Development of a forecasting model for the prediction of product sales using search engine query data

预测交货日期和销售数量是物流规划的一大挑战。在大多数情况下，后勤规划因销售波动而恶化。由于广告或促销活动，销售可能会出现波动。这种不确定性可能导致延迟交货或有关材料的质量问题。因此，后勤绩效下降，例如服务水平下降。由于有效的销售计划越来越重要，许多公司依赖于销售预测的定量方法。这些传统预测方法的缺点，特别是数据的及时性低、细节程度低和预测工作量大。本研究计划的目的是开发和评估一种创新的方法来预测特定产品的销售。预测模型应基于互联网搜索引擎查询数据。搜索引擎查询数据包含与消费意向相关的关键字信息（如时间、数量等）。因此，与现有的预测模型相比，它有望减少预测误差，增加预测范围。分析和揭示搜索引擎查询数据在销售预测方面的潜力。还应了解所取得方法的局限性。该研究为基于搜索引擎查询数据的工业预测模型的开发奠定了基础。

Forecasting delivery dates and quantity of sales is a big challenge concerning the logistical planning. In most cases the logistical planning is aggravated by fluctuations concerning sales. Sales fluctuations may arise due to advertising or promotion events. This uncertainty may cause delayed deliveries or quality issues regarding the material. As a consequence the logistical performance decreases such as the service level diminishes. Giving the growing importance of effective sales planning many companies rely on quantitative methods of sales forecasting. The shortcomings of these conventional forecasting approaches are in particular a low timeliness of data, a low level of detail and a high forecasting effort. The objective of this research proposal is to develop and evaluate an innovative approach for product specified sales forecasting. The forecasting model shall be based on internet search engine query data. Search engine query data contains keyword information (e. g. time, quantity) which are related with a consumption intention. Thus, it is expected to decrease forecasting errors and increase the forecast horizon compared to existing forecasting models. These potentials of search engine query data for sales forecasting shall be analyzed and revealed. Also knowledge shall be generated concerning the limitations of the achieved approach. The research forms the basis for the development of industrial-suited forecasting models which are based on search engine query data.