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项目分析了社交网络在产品推出后的早期阶段可用于有针对性的营销沟通的各种方式。最初，重点放在网络结构和描述其对定向广告的适用性的标准的推导上。制定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，还分析了通信网络中互惠的一些方面，这些方面到目前为止在实证研究中还没有得到充分的考虑。最后，在最后一步中，首先开发了复杂的仿真模型，这些模型有助于显著改进具体的营销决策(例如，新产品的广告溢价奖励或停/走决策)。由于第一个资金期的重点是在产品发布后使用社交网络，即将到来的项目将调查产品发布前的时间。甚至在产品发布之前，社交网络中获得的信息如何被用于构思和产品开发，这是需要调查的。为此，将对现有的文本挖掘方法进行扩展和改进，以实现自动化的需求挖掘，即对潜在客户需求的量化、客观表示。这种自动化的需求挖掘是基于用户生成的内容，随着参与性网络和社交网络的兴起，这些内容现在可以广泛用于评估。通过这种方法，将建立构思和产品开发阶段的“数据通信”，以便定量收集、定性编辑的愿望，从潜在客户的交流中过滤出来的信息可以用作决策支持，而不是有偏见的直觉和创造力。因此，在第二个资助阶段的背景下，将审查以下研究问题：(1)哪些文本挖掘方法适合于需求挖掘概念？(2)在哪个细节级别上需要挖掘是有用的-在宏观层面上用于收集总体趋势和/或在微观层面上用于派生特定的产品特征和功能？(3)在产品开发的哪个阶段应用需求挖掘是有用的？(4)在产品开发的哪个阶段，需求挖掘的应用是有用的为需求挖掘设计反馈回路，测试收集到的需求的实施是否也满足潜在用户的期望？

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?