the needmining to test whether the implementation of the collected needs also meets the expectations of the potential users?