Your consumers spend a lot of time exploring and analyzing suitable information―which books to study, which news articles to read, which songs to play, which movies to watch, which games to play, and so on. Imagine, what their experience would be like, if they don’t need to pick anything on their own, but are presented with options of their liking―be it in education or media or entertainment.
Here are some of the things they can be offered:
• Adaptive text-books, in which content changes based on the pace of learning and comfort level of the reader.
• Dynamic news articles, with customized text, supporting images, graphics, and multimedia.
• Personalized advertisements that factor in the needs and priorities of customers at a particular moment.
Such advancements reduce the overall time spent on information discovery, and increase the scope of effective information consumption (or learning). Domains such as education, publishing, entertainment, and advertisement mostly deal with granular digital assets (text, images, audio, video, multi-media, and so on), and are better prepared to enhance personalization even without creating new content from scratch. For example, as a digital education enterprise, you can dynamically build and render an adaptive textbook by assembling granular content, such as paragraphs, chapters, questions, assignments, images, infographics in real-time based on your reader’s performance statistics. You will only have to reuse your existing content, albeit in different compositions, based on the reader’s performance analysis against statistical recommendation models. Similar offerings can be outlined for industries like entertainment, advertisement, and publishing as well.
Contradictory opinions around loss of choice and exploratory instincts, automation hampering potential creativity, and so on might come forth. However, information in raw form will continue to be available, just like manual transmission automobiles have managed to exist despite widespread adoption and popularity of the automatic ones.
Machine Learning is driving personalization advancements
As the volume, velocity, and variety of information expands, traditional rules-based systems are making way for self-learning intelligent systems that rely only on data. To serve consumers with personalized education, entertainment, and advertisements, you can rely on three major disciplines powered by machine learning, which include:
Computer vision: Computer vision techniques that leverage neural networks-based deep learning can detect the type and intensity of consumer emotion from captured images or video frames. Such insights, in turn, can play an extremely important role in dynamic and real-time personalization of media delivery.
Data analytics: You can use data analytics and statistical models with data points such as click-streams; page views; amount of active and idle time spent; likes and dislikes; social sharing patterns; tactile and 3D responses to cluster consumer segments, profile individual consumers, and predict their preferences.
Text analytics: Technologies that successfully synthesize text into raw data, allow the information to be personalized when presented in different contexts.
Thanks to Big Data and improved computing hardware, you can now perform most of these analytical processes in real-time.
Challenges and mitigating factors
Your enterprise will manage to draw only a partial profile of your consumer, related to the services you offer. But there are often implicit overlaps among various aspects of a consumer’s status, behavior, and lifestyle, drawn by different service providers. By integrating such observations you can uncover interesting synergies and many hidden possibilities, which can potentially benefit your enterprise as well as consumers. But here’s the catch: large scale integration of such data remains a far-fetched idea unless there are robust standards, regulatory consortiums, strict laws, and transparent monitoring mechanisms in place.
New privacy laws, data protection models, and government regulations will be required to ensure secure and legal ways of personal data sharing and analysis. Until then, most consumers will feel reluctant to share personal experiences and usage data despite the promise of more convenience in return.
One of the key factors that substantially boosts the transformative potential of machine learning techniques is the increasing success of deep learning. Apart from using it in computer vision, scientists are exploring deep learning to address large scale text mining and speech recognition problems. Its success will open up new levels of incremental advancements in cognitive computing and affective computing. Once you develop deep learning algorithms for one problem, you can port them to address many other problems having similar constructs.
Move over personalization: Brace yourself for emotion-aware robots
The ability to correctly understand human emotion can result in innovations beyond superior personalization-driven products and services. What’s more – you may even see the development of emotion-aware robots (emotion-aware AI agents) that can understand the mood of their owners by analyzing multiple reactions at real-time, and then act accordingly. Sounds like a big leap from personalization to personal assistants, doesn’t it? Well, it is not impossible, but the road ahead is long, and twisted. For your consumers though, it can be an exciting time, because now you can change their learning, entertainment, and information consumption experiences for the better.