We study whether thank-you communications that charities send to past donors should include a call to action and show that the answer depends on whether the donor has already given in the current giving cycle.
While structured search interfaces—such as filters and sorting tools—help consumers reduce search frictions on digital platforms, they also shape how consumers encounter product information during search. This paper studies how search interface design influences revealed preferences and market outcomes. We develop a theoretical framework linking interface design to attribute preferences through salience reallocation and information-based preference updating. The framework generates two predictions that can be formally derived: restricting faceted filters reduces the concentration of attribute importance weights (i.e., preference dispersion) and increases the diversity of products chosen (i.e., choice diversity). We test these predictions using a randomized, incentive-aligned online experiment in which 1,200 participants searched for wireless headphones on a custom-built e-commerce platform. Restricting filters led consumers to explore more broadly and resulted in more evenly distributed attribute importance weights and more diverse, higher-priced product choices. Mediation analysis further suggests that increased exploration was associated with shifts in attribute preferences. Our findings demonstrate that search interface design can construct, rather than merely reveal, consumer preferences.
This study examines how aspirational-goal communications influence sales agents’ performance and information-seeking behavior. In a field experiment across 100 stores in Sweden, agents in the treatment group received daily personalized text messages with aspirational sales goals tailored to their prior performance. Results show that these communications increase sales performance, particularly among low-performing agents, primarily through higher unit sales. At the same time, they reduce agents’ willingness to monitor within-day sales progress—reflecting the ostrich effect—which can adversely affect performance. Information acquisition, in the form of sales-progress monitoring, typically aids effort provision, but aspirational-goal communications suppress such behavior, complicating performance management. These findings highlight the dual effects of aspirational-goal communications: while they enhance motivation, they may also prompt information avoidance. The study contributes to research on goal setting and sales management by clarifying the trade-offs in designing effective motivational and communication strategies.
As Taboola prepared to launch its GenAI Ad Maker, a tool that lets advertisers create ad images within the platform, it faced a consequential question about whether AI-generated ad images would perform well with real consumers, and under what conditions. Working with Taboola, we construct a quasi-experimental comparison of 4,633 ‘sibling ads,’ meaning AI-generated and human-made images launched by the same advertisers, in identical campaign settings, at the same time. These ads span more than 369 million impressions and 2.5 million clicks. We do not find a detectable average click-through-rate disadvantage for AI-generated images relative to their human-made siblings. However, this average masks an important boundary condition: ads with AI-generated images outperform human-made ads when the AI images do not ‘look like AI.’ We identify key visual features influencing consumers’ perceptions of whether an ad looks AI-generated. While AI generates sharper, clearer, and less blurry images with larger faces, consumers associate these features with human-made ads. On the other hand, in line with consumers’ expectations, aesthetics, lower warmth, and intense color saturation in ads signal AI generation. These findings support Taboola’s roll-out communication, advertiser guidance, and continued refinement of the GenAI Ad Maker.
The multibillion-dollar music industry faces severe content creation bottlenecks, making multimedia marketing assets prohibitively costly for smaller artists. This practice paper introduces a Marketing Asset Generation Engine (MAGE) that can produce over 700 million video ad variations reflecting each artist’s visual identity using only a music track’s audio and album cover as inputs. Collaborating with one of the world’s largest music labels, we conduct four preregistered field studies with over 11 million impressions across 75 campaigns for 33 music tracks on TikTok and Meta to validate MAGE. First, pooled across studies, MAGE-generated ads significantly outperform human-designed ads on TikTok with on average 26% higher click-through rates and 10% higher engagement, while we do not detect statistically significant performance differences on Meta, all at 98% lower production cost. Second, neither marketing professionals nor laypeople can reliably identify winning ads such that random sampling of MAGE’s outputs performs as well as human-curated selections. In contrast, an AI-in-the-loop system trained on past performance data can predict pre-launch which MAGE-generated ads will perform best for specific audience segments. These findings demonstrate that generative AI democratizes music marketing by making effective, on-brand video ads accessible at scale.
