现今，数据成为提升营销效果的新的关键要素。营销数据分析作为企业一项非常重要的活动，是通过特定技术获取和分析有关市场的所有可用信息，以帮助设计或优化特定的营销方案，提高营销效果的一系列活动。本书由入选首批国家级一流本科专业建设点的安徽财经大学市场营销专业李永发教授团队历经近两年时间打磨，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，从而帮助学习者掌握数字经济时代需要的高级营销技能。本书展示了当前实践中营销数据分析的主要内容、常用方法、算法与实操演示，每章都以一个实际问题作为切入点，引出相关理论与算法，最终通过一个案例演示详细的软件解决过程。
李永发，安徽财经大学市场营销系主任、教授、博士、硕士生导师。主要研究方向为：商业模式创新的理论与策略。主持完成国家社会科学基金项目1项，完成省级课题多项，在核心期刊上发表论文20余篇，出版专著2部。曾获安徽省教学成果奖一等奖、安徽省教师教学创新大赛三等奖。已开发教学案例20余篇，其中，2018—2021年连续4次入选“全国百篇优秀管理案例（微案例）”名单；荣获清华大学“卓越开发者”案例大奖赛一等奖1项，二等奖1项，三等奖6项。
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
书单推荐
