modeling

What Did Pollsters Learn from the 2020 Election Polls?

Kathy Frankovic, a polling export who led the survey unit at CBS News for over 30 years and now consults for YouGov, highlighted two plausible hypotheses for the polling industry’s over-estimation of Democratic strength in the election:

  1. Likely voter models built on past voting practices: Likely voter models were based on the norm of Election-Day voting, and were unprepared for an election in which two thirds of 2020 votes were not cast on Election Day. One to two percent of mail-in votes don’t get counted, but in seven states, about 10% or more are rejected.
  2. The “missing” Trump voter (as opposed to the “shy” Trump voter): In states which Trump carried with 55% or more of the vote, YouGov pre-election polls showed him tied with Biden. Trump’s bashing of the polls may have discouraged his supporters from participating in polls.

Consumer Behavioral Shifts: Why Your Marketing Measurement Must Adapt in 2022 and Beyond

Consumer behaviors have changed dramatically creating new challenges for brands and their marketing measurement. During this Insights Studio, we explored the measurement challenges brands have faced in the wake of significant consumer changes. We also discussed best practices brands should be using to ensure their marketing measurement is set up for future success as consumers continue to react to major societal change. Executives from OptiMine, an agile marketing analytics provider, Kepler, a global digital agency, and Beachbody, an innovative health and fitness company, shared their observations on how changes in consumer shopping and media consumption behaviors have been reflected in marketing measurement.

Path Forward: Identity, Representation & Authenticity

NBCUniversal and Magid partnered to determine how diverse consumers self-identify and how that informed their life experiences in relation to representation and authenticity, particularly in their brand and content choices. Research from clients, consumers and field surveys showed that, while multi-cultural audiences are multi-faceted, they are connected by common threads from shared cultural pillars, shared success and struggle, a sense of community and the straddling of two worlds. These findings led the team to a framework for “ideal representation” as a hierarchy of four distinct levels that define what consumers consider most important in being authentic.

The Power of Creative Data: Insights & Applications from 1 Trillion Ad Impressions

In this session, Anastasia Leng of CreativeX argued that marketing and creative have the power to change things. In the face of an increased ad pool, which has gone up 6x in the last 20 years, ads now have a smaller shelf-life and need to be created in a more customized manner. In large part, technology has created this challenging landscape in marketing and advertising, but technology can now also help to address these new challenges. She pointed to computer vision technology which can help by supplying data created from “micro-feedback,” by clustering this information and fusing this feedback to create more useful macro-data to base decisions on.

What is Creative Effectiveness and Why is it Important?

Carolyn Murphy of WARC began her session on stimulating and measuring creativity by diving into the relationship between creativity and effectiveness. While the link between creativity and effectiveness is backed by a strong body of research, the focus on the importance of creativity has waned recently, with the rise of digital commerce, performance marketing and retail media networks. Carolyn suggested that marketers and advertisers regain the focus on the value and benefits of creativity in newly emerging channels, which was backed by more recent research. Carolyn noted that creative is not a replacement for an ad budget, but a way to “supercharge” the effectiveness of that campaign. Success is likely when “when creative is married with your overall strategic planning, in a media plan that’s comprehensive.” To provide a framework for success, WARC, James Hurman and Peter Field created the Creative Effectiveness Ladder. This six-tiered model scales around how to measure your creative to see what effective outcome it will have.

A Layman’s Guide to Cross Media Reach & Frequency Measurement Using Virtual IDs

On May 17, the ARF Analytics Council explored the groundbreaking concept of Virtual IDs (VIDs) and their potential to revolutionize cross-media measurement. The essential mechanics of VIDs were explained in a non-technical manner to help professionals across media and advertising understand it better. Panelists shared how VIDs could overcome barriers in calculating cross-media and device reach and frequency.

Navigating Through Uncertainty with Next-Gen Marketing Mix

Greg Dolan (Keen Decision Systems) and Mark Bennet (Johnsonville Milwaukee) examined how to navigate in uncertain and volatile times in the current marketplace using next-generation marketing mix solutions. In the opening, Greg explored the progression of marketing from the late 90s, through what he dubbed “The Roaring 20s.” He noted that we went from a minimally complex, slower-paced “top-down” approach in the 1990s to a very fast-paced, complex environment with a shift to Retail Media and a unified approach where we apply next-generation predictive analytics in the 2020s. Greg discussed the intricacies of their approach of combining historical data with predictive/prescriptive plans to address drastic changes in the current environment, leveraging ML. He provided a case study that demonstrated the successful application of the next-generation marketing mix. In addition, Mark gave a client perspective on how they are handling market uncertainty.

Harnessing the Full Potential of Marketing Mix Models: How Attention, Creative and Audience Personalization can Drive ROI

Sameer Kothari (PepsiCo) and Todd Kirk (Middlegame Marketing Sciences) examined the application of a transformed rendition of marketing mix modeling created through the development of a proprietary system called the “ROI Engine.” Sameer indicated the desire to harness the “true potential of marketing mix models even beyond measuring past campaigns and using it for strategic planning looking forward.” Sameer discussed this system as having a more “predictive ROI outcome-based approach” by “leveraging an ecosystem of leading indicators for before and during a campaign flight.”

Rebuilding MMM to Handle Fragmented Data: The Challenge of Retailer Media

Liz Riley (OLLY) and Mark Garratt (In4mation Insights) explored rebuilding and reimagining marketing mixed modeling (MMM) to better handle fragmented data, in the era of retail media networks. Mark lauded MMM as an effective technique that has contributed to financial success for many businesses. In light of data becoming increasingly fragmented, he suggested that “some reinvention of the fundamental model framework is going to be required in order to move this old venerable method into the future.” Mark and Liz examined the Bayesian approach to MMM in handling fragmented data. Mark noted that there will not be a situation “where all the data is the same granularity in one place at one time.” The Bayesian approach can “fill in the blanks” of missing or fragmented data using reasonable estimates, creating a more accurate picture, which traditional MMM falls short of in the retail media environment.