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三节课 sanjieke and 接地气的陈老师

实现数据驱动,是很多企业的期望。特别在依赖个人业务能力的B2B领域尤其如此。以精准分析,科学决策代替拍脑袋尤其重要。然而,目前市面上课程,大多以B2C的电商、社交、游戏、零售金融为例子,极少有专门探讨B2B的问题。许多同学在工作中常有这样的困惑:到底B2B的数据驱动该是什么样?为什么我们公司明明做了数据报表,却完全没有驱动的感觉?为什么当我试着基于数据做B2B业务的分析,总感觉各种掣肘?为此,我们邀请到了在B2B领域有丰富经验的陈文老师为大家带来本节课程。课程首先介绍了搭建适应B2B业务指标体系的方法。目前网上流传的有关指标体系的文章、教程决大部分基于B2C的电商、游戏、新媒体领域,对B2B业务该如何搭建指标体系介绍甚少。很多同学照抄B2C的杜邦分析法或者AARRR模型,结果发现:“为什么数据那么少?”“为啥不好用?”。本课程在介绍常用数据指标体系方法的基础上,还详细讲解了搭建B2B数据指标体系特殊考虑,让数据指标体系真正服务于业务。其次,课程不仅仅停留在讨论数据分析方法层面,而是站在B2B业务开展的角度,深入探讨:到底B2B业务数据驱动该如何做。有哪些困难必须克服。可以实现什么效果。从而解答同学们的疑惑。

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What's inside

Learning objectives

  • 深入理解数据指标体系的作用,掌握搭建数据指标体系的基本方法
  • 了解b2b业务的特点与指标体系的特点,理解b2b业务及其特点
  • 清晰愿景,更加具体的了解到b2b数据驱动价值
  • 看清困难,直面b2b数据驱动四大现实难题
  • 建立思路,从四个方向,思考如何推动数据驱动由浅到深

Syllabus

课程导读
课程简介
讲师介绍
课程大纲
Read more
如何从0到1搭建B2B业务数据指标体系?
什么是数据指标体系
搭建数据指标体系的一般方法
B2B数据指标体系
搭建B2B数据指标体系特殊考虑
战略级数据指标体系
营销推广数据指标体系
售前阶段数据指标体系
交付阶段数据指标体系
B2B业务数据驱动如何做?
理解B2B业务
数据驱动对B2B的作用
数据如何驱动B2B工作
为什么数据驱动很难做到?
数据驱动四要素之一:管理思维
数据驱动四要素之二:系统建设
数据驱动四要素之三:运作流程
数据驱动四要素之四:分析方法
回顾总结
课后寄语

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This comprehensive handbook provides a wide range of topics in data science, including data mining, machine learning, and data visualization. Written by experts in the field, it valuable resource for students and practitioners who want to gain a broad understanding of data science.
Provides a comprehensive overview of statistical methods for data analysis, covering topics such as probability distributions, hypothesis testing, and regression analysis. Written by a leading expert in the field, it valuable resource for students and practitioners in various fields.
A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
An authoritative text on statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.
An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.
A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.
Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
This online book provides a comprehensive overview of machine learning concepts and techniques. Written by a leading expert in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
Provides a practical introduction to data science for business professionals. It covers topics such as data mining, data visualization, and statistical modeling. Written by experts in the field, it valuable resource for anyone who wants to gain a better understanding of data science.
Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.
A comprehensive introduction to data analysis using R, this book covers a wide range of topics, including data manipulation, visualization, and statistical modeling. Written by leading experts in the field, it valuable resource for students and practitioners.
Is written by a renowned data visualization expert and focuses on the importance of effective data visualization in data-driven decision making. It provides practical guidance on creating clear and compelling visualizations that communicate insights effectively and support informed decisions.
Provides a structured approach to data-driven decision making, covering the entire process from data collection to analysis and implementation. It includes practical examples and case studies from various industries, making it relevant to readers from different backgrounds seeking to integrate data into their decision-making processes.
Specializes in using data to drive marketing strategies and improve customer engagement. It covers topics such as customer segmentation, personalization, and campaign optimization, providing practical guidance on how to leverage data to increase marketing ROI and build strong customer relationships.
Focuses on data-driven decision making in startups and early-stage businesses. It provides a practical framework for using data to validate ideas, measure progress, and make informed decisions to drive growth and success.
Provides an accessible introduction to predictive analytics, exploring the techniques and applications of using data to predict future events. It covers a wide range of topics, including data mining, machine learning, and forecasting, and discusses the ethical and privacy implications of predictive analytics.
Written by leading experts in analytics, this book examines the competitive advantage of data-driven decision-making. It presents case studies and frameworks to demonstrate how organizations can leverage analytics to gain insights, innovate, and outpace competitors in the digital age.
Written by experts with decades of experience in data analysis and decision-making, this book focuses on the practical aspects of data-driven decision-making. It emphasizes the importance of data quality and provides guidance on extracting meaningful insights from data and effectively communicating findings to stakeholders.

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