Morphing Banner Advertising

Transcription

1 Morphing Banner Advertising Gui Liberali(MIT, Erasmus), Glen Urban(MIT), John Hauser(MIT), Robert Bordley (BAH,GM), Erin MacDonald (Iowa S.) CDB Annual Conference May 23, 2012

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3 Practical problem banner advertising accounts for $6.2B in revenue (24% of online advertising revenue) click-through rates are low and falling (0.005 in 2001 to in 2010) conversion after click-through varies considerably high managerial interest to increase click-through and conversion state-of-the-art static optimization (e.g., genetic algorithms) context targeting by text matching or website relevance behavioral targeting based on demographics, click histories methods not forward-looking nor inferred dynamically from clickstream

4 Scientific problem Website Morphing proposed in 2009; we extend to banner morphing cognitive styles from clickstream website morphing changes look and feel automatically to match cognitive styles (near) optimal tradeoff of exploration vs. exploitation 2009 algorithm was tested via simulation 20% projected improvement for the BT Group data from small-sample priming study morphing is designed for high-traffic websites require 10,000+ for optimal convergence first large-scale field test of any type of morphing on a high-traffic website

5 Morphing in Action Tasks Outcome Priming Study Collect priors on a small-scale survey Distribution of cognitive styles Priors on optimal ads per style Day-to-day operation Exploration Exploitation Learn style-ad optimality Profit from optimal ads Real-time information about optimal ads for each style Highest possible CTR

6 Morphing Process Website Click descriptor Priors on distribution of decision styles Bayesian update Updated user decision-style descriptor P gm : probability of purchase for individual in group g given morph m On-line Optimization Source: Hauser et al., 2009 Optimal morph for current user Ads -> Holistic-Deliberative Holistic-Impulsive Analytical-Deliberative Analytical-Impulsive

7 Banner morphing algorithm review* Priming study (small sample prior to implementation) published projections to date are based only on priming studies identify cognitive styles and identify segment definitions measure preferences among click-alternative features for each segment Assign consumers to cognitive-style segments based on clickstream (during natural visit) observe clickstream, compute likelihood of making observed clicks use Bayes Theorem to compute the probability that a consumer belongs to a segment Identify and assign the best morph to a segment (during natural visit) solve a dynamic program: trade off new-morph exploration with current-knowledge assignments the optimal solution for known segment is to assign the morph with largest Gittins index when segment is partially observable, solve POMDP with expected Gittins index New issues for banner morphing (solved in banner-morphing paper) cookies enable tracking, success if consumer clicks banner at least once revise methods to update the Gittins -index parameters * J.R. Hauser, G.L. Urban, G. Liberali, and M. Braun (2009), Website Morphing, Marketing Science, 28, (2),

8 CNET field test: banner morphing CNET attracts 8 million visitors per day, banner advertising important to CNET. CNET website with natural website visitors (AT&T HTC smart phones). 116,168 consumers 451,624 banners test cognitive styles: (impulsive vs. deliberative) x (analytic vs. holistic) 1,300 users eight potential banners developed by professionals control is no matching (randomly selected banners) observe click-through rates

9 Example banners Analytical, deliberative Holistic, impulsive

10 Interaction makes sense Analytic theory suggests targeting interactions Iyer, Soberman, Villas-Boas Marketing Science 2005 Kenny and Marshall Harvard Business Review 2000 Longstanding recognition of interaction with involvement (relevance) Petty, Cacioppo, Schumann Journal of Consumer Research 1983 Chaiken Journal of Personality and Social Psychology 1980 Cognitive styles facilitate learning of preferences Lambrecht and Tucker 2011, MIT & LBS Working Paper Hauser, Dong, Ding Journal of Product Innovation Management 2012 Academic and industry success matching context and behavior

11 CNET field test results Among targeted consumers click-through per banner morphing: of 1% no morphing: of 1% 83% lift p < 0.01 click-through per consumer morphing: of 1% no morphing: of 1% 97% lift p = Among consumers not targeted click-through per banner lift not significant click-through per consumer lift not significant interactions between morphing and behavioral-targeting are significant. Interactions of morphing x targeting are significant

12 General Motors (Chevrolet) laboratory/field test goals of the laboratory/field test underlying premise of morph-to-segment matching. pre- vs. post- brand consideration and purchase likelihood laboratory stage identify cognitive styles and best banner for each segment in a longitudinal study surrogate for in vivo Bayesian/Gittins optimization field stage consumers exposed to banners while searching an automotive information-and-recommendation website 588 consumers, 8,991 banners test two best banners for each segment segments combine cognitive styles and context (buying stage) all banners matched to body-type preference control GM s current banners matched to body-style preference

13 Example banners on Consumer Research Power Control Collect Compare Commit 2 nd,3 rd,4 th columns are buying stages Details, deliberative Image, impulsive

14 3-Phased GM Longitudinal Study Priming Study 1. Rate all ads for context (buying stage) and body type Exploration 2. Learn - Measure user s cognitive styles with scales - Pre-measures of DV: consideration, purchase likelihood Exploitation 3. Optimize (4 ½ weeks later) - Show optimal ads - Post-measures of DV: consideration, purchase likelihood

15 GM laboratory/field test results Matching on cognitive-style and buying stage. click-through per banner matched: 0.97% control: 0.26% 245% lift p < 0.01 click-through per consumer Control Collect Compare Commit matched: 15.9% control: 9.6% 66% lift p < 0.01 brand consideration matched: 42.8% control: 32.9% 30% lift p < 0.01 purchase likelihood matched: 3.28% control: 3.05% 8% lift p < 0.01

16 Ruling out threats to validity Current banners as a control current banners > best two overall > randomized banner More banners better chance of a click all users, test and control, saw only two banners (but multiple impressions) Longitudinal design banner experiment 4½ weeks after banners rated proximity to rating favors pre-measures Observed and unobserved heterogeneity difference from pre-measures controls for observable demographics

17 Pre-measures and demographics (logit and regression analyses) Click-through All Banners Click-thru per Consumer Brand Consideration Purchase Likelihood Coefficient Significance Coefficient Significance Coefficient Significance Coefficient Significance Intercept < a < a < Test vs. control treatment a < b a < a Pre-measure a < a < 0.01 Buying-process dummies Collect a a < 0.01 Compare a < < b Commit Cognitive-dimension dummies Rational a Intuitive Impulsive b Deliberative Male (vs. Female) b Age b b Income Log-likelihood ratio a a a a a b Significant at the 0.05 level. Significant at the 0.10 level. Sample size impressions or 588 consumers. All equations significant at the 0.01 level. Test vs. control is also significant at the 0.01 level with a differences of differences specification.

18 Summary and implications Matching banners to cognitive styles yields significant lifts Morphing algorithm feasible and effective on high-traffic websites Complementary to context targeting (behavioral targeting) Interactions of cognitive styles and relevance interesting scientifically Challenges stabilize cognitive-style definitions develop norms to streamline priming studies develop design expertise