The rapid development of technology and the growing amount of time consumers spend online have increased the number of touch points and interactions consumers have with brands—especially media and entertainment platforms. This is making more personalized experiences vital for marketers seeking deeper, long-lasting connections with consumers.
As the ability of media and entertainment companies to deliver more sophisticated experiences has evolved, consumers have come to expect real-time, curated experiences across digital channels. But many companies still struggle to deliver game-changing, personalized consumer experiences due to the scale limitations that come with static, rule-based systems, the complexities and costs of implementing a machine learning-based solution, and friction with platform integration.
While companies work to figure out the best way to create personalized experiences for their customers, the payoff is clear: According to a 2019 McKinsey study, companies that implemented personalized recommendations and triggered communications realized 5% to 15% increases in revenue and 10% to 30% improvements in marketing spend efficiency.
The entire business model of the media and entertainment industry, in particular, has been upended. Technological and programming innovations have raised consumer expectations for a virtually endless array of content choices that they can experience anytime, anywhere, on any screen. That is driving historic business and operational changes at media and entertainment companies, which must be able to deliver premium content in a cost-effective manner to an audience who is engaged with the brand.
An infrastructure that flexes with audience expectations
To compete in this universe of technologically driven choice, media and entertainment companies must prepare for a business environment that is defined by constant change. That requires an infrastructure that flexes with audience expectations, accelerates roadmaps, facilitates innovation and evolves along with the business priorities. At the same time, these organizations must transition from a one-to-many relationship with consumers to a direct, one-to-one relationship.
This is giving rise to technical solutions that allow companies to create highly personalized experiences. For example, Amazon Personalize enables developers to build applications with the same machine-learning technology used by Amazon.com for real-time, personalized recommendations, including specific product or content recommendations, relevant product rankings and customized marketing communications.
For the media and entertainment sector in particular, this kind of technology gives companies the ability to build strong, direct connections with consumers and deliver innovative viewing experiences. Let’s take a closer look at common personalization use cases for the media and entertainment industry offered by this machine-learning technology:
- Increased content consumption: Companies can deliver highly relevant, individualized content recommendations for videos, music, e-books and more
- Highly curated content carousels: It allows the creation of personalized content carousels for every user based on their content consumption history.
- Highlighted new content offerings: This helps users find fresh and new content based on their unique tastes and preferences.
- Highly personalized ad placements: Companies can personalize pre-roll, mid-roll and post-roll ad placements within audio and video content.
- Improved marketing communication: Consumers receive personalized push notifications and marketing emails with individualized content recommendations.
- Enhanced genre-based recommendations: Individualized recommendations to genre can be made based on content carousels and lists.
One success story in the media and entertainment space is Pulselive, a subsidiary of Sony and a digital media partner to some of the biggest names in sports. Pulselive creates experiences sports fans can’t live without; whether that’s the official Cricket World Cup website or the English Premier League’s iOS and Android apps. To further enhance customer experience Pulselive began using Amazon Personalize to improve offering recommendations. After two weeks of A/B testing, the company realized the benefits of machine-learning-powered recommendations. It was able to use its more sophisticated recommendation engine to enable one of its clients, a premier European football club with millions of fans globally, to increase video consumption by 20% across its website and mobile app.
The ease of employing the technology is one of its greatest benefits, according to Wyndham Richardson, managing director and co-founder of Pulselive. “We don’t consider ourselves machine learning experts, but we found Amazon Personalize to be straightforward, and the integration was complete in a few days,” Richardson says. “Leveraging Amazon Personalize, we will be able to further push the limits in building data-driven, one-to-one, personalized experienced for sports fans everywhere.”
No matter the sector—media and entertainment, retail and beyond—virtually any industry in which brands engage directly with consumers must figure out how best to create highly personalized experiences for their customers that will enable them to improve engagement with those consumers and to drive revenue now and in the future.
Amazon Personalize custom recommendation and ranking inference runs on Amazon EC2 C5 instances featuring the latest Intel® Xeon® Scalable processors and AVX 512. Amazon EC2 C5 instances deliver cost-effective high performance at a low price per compute ratio for running advanced compute-intensive workloads like machine/deep learning inference.
About AWS | Intel
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