When you go online to read the news, how does Google know what stories you’ll be interested in? When it’s time to do your bookkeeping, how does Quickbooks know which expenses are business-related and which ones are personal? When it’s time to relax with a beer at the end of the long day, who was responsible for what that beer tastes like?
The answer to all of these questions is machine learning. Machine learning is the automated examination of the data generated by actual human behavior. We live in a world where customers expect highly personalized experiences. Creating those highly personalized experiences is such a huge job that it’s well beyond what any human-powered team to could hope to provide. Machine learning is used to identify relevant information out of an infinite number of possibilities.
For example, let’s look at the Google News feed. Google News serves over 150,000,000 users each month. Every one of these users has their own unique perspective on the world: they’re reading news because they want to be well-informed about the events of the day, but they’re most interested in the events of the day that matter to them. This means that that same user who wants to know about the top political news and breaking events also wants to know about biotechnology, golf and their favorite celebrity.
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Machine learning allows Google to determine which topics are most of interest to each individual user. This determination is based on actual user behavior, rather than stated preferences. This is important for creating a user experience that’s of high value for the individual user. It’s common for there to be a disconnect between what users say they want and what they actually consume -the serious selections of political news and biotechnology may not be read as regularly as the golf and pop star headlines.
Google News delivers an individual news feed based on other factors besides personal preferences. Trending headlines – stories of mass interest – and the quality of the news source also factor into the complex algorithms used to determine who sees what stories. It’s a dynamic process, with tweaks and updates continually being made to better provide a better experience – and it’s only one of the thousands of examples of machine learning that are impacting your everyday life.
QuickBooks is, hands down, the most popular small business accounting software. Again, there’s a situation where many different types of businesses come together for a common set of needs. Providing each individual user with relevant support and remaining responsive to an ever-changing set of reporting and tax regulations is only possible with machine learning.
Machine learning is also making inroads into industries you’d never expected. Craft beer is about as old-school traditional as you can get – but machine learning is helping brewers by reviewing actual customer purchasing patterns and flavor profile preferences to determine how successful a particular recipe will be before it’s brought to market. In an industry where 75% of new offerings fail, using machine learning to eliminate pricey experiments that don’t pan out makes sense.
You’re going to be hearing a lot about machine learning and its role in making your business better. Machine learning can impact every aspect of your company, creating behind the scenes efficiencies and more effective ways to connect with your customers. In the weeks to come, we’re going to be focusing specifically on machine learning’s impact on retail and customer service, in both the B2B and B2C settings. We welcome your questions – let’s make this a rich conversation that benefits everyone.