Attribution Modeling P. K. Kannan
Example: a multi touch path to purchase Day 1 Day 1 Day 8 Click on Email Promotion Link Click on TripAdvisor Link Click on Paid Search Link Firm.com Firm.com Firm.com No conversion No conversion Converts Which marketing campaign should get credit for the conversion? How exactly do we value each touch point? 2
Marketing Channels Direct (by typing in URL) Referral E Mail Organic & Pid Paid Search Firm.com Display 3
Key Questions What is the incremental impact of each marketing channel in drawing in visits and purchases? Do we spend too much on Google? 4
Referral 20% Direct 20% Display 20% 7 day average Metric E mail 0% Paid Search 40% Customer 1 Customer 2 Customer 3 Last click Conversions Metric Paid Search Paid Search Direct Customer 4 Referral Customer 5 Display Customer 6 Customer 7 5
Customer 1 E mail Paid Search Customer 7 6
7 Use Carryover and Spillover to Capture the Dynamics in the Ad Information Carryover Email Display Display Paid Search 7 Spillover
8 Modeling the Attribution Problem Available Channels Search, Referral, Direct, E mail, and Display Channels Considered Search, Direct, and E mail Costs Costs Visit Through Channels Spillover Search Direct Effects E Mail Carryover Effects Benefits Benefits Purchase At Website Overall attractiveness of making a purchase 8
The Consideration Stage: Possible channels to reach the website Q customer initiated channels: organic search, paid search, referral, and direct (J Q) firm initiated channels: email and display The utility of considering channel q by customer i c R * iq i iq iq R i : customer specific variables, such as loyalty tiers. The consideration set of customer i 9 C ( c c ) T i i1 ij (,, ) ~ N (0, ) T i1 iq Q An example with J=3 P i (channel 1)+ P i (channel 2)+ P i (channel 3) + P i (channel 1&2)+ P i (channel 1&3) + P i (channel 2&3) + P i (channel 1&2&3)=1 De Los Santos, Hortacsu, and Wildenbeest 2012; van Nierop et al. 2010
The Visit and Purchase Stage: The utility for customer ito visit channel j on occasion n Uijn 0, ij Iijn j Sijn ijn j 1,,J Inclusive value: Cost of Visiting: S The utility for customer ito purchase in channel j on occation n J Indicator of customer i visit W G j 1,,J 10 ijn ij j, k ikn ijn k 1 n 1 ijn j ijn j, k ik, n 1 k 0 Informational Stock: Gikn dikh (1 k ) I log 1 exp( W / ) ijn ijn J exp( jtijn j, k Lik, n 1) k 0 J 1 exp( T L ) h 1 channel k on occation h. ( t t ) ikn ikh Most recently visited channel Cumulative time spent on the firm s website (1 Decay Rate) Ansari, Mela, and Neslin 2008; Erdem and Keane 1996; Moorthy, Ratchford, and Talukdar 1997; Montgomery et al. 2004; Seiler 2013
Data and Estimation Individual level path to purchase data from an international hotel chain June August, 2011 Integrated data feeds Search and display; referral and direct; and e mail Google DoubleClick Adobe Site Catalyst Estimation: MCMC in R 11
Data and Estimation Individual level path to purchase data from an international hotel chain June August, 2011 Integrated data feeds Search and display; referral and direct; and e mail Google DoubleClick Adobe Site Catalyst Estimation: MCMC in R 12
Results Consideration Stage Notes: Bold indicates that the 95% posterior interval excludes zero. 13
Results Visit Stage Carryover Spillover
Results Purchase Stage Carryover Notes: Bold indicates that the 95% posterior interval excludes zero. Spillover 15
Compare Alternative Attribution Methods Channel Observed Direct 347 Organic Search 285 Referral 201 E Mail 138 Paid Search 114 Display 43 Total 1128 Last Touch 31% 25% 18% 12% 10% 4% 100% Proposed Model 28% 16% 24% 19% 6% 7% 100% 16
17 Take away from Example A Significant carryover and spillover effects at both visit and purchase stages. Incremental impact of channels. For the focal firm: Spend less on paid search. Spend more on referral, e mail and display. Measurement framework. 17
This image cannot currently be displayed. Attribution and Resource Allocation in Search Advertising Firm bids on keywords at time t: Bid(K1)=$1; Bid(K2)=$1; Bid(K3)=$1 Search engine ranks the bidders for each keyword Customer clicks on keywords: Customer 1: K1 K2 K3 K3 $10 Customer 2: K2 K2 $10 Customer 3: K2 K2 K3 K1 $0 Revenues based on last touch attr.: R(K1)=$0; R(K2)=$10; R(K3)=$10 Revenues based on first touch attr.: R(K1)=$10; R(K2)=$10; R(K3)=$0 Firm allocates budget Bid on keywords at time (t+1): Bid 1 =$0; Bid 2 =$1.2; Bid 3 =$1.5 Bid on keywords at time (t+1): Bid 1 =$1.6; Bid 2 =$1.3; Bid 3 =$0
New account WOM Consideration Transaction 19
Take away from Example B Which attribution scheme (first click or last click) is better in generating revenues? Broad Keywords Specific Keywords Last Touch First Touch What is the impact of attribution scheme on different marketing responses? New Customer WOM Generation Consideration Purchase Last Touch First Touch
Challenges in implementation Attribution model only as good as the path data How do you integrate all customer online touchpoint data? Incomplete data cookie deletion How about online channels and offline channels TV, for example? Direct to Physicians, Direct to Consumers Shopping across mobile and online channels
Challenges What I see in attribution starts with my marketing mix allocation and targeting How do I disentangle what is due to my actions versus measurement issues?
$ $ Budget Set by Mgmt Monthly/Quarterly
$ $ Budget Set by Mgmt Monthly/Quarterly Consumers Purchase Funnel Modeling Approaches 1. Hidden Markov Models 2. Nested Logit Model 3. Generalized Poisson 4. VAR Models 5. Machine Learning
$ $ Budget Set by Mgmt Daily
Challenges How do I incorporate attribution results into my media mix allocation? How do I target customers? Compatibility of data at different granularity Building brand versus revenue generation Challenge of Big Data
Questions? Setting the stage for discussion on marketing mix allocation
Contact Info P. K. Kannan Ralph J. Tyser Professor of Marketing Science Chair, Department of Marketing Smith School of Business University of Maryland College Park, MD 20742 pkannan@rhsmith.umd.edu Phone: 301 405 2188