The Rebirth of Marketing and the Role of Chief Marketing Officers
Yoram “Jerry” Wind | The Wharton School, University of Pennsylvania
Nick Primola | Association of National Advertisers
The layered crises of 2020 forced companies to change how they worked and operated overnight. In a number of firms, marketing, once underutilized, has been not so much transformed as elevated, allowing it to at last perform its rightful role. Executives and workers now have a much clearer understanding of the true power of modern marketing and, more specifically, of the role of the modern chief marketing officer. Since the tragic January 6th attack on the capital the US has been more divided and polarized than ever. As a result, businesses feel an ever greater pressure to deliver fully on the promise of stakeholder economics and to become beneficial influences on society. Chief marketing officers are essential to making this vision a reality, so it is vital that executives clearly understand and embrace their role. The consequences of failing in this task are growing more dangerous every day.
Racial and Ethnic Self-Expression Offer a Marketing Opportunity
Denise Dahlhoff | The Conference Board
Ivan Pollard | The Conference Board
Almost a third of Black consumers in the US and a quarter of Asian, Hispanic, and white ones express their racial or ethnic identity through products, services, and brands. This behavior and the chosen services vary not just by race or ethnicity, but also by age and income. Younger and higher-income non-white consumers are most likely to convey their racial or ethnic identity through various services. Generational influence and acculturation may contribute to younger people’s racial expression, such that their behavior differs from that of their elders.
This desire to express identity, including ethnic heritage, through purchases presents companies with a growth opportunity, especially in that the desire people feel for self-expression seems to be getting stronger. Companies should therefore consider designing products, services, and customer experiences that help consumers to convey their racial or ethnic identity. However, in order for these initiatives to be received as authentic, they must be part of a comprehensive, committed diversity strategy, and not just a superficial marketing tactic.
Can Artificial Intelligence Overshadow Human Intelligence in Marketing?
V Kumar | St. John’s University
Ashutosh Dixit | Cleveland State University
Rajshekhar “Raj” G. Javalgi | Walsh University
Nazli Z. Turken | Johns Hopkins University
Most artificial intelligence (AI) applications today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Yet managers may have a hard time knowing where and how to effectively apply AI in marketing. Still, by using AI, managers can better prospect customers, profile them by their characteristics, identify their preferences, and match them with products and services. AI collects information about customers from search engines, sensors, applications, and the Internet of Things (IoT), combining it with aggregate and detailed information from the companies themselves. It uses this data to produce useful insights into the needs and wants of individual customers. By searching information databases and matching market offerings to customers’ needs, AI boosts market efficiency at every stage of the purchase funnel. While using AI can be complex and challenging, its strategic importance and contributions to the bottom line are constantly expanding.
Why Working Backwards Works Better
George S. Day | The Wharton School, University of Pennsylvania
To assess innovation projects before they are approved, Amazon uses an approach called “working backwards.” This method requires an innovation team to produce a two-page “future press release” announcing the innovation as though it were ready to be launched, as well as a “frequently asked questions” document that anticipates and answers all the tough questions that leaders are likely to ask. Any firm can use working backwards to assess its innovation projects, but success relies on the organization embracing four essential qualities. Leaders must visibly commit to innovation as a central priority. The firm must support the search for opportunities with significant, sustained investment. Leaders must encourage empathy throughout the organization. And finally, this empathy must be fueled by collective curiosity at every level.
Visibility Isn’t Enough – Supply Chains Also Need Vigilance
Shardul S. Phadnis | Asia School of Business
Paul J.H. Schoemaker | Wharton, University of Pennsylvania
The debilitating logistical bottlenecks caused by the COVID-19 pandemic, like other supply chain disruptions in the past, have renewed the business community’s interest in fragility, robustness, stress-testing, and agility. While many in the field emphasize the importance of supply chain visibility, it is only half of the battle to strategically manage uncertainty. The other, and far less recognized, half is supply chain vigilance. Supply chains need vigilant leadership. By taking an open systems perspective on long-term and strategic issues, supply chain vigilance ensures that organizations are prepared to anticipate and handle disruptions. Organizational vigilance rests on four pillars: leadership commitment, foresight capabilities, flexible strategic planning, and coordination and accountability. Leaders should make these four components the lynchpin of their extended supply chain. Investing in vigilance will complement and even improve leaders’ emphasis on building supply chain visibility and resilience.
Beyond Usual: How Leading Firms Diverge from Business as Usual
Ofer Mintz | UTS Business School, University of Technology Sydney
Eric Knight | Macquarie Business School, Macquarie University
Firms must innovate to sustain their long-term profitability. Yet few firms can innovate continually. We joined a delegation with a unique mission to visit leading innovative firms in Seattle and Silicon Valley and engage with their executives. The trip gave us ample opportunities to discuss with other delegates what we were learning by touring so many innovators in such a short period. Combined with our multidisciplinary academic backgrounds and a comprehensive review of the literature, this trip allowed us to compose six key innovation principles: (i) imagine the world you and your customers want to live in; (ii) the customer reigns supreme and focusing on customers cuts through politics; (iii) it’s not just about risk-taking, but about appetite for risk; (iv) the status quo is the kiss of death; (v) make the world better; and (vi) a mix of leaders can inspire and support innovation. These six principles can teach firms how to innovate continually by creating, maintaining, and facilitating an innovative organizational culture.
The Reality Distortion Field in Care Delivery: Overcoming Obstacles to Improve Healthcare Delivery
Kyle Richardville | Kaiser Permanente
Bradley R. Staats | University of North Carolina
Brian J. Miller | The Johns Hopkins University and the American Enter-prise Institute
Steve Jobs’ “reality distortion field,” is a phenomenon in which an organization is able to achieve what was previously considered impossible because its constituents believe it can be done. The healthcare industry faces a negative reality distortion field, which limits innovation and prevents necessary change. The drivers of this negative reality distortion field include limited measures of quality, a wide power gap between front line clinical workers and managers, poor management training of clinicians, and the broadly held view that regulators and regulatory policy are barriers rather than partners. We propose methods of overcoming these obstacles so as to transform healthcare’s reality distortion field from negative to positive.
Make Your Business Quantum-Ready Today
ManMohan S. Sodhi | Bayes Business School, City, Uni-versity of London
Sridhar R. Tayur | Tepper School of Business, Carnegie Mellon University
Quantum computing merges quantum mechanics with computer science and information theory. Now an emerging technology, it promises benefits great enough to entice managers to invest. At the same time, it is not clear when, or indeed if, quantum computing will become widely available. Managers are therefore likely to feel torn between waiting for the technology to mature, knowing that competitors may take the lead, and investing now despite the fact that returns on their investment are nowhere in sight. We suggest a third option, that executives should invest today in quantum- inspired computing, which emulates quantum computing but on existing specialized digital computers. Making such an investment today will carry over to true quantum computing when it becomes available. And quantum-inspired computing offers computa-tional gains immediately with certain architectures and for specific business applications. Managers who want to get quantum-ready should first investigate the information on providers and uses already available. They should take stock of their companies’ advanced computing needs, considering whether quantum applications would be a good fit for their business requirements. Finally, they should conduct pilot studies using quantum-inspired computing.
Conflict Analytics: When Data Science Meets Dispute Resolution
Maxime C. Cohen | Desautels Faculty of Management, McGill University
Samuel Dahan | Faculty of Law & Smith School of Business, Queen’s University
Colin Rule | Mediate.com
Data science research can shed new light on dispute resolution, whether it be a customer, insurance, trademark, or employment dispute. It can improve legal transparency, make dispute resolution more efﬁcient, and increase access to justice. Many facets of resolving disputes, such as discovery, legal search, generating documents, and predicting case outcomes, have already been improved by data science. Of course, data-driven models must be trained with both legal and negotiation data if they are to produce accurate results. Making predictions based solely on legal precedent tends to yield inaccurate results because most disputes are resolved through negotiation which means, from a data perspective, they are unobserved. Research has shown that data science has clear limitations in the legal field because legal outcomes are not always predictable. In dispute resolution, machine learning algorithms often fail to capture the nuance or underlying sentiments of a case, and these are essential for an accurate legal analysis. Fortunately, cutting-edge innovations in artificial intelligence, such as deep learning models, are bringing us ever closer to comprehensive dispute resolution systems capable of understanding legal concepts.