Cross-channel Multi-device Conversion Attribution Qingchen Wang, Marc Salomon, Ger Koole, Maarten Soomer, Kevin Pak Are the models any good? Conversion Attribution Advertising campaigns are often launched across multiple channels. Billboard, TV, radio, out of home, print, direct mailing. Web search, online display, social media (Facebook, Twitter), video (YouTube), mobile (browser and apps), and direct emailing. What is the incremental effect that an advertising activity (touch point) has on the likelihood of converting? β’ Credit is awarded to the advertising channels/vendors proportionally to their incremental effects. Attribution Results with Different Algorithms 19000 True Expected 17000 Number of Conversions Attributed β’ β’ Last Touch 15000 All Touch 13000 Simple Prob. 11000 Causal CA 9000 Markov Chain 7000 Current CA 5000 Current CA-Alt 3000 Logistic Reg. 1000 Linear Reg. -1000 TP-1% Mean Abs Error Source: http://www.mobyaffiliates.com/blog/appsflyer-launches-multi-touch-attribution-analytics-to-track-mobile-app-installs/ TP-0% Last Touch All Touch Simple Probilistic Causal CA Markov Chain Current CA Current CA-Alt 24320.47 24314.79 28632.73 42261.41 33987.68 14071.27 4243.42 Logistic Linear Regression Regression 8068.55 835.13 Effect of fragmented data 1. Last touch attribution (LTA) Attribution Results with 20% Broken Paths Source: http://www.clickz.com/clickz/column/2282207/embracingthe-reality-of-multitouch-attribution 4. Time-decay attribution 5. Bathtub attribution Attribute conversion to first and last TPs Number of Conversions Attributed 19000 2. First touch attribution Attribute equal value to all TPs TP-5% Source: Shao and Li (2011) CA in the industry (rule-based) 3. Linear attribution (all touch) TP-2.5% True Expected 14000 Last Touch Simple Prob. Causal CA Markov Chain 9000 Current CA Current CA-Alt Logistic Reg. Linear Reg. 4000 6. Engagement mapping Attribute conversion based on userspecified weights -1000 Source: http://www.clickz.com/clickz/column/2282207/embracing-the-reality-of-multitouch-attribution TP-1% Mean Abs Error TP-1% Simple Prob Causal CA Markov Chain Current CA Current CA-Alt 29570.26 22501.12 26867.18 27053.08 31224.05 17921.30 11052.31 Markov chain (Anderl et al 2013) Treat each TP as states in a Markov chain with conversion as the absorbing state. Consumers move from one state to another down the conversion path Hidden Markov models (Abhishek et al 2012) Consumers have latent intrinsic states (disinterested based on which state he/she is in interested) and reach TPs Vector-Autoregressive model (Wiesel et al 2011) TP-0% Logistic Linear Regression Regression 15043.63 10418.12 Attribution Results with Different Algorithms 0.1300 Number of Conversions Attributed Treat CA as a survival analysis problem with conversion as death and estimates the effect of each TP on the rate of death TP-0% Effect of targeting Computes the empirical π ππππ£πππ πππ πππ ) for all π and then get the relative contribution of each TP Survival analysis (Manchanda et al 2006, Papelnjak 2010) TP-5% All Touch Simple probabilistic model (Shao and Li 2011) Treat CA as a binary prediction problem and use the estimated coefficients to derive the marginal contribution of each TP TP-5% Last Touch CA in papers (data-driven) Logistic regression (Li and Kannan 2014, Nottorf 2014) TP-2.5% TP-2.5% 0.1100 True Expected Last Touch 0.0900 All Touch Simple Prob. 0.0700 Causal CA Markov Chain Current CA 0.0500 Current CA-Alt Logistic Reg. 0.0300 Linear Reg. 0.0100 -0.0100 TP-0% Treat CA as a time series problem with advertisement channel spend and sales dependent on spend and sales in previous time step. Uses aggregate-level data Mean Abs Error TP-3% Retarget-0% Retarget-3% Search-0% Search-3% Last Touch All Touch Simple Prob Causal CA Markov Chain Current CA Current CA-Alt 0.0779 0.0628 0.5605 0.0998 0.4833 0.0456 0.0281 Logistic Linear Regression Regression 0.0447 0.0039
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