Learning User Real-Time Intent for Optimal Dynamic Webpage Transformation

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1 Learning User Real-Time Intent for Optimal Dynamic Webpage Transformation Amy Wenxuan Ding, Shibo Li and Patrali Chatterjee Web Appendix: Additional Results for the Two-State Proposed Model A. Program Estimation We used a hierarchical Bayesian approach (Monte Carlo Markov chain) to estimate the models. We ran the models coded in C++ for 50,000 iterations. The first 40,000 iterations served as the "burn-in" period to ensure convergence, and the last 10,000 iterations provided input for our parameter inference. We conducted Geweke s (1992) convergence test and Heidelberger and Welch s (1983) stationary test to determine the convergence of the chains; all the nodes in our analysis pass both tests, so our estimation is reasonably stable and convergent. Readers interested in the detailed estimation procedure should see Li et al. (2011) and Netzer et al. (2008). B. Endogeneity of Marketing and Web Stimuli and Simultaneity of Users In-Session Activities Table A1 summarizes the estimation results for the endogeneity functions for the marketing and web stimuli variables. Higher cumulative pop-up promotions and fewer banner ads at the last page view increase the likelihood of price appearing in the current page view. Fewer prices, more pop-up promotion, and more banner ads encourage the retailer to increase the number of pop-up promotions in the current page view. The cumulative number of solicitations is mainly determined by its lag value. The banner ads, numbers of hypertext links, and pictures are not driven by past marketing and web stimuli though. Table A1. Endogeneity Functions for Marketing and Web Stimuli B Log Price Log Pop-Up Log Log Log iq(t 1) Presence Promotion Banner Solicitation Hypertext Ad Links Intercept (0.10) Lag Log Price - - presence (0.002) Lag Log Pop-up 0.29 promotion (0.001) Lag Log Banner ad (0.003) (0.15) (0.10) Lag Log solicitation (0.35) (0.21) () Lag Log Hypertext links Lag Log Pictures - - Log Pictures The results for the simultaneity functions for the user s in-session activities are in Table A2. For low-intent users, more banner ads and fewer hypertext links on the focal site encourage them to visit competing bookseller sites; marketing and web stimuli on the focal site have insignificant impacts on comparison shopping or visits to non-bookstore sites. Price presence, solicitations, and number of pictures on the page tend to discourage these users from viewing more webpages (lower visit depth); popup promotions and number of hypertext links encourage more webpage views during the session. Banner

2 ads and solicitations discourage them from signing in; solicitations also discourage them from putting more items into the cart to obtain free shipping. Marketing and web stimuli do not have significant impacts on the time low-intent users spent on the last page view. For high-intent users, the results are quite different. More price presence and less pop-up promotion on the focal site encourage them to visit competing book sites. More pop-up promotions, e- mail solicitations, and pictures lead users to comparison shop or visit non-bookstore sites. Fewer solicitations encourage them to view more webpages; more lead them to spend more time at the last page view. Banner ads and solicitations both discourage high-intent users from signing in; solicitations discourage them from putting more items into the cart to obtain free shipping. Table A2. Simultaneity Functions for User In-Session Activities M Log Visits to iq t Other Bookstore Sites Low-intent state Intercept Log Price presence 0.12 (0.21) Log Pop-up promotion - (0.19) Log Banner ad 0.80 (0.28) Log solicitation (0.26) Log Hypertext links Log Pictures Intercept 2.07 (0.85) Log Price presence 0.64 (0.23) Log Pop-up promotion (0.36) Log Banner ad 0.15 Log solicitation 0.35 (0.48) Log Hypertext links Log Pictures () Log Visits to Other Non- Bookstore Sites (0.004) (0.003) - - (0.002) Log Visit Depth 0.08 (0.10) () (0.17) (0.29) Log Time Duration (0.33) 0.26 (0.40) 0.27 (0.47) (0.31) Sign In (0.27) (0.15) Free Shipping (0.19) C. Variance-Covariance Matrix for the Cart Choices The variance-covariance matrix Σ for cart choices, the endogeneity equations of marketing and web stimuli, and the simultaneity equations for in-session activities creates a matrix. In this large estimated matrix, we focus on the estimated variances and covariances for cart choices, covariances between cart choices and the stimuli, and covariances between cart choices and in-session activities, as in Table A3. A user s purchase choice is negatively correlated with continuing to browse without changing the cart and removing items but positively correlated with adding items. Adding items also correlates negatively with continuing to browse and removing items. We also find some endogeneity in pop-up promotion, banner ads, and the number of hypertext links, which show significant correlations with the user s choice of continuing to browse. In contrast, price presence, solicitations, and number of 2

3 pictures do not seem endogeneous and are uncorrelated with any cart choices. We find negative simultaneity between time spent at the last page view and removing items from the cart but positive simultaneity of time spent with the choice to add an item. A user s sign-in to the account correlates positively with the choice of removing an item, indicating some simultaneity between these variables. Table A3. Estimates for the Variance-Covariance Matrix Continue Browsing Remove Item Add Item Purchase Continue browsing 1* (0.00) Remove item 1.01 Add item Purchase Log Price presence - Log Pop-up promotion Log Banner ad Log solicitation - Log Hypertext links - - Log Pictures - Log visits to other book sites - Log visits to other non-book sites - Log visit depth - Log time duration Sign in Free shipping *For identification, this value is normalized to 1. D. Impact of Demographics on Marketing Stimuli Coefficients In the user heterogeneity function in Equation 5, demographic characteristics affect preferences or coefficients in the shopping cart choice utility function. In Table A4, we select marketing stimuli coefficients as examples and summarize the impacts of demographics on these parameters. Among lowintent users, older users with medium income levels are less responsive to price information. Young users with a college education and medium income levels are more responsive to pop-up promotions. Older users with high income and no college education are more responsive to banner ads. In contrast, among high-intent users, older users with medium income levels are more responsive to price information. Younger users with a college education and medium to high income levels are more responsive to pop-up promotions. Users with high to medium income levels are more responsive to banner ads. These results indicate the importance of accounting for individual heterogeneity and incorporating demographic data. 3

4 Table A4. Effects of Demographics on Marketing Stimuli Coefficients HMM State Variable Intercept Age Male College Medium Income High Income Log Price presence (0.35) (0.26) (0.19) (0.32) (0.42) Low-intent state Log Pop-up promotion 4.75 (0.64) (0.32) (0.34) 0.36 (0.27) Log Banner ad 2.95 (0.53) 0.56 (0.67) (0.89) 0.71 (0.74) 2.97 (0.46) Log Price presence Log Pop-up promotion (0.52) (0.31) (0.65) (0.43) 0.63 (0.51) 2.78 (0.22) 1.75 (0.58) 1.03 (0.44) 2.69 (0.47) Log Banner ad (0.54) 0.81 (0.80) 0.50 (0.35) 0.89 (0.43) 1.85 (0.76) E. Impacts of Marketing and Web Stimuli and User Comparison Shopping Activities on HMM States We first consider the impact of solicitation and time duration since the last session on the starting probability of the HMM (Table A5). The starting probability follows a Dirichlet distribution with hyperparameters as a function of solicitations and time duration. To explain their impacts on the starting probabilities, we must consider the estimates across both states. For example, solicitation significantly increases the weight of the hyper-parameter of the high-intent state but has no significant impact on the weight of the low-intent state. Therefore, solicitations significantly increased the probability of starting in the high-intent state and were more likely to attract high-intent consumers. Similarly, the more time between the last session and the current session, the higher the probability that users would start in the low-intent state. Next, we consider the impact of marketing and web stimuli and comparison shopping on the waiting time intensity of the hidden Markov process (Table A5). The waiting time intensity follows a Gamma distribution, so the shape parameter is a function of marketing variables and comparison shopping (Equations 9 and 10). The expectation of waiting time intensity reflects the division of its shape parameter by its scale parameter, given the Gamma distribution assumption. The average waiting time equals the inverse of this intensity parameter, given the exponential distribution assumption in Equation 6. For example, pop-up promotion significantly decreases the shape parameter in the low-intent state with an estimate of but increases it in the high-intent state (0.28). Therefore, pop-up promotion increases the waiting time intensity for the high-intent state. Because the average waiting time represents the inverse of its intensity, pop-up promotion tends to decrease the user s duration in the high-intent state but increase duration in the low-intent state. Similarly, price presence, visit depth, and sign-in increase waiting time intensity and decrease time duration in the high-intent state, but they increase the time spent in the low-intent state. The opposite is true for banner ads and hypertext links. Consumers comparison shopping at competing sites has an insignificant impact on their duration in either state. 4

5 Low-intent state Variables Intercept Starting prob. hyperparam (1.26) Waiting time intensity Table A5. Effects of Covariates on the Hyper-parameters in HMM E- Mail 4.92 (3.16) Time Since Last Session (1.93) Price Popup Banner Hypertext Links Pictures - Visit Depth Sign In 0.83 Comparison Shopping (3.06) Starting prob. hyperparam (2.73) Waiting time intensity (7.94) (6.78) (3.06) References for the Web Appendix Geweke, J. F Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In Bayesian Statistics, Vol. 4, J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F. Smith, eds. Oxford: Oxford University Press. Heidelberger, P., and Welch, P. D Simulation run length control in the presence of an initial transient. Operation Research 31, Li, S., B. Sun, and A. L. Montgomery, Cross-selling the right product to the right customer at the right time. Journal of Marketing Research, 48(4), Netzer, O., Lattin, J. M., and Srinivasan, V A hidden Markov model of customer relationship dynamics. Marketing Science (27:2) pp