array(35) { ["id"]=> string(4) "1826" ["type"]=> string(6) "course" ["title"]=> string(98) "ChatGPT赋能的数字营销多渠道归因分析——基于夏普利值（Shapley Value）模型" ["subtitle"]=> string(0) "" ["creator"]=> array(6) { ["id"]=> string(1) "8" ["nickname"]=> string(7) "mingzhu" ["title"]=> string(6) "教师" ["uuid"]=> string(40) "66f930e6f2d349b45f48f24e125e05d3a92fb8d1" ["destroyed"]=> string(1) "0" ["avatar"]=> array(3) { ["small"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/09105602405b816729.png" ["middle"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/091056023132077454.png" ["large"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/0910560220f8152273.png" } } ["showable"]=> string(1) "1" ["buyable"]=> string(1) "1" ["summary"]=> string(481) "

 

本案例基于夏普利值模型(Shapley Value)，收集数字营销数据，计算营销渠道的夏普利值，构建多渠道归因模型。本例，首先介绍多渠道归因与夏普利值模型的基本原理；然后应用ChatGPT(GPT-4版)智能问答AI系统提出不同的提示(Prompt)对数据类型进行提取分析，并自动生成基于夏普利值多渠道归因模型的Python代码，并使用不同提示语解决生成代码中的问题。

" ["minPrice"]=> string(4) "0.00" ["maxPrice"]=> string(4) "0.00" ["discountId"]=> string(1) "0" ["images"]=> array(3) { ["large"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/1028237338aa537974.jpeg" ["middle"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/1028237344b8930367.jpeg" ["small"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/102823734b64794573.jpeg" } ["ratingNum"]=> string(1) "0" ["rating"]=> string(1) "0" ["hitNum"]=> string(3) "372" ["hotSeq"]=> string(1) "0" ["maxPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["minPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["minDisplayPrice"]=> string(4) "0.00" ["maxDisplayPrice"]=> string(4) "0.00" ["minDisplayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["maxDisplayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["canManage"]=> bool(false) ["peopleShowNum"]=> string(3) "144" ["isMember"]=> bool(false) ["status"]=> string(9) "published" ["orgId"]=> string(1) "1" ["orgCode"]=> string(2) "1." ["recommendWeight"]=> string(1) "0" ["recommendedTime"]=> string(1) "0" ["createdTime"]=> string(25) "2023-10-17T16:08:38+08:00" ["updatedTime"]=> string(25) "2025-12-08T01:10:41+08:00" ["product"]=> array(7) { ["id"]=> string(4) "1826" ["targetType"]=> string(6) "course" ["title"]=> string(98) "ChatGPT赋能的数字营销多渠道归因分析——基于夏普利值（Shapley Value）模型" ["owner"]=> string(1) "8" ["createdTime"]=> string(10) "1697530118" ["updatedTime"]=> string(10) "1742783313" ["target"]=> array(17) { ["id"]=> string(4) "1926" ["type"]=> string(6) "normal" ["title"]=> string(98) "ChatGPT赋能的数字营销多渠道归因分析——基于夏普利值（Shapley Value）模型" ["subtitle"]=> string(0) "" ["summary"]=> string(481) "

 

本案例基于夏普利值模型(Shapley Value)，收集数字营销数据，计算营销渠道的夏普利值，构建多渠道归因模型。本例，首先介绍多渠道归因与夏普利值模型的基本原理；然后应用ChatGPT(GPT-4版)智能问答AI系统提出不同的提示(Prompt)对数据类型进行提取分析，并自动生成基于夏普利值多渠道归因模型的Python代码，并使用不同提示语解决生成代码中的问题。

" ["cover"]=> array(3) { ["large"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/1028237338aa537974.jpeg" ["middle"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/1028237344b8930367.jpeg" ["small"]=> string(76) "http://www.chinadatacase.com/files/course/2025/03-24/102823734b64794573.jpeg" } ["status"]=> string(9) "published" ["studentNum"]=> string(3) "144" ["discountType"]=> string(8) "discount" ["discount"]=> string(2) "10" ["minCoursePrice"]=> string(4) "0.00" ["maxCoursePrice"]=> string(4) "0.00" ["defaultCourseId"]=> string(4) "1944" ["productId"]=> string(4) "1826" ["goodsId"]=> string(4) "1826" ["minCoursePrice2"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["maxCoursePrice2"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } } } ["extensions"]=> array(3) { [0]=> string(8) "teachers" [1]=> string(14) "recommendGoods" [2]=> string(10) "isFavorite" } ["specs"]=> array(1) { [0]=> array(26) { ["id"]=> string(4) "1844" ["goodsId"]=> string(4) "1826" ["targetId"]=> string(4) "1944" ["title"]=> string(0) "" ["seq"]=> string(1) "1" ["status"]=> string(9) "published" ["price"]=> string(4) "0.00" ["coinPrice"]=> string(4) "0.00" ["usageMode"]=> string(7) "forever" ["usageDays"]=> string(1) "0" ["usageStartTime"]=> string(1) "0" ["usageEndTime"]=> string(1) "0" ["buyableStartTime"]=> string(1) "0" ["buyableEndTime"]=> string(1) "0" ["buyableMode"]=> NULL ["buyable"]=> string(1) "1" ["maxJoinNum"]=> string(1) "0" ["services"]=> array(0) { } ["priceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["displayPrice"]=> string(4) "0.00" ["displayPriceObj"]=> array(2) { ["currency"]=> string(3) "RMB" ["amount"]=> string(4) "0.00" } ["isMember"]=> bool(false) ["access"]=> array(2) { ["code"]=> string(14) "user.not_login" ["msg"]=> string(15) "用户未登录" } ["hasCertificate"]=> bool(false) ["learnUrl"]=> string(43) "http://www.chinadatacase.com/my/course/1944" ["teachers"]=> array(1) { [0]=> array(6) { ["id"]=> string(1) "8" ["nickname"]=> string(7) "mingzhu" ["title"]=> string(6) "教师" ["uuid"]=> string(40) "66f930e6f2d349b45f48f24e125e05d3a92fb8d1" ["destroyed"]=> string(1) "0" ["avatar"]=> array(3) { ["small"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/09105602405b816729.png" ["middle"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/091056023132077454.png" ["large"]=> string(76) "http://www.chinadatacase.com/files/default/2021/11-26/0910560220f8152273.png" } } } } } } ChatGPT赋能的数字营销多渠道归因分析——基于夏普利值（Shapley Value）模型 - 中国经管实验教学案例平台

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