Blog Post:When TV viewers go to their favorite screen to watch a TV show, they want something good right there in front of them. They want personalized TV. In our guide, Television Gets Personal, we share proof that consumers want personalized TV and cover several ways that media companies can successfully deliver it, helping you envision a TV experience that’s intuitive, intelligent, and interactive. Of course, personalized TV wouldn’t be possible without data and technology. Media companies need data to understand viewers' taste preferences. And, they need technology to infuse those preferences into an experience that delights the viewer. If you're a broadcaster, cable network or operator looking to infuse more personalization features into your TV service, the following seven ways are a good place to start. 1. User data - Get the most accurate and complete data on your users by keeping information such as their name, age, gender, location, and previously shared preferences in a customer database. The more accurate and complete this data is, the better it can inform your segmentation. Then, you can anonymize the data and use it for segmentation with a data management platform such as Adobe Audience Manager. 2. Session context - With each viewing session, you can capture more data, which will lead to better personalization. Capture the browser type, device type, IP address, and time zone. This data can feed into a personalization algorithm that can recommend different content to the same user based on whether he or she is on a mobile device or a PC. Or, based on whether he or she is watching in the morning or watching at night. 3. Third-party data - Data from external providers can be used to further understand your users or your content. For example, you can purchase data about the products your audience buys or the types of websites that they visit. You'll also want to purchase content metadata so that you can make personalization decisions based on the genres, subgenres, cast, awards, year of release, and user ratings of shows that your users have watched. 4. Segmentation - You can use segmentation to orchestrate different types of personalization experiences for different segments of users. Segments can be generated based on behavioral attributes about your audience such as how frequently they visit your service or what type of content they most frequently watch. Segments can also be generated based on contextual attributes you know about your visitors such as device, time of day they most frequently visit, or geo location. To drive the most engagement from your audience, you can drop each viewer into an experience that's proven to work with other people in their segment. 5. A/B testing - A/B testing lets you test your hypotheses about how your data can be used to improve personalization. For example, you may have a hunch that viewers who browse sporting websites are more likely to respond to recommended sports content. You can test this hunch by exposing a control audience to your existing personalization algorithm, which doesn't consider sports browsing data. At the same time, you expose a test audience to a new personalization algorithm, which does consider sports browsing data. If the test audience outperforms the control audience on one or more metrics that you care about, such as viewing time, then the new algorithm is a success. 6. Algorithms - Algorithms make up the decision engine for personalization features. Once an algorithm has been programmed to know what data to consider and how to consider it, then it can make personalization decisions on the fly for every viewer of a personalized TV service. You don't have to be able to develop algorithms in order to personalize a TV service. Solutions like Adobe Primetime recommendations include built-in algorithms that you can use. 7. Automated optimization - Optimization technology makes personalized TV smarter over time. For example, Primetime recommendations automatically conducts A/B testing to continuously improve the decisions that it makes regarding video recommendations and visual layouts. These seven data sources and technology capabilities put personalization within reach of any TV service. In fact, many media companies already have the data they need to do personalization well. Others can get the data they need. If you're embarking on implementing more personalized TV services into your brand, we'd love to hear from you. Author: Date Created:July 11, 2017 Date Published: Headline:7 ways to personalize TV Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2017/07/Image-7-ways-to-personalize-TV--e1499552717159.jpeg

When TV viewers go to their favorite screen to watch a TV show, they want something good right there in front of them. They want personalized TV.

In our guide, Television Gets Personal, we share proof that consumers want personalized TV and cover several ways that media companies can successfully deliver it, helping you envision a TV experience that’s intuitive, intelligent, and interactive.

Of course, personalized TV wouldn’t be possible without data and technology. Media companies need data to understand viewers’ taste preferences. And, they need technology to infuse those preferences into an experience that delights the viewer.

If you’re a broadcaster, cable network or operator looking to infuse more personalization features into your TV service, the following seven ways are a good place to start.

1. User data – Get the most accurate and complete data on your users by keeping information such as their name, age, gender, location, and previously shared preferences in a customer database. The more accurate and complete this data is, the better it can inform your segmentation. Then, you can anonymize the data and use it for segmentation with a data management platform such as Adobe Audience Manager.

2. Session context – With each viewing session, you can capture more data, which will lead to better personalization. Capture the browser type, device type, IP address, and time zone. This data can feed into a personalization algorithm that can recommend different content to the same user based on whether he or she is on a mobile device or a PC. Or, based on whether he or she is watching in the morning or watching at night.

3. Third-party data – Data from external providers can be used to further understand your users or your content. For example, you can purchase data about the products your audience buys or the types of websites that they visit. You’ll also want to purchase content metadata so that you can make personalization decisions based on the genres, subgenres, cast, awards, year of release, and user ratings of shows that your users have watched.

4. Segmentation – You can use segmentation to orchestrate different types of personalization experiences for different segments of users. Segments can be generated based on behavioral attributes about your audience such as how frequently they visit your service or what type of content they most frequently watch. Segments can also be generated based on contextual attributes you know about your visitors such as device, time of day they most frequently visit, or geo location. To drive the most engagement from your audience, you can drop each viewer into an experience that’s proven to work with other people in their segment.

5. A/B testing – A/B testing lets you test your hypotheses about how your data can be used to improve personalization. For example, you may have a hunch that viewers who browse sporting websites are more likely to respond to recommended sports content. You can test this hunch by exposing a control audience to your existing personalization algorithm, which doesn’t consider sports browsing data. At the same time, you expose a test audience to a new personalization algorithm, which does consider sports browsing data. If the test audience outperforms the control audience on one or more metrics that you care about, such as viewing time, then the new algorithm is a success.

6. Algorithms – Algorithms make up the decision engine for personalization features. Once an algorithm has been programmed to know what data to consider and how to consider it, then it can make personalization decisions on the fly for every viewer of a personalized TV service. You don’t have to be able to develop algorithms in order to personalize a TV service. Solutions like Adobe Primetime recommendations include built-in algorithms that you can use.

7. Automated optimization – Optimization technology makes personalized TV smarter over time. For example, Primetime recommendations automatically conducts A/B testing to continuously improve the decisions that it makes regarding video recommendations and visual layouts.

These seven data sources and technology capabilities put personalization within reach of any TV service. In fact, many media companies already have the data they need to do personalization well. Others can get the data they need.

If you’re embarking on implementing more personalized TV services into your brand, we’d love to hear from you.