When fake ratings become the new normal, how do we evaluate the commercial value of film and television dramas?

On December 4 last year, "Beauty's Private Recipes", co-starred by Zheng Shuang, a "self-free" actress, and Ma Tianyu, a popular young actor, was broadcast on Zhejiang Satellite TV's prime time. The ratings on that day were disappointing at only 0.184, ranking 20th in the country. During this period, it even set the "lowest ratings in 50 years" for Zhejiang Satellite TV. On December 9, "Beauty's Private Recipes" was officially withdrawn from Zhejiang Satellite TV. Mr. Yan Conghua, the producer of "Beauty's Private Recipes", later posted on Weibo that he insisted on original works and insisted on not buying ratings. From the producer's words, it is not difficult to see that there has always been a problem of chaos in TV drama ratings. Next, the author will reveal the mystery of the chaos in the OTT market for you.

The origin of ratings

Ratings surveys originated in the mid-20th century. The earliest reason for the rise of ratings was the demand of advertisers. Ratings were used to measure the broadcast effects of TV programs and TV dramas, and advertisers made advertising decisions based on ratings. The United States took the lead in opening the precedent of ratings surveys. After more than half a century of development, the ratings survey method has evolved from the diary card method with lag to the personnel measurement instrument method that can be accurate to the second. In addition, with the continuous updating and innovation of the times, the measurement instrument method has greatly improved the coverage scale and data sample collection compared with previous data collection methods.

Traditional audience ratings surveys are difficult to provide precise marketing

From the perspective of data accuracy and stability, in traditional ratings surveys, sampling surveys are used, and there is generally an allowable error value. If the allowable error value is to be reduced, the required sample size will be larger, and the sample size required for each one percentage point reduction in the error value will double the previous one. This puts a certain amount of pressure on the initial basic survey, fixed samples, data collection, and labor costs. Today's advertising placements are all about precision, personalization, and segmented communication. It is too thin to simply rely on the ratings to influence advertising decisions.

As the penetration rate of smart TVs continues to increase, smart TVs provide people with more than just live broadcast services. They cover functions such as life (shopping, medical treatment), entertainment (games), and education, and content resources are also developing towards diversity and personalization. In the era of fragmentation, in the tug-of-war over users' large-screen usage behavior, on-demand has become the big winner. In order to attract users' attention, convenient services are provided during users' fragmented time, and a more humanized viewing experience and a sticky relationship with users are all reasons why on-demand is more popular. According to data from a research institute, the household coverage rate of the large-screen market will reach 45% in 2017, and China's OTT monthly active terminals will cover 236 million users, and daily active terminals will cover 153 million users. TV audiences are shifting from traditional TV to OTT terminals. Facing large screens, users' smart on-demand behavior has significantly surpassed traditional live viewing behavior, and the time users spend on large screens is increasingly leaning towards on-demand behavior.

It now seems that the dimensions of traditional ratings surveys are no longer sufficient to support the precision marketing needs of advertisers, TV stations, etc. The development of contemporary technology has forced the industry to need a new multi-dimensional data system to improve the rules of the smart TV market economy, and the arrival of big data is based on the upgrade and supplement of the traditional ratings survey level. For example, the behavior from the time the user turns on the TV to the time the user turns off the TV is extended, and what behaviors are performed after turning on the TV, whether it is on-demand, live broadcast, or other, and trend division is based on a large number of user behaviors, and the overall market situation and user behavior orientation are explained through trends. For different groups, by focusing on and tracking the user's behavior trajectory, personalized services and precise push notifications can be formulated based on precise needs.

The fragmented analysis system needs to be improved

The main factors that affect the ratings of TV dramas and programs include: region, season, time period, channel, and broadcast round. Fundamentally speaking, the authenticity and universality of data sources and data samples are also indispensable and important factors affecting ratings. In traditional ratings surveys, basic surveys are conducted to determine the fixed samples of ratings surveys, and representative household users with stability and active influence on viewing behavior are selected. In sample rotation, the problem of reduced representativeness caused by sample aging and reduced data quality caused by respondents is solved. Those sample households with the longest survey time are selected to exit first, and users who ensure that the quota indicator structure of the fixed sample group is consistent with the overall indicator structure enter the fixed sample group. This is the traditional ratings survey. The path determined by the fixed sample and the difficulty of changing the sample are important reasons for the lag in data update iteration. At present, Internet TV is developing rapidly and occupies a high proportion in the market. In addition, the functions and applications of Internet TV are diversified, and the user's behavior habits are more complicated than before. The viewing behavior habits of traditional TV users are not enough to support the current status of the viewing market. The demand for TV terminal data is not limited to the ratings survey system, and the analysis system needs to be improved.

Based on the era of big data, the new era is bound to add the need for multiple dimensions, which is an urgent need driven by the market. With the entry of big data into the ratings survey market, the survey dimension injected with the Internet and big data thinking has overturned the limitations of traditional ratings. There is no doubt that big data companies have inherent advantages in data samples and data sources, with wide coverage and real-time data feedback. However, it cannot be ignored that there are also data samples limited by hardware brands, data companies operating independently, single brand data volume, resulting in single data source samples, and the entire ratings market data is fragmented. From the perspective of big data, big data means full samples and full coverage. At present, the combination of companies with massive data is the key point that needs to be solved in the measurement standard market. The fullness and breadth of data samples make the data universal and more valuable in measuring the true effectiveness of the market. The function of "ratings" as a "universal currency" is more authoritative.

A new audience analysis system

The arrival of big data can provide a more comprehensive data dimension, which is user-oriented, bidirectionally addressable, and can analyze user smart TV behavior in real time. From the original single data system, to a rich new user smart behavior life cycle, through user behavior, from user inflow to outflow path, from user power-on to power-off analysis of the entire life cycle, a large amount of user data is accumulated. Based on big data thinking analysis, the new audience analysis system adds a new analysis perspective, predicts user behavior habits, viewing preferences, life status, focus categories, consumption attributes and tendencies, etc., to provide advertisers with a more refined basis for advertising placement, and provide TV stations with a reference system for program scheduling.

The concept of measuring the value of a program has shifted from ratings to a user-centric value orientation, supplementing the traditional sample size of ratings data, from representative sampling selection to the full sample size of all users, and from being extended to the overall behavior presentation of all users today. Real-time data feedback is more effective and authentic, allowing TV stations and advertisers to face real users directly, and have more traces and rules to follow in purchasing dramas, arranging program systems, program sponsorship, advertising placement, and effect evaluation.

Advertisers and TV stations are most concerned about the audience groups and viewing time. In the tug-of-war between intelligent behavior and live broadcast behavior, the industry needs a new and comprehensive data dimension system to support the healthy operation of the intelligent ecological market. In an era where content is king and resources are abundant, whoever can retain users will have a say. It is a general trend to add big data dimensions such as real-time audience behavior, audience popularity, hot discussion, interactive behavior, and consumer attributes to the traditional survey and research methods.

Big data is expected to break the chaos in the OTT market

Ratings function as a "universal currency" for advertising. They are a form of reflection of the commercial value of a television program, TV series, etc.

For TV stations and program producers, only by analyzing data can they know user preferences and make targeted and user-centric adjustments in future program planning. For advertisers, targeted and comprehensive data analysis is conducive to advertising.

With the development of big data technology, it is expected that the accuracy and transparency of ratings will be improved from a technological perspective in the future. In addition, with the diversification of Internet TV program functions and changes in user behavior, a small sample library survey is difficult to represent the entire industry situation. The expansion of the sample library has become inevitable, and it is even possible to achieve full data statistical analysis. We hope that the chaos in the OTT market will be improved as soon as possible.

As a winner of Toutiao's Qingyun Plan and Baijiahao's Bai+ Plan, the 2019 Baidu Digital Author of the Year, the Baijiahao's Most Popular Author in the Technology Field, the 2019 Sogou Technology and Culture Author, and the 2021 Baijiahao Quarterly Influential Creator, he has won many awards, including the 2013 Sohu Best Industry Media Person, the 2015 China New Media Entrepreneurship Competition Beijing Third Place, the 2015 Guangmang Experience Award, the 2015 China New Media Entrepreneurship Competition Finals Third Place, and the 2018 Baidu Dynamic Annual Powerful Celebrity.