By Whitney Jones
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
Brands are leveraging machine learning, via powerful technology platforms, to use consumers’ behavior to customize an individual consumer’s experience. As a result, consumers are presented with better recommendations, more relevant information, and more individualized content.
Here are some examples…
@Walmartlabs, a technical division of the country’s largest retailer, is chartered with finding and building upon the cross-section of consumer behavior in social and mobile media and retail – their keystone is what’s being called the “Social Genome.” @WalmartLabs is using machine learning to constantly analyze consumer generated social media signals – feeding the genome. In turn, Walmart becomes better able to match consumers to products, and position products to consumers, in their advertising.
Are you planning a trip, making a reservation, looking for your next book? Hunch.com uses information you provide, your social activity, social activity of your friends, and social activity of those like you to generate individualized recommendations. How is this possible? Hunch aggregates all of these data points, uses your social graph to connect them, identifies your characteristics, and then taps into similar, ever-growing, graphs to find relevant information for you – that’s the power of machine learning.
Artificial Intelligence, real relationships. Companies like Intro Analytics, out of the UK, are applying machine learning to the wild world of online dating with algorithms that fuse machine learning and psychology. The popular online dating site eHarmony is on the same page and uses machine learning to match its users, and apparently to great success. According to its website, eHarmony is responsible for almost 5% of marriages in the US.
Here’s the common theme: machine learning supports relevant, individualized experiences. This is because it’s dynamic. Machine learning goes beyond even the most sophisticated rules-based tools to leverage data in a way that’s unique and evolves with consumers.