Archive for the ‘ Conference Articles ’ Category

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

Who’s on Netflix vs. Hulu vs. Other? A Panel based examination of SVOD users

Abstract:

The media industry is in a state of flux with continued fragmentation of consumer time and attention around media and across various devices and services. One such service that is popular among consumers today is SVOD (Subscription Video On-Demand) which enables on-demand access to both native digital content and TV-produced content. Forty eight percent of US homes have access to at least one SVOD service from providers such as Netflix, Amazon Prime and Hulu, up from 42% a year ago, according to Nielsen’s report. As consumers are shifting from live viewing to SVOD consumption, researchers are interested in understanding the underlying behavioral changes that are differentiating SVOD service providers. For instance, are consumers watch similar programs between Netflix and Hulu? Are there overlaps and/or uniqueness in consumer behaviors across these service providers? Answering these and many other questions is at the heart of this study and analysis. Read more

David vs. Goliath? Is Over-The-Top Challenging Traditional TV? A Case Study

Abstract:

Over the past few years, we have witnessed an expanding range of viewing devices and new content offerings by online streaming services (such as Netflix, Amazon and Hulu) through over-the-top (OTT) devices. Nearly 20% of U.S. households own at least one OTT device, such as a Roku, Amazon Fire TV, or Apple TV (Park Associates 2015). As these trends keep increasing, there have been debates on whether online streaming will replace traditional (or cable) TV in near future. Furthermore, questions have been raised around whether OTT viewing, via Apps, is cannibalizing or complementing network oriented TV viewing. Does multiple layers of ownership/access (ex: device, App, etc.) in OTT viewing play a role in their viewing/usage behavior to be different from traditional TV viewing? Does these two forms of TV viewership different in terms of types of programs watched, when they are watched, and how often they are watched? These are all questions of great importance to online publishers and advertisers, and, in general, to researchers working with large volume and variety of TV viewing data. Answering these questions is at the heart of this study and analysis. Read more

Only for the Young at Heart: Co-Viewing on Mobile Devices and Viewing on the Go?

Abstract:

With the relative ease and accessibility of a variety of content available to users of smartphones and tablets, there has been a subtle behavioral change in how people use these devices. The concept of viewing together or having more than one viewer for a mobile device is a phenomenon referred to as “co-viewing” and is a new area that warrants further investigation. Very little information is available on who is likely to engage in co-viewing behaviors, what types of mobile devices are used, what content is likely to be viewed and if those who engage in this activity / behavior are fundamentally different than those who are less likely—what are the behavioral or demographic differences among those who participate in these activities. Thus the focus here is to examine and provide a baseline understanding around the concept of co-viewing with specific focus of content viewing on the “go” or away from home. Read more

Who Is behind That Screen? Solving the Puzzle of Within-Home Computer Sharing among Household Members

Abstract:

The number of US households with access to computers at home has continued to grow. According to the 2011 Computer and Internet Use report published by US department of Commerce, 77% of US homes have computers in their home, compared to 62% in 2003. Many households, however, do not have multiple computers dedicated to each member living in the house. As such, sharing of computers amongst household members can be a prevalent phenomenon in home computer usage. Understanding this within-house computer sharing phenomenon and identifying the mostly likely person behind the computer screen can be of interest to market researchers and practitioners, particularly those interested in studying effective ways to target online ads based on users, online activities. For survey researchers who are attempting to recruit hard-to-reach individuals like teens and young adults, understanding of computer sharing could help establish contact at times when those individuals are more likely to be behind the computer. Despite its prevalence, within-house computer sharing has barely received any research attention. This study hopes to break through the barriers preventing the light of scientific inquiry into this phenomenon. Read more

Is It Too Much to Ask? The Role of Question Difficulty in Survey Response Accuracy for Measures of Online Behavior

Abstract:

While market research capabilities of online panels have never been greater, the challenges facing these panels in many ways are just as great. Over the past few years, online panels that recruit members using nonprobability/opt-in based methods have come under increased scrutiny and criticism over data quality concerns such as respondent identity and increased satisficing. These concerns have drawn attention to the heart of the issue, which is: the accuracy or truthfulness of data provided by opt-in panel respondents. This issue is of utmost importance given the recently established link between opt-in panel sample and poor survey data quality (see Yeager et. al. 2011). Read more

Evaluation of Alternative Weighting Approaches to Reduce Nonresponse Bias

Abstract:

With declining response rates, surveys increasingly rely on weighting adjustments to correct for potential nonresponse bias. The resulting increased need to improve survey weights faces two key challenges. First, additional auxiliary data are needed to augment the models used to estimate the weights. Depending on the properties of these auxiliary data, nonresponse bias can be reduced, left the same, or even increased. Thus, the second challenge is to be able to evaluate the alternative weights, when the assumption of “different estimates means less bias” may not hold. Ideally, data need to be collected from as many nonrespondents as possible to provide direct estimates of nonresponse bias. Read more

Difficult Data: Comparing the Quality of Behavioral, Recall, and Proxy Data Across Survey Modes

Abstract:

The mode choice literature is rife with evidence on the impact of different survey modes on response rates, respondent cooperation, and data quality. However, insufficient attention has been paid to the quality of “difficult data” provided when respondents cannot choose the mode and thus cannot maximize their comfort with the survey. Here, “difficult data” correspond to questions that are burdensome for respondents to think about – e.g., very specific details on a behavior, on past events, or on the behavior of other persons. Read more

The Use of GIS Information as Auxiliary Data for Nonresponse Bias Analysis

Abstract:

The Nielsen Company, like most other market research firms, is concerned with falling response rates and the threat of nonresponse bias. The challenge in understanding and adjusting for nonresponse is always the lack of information about the non-responding cases. In recent years, researchers have considered the use of paradata, interviewer observations, and aggregate Census information to provide information about the non-responders. This study introduces and evaluates the use of a new type of GIS-based data, called POI (Points-of-Interest), as a potential auxiliary data source for nonresponse bias analysis. Read more