This paper is about the practical application of artificial intelligence in TV market and TV audience research.
Digital transformation has a colossal impact on the media industry, the World Wide Web is rapidly changing the media landscape and introducing new game rules. Traditional media are losing their audience, their weight in the eyes of customers and consequently, the advertising revenues. Media companies other than from the internet sector need to counteract and adapt, especially, as far as the audience measurement is concerned.
This project reviews the necessary changes in audience management that are supported by Artificial Intelligence technology. Specific solutions offered in the Russian media market have served as an example for possible development directions. It was concluded that new products such as multi-source evaluation, the transition from a limited sample to the whole universe measurement are required to be put into effect in order to deliver holistic and consumer-focused data by research companies.
This tremendous change in the mindset of the researchers will enable usage of a single metric like the Reach across all media by advertising managers. Moreover, the dynamic pricing at TV advertising will become possible too. Given the magnitude of the data to be processed only AI is capable of offering economically reasonable industrial solutions.
Table of Contents
1. Introduction
2. Methodology applied
3. Background and Literature review
3.1. Relevance of Concept of Artificial Intelligence for Media Industry
3.2. General vision of Digital marketing for TV Industry
3.3. Inherited weaknesses of classic TV audience measurements
4. Audience Researchers’ Response to Digital Challenge (Mediahills’ Business Case)
4.1. Multi-source Data mining
4.2. AI for mapping and allocating household viewing to individuals
4.3. De-duplicating of multi-source data
4.4. Audience forecast using natural language processing and artificial neural networks
5. Compatibility and Comparability of TV and internet metrics
6. Final remarks about Mediahills customers’ survey
7. Conclusions
Research Objectives and Themes
This research investigates how Artificial Intelligence (AI) can address the challenges currently faced by the television industry regarding audience measurement and the transformation of its value chain. It explores how digital competition and shifting consumption habits necessitate a transition from traditional, limited-sample survey methods to more granular, real-time data processing enabled by AI, using the Russian media market as a specific case study.
- Transformation of TV audience measurement in the digital age.
- Application of Artificial Intelligence and Big Data in media research.
- Practical implementation of AI via the Mediahills business case.
- Metric convergence: From traditional TV ratings to cross-platform "Reach".
- Impact of programmatic trading on TV advertising value chains.
Excerpt from the Book
4.2. AI for mapping and allocating household viewing to individuals
Missing registration of users creates a mapping issue for the selected measurement technology. Device-based data cannot tell anything about the number of people watching or who they are. However, this insight is crucial for advertisers and producers as they plan for viewers to watch the content when they draft media plans or broadcast shows.
Mediahills utilizes a neural network to ascribe the registered viewing on a device, especially, not personalized as STB, to specific individuals based on the analysis of actual TV viewing over the past three months using neural network technologies. They resort to metrics received from multi-system operators, especially, from cellular phone operators.
For example, Mediahills tracks the location of a relevant mobile phone holder and estimates the probability of whether he is at the front of his TV set, and what he is watching by mapping his personal preferences with the TV program on-air. Concurrently, knowing the composition of the household the researcher assigns the socio-demographic parameters based on the census and the establishing survey.
Mediahills trains the artificial neural network (ANN) for Machine learning (ML) capable of processing large amounts of input data by using probabilistic techniques that allow the machine to learn and develop algorithms by itself. By feeding the data from cellular operators and data of the establishing survey of 5000 households their ANN can predict the composition of the household and viewing pattern of each family member (time spent for TV viewing) with an acceptable tolerance.
Summary of Chapters
1. Introduction: Outlines the challenges mass media and traditional TV face due to the internet and the urgent need for updated audience measurement techniques.
2. Methodology applied: Describes the qualitative research approach, including literature analysis and a case study of the Russian media research company Mediahills.
3. Background and Literature review: Provides the theoretical foundation regarding AI, digital marketing concepts, and the inherent flaws in classic television audience measurement models.
3.1. Relevance of Concept of Artificial Intelligence for Media Industry: Explores definitions of AI and its potential impact on business processes and industry value chains.
3.2. General vision of Digital marketing for TV Industry: Connects Phillip Kotler's Marketing 4.0 approach with AI applications like programmatic sales and targeted advertising.
3.3. Inherited weaknesses of classic TV audience measurements: Analyzes technical errors and limitations in traditional panel-based TV ratings and the pressure to adopt computational models.
4. Audience Researchers’ Response to Digital Challenge (Mediahills’ Business Case): Examines the practical application of AI solutions by Mediahills to improve data collection and reporting.
4.1. Multi-source Data mining: Discusses how Mediahills gathers data from diverse digital sources to replace traditional, restricted panel samples.
4.2. AI for mapping and allocating household viewing to individuals: Explains the use of neural networks to estimate individual viewing behavior from device data.
4.3. De-duplicating of multi-source data: Details how deep-learning techniques identify and remove duplicate data across multiple media platforms.
4.4. Audience forecast using natural language processing and artificial neural networks: Describes how AI-driven predictive modeling forecasts audience behavior for specific shows and commercials.
5. Compatibility and Comparability of TV and internet metrics: Addresses the discrepancy between TV and web metrics and the necessity for a common, granular "Reach" metric.
6. Final remarks about Mediahills customers’ survey: Presents findings from a customer survey indicating industry support for AI-driven, multi-source measurement methods.
7. Conclusions: Summarizes that AI is indispensable for the future of TV audience measurement to remain competitive in the digital advertising landscape.
Keywords
Artificial Intelligence, TV Audience Research, Digital Transformation, Media Economics, Programmatic Trading, Big Data, Neural Networks, Advertising Spendings, Reach Metric, Mediahills, Audience Measurement, Predictive Modeling, Machine Learning, Audience Management, Cross-platform Consumption.
Frequently Asked Questions
What is the core focus of this dissertation?
The research focuses on how Artificial Intelligence and digital transformation are reshaping TV audience measurement and the television advertising value chain.
What are the primary themes discussed in this work?
Key themes include the shift from traditional sample-based ratings to multi-source data mining, the implementation of programmatic trading in television, and the necessity for unified, cross-platform metrics.
What is the ultimate goal of the research?
The goal is to demonstrate how media researchers can leverage AI to provide more accurate, real-time, and consumer-centric audience data to remain competitive against internet advertising platforms.
Which research methodology was employed?
The author utilized systematic qualitative analysis, extensive literature review, and a specific case study of the Russian media research company Mediahills.
What does the main body of the text address?
The main body examines AI definitions, marketing models, the technical weaknesses of traditional TV metrics, and the practical application of AI technologies like neural networks for data mapping and audience forecasting.
Which keywords best describe this study?
Core keywords include Artificial Intelligence, TV Audience Research, Programmatic Trading, Big Data, and Reach Metric.
How does Mediahills specifically improve audience data collection?
Mediahills collects device-based data from millions of households via set-top boxes and other connected devices, significantly expanding the sample size compared to traditional panel-based methods.
What role do neural networks play in this new measurement model?
Neural networks are used to map device-based data to individual users, de-duplicate consumption data across platforms, and predict future viewing patterns for specific content.
What are the main takeaways from the customer survey?
The survey of industry professionals showed a strong preference for multi-source measurement, real-time commercial optimization, and the expectation that "Reach" will become the universal metric across all media.
- Quote paper
- Igor Kolesnichenko (Author), 2020, Practical application of artificial intelligence in TV market and TV audience research. IT and digital marketing, Munich, GRIN Verlag, https://www.grin.com/document/1147042
