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AI/ML

Can Observational Methods Assess the Causal Effect of Ads?

  • MSI

While Randomized Controlled Trials (RCTs) are ideal for advertising measurement, it is not always feasible to conduct one. Until advertising platforms are willing to provide more information about how they deliver targeted ads or implement auctions, alternative observational methods are unlikely to reliably estimate causal effects. That includes utilizing newer machine learning techniques.

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Optimizing Interventions Along the Customer Journey

  • MSI

Random controlled experiments for A/B testing help improve things like a company's marketing or customer service. However, individually optimizing interventions may not always capture interactions across the entire purchase decision journey. To optimize interventions more holistically, use a Bayesian reinforcement learning model. It can integrate multiple historical experiments, which can improve both current impact as well as future learning.

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  • Article

Top Trends in Data and Analytics for 2022

Research and advisory company Gartner shared their insights on top trends in data and analytics technology and practices. These can help anticipate change and transform uncertainty into opportunity, driving new growth and increasing efficiency, resilience and innovation. Gartner points out that the past year has seen an accelerated pace of disruption across all vectors of business, government and society, with the potential for even more profound shockwaves to come because of the Russian invasion of Ukraine. The global health crisis has been displaced for the moment by a geopolitical one, and the combination continues to shift people’s priorities, their values and their roles as family members, customers, employees and citizens. These conditions are driving extreme uncertainty, but also opportunity. Opportunities

  • Connections between diverse and distributed data and people create truly impactful insights and innovation. These connections are critical to assisting humans and machines to make quicker, more accurate, trustworthy and contextualized decisions while taking an increasing number of factors, stakeholders and data sources into account.
  • CEOs’ highest priority is to return to and accelerate growth, but they must do so in an extremely uncertain and volatile environment. Capabilities that enable navigating and responding to accelerated disruption across all aspects of the geopolitical environment, business, government and society are the foundations of success.
  • Prioritizing trust and security in these unprecedented times of global chaos is fundamental to the strategic role of data and analytics to realize new sources of value.
Recommendations Data and analytics leaders looking for new opportunities for their D&A programs and practices should:
  • Improve situational awareness to rapidly adjust to disruption and uncertainty by prioritizing investment in data and analytics diversity and dynamism, including adaptive AI systems, expanded data sharing and data fabrics.
  • Drive new sources of innovation and value for stakeholders by implementing context-driven and domain-relevant analytics to be composed of modular capabilities by the business. Addressing the scarcity of skills and hirable D&A talent is a top existential priority.
  • Institutionalize trust to achieve pervasive adoption and value at scale by managing AI risk and security, and enacting connected governance across distributed systems, edge environments and emerging ecosystems. 
Source: Sallam, R. and Friedman, T. (2022, March 11). The Gartner Top Trends in Data & Analytics,  2022. Gartner. 

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  • Article

New Research Insights from MSI

MSI Working Papers provide new insights from high quality research projects. Here are three examples.  

  • A study investigated if negative online reviews would have a measurable impact on sales. An experimental approach was used to assess the impact of one single negative review – with surprising results.
  • Another paper describes how machine learning techniques can help researchers identify and disentangle consumer preference data without human intervention.
  • The third paper explores research approaches to make causal inferences about ad impact when Randomized Controlled Trials (RCTs) are not feasible. The authors conclude that even machine learning techniques and observational methods are unlikely to reliably estimate causal effects, until ad platforms provide more information about how they implement auctions and deliver targeted ads.
Sources: Albuquerque, P. & Varga, M. (2019, January 17). Measuring the Impact of a Single Negative Consumer Review on Online Search and Purchase Decisions Through a Quasi-Natural Experiment. Working Papers: Library, MSI. Gordon, B., Moakler, R. & Zettelmeyer, F. (2022, March 28). Close Enough? A large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement. Working Papers: Library, MSI. Burnap, A., Kumar, V. & Sisodia, A. (2022, March 28). Automatically Discovering Unknown Product Attributes Impacting Consumer Preferences. Working Papers: Library, MSI. 

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Contending with Algorithmic Bias

  • Cultural Effectiveness Council

On March 16, 2022, the ARF Cultural Effectiveness Council hosted a discussion on bias in the algorithms and models used by organizations, particularly those in advertising and marketing, to make selection or recommendation decisions.  Speakers from Publicis Media, Twitter, Wunderman Thompson, Cassandra, and the University of Southern California shed light on why this issue arises, what its effects can be and how to contend with it.  The session was moderated by Council Co-Chair Janelle James of Ipsos.

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Does Alexa Make Humans More Humane?

  • MSI

Do digital voice assistants make website navigation easier? Not necessarily, according to this MSI working paper. Researchers found that a sense of “social presence” created by such assistants can evoke social norms. And so, these devices can in fact predispose consumers to more prosocial behavior, such as donating to important causes and tipping.

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  • Article

NYCU: Levi Strauss & Co. AI Chief Says Algorithms Boosted Revenue

The use of AI has helped the apparel company make better decisions in areas such as pricing and shipping, Chief Strategy and AI Officer Katia Walsh, Ph.D., says. One key to the effort is a massive data repository that the company has built on Alphabet Inc.'s Google Cloud, according to Katia Walsh. It contains inventory and sales information from Levi Strauss & Co.'s stores as well as some stores operated by other retailers. The repository includes information that Levi's shoppers share with the company. It also houses a range of external data, derived from public and private sources, that track consumer buying patterns and behaviors, weather and climate forecasts, economic trends and more. This cache, Ms. Walsh said, is vital to implementing Levi's enterprise-wide AI capability. The application of machine learning and automation to the data helped the company enhance personalization of consumer marketing, make informed pricing decisions, predict demand and optimize fulfillment, all of which have helped the business, she said. The company employs machine learning, a subset of AI, that uses statistics and probability to automatically recognize patterns in data and make predictions. The company wouldn't be able to spot such trends without machine learning, given the volume of products that the company sells online and in some 50,000 retail locations in more than 110 countries, according to Ms. Walsh. Levi Strauss & Co. also uses the data repository and machine learning to support pricing and shipping decisions. This helps better manage store inventory and control shipping costs—but also often results in shoppers receiving their merchandise sooner. The use of AI has given the company a new ability to precisely target customers, according to Ms. Walsh. "That has helped increase revenues," she said.

Source: McCormick, J. (2021, December 17). Levi's AI Chief Says Algorithms Have Helped Boost RevenueCIO Journal, The Wall Street Journal.

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Does Advice from Devices Encourage our Vices?

  • MSI

The latest enabling technologies, such as voice-based smart assistants like Alexa and Siri, along with intelligent shopping carts, make some consumers’ purchasing decisions easier. But they can also potentially lead to harmful outcomes. In this latest MSI working paper, researchers Iman Paul, Rumela Sengupta, Samuel Bond and Satadruta Mookherjee, examined the potential pitfalls of smart device usage in a series of online experiments with paid subjects.

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