Author Archive

Is Less the Merrier for Ad Exposure and Audience Attention? A Media Measurement Case Study

Abstract:

Advertisement-supported media models are built upon an implicit agreement that media publishers make with their audience: the price of free or subsidized content is the audience’s attention to advertisements. A key component in the evaluation of an advertiser’s return on investment is whether an advertisement held the audience’s attention well enough to generate a conversion (e.g., a sale, website/store visit, newsletter sign-up). In our modern era of near-constant access to information, arguably one of the greatest threats to audience attention is information overload. This threat is likely to increase in severity as ad content increases in quantity. Recent research by Abcarian and Rao (in progress) has demonstrated an increase in the number of ad spots generated by media companies over time. How do audiences react to increased ad exposure? Is there an optimal ad duration that elicits higher levels of audience attention? This study seeks to answer these questions. This study adds to the growing body of research on audience attention in the context of ad exposure measurement. Preliminary results indicate that shorter-duration ads (15 seconds) are more effective in garnering audience attention than ads of longer duration (30+ seconds). Our paper includes a more extensive discussion of our findings along with recommendations for future research.

Recommended Citation:

Nandi, S., Craft, M., & Rao, K. (2023). Is Less the Merrier for Ad Exposure and Audience Attention? A Media Measurement Case Study. American Association for Public Opinion Research, Philadelphia, PA.

Attached Documents/Links:

  • AAPOR 2023 Program
  • For a copy of this presentation, please send me a comment with your email address in the box below or an email to kumarrons at gmail dot com.

Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements

Abstract:

In media measurement, examinations of relationships between audience behaviors (e.g., viewership of TV advertisements) and demographics (e.g., age and gender) are common practice. These examinations can follow a variable-centered approach that differentiates variables into dependent and independent variables (e.g., linear regression) or a person-centered approach which groups observations together according to their configural profile of characteristics (e.g., K-means; Morris & Kavussanu, 2008). This paper extends the boundaries of media research by using a latent clustering method — Latent Class Analysis (LCA) for analyzing advertisement viewership data. LCA is a probabilistic mixture modeling technique used to identify a set of discrete latent classes of observations, in this case advertisements, based on the values of a set of categorical indicators. The technique focuses on generating classes of advertisements that share similar patterns of responses and comparing with other classes to subsequently determine how they are differentially related to predictors and outcomes (Wang & Hanges, 2011).

The dataset used in our analysis is characterized by extreme volume (7M rows) and a variety of variable types (variables are a combination of continuous, categorical, and count). Preliminary findings indicate the classification of advertisements into two latent classes as defined by a set of categorical indicators of interest. The most salient features of Class 1 in comparison to Class 2 are a greater likelihood to air within News/Information and Sports TV genres as well as a greater likelihood to air on a weekday as opposed to a weekend or weeknight. We conduct additional analyses to understand how these latent classifications are related to a measure of what the media world refers to as co-viewing. This paper concludes with a discussion of the results and their implications along with recommendations for future research. R code and a sample dataset is provided for interested readers and practitioners.

Recommended Citation:

Craft, M., & Rao, K. (2023). Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements. American Association for Public Opinion Research, Philadelphia, PA.

Attached Documents/Links:

  • AAPOR 2023 Program
  • For a copy of this presentation, please send me a comment with your email address in the box below or an email to kumarrons at gmail dot com.

Does Ancillary Data about Ancillary Data Help Treat Missing Responses in Ancillary Data?

Abstract:

Consumer-file marketing companies collect and sell customer’s demographic and behavioral information wherever they can: through loyalty cards, website visits, social media posts, etc. These ancillary data are tremendously valuable and have a variety of applications in the field of television and digital media measurement that include targeting households or individuals for marketing campaigns, adjusting for nonresponse bias, and projecting estimates from a sample to an underlying universe. These potential advantages, however, depend on the completeness of the information contained in the data. Today, most applications involving the use of ancillary data exclude incomplete records from the substantive analysis. Exclusion as a missing data handling strategy has been known to result in inaccurate conclusions depending on the nature of the mechanism assumed to have generated the missing data. In this paper, we demonstrate a model-based alternative to exclusion, called multiple imputation (MI), which is used to fill in the missing values. A correctly specified imputation model preserves important characteristics of the data (e.g., variances, correlations, interactions) and can mitigate any bias resulting from exclusion-based missing data handling strategies. Our paper demonstrates MI in the context of a substantive analysis of the relationship between a completely observed measure of the number of advertisement impressions served across television networks and an incomplete measure of the number of children per household. We include likely correlates of the suspected cause of the missing data in the imputation model to evaluate whether their inclusion improves the quality of the imputed values. These correlates are measures pulled from a second, more completely observed, ancillary dataset containing television and digital media audience measurements. This paper concludes with a discussion of our findings, which are likely to have important implications for the use of ancillary data in television and digital media audience measurement.

Recommended Citation:

Craft, M., Raghunath, A., & Rao, K. (2023). Does Ancillary Data about Ancillary Data Help Treat Missing Responses in Ancillary Data? American Association for Public Opinion Research, Philadelphia, PA.

Attached Documents/Links:

  • AAPOR 2023 Program
  • For a copy of this presentation, please send me a comment with your email address in the box below or an email to kumarrons at gmail dot com.

Ad-Supported vs. Non-Ad Supported OTT App Viewership Behavior

In this white-paper, we examine how OTT audiences consumed ad-supported versus non-ad-supported services over a period of two years. OTT streaming services, as well as advertisers and their agencies, use Comscore’s trusted measurement to understand the OTT landscape and make informed ad buying and sponsorship decisions. 

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The Rise of Streaming Services

To state the obvious, 2020 has been like no other year. With most Americans confined to their homes because of the coronavirus pandemic creating a “captive” audience, the demand for streaming content from home skyrocketed, leading to abrupt changes in viewership trends and audience profile, not to mention wider industry implications. In the worst of times, it was the best of times—for streaming.

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Media Industry Shift to Impression-Based Evaluation

It is no surprise that technology is radically changing the media industry. More people are consuming video content on platforms other than traditional TV sets, such as computers, smartphones, tablets and over-the-top (OTT) devices. Measuring audiences across these new platforms is critical for both the sell-side and buy-side of the business, and new metrics of success will help better define consumer outcomes from advanced advertising campaigns.

This shift to cross-platform viewing has highlighted the critical need for a common metric that media buyers and sellers can use to seamlessly transact across all platforms. The traditional ratings metric (which is defined as the percentage of various age/gender populations) is quickly migrating to impressions—the actual or projected number noted as (000). Major industry players have announced their move to an impressions-based ad sales model with the hope that this will give media sellers more pricing power, more audience granularity and a frictionless cross-media planning and buying experience. The move to impressions is a natural evolution for local TV groups that have seen audiences migrate to digital and cross-platform viewing and is a step closer to a true, cross-platform apples-to-apples currency.

Method, Medium, and Apparatus to Generate Electronic Mobile Measurement Census Data

Patent Number: 10045082
Date Issued: August 7, 2018
Patent Title: Method, Medium, and Apparatus to Generate Electronic Mobile Measurement Census Data

Citation:
Rao, Kumar, Tianjue Luo, Albert Ronald Perez, and Scott Bell. Method, Medium, and Apparatus to Generate Electronic Mobile Measurement Census Data. (2019).10217122B2. U.S. Patent and Trademark Office.

Methods and Apparatus to Correct Errors in Audience Measurements for Media Accessed Using Over-the-Top Devices

Patent Number: 10045082
Date Issued: August 7, 2018
Patent Title: Methods and apparatus to determine an adjustment factor for media impressions

Citation:
Rao, Kumar, Kamer Toker Yildiz, Jennifer Haskell, Cristina Ion, and Mimi Zhang. Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices.  (2018).10045082. U.S. Patent and Trademark Office.

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Media Viewership in the Connected World: A Big Data Case Study

Abstract:

U.S. consumers are adding time to their media day and making time to connect with their favorite content, no matter where it exists (Nielsen 2014). But how they’re consuming media is ever-changing thanks to the continued proliferation of technological devices, 24/7 availability of the media content, ease-of-access, and economics. Whether streaming or satellite, over-the-air or over-the-top, understanding how consumers are consuming media is more important than ever, particularly for companies providing these services since advertising is their major source of revenue. For researchers, this consumption ecosystem has given rise to big datasets consisting of millions and millions of viewing records to mine thru in order to discover trends, viewing patterns, and relationships. In this study, we are attempting to do just that. Read more

A Panel Examination of Over-the-Top Audience

Abstract:

The new reality for consumers is they not only have access to more content than ever before, but they can also select the content they want, when they want, and watch in the device they want. One such device that has become increasingly popular for media consumption is Over-the-Top (OTT) media players. These are devices that deliver video content via the internet to television sets. Today, there exists an ever-growing number of various OTT devices from Roku players, the Apple TV, the Amazon Fire TV box, Chromecast, and game consoles. However, with this increased availability of choice comes the growing fragmentation of consumer time and attention. This leaves advertisers with the complex task of breaking through the clutter of advertisements and finding a way to reach the OTT device-specific audience. However, reaching an audience behind an OTT device requires a thorough understanding of the viewers. To date, there has been no study examining the differences between various types of OTT device owners and their viewing behaviors. Read more