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Online to-offline commerce in automobile industry
- 1. Cross-Channel Impacts of Online Advertising, Salesforce and Product Line Strategies in O2O Retailing Environments Yunkun Zhao*, Khim Yong Goh* and Liwen Hou** * National University of Singapore, ** Shanghai Jiaotong University Contact Author: mozartkun@gmail.com Motivation Brand Ad Product Line Product Ad Research Objectives and Hypotheses ? Evaluating the impacts of salesman attributes on effectiveness of advertising strategies ? Evaluating the impacts of product line length on effectiveness of advertising strategies H1A: Brand Ad + Salesman Train + Brand Ad + Salesman Tenure + H1B: Product Ad + Salesman Train + Product Ad + Salesman Tenure + H2A: Brand Ad + Length Across Different Brand - H2B: Product Ad + Length Across Different Brand - H2C: Brand Ad + Length Within Same Brand + H2D: Product Ad + Length Within Same Brand - Data Background Data provided by a multi-brand multi-product O2O automobile manufacturer in China, selling 3 brands, 35 models in 1,980 dealership stores throughout China. We have (1) Purchase history, (2) Offline visit records, (3) Automobile information, and (4) Salesman information Individual Level Analysis Aggregate Level Analysis Sample, Dependent (Y) and Independent Variables (X) Sample: (1) Customer-product-day level data from Jan, 2014 till Jun, 2016; (2) 551,056 observations from 524,991 customers Choiceijt Y1: Whether customer i choose to buy product j on day t (=1 yes, =0 no) BrandAdijt X1: Whether customer i is lead by brand-oriented advertising for product j into offline official store to visit at day t ProductAdijt X2: Whether customer i is lead by product-oriented advertising for product j into offline official store to visit at day t SpBrandTenureit X3: The number of months the salesperson who serves customer i at day t has been responsible for the car product j SpPassTrainit X4: Whether the salesperson who serves customer i at day t passes sales training program CarLineDiffBrandit X5: The number of same-line different-brand car models of the car j that the customer i intends to buy at day t CarLineSameBrandit X6: The number of same-line same-brand car models of the car j that the customer i intends to buy at day t Model Specification: Pr( 1) ( )ijt ijtChoice X ??? ? ? 0 1 2 3 4 5 6 7 8 9 10 11 = * * ijt ijt ijt ijt it it it it it it ijt it ijt it X BrandAd ProductAd DeciLevel SpMidSchool SpCollege SpGraduate SpAge SpBrandTenure SpPassTrain BrandAd SpBrandTenure BrandAd SpPassTrain ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 12 13 14 15 16 17 18 19 * * ln( ) ln( ) ln( ) ijt it ijt it jt jt jt j j j i j t ijt ProductAd SpBrandTenure ProductAd SpPassTrain CarLineDiffBrand CarLineSameBrand CarPrice CarDisplacement CarFuelEconomy CarSeats ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Sample, Dependent (Y) and Independent Variables (X) Sample: (1) Car-week level data from Jan, 2014 till Jun, 2016; (2) 2,493 observations from sales of 35 unique car models Transactionjt Y1: The total number of transactions of car model j on month t Visitjt Y2: The total number of offline store visits of car model j on month t TotalBrandAdjt X1: The total number of brand-oriented advertising exposures of car model j on month t TotalProductAdjt X2: The total number of product-oriented advertising exposures of car model j on month t CarLineDiffBrandjt X3: The number of same-line different-brand car models of the car j at month t CarLineSameBrandjt X4: The number of same-line same-brand car models of the car j at month t Model Specification: andln(1 )jt jtTransaction X ??? ? ln( )jt jtVisit X ??? 0 1 2 3 4 5 6 7 8 = * * * jt jt jt jt jt jt jt jt jt jt jt X TotalBrandAd TotalProductAd CarLineDiffBrand CarLineSameBrand TotalBrandAd CarLineDiffBrand TotalProductAd CarLineDiffBrand TotalBrandAd CarLineSameBrand T ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 9 10 11 12 13 14 * ln( ) ln( ) ln( ) jt jt jt jt jt j j j t jt otalProductAd CarLineSameBrand RivalBrandAdSType RivalProductAdSType CarPrice CarDisplacement CarFuelEconomy CarSeats ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Results and Findings Individual-Level Aggregate-Level Variables (1) Logit Choice (2) Probit Choice BrandAd 0.743*** 0.362*** ProductAd 0.428*** 0.259*** SpBrandTenure -0.002 -0.002 SpPassTrain 0.153*** 0.080*** BrandAd × SpBrandTenure -0.005 -0.003 BrandAd × SpPassTrain -0.098 -0.067 ProductAd × SpBrandTenure -4.19e-04 -8.59e-04 ProductAd × SpPassTrain 0.231*** 0.092*** Controls √ √ Constant -10.21*** -4.177*** BIC 7099.215 8359.542 Observations 551,056 551,056 Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1 Variables (1) Ln(1+Transac tion) FE (2) Ln(1+Transa ction) RE (3) Ln(Visit) FE (4) Ln(Visit) RE TotalBrandAd 1.57e-04 9.86e-04*** 1.52e-03*** 3.02e-03*** TotalProductAd 7.57e-05 5.37e-04*** 8.97e-04*** 1.70e-03*** CarLineDiffBrand -0.011 0.156*** 0.014 0.293*** CarLineSameBrand 0.032 0.098*** 0.477*** 0.751*** TotalBrandAd × CarLineDiffBrand -5.02e-05 -2.50e-04*** -2.70e-04*** -5.80e-04*** TotalProductAd × CarLineDiffBrand 1.08e-05 7.82e-06 1.03e-05 -1.17e-05 TotalBrandAd × CarLineSameBrand 1.24e-04*** 1.64e-04*** 3.38e-05 -8.32e-06 TotalProductAd × CarLineSameBrand 1.51e-05 -1.12e-04*** -2.04e-04*** -4.16e-04*** Controls √ √ √ √ Constant 0.286 14.51*** 3.437*** 25.69*** Overall-R2 0.282 0.532 0.319 0.497 Observations 2,493 2,493 2,493 2,493 Identifications and Robustness Instrument Variable Estimation Non- randomly Assigned Salesman Endogeneity Concern of Product Line Length Simultaneity Alternative Model Hierarchical Linear Bayesian MCMC Estimation √ √ √ √ √ √ √ Contributions ? Contribute to the literature on the interdependencies between online advertising and offline salesman interactions by granularity testing ? Contribute to the literature on the interdependencies between online advertising strategies and product line management strategies ? Managers should evaluate trade-offs of different advertising strategies, offline salesman training and product line management