Improving the efficiency & efetiveness of VPC processes through Big Data - Case study within Philips Innovation Services

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The purpose of this research was to improve the value proposition creation (VPC) process by using Big Data to identify customer insights more effective and efficient. This explorative research is conducted through a multiple case study, in fourteen semi-structured interviews have been conducted in two multinational companies. Current VPC processes are described and the way how customer insights are currently identified has been researched. The current VPC processes within the multinational companies are benchmarked in terms of efficiency and effectiveness. After which, it was investigated how these processes could be improved by implementing Big Data. A company willing to use Big Data in the customer insight identification activity should meet several mandatory prerequisites, first of all, data that is analysed should consist of internal as well as external data to initiate adjacent innovations. Besides, the insights that follow out of a Big Data analysis should be backed up with qualitative research when motivational and emotional aspects are of importance. This research provides a new perspective on the use of Big Data in the customer insight identification in the VPC activity. It is suggested to use Natural Language Processing (NLP) and Machine Learning (ML) within Artificial Intelligence (AI) since these advanced analytics tools are best suited for the purpose of finding customer insights. When implemented properly and considering the prerequisites Big Data has the ability of improving the efficiency and effectiveness of the VPC process.
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