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营销数据分析

作译者：李永发

出版时间：2023-12 千字数：295 版次：01-01 页数：204

开本：16开装帧： I S B N ：9787121467929

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纸质书定价：￥59.0

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内容简介

前言

上架建议

作者简介

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第1 章 绪论········································1
1.1 营销数据分析的相关概念··············2
1.1.1 数据分析与数据挖掘···········2
1.1.2 营销数据与数据营销···········3
1.2 营销数据分析的应用领域··············3
1.3 营销数据分析的方法····················5
1.3.1 基本方法··························5
1.3.2 高级方法··························6
1.4 营销数据分析的流程····················7
1.5 营销数据分析的影响····················9
本章小结······································.11
实训目的······································.11
思考与练习···································.11
参考资料······································.12
第2 章 基于聚类算法的价格带分析··.13
2.1 问题的提出·····························.14
2.1.1 价格带分析····················.14
2.1.2 问题设计·······················.14
2.1.3 问题解决思路·················.15
2.2 聚类算法································.15
2.2.1 聚类算法简介·················.15
2.2.2 K 均值算法原理··············.16
2.2.3 聚类算法的分类··············.17
2.2.4 聚类算法的应用··············.18
2.3 价格带分析案例·······················.18
本章小结······································.24
实训目的······································.25
思考与练习···································.25
参考资料······································.25
第3 章 用户画像分析·······················.27
3.1 问题的提出·····························.28
3.1.1 用户画像·······················.28
3.1.2 问题设计·······················.30
3.1.3 问题解决思路·················.30
3.2 用户画像构建过程····················.31
3.2.1 明确营销需求·················.31
3.2.2 确定用户画像的维度和度量
指标·····························.32
3.3 用户画像案例··························.35
本章小结······································.46
实训目的······································.47
思考与练习···································.47
参考资料······································.47
第4 章 基于ARIMA 模型的产品生命
周期预测······························.48
4.1 问题的提出·····························.49
4.1.1 产品生命周期理论···········.49
4.1.2 问题设计·······················.49
4.1.3 问题解决思路·················.50
4.2 时间序列法与ARIMA 模型·········.50
4.2.1 时间序列法····················.50
4.2.2 ARIMA 模型··················.51
4.3 产品生命周期预测案例··············.52
本章小结······································.62
实训目的······································.64
思考与练习···································.64
参考资料······································.65
第5 章 基于关联规则的购物篮分析··.66
5.1 问题的提出·····························.67
5.1.1 购物篮分析····················.67
5.1.2 问题设计·······················.67
5.1.3 问题解决思路·················.68
5.2 关联分析································.68
5.2.1 关联分析步骤与关联强度··.68
5.2.2 关联分析的核心算法········.69
5.2.3 关联分析在营销中的应用··.71
5.3 购物篮分析案例·······················.71
本章小结······································.79
实训目的······································.80
思考与练习···································.80
参考资料······································.80
第6 章 基于文本挖掘的消费者情感
分析····································.81
6.1 问题的提出·····························.82
6.1.1 商品评价中的情感···········.82
6.1.2 问题设计·······················.82
6.1.3 问题解决思路·················.82
6.2 文本分析法·····························.83
6.2.1 文本分析原理·················.83
6.2.2 文本数据的分析类型与一般
流程·····························.84
6.2.3 文本情感分析的三种方法··.84
6.3 消费者情感分析案例·················.85
本章小结······································.94
实训目的······································.96
思考与练习···································.96
参考资料······································.96
第7 章 基于PSM 的定价策略··········.97
7.1 问题的提出·····························.98
7.1.1 定价·····························.98
7.1.2 问题设计·······················.98
7.1.3 问题解决思路·················.99
7.2 PSM 的原理、流程与优缺点········.99
7.2.1 PSM 的原理···················.99
7.2.2 PSM 的流程···················100
7.2.3 PSM 的优缺点················100
7.3 定价案例································101
本章小结······································109
实训目的······································110
思考与练习···································110
参考资料······································110
第8 章 基于决策树的消费者响应
预测····································112
8.1 问题的提出·····························113
8.1.1 促销与消费者响应···········113
8.1.2 问题设计·······················113
8.1.3 问题解决思路·················114
8.2 决策树工作原理与算法··············114
8.2.1 决策树的工作原理···········114
8.2.2 构建决策树的多种算法·····115
8.3 消费者响应预测案例·················116
本章小结······································132
实训目的······································133
思考与练习···································133
参考资料······································133
第9 章 品牌推广策略优化················134
9.1 问题的提出·····························135
9.1.1 品牌营销·······················135
9.1.2 问题设计·······················135
9.1.3 问题解决思路·················136
9.2 数据可视化·····························136
9.2.1 数据可视化的定义···········136
9.2.2 数据可视化的步骤···········137
9.2.3 数据可视化的呈现要点·····137
9.2.4 数据可视化的报告撰写·····138
9.3 品牌营销效果分析案例··············139
本章小结······································146
实训目的······································147
思考与练习···································147
参考资料······································147
第10 章 客户关系管理·····················148
10.1 问题的提出····························149
10.1.1 客户关系管理···············149
10.1.2 问题设计·····················149
10.1.3 问题解决思路···············150
10.2 客户购买预测·························151
10.2.1 逻辑回归算法简介·········151
10.2.2 基于逻辑回归算法的潜在
客户识别案例···············153
10.3 基于RFM 模型的客户分类········164
10.3.1 RFM 模型····················164
10.3.2 客户分类案例···············166
10.3.3 差异化营销策略············173
10.4 客户流失预测案例···················174
本章小结······································181
实训目的······································182
思考与练习···································182
参考资料······································182
第11 章 营销大数据伦理··················183
11.1 营销大数据中的伦理道德问题····184
11.1.1 大数据收集中的伦理道德
问题···························184
11.1.2 大数据处理与分析中的伦
理道德问题··················185
11.1.3 大数据应用中的伦理道德
问题···························186
11.2 营销大数据伦理道德问题的
危害·····································187
11.2.1 个人层面·····················187
11.2.2 企业层面·····················188
11.2.3 行业层面·····················189
11.2.4 社会层面·····················190
11.2.5 国家层面·····················191
11.3 营销大数据伦理道德问题的
治理·····································192
11.3.1 完善法律规制················192
11.3.2 加强行业自律················193
11.3.3 加强技术监控和保护·······194
11.3.4 提高个人信息安全和信息
保护意识·····················195
本章小结······································195
实训目的······································195
思考与练习···································196
参考资料······································196

菲利普·科特勒认为，营销已从1.0 迈向5.0。其中，营销1.0 是产品驱动型营销，客户重在寻求功能上的满足；营销2.0 是客户导向型营销，客户需要实现情感上的满足；营销3.0 是以人为中心的营销，客户不仅寻求功能与情感上的满足，而且想从所选品牌中获得某种精神上的满足；营销4.0 是数字世界中的全渠道整合营销，是将传统物理世界与数字世界连接在一起，贯穿于整个客户旅程的营销活动；营销5.0 是人性技术型营销，通过人性模仿技术的应用，在整个客户旅程中创造、沟通、传递与增强价值。
随着整个社会的营销与传播系统迈向数字网络，以人工智能、区块链、云平台、大数据等为核心的数字技术广泛运用，预测与优化成为关键营销活动，数据成为提升营销效果的新的关键要素。花时间洞察客户，周到考虑与客户任何可能的接触点，挖掘各种各样以客户为中心的合法合规数据，满足客户需求，创造客户喜悦，才能占领客户心智。在这种背景下，营销专业教学内容与课程体系做出重大调整就成为一种必然。随着技术的发展，社会营销高技能人才的需求巨大，对能及时反映营销新技术、新方法、新标准的教材的需求日益迫切。本教材贯彻落实《国家中长期教育改革和发展规划纲要（2010—2020 年）》文件精神，全面提升本科教材质量，充分发挥教材在提高人才培养质量中的
基础性作用，采用理论与案例相结合的方式，全方位介绍营销数据分析的理论策略和实操过程，并深入利用多个技术工具，挖掘营销数据背后的市场规律与营销逻辑，从而帮助读者快速了解营销数据分析知识，全面掌握营销数据分析技能，提升营销相关决策的能力与水平。
本教材共11 章，力图展示当前实践中营销数据分析的主要内容、常用方法与算法。第1 章为绪论，主要阐述营销数据分析的相关概念、应用领域、方法、流程及影响。第2 章为基于聚类算法的价格带分析，根据与电脑椅相关的400 条商品数据记录，利用K均值算法对相关指标进行聚类分析，做市场价格细分。第3 章为用户画像分析，基于中
国某景区旅游消费数据，通过可视化分析方法，按地理、人口、心理、行为四种要素对游客人群进行细分，从而优化景区营销对策，满足游客个性化需求。第4 章为基于ARIMA模型的产品生命周期预测，根据液晶电视产品的出口数据，利用ARIMA 模型预测产品生命周期，制定并优化相应的市场营销策略。第5 章为基于关联规则的购物篮分析，根据7985 条天猫超市的消费记录数据，利用FP-Growth 算法，优化商品陈列布局、交叉销售、捆绑销售、个性化商品推荐等相关经营决策。第6 章为基于文本挖掘的消费者情感分析，根据淘宝某店铺1599 条烤肠评价的初始数据，可视化呈现消费者的消费态度，进而优化产品。第7 章为基于PSM 的定价策略，根据某企业关于产品价格敏感的调查问卷数据，利用PSM 生成价格敏感度折线图，并根据分析结果确定产品的合理价格区间。第8 章为基于决策树的消费者响应预测，根据某电商企业数据，利用决策树算法预测客户对营销活动的响应，通过建模判断出企业提供的附加价值的边际临界值。第9 章为品牌推广策略优化，基于某一次性用品品牌与行业数据，利用数据可视化技术呈现品牌营销的效果，为该品牌和行业的营销与管理提供更好的营销推广策略。第10 章为客户关系管理，本章案例分三个环节：首先利用逻辑回归算法预测客户购买行为；其次基于RFM模型对客户进行分类；最后利用决策树算法预测客户流失，并提出流失客户的挽留方案。第11 章为营销大数据伦理，在大数据时代背景下，在大数据收集、处理、分析和应用的过程中会出现不同的伦理道德问题，针对这些问题，有关政府部门、行业、个人都要承担各自的责任，为预防伦理道德问题的产生做出贡献。
作为营销专业的核心课程教材，本教材在编排思路、内容设计、案例选择与软件解决过程等方面具有四个创新特色。
第一，整体统一，各章独立。全书是一个有机统一体，包括营销数据分析的核心内容、常用方法与实操演示，而每章以一个实际问题为切入点，引出相关理论与算法，最终通过一个案例演示软件解决的详细过程。
第二，理论与实践紧密结合。第2～10 章中的每章都围绕特定的实践现象或问题，通俗易懂地介绍相关理论知识、方法与算法，利用软件图文并茂地展现问题解决的实操过程。本教材中的案例、数据均取材于真实的商业世界，每章最后列出实训目的，做到实训与实战相统一。
第三，教材思政，案例主导。我们认真贯彻党的教育方针，坚守为党育人、为国育才的初心使命，落实立德树人根本任务，主动将思政元素嵌入教材中，特别是第1 章与第11 章包含大量思政内容。第2～10 章每章都有案例、实训数据，引导学习者根据应用场景进行自主、灵活的实验设计。
第四，教产融合，优势互补。李永发教授及团队负责教材架构与理论知识编排部分，浙江师范大学经济与管理学院院长助理、浙江思睿智训科技有限公司创始人邹益民及团队负责数据抓取、软件实验部分。具体分工如下：李永发教授负责整体目录规划设计、人员统筹分工，具体负责第1～4、6～8 章理论和案例分析部分的编写；冯林燕负责第5、9～11 章理论和案例分析部分的编写；邹益民负责第2～6 章案例数据抓取、软件实验部分的编写；王天骄负责第7～
9 章案例数据抓取、软件实验部分的编写；隋东旭负责第10 章案例数据抓取、软件实验部分的编写。
由于水平所限，本教材或有疏漏之处，我们真诚期待有识之士提出宝贵意见，以使本教材不断完善，更好地满足教学需要！在编写教材的过程中，我们参考了大量文献，向列出的和由于篇幅限制未列出的所有参考文献作者表示诚挚的感谢！

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