data quality

Cross Channel Measurement in a Time of Data Collection Challenges

  • Posted: July 5, 2023

The average home has over 300,000 items, and consumers may be exposed to 6-10,000 ads daily. We need to overcome measurement silos to truly understand what triggers the different paths to purchase for the same products. Third party data sources need to be vetted on their sources and collection techniques, their validation methods and how they help us understand traditional metrics such as recency, frequency and consistency. Loyalty card data can help CPG companies track the 90% of purchases that still occur offline. IRI’s retailer and other partnerships offer a more holistic view of purchase behavior. In a masked case study, COVID reduced linear and cable but increased connected TV viewing, putting a premium on “equity spots” for in-home occasions such as food preparation/consumption. Multi-touchpoint fractional attribution (MTA) can distinguish the impact of creative from other aspects of digital ads.

The 80/20 Challenge: Building a Better Measurement Blueprint

  • Posted: July 3, 2023

NBCU’s Kelly Abcarian marshalled the call for revolutionizing opportunities in the measurement space and challenged attendees to drive change by questioning how they consume content, how they connect with that content, and ultimately, why, with current technology’s available options, is measurement still the same.

Improving Viewership Projections: Forecasting for Data-Driven Audience Segments

  • Posted: June 26, 2023

Diana Saafi, Data Science Lead at Discovery, built on Cliff Young’s comments about the importance of multiple indicators for forecasting. Saafi forecasts linear television audiences in segments of interest to advertisers (beyond age-sex segments), rather than political outcomes. She and her team have found that their models have benefited from using multiple sources of data.  She recommended identifying signals that are most predictive, experimenting with different types of models (such as ARIMA models and AI models), continually refreshing the data in the models, and continually updating the models. While this process is now automated at Discovery, there are people who monitor changes in the predictions, which she referred to as “human-in-the-loop automation.”

In Defense of Polling

  • Posted: June 26, 2023

David Dutwin, SVP of Strategic Initiatives at NORC, and a past president of AAPOR and survey research expert, in an interview with ARF CEO & President Scott McDonald, Ph.D., encouraged the advertising and marketing industry to maintain their faith in survey research. Surveys for marketing and advertising do not have to contend with two problems with election forecasting based on polls:

  1. Unlike market research surveys, pre-election polls are, “measuring a population that doesn’t [yet] exist,” – the population that will vote in an election.
  2. Given that lack of trust in major media is stronger at one end of the political spectrum than the other, non-response to surveys may well be correlated with political opinions but not with the subjects of most media and advertising surveys. Non-response therefore may well be less damaging for market research surveys.

There Were Always Mixed Signals: Triangulating Uncertainty

  • Posted: June 26, 2023

Cliff Young, President of US Public Affairs at Ipsos, proposed that multiple indicators be used to forecast elections, not just data from the horse-race question alone. In particular, leaders’ approval ratings are strong predictors of their probabilities of winning, and Trump’s approval rating exceeded his horse-race preference in several swing states. Taking this variable and others (incumbency, perceptions of the most important problems facing the country) into account, Ipsos created a model based on results of over 800 elections across the globe. The model had predicted that a narrow Biden win with Republicans retaining control of the Senate was more likely than a Blue Wave.

What Did Pollsters Learn from the 2020 Election Polls?

  • Posted: June 26, 2023

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.

The Exploding Complexity of Programming Research, and How to Measure It, When Content is King

  • Posted: June 20, 2023

Programming researchers are not getting the data they need to make informed decisions and Joan FitzGerald (Data ImpacX) uses streaming’s complex ecosystem to explain the conundrum facing programmers. Key insights into monetization and performance are not supported despite the inundation of new forms of data, leaving programmers without a comprehensive picture of their audience. Together with Michael McGuire at MSA, Joan outlined a methodology funnel that combined 1st, 2nd and 3rd party data to create equivalized metrics that, once leveraged, could meet critical programming research demands.

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