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，A1项目分析了在产品发布后的早期阶段，社会网络可以用于有针对性的营销传播的各种方式。最初，重点是网络结构和描述其适合定向广告的标准的推导。开发了网络分类的正式标准，并在模拟范围内成功地进行了测试。此外，本文还分析了目前实证研究中尚未充分考虑到的通信网络中互惠性的几个方面。最后，在最后一步中，开发了第一个复杂的模拟模型，这有助于显着改善具体的营销决策（例如，广告溢价的奖励或新产品的停/走决策）。第一个融资阶段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。这是调查如何在社交网络中获得的信息可以使用，甚至在产品发布之前的创意和产品开发。为此目的，文本挖掘方法的现有方法将得到扩展和改进，以实现自动化的需求挖掘，即对潜在客户需求进行定量、客观的表示。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，这些内容如今被广泛用于评估。通过这种方法，可以建立一个概念和产品开发阶段的“数据化”，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，可以用作决策支持，而不是偏见的本能和创造力。因此，在第二个资助阶段的背景下，将审查以下研究问题：(1)哪种文本挖掘的系统方法适合需求挖掘概念？(2)需求挖掘在哪个细节层面上是有用的——在宏观层面上收集总体趋势和/或在微观层面上推导特定的产品特性和功能？(3)在产品开发的哪个阶段应用需求挖掘是有用的？(4)如何设计需求挖掘的反馈回路，以测试收集到的需求的实施是否也符合潜在用户的期望？

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?