Understanding machine learning: The ‘What’, ‘How’ and ‘Why’

Machine learning – you’ve probably come across this term anywhere from a passing tweet to technology articles to CNN interviews and places between. The concept of machine learning is far from new, but as the field continues to grow and find progressively more avenues where it has the potential to add value, machine learning processes can be found in a growing number of applications across a wide variety of sectors, like speech recognition and natural language processing, healthcare, social media and advertising, online streaming, traffic alerts, product recommendations, investing and fraud detection, and customer service.

If you’ve ever wondered what machine learning really is and whether your organization could benefit from incorporating tools that use machine learning, you’re not alone. If you’ve used a mobile banking or financial investing app or website, you’ve seen machine learning in action – or at least the result of machine learning models. Machine learning algorithms track funds as they transfer in and out, builds and report on budgets, and learns from typical spending habits and flags questionable or uncharacteristic spending. The more you use these types of services, the more familiar these machine learning algorithms become with your financial habits, and the more opportunity they have to learn, assess and develop models to help you manage your funds and protect you from risk. In fact, as of 2020 70% of financial services firms used machine learning to predict cash flow events, fine-tune credit scores and detect fraud. 

When it comes to sports and entertainment in particular, machine learning can help to address a variety of opportunities and challenges, including determining the best channels for engagement and conversion; streamlining dynamic ticket pricing; flagging package accounts that may not renew the following season; testing and evaluating which messaging is most effective with different groups of fans; and more.

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy… Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. (Source: IBM)

Essentially, machine learning takes on the bulk of work required to track, analyze and interpolate data within set parameters; think of it as a kind of digital assistant that helps you streamline and fast-track your data analytics and business intelligence. For a user-friendly deep-dive complete with real-world examples, helpful diagrams, and a little colorful language, we recommend checking out this post from vas3k.com.

Machine learning, artificial intelligence, and the robot apocalypse

Popular culture has offered up a number of potential futures where AI goes awry – notably the Terminator and Matrix series of films, among others – and while it’s true machine learning is part of the artificial intelligence ecosystem, today’s machine learning algorithms are unlikely to pose a Skynet-level threat; they don’t make decisions, they simply follow the rules set out to crunch numbers and present models for consideration. It’s up to us (as their human overlords) to determine what to do next with that data – whether we action it ourselves or activate an automated system to parse and distribute content based on machine learning modeling results.

Imitation is the sincerest form of flattery: Machines that learn like humans

It’s perhaps a lesser-known fact that machines learn in much the same way humans do – they are programmed by humans, after all – through an ongoing process of trial and error/response. Similar to how children learn over time to identify sounds, repeat words, build speech skills and develop a vocabulary, machine learning algorithms are trained to identity traits found in sample records, learn from them, and test additional factors within set parameters to build an increasingly larger vocabulary regarding how the data its tasked with reviewing relates to the problem we’ve set it on.

The greater the variety in data samples you use, the easier it is for machine learning algorithms to find relevant patterns and predict results. It’s extremely difficult, time-consuming, and often cost-prohibitive to manually collect the volume and variety of data required to build accurate and usable algorithms, which is why many organizations choose to adopt a tool that harnesses machine learning to do the heavy lifting for them. At StellarAlgo, our machine learning algorithms are trained on more than 100-million unique fan records before they even begin monitoring and analyzing your team-specific fan data.

Because StellarAlgo’s CDP focuses exclusively on data sources specific to sports and live event audiences across a broad range of demographics, it ensures our dataset not only meets the quantity and variety requirements to generate effective machine learning models but also comes pre-loaded with algorithms trained on the most relevant and up-to-date fan data. This means you’ll have immediate access to valuable psychographic data you can use to craft meaningful, personalized engagements with your fans from the moment you begin using the platform. From there, the machine learning algorithms go to work continually monitoring your fan data to learn, analyze, and generate predictive models that are customized to the behavioral patterns of your specific fans.

Increasing efficiency with machine learning-supported data science

We believe data is key to building a remarkable end-to-end fan experience. Segmentation, personalization, and nurturing demonstrate a thoughtfulness toward ensuring fans feel truly connected to and get the most out of their interactions with their favorite brands, teams, and players. But having a wealth of data means little if you don’t know how to interpret it, action it, and measure efficacy. 

As mentioned above, machine learning models crunch numbers and identify patterns – what you do with that data is up to you. With that said, imagine the time (and cost) savings by having those insights at your fingertips when you need them – to be notified when the model identifies upward trends like increased spending and rising avidity, flags fans who show the greatest propensity for conversion on specific products or events, and alerts you to signs that may signal a decrease in fan loyalty, like declining engagement rates. The earlier you and your team are aware of such risks and opportunities, the better equipped you’ll be to respond to the personalized needs of your fans, at the right time, using the best channel to reach them. Machine learning won’t do your job for you, but it has the potential to help make your organization more efficient and effective.