AI's Transformational Impact on Marketing by instituteforexperientialai

AI’s Transformational Impact on Marketing

Yakov Bart Associate Professor of Marketing Thomas E. Moore Faculty Fellow Faculty Director of DATA Initiative

D’Amore-McKim School of Business

November |15, 2023

Our focus today Novel framework that may help various stakeholders to better understand the impact of current and future AI advancements from a consumer-firm perspective Examples of how advances in machine learning allow us to: Uncover industry leaders and laggards in racial diversity in advertising, and examine its impact on consumer purchase intentions Investigate how emphasizing nonmaterial and material ESG factors may affect firm value

AI radically changed how consumers search for information, evaluate products and services, make purchase decisions, and share their experiences with others

While firms invest in AI for various marketing purposes, the potential returns on their investments hinge on understanding how these new technologies can be used to better manage technology-enabled customer journey

Growing popularity of consumer-facing technologies From digital assistants and smart thermostats to wearables and connected devices, consumers continually interact with devices capable of collecting and transmitting information pertaining to their actions and intentions

The number of Internet of Things (IoT) devices worldwide is forecast to almost double from 15.1 billion in 2023 to more than 29 billion IoT devices in 2030

We examine the customer journey through the lens of the signals emitted by consumers and the ability of firms to act on those signals Consumer digital signals = tangible and intangible interactions between consumers and firms that may be observable (depending on firms’ capabilities) Consumers emit digital signals from a broad range of everyday behaviors, which may be incredibly informative of their future behavior (indicators of consumer interests, preferences, and activities during their journey) Firm’s ability to leverage consumer digital signals for personalized and targeted marketing efforts depends on its capability to gather, analyze, and act on such data Signals may be overtly or covertly collected by firms Consumers may vary in their awareness and knowledge with which they generate and emit signals How firms interpret and act on these signals may affect how consumers react, and in turn, the signals they subsequently emit

Consumer perspectives regarding the digital signals they emit Awareness of signal emission Surveys show that while consumers express concern about how their data can be used by businesses, most are not aware to what extend their digital signals are collected and used for targeting marketing efforts

Privacy concerns related to emitted signals Privacy concerns decrease when consumers believe they have control over their privacy Consumers may have trouble making informed decisions about privacy due to information asymmetries and switching costs

Privacy-personalization tradeoff Privacy calculus: weighing of risks associated with sacrificing privacy against benefits such as convenience, personalized recommendations

Consumer signals throughout the journey - aggregation At the most detailed level, signals are produced by an individual consumer, enabling firms to attribute behavior to a specific person At the other extreme are signals that have been aggregated across consumers, limiting the extent to which they can be used to personalize the customer experience The middle ground is where signals produced by consumers have been aggregated into segments; while not providing the clarity of signals emanating from individuals, segmented signals can still support personalization depending on the segmentation criteria

Google Chrome’s Federated Learning of Cohorts (FLoC), now Topics API

Consumer signals throughout the journey - visibility

Pre-purchase

Purchase

Post-purchase

Challenges in managing consumer relationships

Our framework

Opportunities for future research

Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers’ desire for privacy can restrict the opportunities technology provides marketers, other consumers’ desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers’ willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators.

Motivation Consumer demands societal change and increased diversity Brands are responding Communicate commitment through visual marketing Promoting societal change while boosting the bottom line: Win-win? Or maybe not: issues with lack of authenticity, woke-washing, tokenism

Research objectives Examine the impact of racial diversity in TV ads on consumer purchase intention Explore the moderating role of consumer processing and public attention to racial inequality Analyze changes in consumer responses pre- and post-George Floyd’s murder

Data collection Data on 50,284 TV ad creatives from 666 brands Airing details and viewer engagement metrics from 2018-2021Q2 Daily consumer purchase intention indicators from YouGov’s BrandIndex

Operationalizing racial diversity YOLOv5Face and LightFace models to detect faces Identification and racial classification of actors Fairfaces data (Karkkainen & Joo 2021)

Calculation of daily Black actor share of ads (BASOA)

Daily race percentages of actors over time

Brand dynamics for BASOA in the fast food industry The color of the nodes in the graph represents the social innovation categorization: • • • •

leader is green early adopters are orange late adopters are purple laggards are grey

Measuring purchase intention YouGov’s BrandIndex tool for monitoring brands Purchase intention as a proxy for consumer behavior Daily mean of purchase intention

Understanding consumer processing Measurement of ad “zapping” or turning off Attention Tuning Rate • Operationalization: ATR75 = #TVs_75% / #TVs_Start • Low attention → ELM peripheral processing • High attention → ELM central processing

Conceptual model

Viewer attention reflects the motivation to process information based on the Elaboration Likelihood Model (ELM) Framework

George Floyd and racial inequality Examining the impact of George Floyd’s murder Changes in consumer responses due to heightened public attention

Empirical specification and models Dynamic panel data (Arellano & Bond 1991) Evaluate how BASOA affects purchase intention and test the moderating effect of consumer processing Include the impact of murder of George Floyd and assess changes

Panel data model estimation

Estimation and robustness checks System GMM: Integrates level and differenced equations to exploit all moment conditions Accounts for endogeneity and bias from unobserved heterogeneity: • lagged levels as instruments for the differenced equation • lagged differences as instruments for the level equation Robustness: Hansen-J test for instrument validity Sensitivity of findings to the number of instruments Variable operationalization |

Marginal effects at different attention levels

Marginal effects at different attention levels pre- and post- George Floyd

Summary of findings Placed in the context of George Floyd and the following social unrest: Before, the effect of Black actors on purchase intention is positive and significant when processing is more peripheral, and insignificant when processing is more central After, the effect of Black actors on purchase intention is negative and significant when processing is mostly central, and non-existent otherwise

Our next steps Does it matter which Black actors appear in the ads? Does the situational context (roles, focus, dynamics) in which Black actors interact in the ads matter?

How does a company benefit or harm the environment? Air quality Ecological Impacts Energy Management GHG Emissions Water & Wastewater Management

How does a company interact with its employees and broader community? Human Rights & Community Relations Employee Engagement, Diversity, and Inclusion Employee Health & Safety Customer Privacy Customer Welfare Labor Practices

How does a company align stakeholder interest to drive sustained business growth? Business Model Resilience Material Sourcing & Efficiency Supply Chain Management Critical Incident Risk Management

The ESG Dilemma Investors are demanding that companies must prioritize ESGrelated concerns and expand disclosures

Investors are reluctant to sacrifice financial performance in the pursuit of ESG, as studies show no incremental returns from ESG

How to quantify ESG emphasis? Natural Language Processing can be used to analyze relevant texts to extract out important ESG centric insights BERT (Bidirectional Encoder Representations from Transformers) meets Sustainable Investing -> ESG-BERT This model is specifically trained on the sustainable investing text corpus to analyze and understand text data related to ESG topics We extract the presence of 26 ESG factors from 10,142,523 sentences in 44,570 conference call transcripts covering 6,730 firms across 67 industries from 2005 to 2021

Material and nonmaterial ESG emphasis

Material

Nonmaterial

If omission from corporate disclosure would substantially change the overall mix of information available to a reasonable investor

If omission from corporate disclosure would not substantially change the overall mix of information available to a reasonable investor

AIRLINES

TELECOMMUNICATIONS

Material and nonmaterial ESG emphasis over time Stakeholder Capitalism arrives at Davos

Republican politicians argue that Bank of America prioritizes nonmaterial ESG (climate change, and workforce diversity) over shareholder returns (Schroeder, Nguyen, and Azhar 2022) Consistent material emphasis echoes their CEO's statement “We are capitalists” (Nguyen and Anand 2023)

ESG emphasis and costs Emphasizing ESG factors is associated with increased costs (Aupperle, Carroll, and Hatfield 1985; Friedman 1970; Jensen 2002; McWilliams and Siegel 1997) Consequently, investors seek signals to gauge a firm's ability to mitigate/recover these costs

Other stakeholders may shape the firms’ emphasis on ESG CUSTOMERS

EMPLOYEES

REGULATIONS

Is nonmaterial ESG really nonmaterial?

When firms emphasize nonmaterial (vs. material) ESG factors, investors may perceive it as a negative (vs. positive) signal, signifying potential cost disadvantages (vs. advantages), resource allocation inefficiencies (vs. efficiencies), and managerial distraction (vs. attention)

Results Firm Value

Nonmaterial ESG emphasis Material ESG emphasis Nonmaterial ESG emphasis × time elapsed Material ESG emphasis × time elapsed Nonmaterial ESG emphasis × regulation Material ESG emphasis × regulation No. of obs. Adjusted-R2 Year fixed effects Firm fixed effects Controls

-.299*** .141** -.003** -.0002 -.034*** .029*** 39,673 .094 Yes Yes Yes

Notes: Control functions are estimated using ESG emphasis of peer firms as instruments. Elimination of coefficient of regulation due to fixed effects model. *p < .10, **p < .05, ***p < .01.

ESG Emphasis Finder: https://esg-analytics.shinyapps.io/ESG-Emphasis-Finder