Key Areas of Research
AI for Customer Journeys: A Transformer Approach
Journal of Marketing Research
AI for Customer Journeys: A Transformer Approach
Zipei Lu and P. K. Kannan, Smith School of Business, University of Maryland
(forthcoming Journal of Marketing Research)
AI for Customer Journeys: A Transformer Approach introduces a novel artificial intelligence (AI) framework for modeling customer journeys in digital marketing. Leveraging transformer-based models – originally developed for natural language processing - this approach analyzes complex sequences of customer interactions across multiple channels (e.g., search, email, display ads). Unlike traditional models, this method considers both the timing and type of interactions, making it uniquely suited to modern multi-touchpoint environments.
“Transformers give us the ability to see the journey as a whole, not just as a series of isolated interactions. That’s a major leap in marketing analytics.”
— PK Kannan
The core innovation lies in its use of multi-head self-attention mechanisms, which model each customer’s journey as a dynamic sequence of touchpoints. This allows marketers to not only predict the likelihood of purchase but also identify when and through which channels interventions are most effective. Furthermore, the model is extended to capture individual-level heterogeneity, enabling personalized insights into how different customers respond to marketing efforts.
“We designed the model to capture the complexity and individuality of digital customer journeys—something traditional models often overlook.”
— Zipei Lu
Using rich data from a major hospitality firm – including over 92,000 users and over half a million visits – the model demonstrates substantial improvements over traditional approaches (e.g., Hidden Markov Models, Poisson Point Process Models, and LSTMs). For example, the proposed model achieves an AUC of 0.92 for out-of-sample conversion predictions compared to 0.85 for LSTM and <0.70 for others. Moreover, it identifies high-potential customers with far greater precision – top-decile predictions yield an 88% true conversion rate versus 34% for LSTM.
Beyond prediction, the model offers descriptive marketing insights, such as how the effectiveness of email or display ads varies over time and across customers. For instance, the study finds that customer-initiated interactions (like direct visits) have stronger and longer-lasting effects than firm-initiated ones (like emails), and the optimal window for intervention is typically within 7 to 14 days before purchase.
The model’s structure also enables profiling and customer segmentation based on latent self-attention patterns, helping marketers understand nuanced motivations like last-minute business bookings versus long-term vacation planning. This insight can inform targeted messaging and A/B testing strategies.
Overall, this AI framework not only enhances predictive accuracy but also delivers actionable insights that can improve ROI, optimize channel mix, and enable real-time personalization in customer engagement.
Zipei Lu, Ph. D. Candidate in Marketing; P. K. Kannan, Dean's Chair in Marketing Science, both at the Smith School
The Influential Solo Consumer: When Engaging in Activities Alone (vs. Accompanied) Increases the Impact of Recommendations
Journal of Marketing Research
Information about the social context of consumption is often seen on review websites or social media when consumers sharing word-of-mouth about an experience indicate whether they engaged in the activity solo or with companions. Across a secondary dataset scraped from Tripadvisor.com, five main experiments, and one supplemental experiment, the current research finds that individuals who engage in consumption activities alone can be a more influential source of recommendations than people who engage in these same activities with others. The results support an attribution-based process, such that people are more likely to attribute a solo (vs. accompanied) review to the quality of the activity itself, leading the solo (vs. accompanied) person’s review to be particularly influential. Further, the studies test the theorizing that perceived interest on the part of the solo (vs. accompanied) consumer leads to the stronger attribution to quality, and therefore that additional cues to intrinsic interest (e.g., presence of a cue to intrinsic or extrinsic motivation) attenuate the influence of solo (vs. accompanied) word-of-mouth. This work has theoretical and managerial relevance for those who seek to understand how the social context of consumption influences other consumers.
Rebecca Ratner, Dean's Professor of Marketing, Robert H. Smith School of Business; Yuechen Wu, assistant professor, Spears School of Business, Oklahoma State
Identifying Competitors in Geographical Markets Using the CSIS Method
Journal of Marketing Research
Businesses with physical footprints – hotels, retailers, and restaurants – must identify the competitors that matter most. Traditional approaches using brand tier or proximity often fail in dynamic or asymmetric markets. We introduce the Conditional Sure Independence Screening (CSIS) method to marketing to identify true competitors based on their pricing influence on a focal firm’s demand. CSIS is computationally efficient, robust to spatial mis-specifications, and effective for identifying, asymmetric, even non-local, and segment-specific competition. It is also an effective variable selection technique.
In applying CSIS to U.S. hotel data our analysis shows that competition intensity varies not only by location or market segments, but that asymmetry is common – many hotels influence others without being influenced in return. Our methodology enables smarter, data-driven pricing and benchmarking and helps tailor strategy to segment and seasonality. In addition, it is scalable across industries such as retail, services, and hospitality.
Xian Gu, Assistant Professor, Kelley School of Business, Indiana University; P.K. Kannan, Dean's Chair in Marketing Science, Smith School of Business, University of Maryland
The Impact of App Crashes on Consumer Engagement
Journal of Marketing
The authors develop and test a theoretical framework to examine the impact of app crashes on app engagement. The framework predicts that consumers increase engagement after encountering a single crash due to their need-for-closure and curiosity, yet reduce engagement after experiencing repeated and concentrated crashes, primarily because of frustration and perceived task unattainability; the recency of crashes moderates these effects. Field data analysis reveals that while a crash truncates a session and reduces content consumption, it increases page views in the following session. However, this increase in page views does not compensate for the loss during the crashed session. Frequent and more concentrated crashes curtail engagement. Three experiments in which crashes are exogenously manipulated in a different context support the validity and generalizability of these findings, confirm the proposed mediators, and demonstrate how to lessen the negative impact of repeated crashes with post-crash messages. The research adds new dimensions to the task pursuit literature and provides managers with a framework to quantify the economic impact of crashes, analyze content substitution behavior, and assess the bias of a transactional view of crash incidents. Additionally, it offers insights into targeted feature release to more tolerant users and strategic design of post-crash messages.
Savannah Wei Shi, Associate Professor of Marketing & J.C. Penney Research Professor, Leavey School of Business Santa Clara University
Seoungwoo Lee*, Assistant Professor, Yonsei School of Business, Yonsei University Seoul
Kirthi Kalyanam, L.J. Skaggs Distinguished Professor Leavey School of Business, Santa Clara University
Michel Wedel, PepsiCo Chaired Professor of Consumer Science, Robert H. Smith School of Business, University of Maryland
Transforming Products into Platforms: Unearthing New Avenues for Business Innovation
NIM Marketing Intelligence Review, October 2024
It is impossible for brands to ignore digital platform opportunities. Network effects are one of the strongest sources of power and defensibility ever invented and underlie some of the most valuable businesses in the world. Managers and entrepreneurs can leverage the power of platforms by adding some platform elements to their existing products or services, by distributing their brands via existing platforms or by developing their own new platforms. By using one’s own brands as platforms requires creativity but can help businesses unlock new value and build resilient ecosystems around their products. There are three key methods. The first is to invite third-party sellers to enhance existing products. Examples include selling advertising space around products or creating app stores to extend offers. The second is to connect one’s customers by enabling interactions among users to add value. Third, brands might reach out to customers’ customers by enhancing the end-user experience in a way that benefits both themselves and their direct customers. If thoughtfully implemented, any platform strategy will create self-reinforcing feedback loops sparking growth and keeping competitors at bay.
Andrei Hagiu, Associate Professor of Information System, Boston University; Bobby Zhou, Associate Professor of Marketing, University of Maryland
Liability of Foreignness in Immersive Technologies: Evidence from Extended Reality Innovations
Journal of International Business Studies
This study investigates the persistence of the Liability of Foreignness (LOF) in the realm of immersive technologies like Extended Reality (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). Challenging the assumption that digitalization eliminates traditional barriers for foreign firms, we argue that LOF in XR stems from foreign companies' difficulties in providing a "mentally fluent" experience to consumers in foreign markets. Cultural mismatches can disrupt smooth information processing and diminish the effectiveness of XR innovations. Our research identifies specific XR technological features—realism, interactivity, and vividness—and brand-related factors like brand newness and platform orientation that can either exacerbate or mitigate LOF. Confirming the existence of LOF in XR innovations, we find that foreign brands in the South Korean beauty market are at a disadvantage in generating positive brand engagement through XR compared to local brands. XR innovations that are less realistic, more interactive, and highly vivid tend to amplify LOF due to the need for deeper cultural understanding. Conversely, higher realism in XR experiences helps reduce LOF by offering universally relatable content. Newer foreign brands and those using communication-centered platforms experience less LOF, as consumers may overlook cultural mismatches to resolve information uncertainty and develop attitudinal loyalty.
Hyoryung Nam, Assistant Professor, Martin J. Whitman School of Management at Syracuse University (Ph.d. from Smith – Marketing Department), Yiling Li, Doctoral Student, Yonsei Business School, Yonsei University, Seoul, Korea, P.K. Kannan, Dean’s Chair in Marketing Science, Robert H. Smith School of Business, University of Maryland, Jeonghye Choi, Professor of Marketing, Yonsei Business School, Yonsei University, Seoul, Korea