The Future of Marketing Analytics and Public Policy

Abstract

Access to large, diverse data has increased exponentially, due to the expansion of digital environments (e.g., websites, online forums, social media, mobile apps, commercial peer-to-peer mutualization systems). More sophisticated analytics tools have emerged to both collect and extract meaning from these data. In parallel, new digital technologies bring diverse public policy and social issues to the forefront, including those endogenous to the digital arena (e.g., social media addiction, fake news, cyberbullying, risks of the sharing economy) and those that have long existed but taken on new forms in the digital space (e.g., election rigging on Facebook, obesity insights from Uber Eats data, social media listening about adolescent smoking, geographic information systems revealing consumers’ access to firearms retailers). In combination, these developments suggest that the public policy and marketing literature can benefit from integrating the remarkable precision, generalizability, reliability, and realism offered by current analytics to gain insights into the problems and opportunities that face modern society and firm decision makers.

Noting that analytics approaches in general marketing literature (Bradlow et al. 2017; Grewal, Hulland, et al. 2020; Wedel and Kannan 2016) rarely appear in research pertaining to social problems, policy, and marketing, we conceived this special issue to address research questions at the intersection of public policy and marketing. We were especially interested in approaches that involve the application of unique data sets, new approaches to extracting meaning from data, and novel data visualization techniques. We also observed that the importance of analytics is driven by extracting greater insight from existing data and accessing new types of data. We called for studies that applied analytics approaches to data to derive insights into questions of interest to readers of the Journal of Public Policy & Marketing.

The result is a collection of articles that approach a wide range of policy and marketing issues using recent analytics techniques. Both public policy makers and marketers can benefit from the perspectives that new data analytics approaches offer, particularly with regard to their promise for addressing the global well-being of consumers and society using large, novel data sets. In this introduction, in addition to presenting these articles briefly, we leverage the opportunities they highlight to introduce six areas that continued research efforts involving marketing analytics and public policy should pursue (see Figure 1): retail analytics; social media analytics; marketing-mix analytics; services including health care; nonprofits and politics; and artificial intelligence and robots.

Before introducing this framework, we note the contribution of Chen, Sridhar, and Mittal (2021) in taking a broader approach to “Treatment Effect Heterogeneity in Randomized Field Experiments: A Methodological Comparison and Public Policy Implications.” They expand the analytic techniques available for field experiments to nearly any public policy context. They specify three ways to account for treatment effect heterogeneity in field experiments: analysis of variance with covariates, random coefficients models, and causal forests. By applying such approaches, marketing and policy researchers can make more informed decisions about how to analyze randomized field experiments, which are crucial for establishing relevant, realistic recommendations for policy makers.

Kopalle and Lehmann (2021) provide insightful directions in “Big Data, Marketing Analytics, and Public Policy: Implications for Health Care.” Their commentary, in conjunction with Chen, Sridhar, and Mittal (2021), provides the field directions on how analytics can combine technology and data to arrive at marketing insights. When applied to expansive data sets, marketing analytics also can shed light on policy issues and yield actionable recommendations. For example, by leveraging big data and artificial intelligence (AI) as input to predictive models, policy makers might develop marketing tactics to optimize patient adherence to beneficial health practices.

Marketing analytics often combines various fields (pertaining to, e.g., customers, products, time, geospatial locations, channels) across multiple data sources, then apply large-scale updating techniques such as Bayesian analyses (Bradlow et al. 2017). With more retail data sets, for example, the potential for policy insights increases exponentially if common elements like zip codes can be linked with data describing maladaptive consumption such as tobacco usage (Davis and Grier 2015). Analytics also allows researchers to explore vulnerable populations (e.g., adolescents) for whom primary data collection is restricted, because researchers can turn to secondary data (Grier and Davis 2013).

Overall, analytics approaches yield substantial insight, because they are based on real-world, high-volume data that are intricately linked to the consumers whom policy makers target with interventions. In Table 1, we specify how the special issue articles and commentaries contribute to these research fields.

Table 1.

A Summary of Articles from the Special Issue on Analytics Insights for Public Policy and Marketing.

Special Issue Article	Context	Analytics	Limitations Noted	Positive Insight
Kopalle and Lehmann	Health care	Big data, artificial intelligence (AI)	Analytics is limited by multiple and past-looking objectives; limited adoption of AI due to consumer desire for autonomy	Data have become more micro and analysis simpler.
Chen, Sridhar, and Mittal	Screening for liver cancer	Randomized field experiments	Causal forest output is difficult to interpret and underperforms in some feature space regions	Causal forests uncover individual-level treatment effects in field experiments.
Panzone et al.	Online grocery and other retail shopping	Online field experiments, difference-in-difference	Carbon tax reduces carbon footprint but also reduces overall store spend	Online field experiments show that marketplace design helps policy makers achieve greenhouse gas emission reductions.
Rossi et al.	Gambling, Twitter	Content analysis of social media data	Significant numbers of adolescents follow gambling sites on Twitter	Provides six public policy recommendations that can help curb online adolescent gambling.
Crow et al.	Political tweets	Social media analytics	Political party division is essential because parties speak different goal pursuit languages when sharing ideas on social media	Tweets, text preprocessing, regression models, and endogeneity correction help policy makers successfully manage democratic discourse.
Ghotbi, Dhar, and Weinberg	Diet soft drinks and calorie consumption	Choice modeling of individual-level panel data	Time-invariant and -variant unobservable data can limit analysis of big data	Econometric modeling methods help with policy recommendations.
Iacocca and Vallen	Prescription drug pricing	Data scraping, visualization, and machine learning	Pricing differs across drug manufacturers, generics are sometimes priced higher, depending on condition class	Analytics uncovered pricing problems that policy makers can now correct.
Liu, Gauri, and Jindal	Hospital patients	Propensity score matching and difference-in-difference models	Pain management is the only improvement metric for Medicare's pay-for-performance system, pressuring physicians to prescribe opioids	Data modeling results suggest removing or revising hospitals’ patient pain management measures required by Medicare.
Shah and George	Nonprofit serving arthritis consumers	Copula method, Poisson and Tobin regression models	Weak marketing practices among nonprofits because it is seen as avoidable and costly	Nonprofit marketing improves health behaviors.

Framework and Representative Literature

Retail Analytics

Retail analytics refers to the application of data science (e.g., machine learning, deep learning, text mining) to online and offline customer, product, time, location, and channel data in a retail environment (Bradlow et al. 2017; Wang et al. 2020). They benefit from new sources of data, computational applications of statistics, and domain-specific theoretical insights. Accordingly, effective retail analytics requires data from innovative in-store technology (Grewal, Noble, et al. 2020) as in-store retail technologies facilitate the shopping process, help consumers move along their customer journeys, and identify policy issues related to products, including adolescent tobacco use or addictive gambling and alcohol consumption.

In this special issue, Panzone et al. (2021) study the intersection of retail analytics and sustainability, in “Sustainable by Design: Choice Architecture and the Carbon Footprint of Grocery Shopping.” Leveraging customer choice data in online contexts, they conduct field experiments to understand how consumers make product choices, considering their carbon footprints. In these tests, in which they implement experimental public interventions in online stores that feature hundreds of grocery and nongrocery products, they find that choice architectures that present products with high, medium, or low carbon footprints lead consumers to choose fewer high-carbon-footprint products. In addition, a carbon tax and goal priming banners reduce high carbon footprint purchases further. Their online experiment allows participants to shop over three weeks. With difference-in-difference estimators, generalized for multiple groups and time periods, they find the average change for participants subject to the intervention versus those in the control group. Both marketers and policy makers can adopt similar methods to design effective interventions with a high likelihood of impact.

By applying retail analytics to fast-food contexts, Ghotbi, Dhar, and Weinberg (2021) aim to determine, “Do Consumers Order More Calories in a Meal with a Diet or Regular Soft Drink? An Empirical Investigation Using Large-Scale Field Data.” With their real-world evidence, they put popular health halo theories—such as the “Big Mac and Diet Coke” notion—to the test, to examine whether consumers really do order more calories to compensate for diet cola options. Instead, they find that people who select diet colas purchase meals with fewer overall calories. Therefore, their findings affirm that diet cola availability can result in calorie reduction benefits for consumers.

Social Media Analytics

Social media analytics involves data generated by social media (e.g., Facebook, Twitter, Instagram, Pinterest, YouTube, WhatsApp, TikTok, Weixin/WeChat), analyzed to gain marketing insights. Scholars have employed social media analytics for general marketing insights (Grewal, Hulland, et al. 2020; Villarroel Ordenes et al. 2019) and have suggested the role of combining consumer data with social media data, though they also raise concerns about key public policy issues, such as efforts to exploit social media in the political area and the need to understand the role of content provided and created by bots, and privacy risks. Appel et al. (2020), predicting the future of social media in marketing, highlight these and other concerns in both the immediate and far future. For example, an omnisocial presence is an immediate concern for individual consumers, and privacy concerns are pressing for policy makers. Over time, other concerns will continue to arise, such as the role of influencers, consumers’ sense of loneliness, integrated customer care efforts, or consumers’ sensory overload.

In line with this prior evidence that social media analytics provide a powerful means to understand real-world consumer responses to brands and products’ online social presence, two contributions to this special issue leverage social media analytics for marketing and public policy insights. In “‘Get a £10 Free Bet Every Week!’—Gambling Advertising on Twitter: Volume, Content, Followers, Engagement, and Regulatory Compliance,” Rossi et al. (2021) review behaviors by 620,000 followers and 457,000 engagements (replies and retweets) to discover that around 41,000 U.K. adolescents follow Twitter gambling accounts. Drawing on their content analysis of these social media data, they make six public policy recommendations: (1) limit advertising by specific gambling firms, (2) increase regulation of social media in general, (3) change policy on content targeting children, (4) block minors from seeing ads about gambling, (5) require labels for organic gambling tweets, and (6) encourage better policy enforcement.

Likewise, Crow et al. (2021), in their article “Power and the Tweet: How Viral Messaging Conveys Political Advantage,” analyze the social media pages of the Republican and Democratic Parties in the United States, with a sample of 54,464 tweets. They determine that Republican Party tweets are more likely to be retweeted, seemingly because they predominantly use language of assessment (e.g., emphasizing deliberation of alternatives over taking action). The social media analytics tools they introduce can assess the power structures in the U.S. political system, because the vast amounts of rich social media data they mine are deeply informative. These authors also make recommendations to policy makers for how they should conduct and encourage policy debates.

Marketing-Mix Analytics

Marketing-mix analytics yields insights by applying data science to information about brands, channels, price, and promotions. In citing key domains that can benefit from marketing analytics, Wedel and Kannan (2016) include the marketing mix in two of them, noting that marketing-mix analytics can optimize the allocation of marketing resources and personalize offerings for individual consumers. For example, by combining aggregate data with disaggregate and unstructured data, new machine learning techniques, multi–time period and cross-category optimization, and planning cycles, it becomes possible to develop optimized marketing mixes. Marketing-mix analytics also can facilitate fully automated marketing, personalize content, and introduce location-based personalization with mobile technologies.

In describing options for “Using Analytics to Gain Insights on U.S. Prescription Drug Prices: An Inductive Analysis,” Iacocca and Vallen (2021) combine data scraping, visualization, and machine learning techniques to investigate how brand characteristics (e.g., manufacturer vs. generic), product attributions (dosing levels, ingredients), condition classifications (cancer, blood modifying), and marketplace factors (approval year, number of competitive offerings) drive prescription drug prices. They conclude that pricing differs across manufacturers, such that generics are sometimes priced higher than manufacturer brands. Pricing also appears to depend on the amount of active ingredients, and product variety and strength matter too. Some condition classes price the same drug higher than others, but prices trend lower with more competition. Newer drugs also are priced higher than older ones. These insights have policy implications for health care, as we discuss next, as well as consumer protection.

Services and Health Care

Service interactions though digital channels generate massive data, so they require advanced analytics. Services researchers accordingly identify analytics as critical for understanding not just the role of services but also consumer well-being in services marketplaces (Petrescu, Krishen, and Bui 2020), which depends on protections against unwarranted data capturing, privacy violations, or insecure access). Security, information privacy, and personal information represent key policy concerns related to services consumers’ data (Okazaki et al. 2020).

Health care marketing may be an area of policy concern when it comes to services. In identifying key topics related to the future of technology and marketing, Grewal, Hulland, et al. (2020) refer to how health technology can change what care gets delivered, where, and how. Service analytics then might inform public policy and enhance health care consumers’ well-being. Agarwal et al. (2020) offer a value-centered marketing framework to encourage regulatory shifts toward value over volume, in which analytics functions to help health care systems provide better care for patients. Meyer et al. (2020) use biomedical and biopsychosocial analytics models to improve antistigma campaigns.

In this special issue, Iacocca and Vallen (2021), Kopalle and Lehmann (2021), and Liu, Gauri and Jindal (2021) all offer insights on health care services analytics and policy. As noted, Iacocca and Vallen consider prescription drug pricing and seek benefits for both health care service providers and consumer/patients. They propose the use of data visualization and machine learning to increase the transparency of health care services.

Kopalle and Lehmann (2021) identify advantages of big data and analytics, such as aiding disease diagnosis and treatment, but they also note concerns related to privacy, proprietary interests, and record-keeping systems. Such issues could be addressed with AI. They also identify marketing analytics as important for steering patients toward the best wellness practices, while also ameliorating their privacy concerns, though they acknowledge that marketing analytics cannot effectively address multiple patient objectives, such as longevity, quality of life, economic welfare, and resistance to nonhuman decision makers.

Finally, in “The Role of Patient Satisfaction in Hospitals’ Medicare Reimbursements,” Liu, Gauri, and Jindal (2021) consider how Medicare reimbursements and patient satisfaction might fuel opioid crises. With propensity score matching and difference-in-difference models, applied to several years of data from various sources, they identify pain management as the only significant improvement metric for Medicare's pay-for-performance system. Thus, to reduce pressures on physicians to prescribe opioids for pain management, they recommend removing patient satisfaction measures from Hospital Value-Based Purchasing, such that payments for patient care are linked instead to patients’ perceived quality measures, or else revising the pain management questions that inform these measures.

Nonprofits and Politics

Researchers in marketing have only scratched the surface of the potential for applying marketing analytics to nonprofit and political organizations, even though nonprofit organizations represent relevant stakeholders when it comes to complex uses of data and marketing analytics. Ulver and Laurell (2020) adopt social media analytics to explore far-right political opposition to multicultural marketing. Specifically, they collect public content that consumers post in response to multicultural advertisements, then classify a subset of consumers they can verify as politically far-right. In determining the frequencies of these posts, they find that “politically correct propaganda,” “supportive of criminal immigrants,” and “compulsive non-White representation” describe the bulk (85.2%) of far-right responses to multicultural advertising.

Shah and George (2021) break new ground in “Linking Marketing to Nonprofit Performance,” by introducing a methodology for attributing the unique performance outcomes of nonprofits to marketing efforts. They use the copula method and Poisson and Tobin regression models to calculate marginal direct and indirect effects of marketing on nonprofit outcomes. In their study context (i.e., a nonprofit serving consumers with arthritis), publications, products, and online engagement by the nonprofit organization significantly and positively change the beneficiaries’ behaviors (e.g., losing weight, adhering to healthy diet plans, exercising), thereby improving their well-being. We also previously noted how Crow et al. (2021) gather tweets from the social media pages maintained by U.S. political parties to better understand democratic discourses, which enables them to assess power in politics and suggest policies for regulating political debates, in line with their social media analytics efforts.

AI and Robots

AI (Rai 2020) and robots (Mende et al. 2019) use computer science and statistics, as obtained from machine and deep learning, to create remarkable marketing opportunities, but also unforeseen risks. Davenport et al. (2020) predict that AI will change the future of marketing through task automation and context awareness (i.e., computers sense and react on the basis of their surroundings). In particular, AI-powered robots might take control over the flow of numerical data in several ways, through (1) machine learning (e.g., helping people find optimal insurance prices in real time); (2) text, voice, face, and image analysis through cloud-based recognition capabilities powered by deep-learning neural networks (e.g., automated data management platforms that interpret email responses to identify promising leads); and (3) customer interactions in retail environments (e.g., robotic baristas that take numeric inputs to customize a beverage creation, retail store robots that walk customers to the location of a product in a store).

Guha et al. (2021) predict similar impacts on retailing, especially in applications of AI that help, rather than replace, human managers and service providers. When AI and robots facilitate marketing-based policy solutions, policies will be required to protect consumers and employees; Grewal, Hulland, et al. (2020) also identify consumer discontent due to uses of AI and robotics that automate rule-based tasks.

Huang and Rust (2021) suggest that AI can take on repetitive marketing functions such as data collection (“mechanical AI”), replicate cognitive processes for low-level decision making and market analyses (“thinking AI”), and analyze human interactions and emotions (“feeling AI”). Looked at in another way, AI might inform critical segmentation, targeting, and positioning strategies. In terms of policy implications, consumers will need policy makers to ensure equitable and safe practices in this emerging field. For example, Kopalle and Lehmann (2021) describe how health care researchers integrate AI into robots and have them perform routine tasks, such as wound stitching. Therefore, they argue that policy makers should encourage a balance between AI and human interaction. They also identify limitations of AI during major health crises, such as the COVID-19 pandemic, which lack prior data to support machine learning algorithms.

Conclusion

We identify six ways analytics can influence marketing and public policy, in the forms of retail analytics, social media analytics, marketing-mix analytics, services and health care, nonprofits and politics, and AI and robotics. The increasing spread of new technologies throughout the digital world raises issues, both endogenous to the digital world and coming from the physical world but newly emergent in digital spaces. For all such issues, effective analytics can provide important insights into both problems and opportunities. To start to address the underrepresentation of analytics approaches in literature pertaining to social problems, policy, and marketing, this special issue presents articles that approach policy and marketing issues using modern analytics techniques. The analytics methodologies described apply to marketing topics and public policy issues of relevance for readers of Journal of Public Policy & Marketing. Public policy and marketing literature can benefit from integrating new data analytics approaches to address the global well-being of consumers and society at large, using novel and massive data sets.

Footnotes

Appendix A: List of Reviewers

We are incredibly grateful for the timely and constructive feedback provided by all the reviewers, listed next:

Dipayan Biswas, University of South Florida

Kealy Carter, University of South Carolina

Mark Conley, Stockholm School of Economics

Ko de Ruyter, King's College London

Minette Drumwright, University of Texas at Austin

Eric Eisenstein, Temple University

M. Paula Fitzgerald, West Virginia University

George Frank, University of Alabama

Dinesh Guari, University of Arkansas

Conor Henderson, University of Oregon

Dennis Herhausen, The Free University

Steve Hoeffler, Vanderbilt University

Felipe Thomaz, University of Oxford

Easwar Iyer, University of Massachusetts Amherst

Rupinder Jindal, University of Washington Tacoma

Praveen Kopalle, Dartmouth College

Kathy LaTour, Cornell University

Michael Luchs, College of William & Mary

Yu Ma, McGill University

Dominik Mahr, Maastricht University

George Milne, University of Massachusetts Amherst

Vikas Mittal, Rice University

Maureen Morrin, Rutgers University

Genevieve O’Connor, Fordham University

Cornelia Pechmann, University of California, Irvine

Laura Peracchio, University of Wisconsin–Milwaukee

Linda Salisbury, Boston College

Sankar Sen, Baruch College

Danish Shah, Georgia State University

Karthik Sridhar, Baruch College

Shrihari Sridhar, Texas A&M University

Seshadri Tirunillai, University of Houston

Beth Vallen, Villanova University

Kristen Walker, California State University, Northridge

Corey White, California Polytechnic State University

Katherine White, University of British Columbia

Acknowledgments

The authors offer special thanks to Praveen Kopalle, Tuck School of Business, Dartmouth College, and Yu Ma, Desautels Faculty of Management, McGill University, for serving as experts in ad hoc roles as associate editors for several articles in this special issue.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

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