Prediction of consumption based of customers' segmentation

基于顾客细分的消费预测

• 批准号: 492021-2015
• 负责人: Agard, Bruno
• 金额: $ 4.9万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618218
• 关键词: Prediction; consumption; based; customers; segmentation

Strategic forecasts are best developed by combining historical data, collaborative supply chain information, and qualitative inputs. However, supply chain partners are not always willing or able to share information, and access to knowledgeable individuals for qualitative inputs is sometimes difficult since they are often senior management. This limits the information sources to historical data, processed through time-series analysis. While there have been many advances in the field of time-series forecasting over the past 40 years, the techniques all share the same limitation that they attempt to use past events to predict the future; hence, time-series analysis can only generate accurate forecasts for a few future periods. The best way to understand customers' behavior would be to monitor each customer ... even if this technique is applied in some specific domains (Hydro Quebec with its smart meters, for example), it is not always possible. Then each company needs to "evaluate" the customer behavior based on available data. Some data that are often available are delivery data. Delivery data may contain the product description, quantity delivered, customer address, and sometimes other information.The focus of this project is to develop methods and tools to analyze delivery data (what, how many/much, where) in order to get a better understanding of the customer, and propose a highly efficient customers' requirements prediction tool.Different questions need to be answered: Q1) how to segment customers with similar behaviors, Q2) how to transform delivery data to usage data, Q3) how to predict requirements for a segment, introducing external data, and Q4) how to derive predictions for each customer. None of those questions are actually completely solved. The scientific community already provides partial answers to each of them, but available tools need to be improved and integrated all together in order to provide performant answers.This project is developed in collaboration with an industrial partner that delivers bulk materials.

战略预测最好通过结合历史数据、协作供应链信息和定性输入来制定。然而，供应链伙伴并不总是愿意或能够分享信息，有时很难接触到知识渊博的个人，因为他们往往是高级管理人员。这将信息来源限制为通过时间序列分析处理的历史数据。虽然在过去的40年里，时间序列预测领域取得了许多进展，但这些技术都有一个共同的局限性，即它们试图利用过去的事件来预测未来;因此，时间序列分析只能为未来的几个时期产生准确的预测。了解客户行为的最佳方法是监控每位客户.即使该技术应用于某些特定领域（例如，具有智能仪表的Hydro魁北克），也不总是可行的。然后每个公司需要“评估”客户行为的基础上可用的数据.通常可用的一些数据是交付数据。交付数据可能包含产品描述、交付数量、客户地址，有时还包含其他信息。本项目的重点是开发分析交付数据的方法和工具（什么、多少/多少、哪里），以便更好地了解客户，并提出一个高效的客户需求预测工具。需要回答不同的问题：Q1）如何对具有相似行为的客户进行细分，Q2）如何将交付数据转换为使用数据，Q3）如何预测细分的需求，引入外部数据，以及Q4）如何为每个客户进行预测。这些问题实际上都没有完全解决。科学界已经为每一个问题提供了部分答案，但现有的工具需要改进和整合，以便提供有效的答案。该项目是与提供散装材料的工业合作伙伴合作开发的。