基于渠道点击路径数据的多渠道网络广告组合效应测评研究

doi:10.16381/j.cnki.issn1003-207x.2022.0304

Abstract

Abstract:

The rapid development of the Internet， mobile and social media has brought many new online advertising and marketing channels to e-commerce enterprises. Under the background of integrated marketing and multi-channel online advertising， users may not only contact the online advertising channels once before purchase， and the channels contacted successively will affect consumers jointly but not independently. Therefore， in the effectiveness study of multi-channel online advertising， it is also necessary to consider the multi-channel combination effect of online advertising.The click-stream data of this study comes from the third party insurance agency website in Nanjing. After extracting， cleaning and transforming， the basic variables of this study are obtained.To explore the combinatorial effect of multi-channel online advertising， according to the choice set theory， related research hypotheses based on the classified combination effects of online advertising channels are proposed， and then the COX model is constructed. Based on the channel click path data of individual users in e-commerce enterprises， the relevant variables are extracted， and the COX model is verified by empirical analysis. Then the channel combination effect of the same channel， cross-channel and channel classification is analyzed.The results show that the combination of advertising channels from firm-initiated channels to brand search of customer-initiated channels will have a positive combination effect on purchases， while the combination of advertising channels from customer-initiated channels to firm-initiated channels will have a negative impact on purchases. In addition， the order of channels that move from general-purpose search to brand-type search has a positive combination effect on purchases. The data are grouped by gender to explore the differences in advertising effects of users of different genders. The results can help enterprises establish their marketing mix of advertising channels and mitigate the cost of ineffective advertising channel mix.

Key words: multi-channel online advertising, advertising combination effect, COX model, click path data

CLC Number:

F274

Li Li, Bingkun Cao, Zhenyi Yang. Research on the Evaluation of Multi-channel Online Advertising Combination Effects Based on Channel Click Path[J]. Chinese Journal of Management Science, 2025, 33(9): 121-134.

Figures/Tables 8

分类	变量	风险率	系数	95%置信区间
CIC Brand	BrandSEA	1.495609	0.4025332	[1.459579,1.532528]
	BrandSEO	1.410488	0.3439355	[1.346172,1.477876]
	Direct	1.055243	0.0537712	[1.051508,1.058991]
CIC Generic	GenericSEA	1.323395	0.2802002	[1.266034,1.383354]
CIC Generic	GenericSEO	1.248082	0.2216081	[1.231896,1.264481]
FIC	SMS	0.9008651	-0.1043997	[0.8423919,0.9633971]
	EMA	0.9737893	-0.0265604	[0.906867,1.04565]
	Wechat	1.052956	0.0516013	[1.043588,1.062408]
	Referer	1.149355	0.139201	[1.090619,1.211255]
CIC Brand (Past)	PastBrandSEA	1.125024	0.1178047	[1.114671,1.135474]
	PastBrandSEO	1.120869	0.114104	[1.09891,1.143266]
	PastDirect	1.031976	0.0314749	[1.029824,1.034131]
CIC Generic (Past)	PastGenericSEA	1.020664	0.0204532	[1.006186,1.03535]
CIC Generic (Past)	PastGenericSEO	1.134267	0.1259864	[1.122828,1.145822]
FIC (Past)	PastSMS	1.00994	0.0098909	[0.9761242,1.044927]
	PastEMA	1.08972	0.0859211	[1.063675,1.116403]
	PastWechat	1.016902	0.0167605	[1.011276,1.022559]
	PastReferer	1.080558	0.0774777	[1.063972,1.097403]
控制变量	Tvc(PastPurchase)	1.032877	0.0323483	[1.030085,1.035676]
	PastCollect	1.034653	0.0340663	[0.9833217,1.088664]
	PastShare	0.7951518	-0.2292222	[0.737499,0.8573116]
	Age	1.019605	0.0194149	[1.018276,1.020935]
	Gender	0.7386099	-0.3029854	[0.7157744,0.7621739]
交互项（同渠道组合）	BrandSEA×PastBrandSEA	0.976354	-0.02393	[0.9701595,0.9825881]
	BrandSEO×PastBrandSEO	0.9854322	-0.0146749	[0.976238,0.9947131]
	Direct×PastDirect	0.9989715	-0.001029	[0.9988066,0.9991364]
	GenericSEA×PastGenericSEA	1.000192	0.0001916	[0.9916995,1.008757]
	GenericSEO×PastGenericSEO	0.9911484	-0.008891	[0.9870659,0.9952479]
	SMS×PastSMS	1.01099	0.0109301	[0.9918164,1.030534]
	EMA×PastEMA	0.9865158	-0.0135759	[0.9728393,1.000385]
	Wechat×PastWechat	1.001467	0.0014656	[1.000889,1.002045]
	Refer×PastRefer	1.017111	0.0169667	[1.008446,1.025852]
交互项（跨渠道组合）	BrandSEA×PastNoBrandSEA	0.9868122	-0.0132755	[0.9824447,0.9911992]
	BrandSEO×PastNoBrandSEO	0.9781465	-0.0220958	[0.9704117,0.9859429]
	Direct×PastNoDirect	0.9995069	-0.0004932	[0.9985183,1.000497]
	GenericSEA×PastNoGenericsea	0.975973	-0.0243204	[0.9654965,0.9865632]
	GenericSEO×PastNoGenericseo	0.9930447	-0.0069796	[0.9922149,0.9938753]
	SMS×PastNoSMS	1.011966	0.011895	[1.005377,1.018598]
	EMA×PastNoEMA	1.032431	0.0319161	[1.017919,1.047149]
	Wechat×PastNoWechat	0.9908988	-0.0091428	[0.9884687,0.993335]
	Refer×PastNoRefer	0.9902901	-0.0097573	[0.9835826,0.9970435]

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变量类型	一级变量	描述	二级变量	描述
解释变量	CICBrand	当前用户发起类品牌型渠道点击量	BrandSEA	当前品牌付费搜索广告的点击量
			BrandSEO	当前品牌自然搜索次数
			Direct	当前直接访问次数
	CICGeneric	当前用户发起类通用型渠道点击量	GenericSEA	当前通用付费搜索广告的点击量
	CICGeneric	当前用户发起类通用型渠道点击量	GenericSEO	当前通用自然搜索次数
	FIC	当前企业发起类渠道点击量	SMS	当前短信广告的点击量
			EMA	当前电子邮件广告的点击量
			Wechat	当前微信推送点击量
			Referer	当前引荐广告的点击量
	PastCICBrand	过去用户发起的品牌型搜索类渠道点击量	PastBrandSEA	过去品牌付费搜索广告的点击量
			PastBrandSEO	过去品牌自然搜索次数
			PastDirect	过去直接访问次数
	PastCICGeneric	过去用户发起的通用型搜索类渠道点击量	PastGenericSEA	过去通用付费搜索广告的点击量
	PastCICGeneric	过去用户发起的通用型搜索类渠道点击量	PastGenericSEO	过去通用自然搜索次数
	PastFIC	过去企业发起类渠道点击量	PastSMS	过去短信广告的点击量
			PastEMA	过去电子邮件广告的点击量
			PastWechat	过去微信推送点击量
			PastReferer	过去引荐广告的点击量
		当前渠道的过去跨渠道点击量	PastNoBrandSEA	品牌付费搜索广告的过去跨渠道点击量
			PastNoBrandSEO	品牌自然搜索的过去跨渠道点击量
			PastNoDirect	直接访问的过去跨渠道点击量
			PastNoGenericSEA	通用付费搜索广告的过去跨渠道点击量
			PastNoGenericSEO	通用自然搜索过去跨渠道点击量
			PastNoSMS	短信广告的过去跨渠道点击量
			PastNoEMA	电子邮件广告的过去跨渠道点击量
			PastNoWechat	微信推送的过去跨渠道点击量
			PastNoReferer	引荐广告的过去跨渠道点击量

变量类型	划分依据	变量	描述
控制变量	个体异质性特征	PastPurchase	用户过去是否发生过购买行为
		PastCollect	用户过去是否发生过收藏行为
		PastShare	用户过去是否发生过分享行为
		Gender	用户性别 0-女；1-男
		Age	用户年龄
被解释变量		Convert	用户最终是否发生购买转化

分类	变量	风险率	系数	95%置信区间
CIC Brand	BrandSEA	1.346447	0.297469	[1.318364,1.375127]
	BrandSEO	1.21968	0.1985883	[1.187041,1.253215]
	Direct	1.044702	0.0437318	[1.041057,1.04836]
CIC Generic	GenericSEA	1.229635	0.2067177	[1.183997,1.277033]
CIC Generic	GenericSEO	1.076133	0.0733738	[1.066395,1.085959]
FIC	SMS	0.9003422	-0.1049804	[0.8445306,0.9598422]
	EMA	0.9665695	-0.0340021	[0.9075077,1.029475]
	Wechat	1.026653	0.0263038	[1.016372,1.037037]
	Referer	1.072776	0.07025	[1.054619,1.091247]
CIC Brand (Past)	PastBrandSEA	1.09085	0.0869576	[1.084562,1.097175]
	PastBrandSEO	1.031748	0.0312549	[1.010217,1.053739]
	PastDirect	1.008229	0.0081954	[1.00734,1.009119]
CIC Generic (Past)	PastGenericSEA	1.019775	0.0195815	[1.005542,1.034209]
CIC Generic (Past)	PastGenericSEO	1.124814	0.1176173	[1.114052,1.135679]
FIC (Past)	PastSMS	1.039186	0.0384373	[1.020704,1.058002]
	PastEMA	1.09627	0.0919131	[1.07569,1.117243]
	PastWechat	1.012529	0.0124508	[1.009428,1.015639]
	PastReferer	1.089078	0.0853313	[1.073539,1.104841]
控制变量	Tvc(PastPurchase)	1.033397	0.0328512	[1.030599,1.036202]
	PastCollect	1.062955	0.0610529	[1.010622,1.117998]
	PastShare	0.7658462	-0.2667739	[0.7100619,0.8260131]
	Age	1.020624	0.0204143	[1.019302,1.021948]
	Gender	0.7420095	-0.2983932	[0.7190838,0.7656661]

分类	变量	风险率	系数	95%置信区间
CIC Brand	BrandSEA	1.376832	0.3197853	[1.319545,1.436606]
	BrandSEO	1.234039	0.2102924	[1.173642,1.297543]
	Direct	1.056626	0.055081	[1.047285,1.066051]
CIC Generic	GenericSEA	1.30149	0.2635098	[1.186306,1.427858]
CIC Generic	GenericSEO	1.328776	0.2842584	[1.291911,1.366694]
FIC	SMS	0.9464716	-0.0550143	[0.8503287,1.053485]
	EMA	1.007904	0.0078733	[0.9439406,1.076202]
	Wechat	1.051919	0.0506165	[1.039848,1.064131]
	Referer	1.139992	0.1310214	[1.101564,1.17976]
CIC Brand (Past)	PastBrandSEA	1.08336	0.0800669	[1.06179,1.105367]
	PastBrandSEO	1.098837	0.0942524	[1.062654,1.136252]
	PastDirect	1.032823	0.0322963	[1.026494,1.039191]
CIC Generic (Past)	PastGenericSEA	1.030065	0.0296216	[0.997417,1.063781]
CIC Generic (Past)	PastGenericSEO	1.153627	0.1429112	[1.130881,1.176831]
FIC (Past)	PastSMS	1.02877	0.0283635	[1.004936,1.053168]
	PastEMA	1.083799	0.0804722	[1.060861,1.107233]
	PastWechat	1.013714	0.0136205	[1.007235,1.020234]
	PastReferer	1.08012	0.0770725	[1.047068,1.114216]
控制变量	Tvc(PastPurchase)	1.033188	0.032649	[1.030245,1.036139]
	PastCollect	1.049828	0.0486267	[0.9944068,1.108339]
	PastShare	0.7780524	-0.2509614	[0.7181272,0.8429781]
	Age	1.020356	0.0201518	[1.018926,1.021788]
	Gender	0.7441685	-0.2954877	[0.720327,0.7687992]
交互项（基于渠道分类的特定渠道顺序）	CICBrand×PastCICBrand	0.9988621	-0.0011385	[0.9985018,0.9992226]
	CICBrand×PastCICGeneric	0.989975	-0.0100756	[0.9830055,0.996994]
	CICBrand×PastFIC	1.000373	0.000373	[0.9995354,1.001211]
	CICGeneric×PastCICBrand	0.9913733	-0.0086641	[0.9901382,0.99261]
	CICGeneric×PastCICGeneric	0.9873087	-0.0127725	[0.9825931,0.992047]
	CICGeneric×PastFIC	0.9755327	-0.0247716	[0.9446239,1.007453]
	FIC×PastCICBrand	0.9917452	-0.008289	[0.9876548,0.9958526]
	FIC×PastCICGeneric	0.9940015	-0.0060166	[0.9660304,1.022782]
	FIC×PastFIC	1.001384	0.0013835	[1.000503,1.002266]

分类	变量	系数
分类	变量	男	女
CIC Brand	BrandSEA	0.3486749	0.3029304
	BrandSEO	0.1993119	0.2007566
	Direct	0.0600732	0.0525167
CIC Generic	GenericSEA	0.2297741	0.3453779
CIC Generic	GenericSEO	0.2755656	0.3027294
FIC	SMS	0.0100421	-0.1753532
	EMA	-0.009854	0.0118566
	Wechat	0.0571562	0.0417457
	Referer	0.1341778	0.1381804
CIC Brand (Past)	PastBrandSEA	0.121433	0.0716659
	PastBrandSEO	0.1194788	0.0896197
	PastDirect	0.0301799	0.03712
CIC Generic (Past)	PastGenericSEA	0.0080339	0.0339193
CIC Generic (Past)	PastGenericSEO	0.1500571	0.1384109
FIC (Past)	PastSMS	0.1092676	0.0063826
	PastEMA	0.0812032	0.0810788
	PastWechat	0.0271742	0.000853
	PastReferer	0.096782	0.0677919
控制变量	Tvc(PastPurchase)	0.0316208	0.0329459
	PastCollect	0.1184742	-0.0176073
	PastShare	-0.193947	-0.2853876
	Age	0.022278	0.0178563
交互项（基于渠道分类的特定渠道顺序）	CICBrand×PastCICBrand	-0.0010853	-0.001394
	CICBrand×PastCICGeneric	-0.0138712	-0.0072687
	CICBrand×PastFIC	-0.0015139	0.0013761
	CICGeneric×PastCICBrand	-0.0061048	-0.0088922
	CICGeneric×PastCICGeneric	-0.010905	-0.0154069
	CICGeneric×PastFIC	-0.0589481	0.0367443
	FIC×PastCICBrand	-0.0083223	-0.0094824
	FIC×PastCICGeneric	-0.0011416	-0.0024805
	FIC×PastFIC	0.0008727	0.0038986

Research on the Evaluation of Multi-channel Online Advertising Combination Effects Based on Channel Click Path

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