Leveraging OTT Platform for Valuable Data Insights
Modern companies live in a world of metrics. ARPU, MAU, CLV, CAC, CR, Engagement rates — all these intricate abbreviations have become a common language among companies that provide communication solutions.These metrics are based on data that the company aggregates in its systems in one way or another. Availability and ability to use such data for the company's growth is a fundamental competitive advantage. This allows you to know more and see beyond.
Under certain circumstances, it is possible even to sell or monetize this data to third-party companies, such as advertisers or content providers.
Under certain circumstances, it is possible even to sell or monetize this data to third-party companies, such as advertisers or content providers.
Evolution
The French analytical company Capgemini, the world leader in consulting in the field of digital transformation, at the end of 2020 published the study "OTT Streaming Wars: Raise or Fold How Data is Reshuffling the Cards of the M&E Industry."
The company conducted in-depth interviews with 50 high-ranking executives of telecommunications and media companies in different countries.
The study concluded that 65% of the surveyed companies, including high-level ones, use data only as general information of the "check-box" level.
The company conducted in-depth interviews with 50 high-ranking executives of telecommunications and media companies in different countries.
The study concluded that 65% of the surveyed companies, including high-level ones, use data only as general information of the "check-box" level.
Source: Capgemini OTT Study
Methodologically, the researchers identified five levels of data use by companies:
The first level of working with data is the use of external sources of information or internal accounting reports. A typical example of using external sources is the first attempt to measure audience data for a show, channel, or broadcast time, conducted by Nielsen Media Research in 1950.
The first level of working with data is the use of external sources of information or internal accounting reports. A typical example of using external sources is the first attempt to measure audience data for a show, channel, or broadcast time, conducted by Nielsen Media Research in 1950.
In the 1950s, Arthur Nielsen, the founder of Nielsen Media Research, who had previously been engaged in radio audience research and other marketing research, came to the conclusion that it was necessary to study the audience of TV shows.
This information was useful for both TV show producers and advertisers who chose channels to promote products and services. The studies were conducted either by recording the participants of the experiments of their activity in watching TV shows in special diaries or by installing devices directly on the TV in American households.
The company's research helped to determine the reach and audience composition of American channels and TV shows, and also made a great contribution to the development of qualitative statistical measurements.
This information was useful for both TV show producers and advertisers who chose channels to promote products and services. The studies were conducted either by recording the participants of the experiments of their activity in watching TV shows in special diaries or by installing devices directly on the TV in American households.
The company's research helped to determine the reach and audience composition of American channels and TV shows, and also made a great contribution to the development of qualitative statistical measurements.
The second level — companies collect their own data about subscribers and compare it with data obtained from external sources. At this level, data sets begin to be used for decision-making.
The third level — the data obtained from the system is supplemented with data on the behavioral characteristics of users. At this stage, there is a transition from using data only as statistics about the audience in general to data about understanding the profile of a particular consumer and making decisions with this in mind.
The fourth and fifth levels are Netflix and Facebook levels. The ongoing research allows them to create a database that is able to correlate data at different levels and in itself already becomes a strategic asset that has a separate value.
The ability to get the final data is a key advantage of OTT technology. The use of feedback data transfer protocols, which are the basis of OTT, has become a breakthrough in telecommunications. This allowed telecommunications companies to register statistics of views and their quality, and users to manage content-record broadcasts, add interactive content, and use AI tools.
Do not use the ability to collect statistics for the intended purpose or use it in a limited way — an unacceptable action, which in a rapidly changing reality can significantly slow down the growth rate of companies.
The third level — the data obtained from the system is supplemented with data on the behavioral characteristics of users. At this stage, there is a transition from using data only as statistics about the audience in general to data about understanding the profile of a particular consumer and making decisions with this in mind.
The fourth and fifth levels are Netflix and Facebook levels. The ongoing research allows them to create a database that is able to correlate data at different levels and in itself already becomes a strategic asset that has a separate value.
The ability to get the final data is a key advantage of OTT technology. The use of feedback data transfer protocols, which are the basis of OTT, has become a breakthrough in telecommunications. This allowed telecommunications companies to register statistics of views and their quality, and users to manage content-record broadcasts, add interactive content, and use AI tools.
Do not use the ability to collect statistics for the intended purpose or use it in a limited way — an unacceptable action, which in a rapidly changing reality can significantly slow down the growth rate of companies.
Source: Capgemini OTT Study
Converting data to metrics
Large amounts of data need to be compared with each other and observed over time.To do this, in addition to working with pure data, analysts add the calculation of metrics.
A metric is a qualitative or quantitative indicator that reflects a particular characteristic and the level of success of a product or company. Observing them in dynamics, comparing metrics between companies in the industry should give an understanding of where the product is moving at a given time, and set the direction of development.
Metrics help you distinguish the subjective from the objective and make decisions based on real data. That is why they are so often in demand by investors, advertisers, and content providers. All these market participants evaluate the potential capabilities of the company or product. comparing it with similary on the market.
The sources of information for calculating metrics are user data that allows you to aggregate modern OTT video platforms. To do this, the Middleware management module usually has an additional section that allows you to monitor and analyze such data.
A metric is a qualitative or quantitative indicator that reflects a particular characteristic and the level of success of a product or company. Observing them in dynamics, comparing metrics between companies in the industry should give an understanding of where the product is moving at a given time, and set the direction of development.
Metrics help you distinguish the subjective from the objective and make decisions based on real data. That is why they are so often in demand by investors, advertisers, and content providers. All these market participants evaluate the potential capabilities of the company or product. comparing it with similary on the market.
The sources of information for calculating metrics are user data that allows you to aggregate modern OTT video platforms. To do this, the Middleware management module usually has an additional section that allows you to monitor and analyze such data.
Basic metrics of OTT services
There are many ways to use metrics: they depend on the product, approach, logic, and even the imagination of the product manager or marketer. How many times a year, how, and in what ratios to measure metrics is decided by those who deal with the product.
Below we will look at the main metrics used on OTT platforms, and also use the example of the Telebreeze statistics module to illustrate how the OTT platform's Middleware works as a source of statistics.
1. The number and hours of viewing of your content is an important indicator for assessing the level of engagement (engagement rate). The system allows you to see both aggregated view metrics and views for each individual user and content unit. By measuring user behavior, you can better evaluate subscriber activity and develop a more productive relationship with the customer.
Below we will look at the main metrics used on OTT platforms, and also use the example of the Telebreeze statistics module to illustrate how the OTT platform's Middleware works as a source of statistics.
1. The number and hours of viewing of your content is an important indicator for assessing the level of engagement (engagement rate). The system allows you to see both aggregated view metrics and views for each individual user and content unit. By measuring user behavior, you can better evaluate subscriber activity and develop a more productive relationship with the customer.
2. The number of Monthly Active Users (MAU) who used the service in the past month or on the current date is the most popular indicator. It provides you with information about the "health" of your business, the effectiveness of your marketing, and customer base management, as it demonstrates the engagement and activity of users.
Dashboard demonstrates quantity and statuses of the subscribers.
Using statistics about users, you can calculate such important indicators as Conversion Rate (CR) and Churn rate. CR = 10%, if your website has been visited by 1000 users per month, and 100 agreed to subscribe. An example of conversion rate in OTT: the percentage of registered users who bought a paid subscription, or the percentage of users with a demo subscription who switched to a paid plan.
3. Churn rate is the percentage of users who opt out of your service over a certain period of time. This is an indicator of the viability of your business. It is widely used in the industry as a statistical metric and is one of the main ones when analyzing the value of companies.If this indicator is higher than the market average or is growing in dynamics, it indicates problems with the retention of subscribers, and as a result-the stability of the subscriber base, which is the basis of the media business.At the end of 2020, the overall annual churn rate for OTT services fell to 38%, down from 46% in 2019, according to Parks Associates. http://www.parksassociates.com/blog/article/pr-120...
4. An important element of viewing quality assessment is understanding the dynamics of viewing content units. This can also be seen in the system. This data allows you to monitor such an important indicator as the Abandon Rate. This is the percentage of subtasks that were canceled by the user before the main task was completed — for example, the percentage of canceled trial subscriptions or the percentage of movie viewing cancellations before it started in the case of AVOD services.
3. Churn rate is the percentage of users who opt out of your service over a certain period of time. This is an indicator of the viability of your business. It is widely used in the industry as a statistical metric and is one of the main ones when analyzing the value of companies.If this indicator is higher than the market average or is growing in dynamics, it indicates problems with the retention of subscribers, and as a result-the stability of the subscriber base, which is the basis of the media business.At the end of 2020, the overall annual churn rate for OTT services fell to 38%, down from 46% in 2019, according to Parks Associates. http://www.parksassociates.com/blog/article/pr-120...
4. An important element of viewing quality assessment is understanding the dynamics of viewing content units. This can also be seen in the system. This data allows you to monitor such an important indicator as the Abandon Rate. This is the percentage of subtasks that were canceled by the user before the main task was completed — for example, the percentage of canceled trial subscriptions or the percentage of movie viewing cancellations before it started in the case of AVOD services.
5. Information about purchases is also available from the video statistics module of the platform and is used to calculate such metrics as Average Revenue Per User (ARPU), Customer Lifetime Value (CLV), Customer Acquisition Cost (CAC). The last two metrics are important for understanding a) the return on further investment in retaining a particular customer group, and b) the return on investment in marketing and advertising to attract customers (calculated by dividing the funds spent on marketing by the number of customers attracted over a certain period). Of course, such information can be obtained from the company's accounting data, but more detailed statistics available in the module can contribute to the detailed study of data within user segments and content types.
6. Considering the importance of the distribution channels of the media service, it is important to monitor the statistics of views through various devices: connected TV, PC, mobile devices, compare your information with the analysis of the share of the popularity of platforms: Android, webOS, Roku, Tizen, etc. for each country separately. The collection and analysis of relevant statistics will allow you to properly plan the roadmap for the development of the distribution network.
If, for example, you are located in North America, where, according to the analytical service Conviva, the share of set-top boxes for streaming TV in the total consumption of streaming services is 58%, but when analyzing your own statistics on the use of platforms, you see that you have few subscribers using set-top boxes, then it may make sense to make sure that your application is available through the main popular set-top boxes in your region. For example, you should make sure that your app is successfully uploaded to the Roku app store since these devices (48%) are most in-demand in the United States.
Analysis of view statistics will allow you to plan the resources that should be allocated to support the most important distribution channels. In the Telebreeze statistics module, this data is available in the context of countries, content units, and user groups.
If, for example, you are located in North America, where, according to the analytical service Conviva, the share of set-top boxes for streaming TV in the total consumption of streaming services is 58%, but when analyzing your own statistics on the use of platforms, you see that you have few subscribers using set-top boxes, then it may make sense to make sure that your application is available through the main popular set-top boxes in your region. For example, you should make sure that your app is successfully uploaded to the Roku app store since these devices (48%) are most in-demand in the United States.
Analysis of view statistics will allow you to plan the resources that should be allocated to support the most important distribution channels. In the Telebreeze statistics module, this data is available in the context of countries, content units, and user groups.
Conclusion
Working with data requires a certain discipline. Discipline is one of the most important qualities of management: methodically track the dynamics of changes and be able to draw important conclusions, through the nuances of changes to see more or notice first.
The second important component of successful management is data sources. OTT technology, which was based on the use of data transmission protocols with feedback, made the use of statistics available to every operator. This opportunity should not be neglected in a highly competitive communications and media market.
In addition, the presence of well-organized statistics about users and content attracts advertisers and content owners, allows you to conduct reasoned negotiations and achieve the best conditions for building your ecosystem and business development.
The second important component of successful management is data sources. OTT technology, which was based on the use of data transmission protocols with feedback, made the use of statistics available to every operator. This opportunity should not be neglected in a highly competitive communications and media market.
In addition, the presence of well-organized statistics about users and content attracts advertisers and content owners, allows you to conduct reasoned negotiations and achieve the best conditions for building your ecosystem and business development.