Machine learning is a phrase that's tossed around left and right these days, but what exactly is it? To demystify this concept and understand why it's important to you, here are the three main points you need to understand.
Machine Learning Tasks
At the most basic level, machine learning technology does one of two things. It's either trying to predict future results or trying to discover patterns by using data. The exact details on how it accomplishes this task through algorithms get more technical than you need to know if you're not a data scientist.
An easy way to understand the prediction aspect of machine learning is to think about a fantasy sports player. They look at their team members' past performance to make educated guesses about how well they're going to do as a team. The player pours over statistics from the past and bases their decisions on this information. When they're good at this process, they end up choosing fantasy sports players who combine to work well together to deliver successfull results. Machine learning logic searches for the best possible predictors of success and applies them.
With machine learning, you have computer software that goes through a vast amount of data to achieve a similar goal. It does so quite a bit faster than your fantasy sports friend, looking at historical and real-time data to generate its conclusions through an automated process. And unlike your friend, a computer can ingest thousands, even millions, of data points to calculate recommendations, whereas your friend is only dependent on the sports shows or news she reads.
So, as the fantasy football season progresses, the data points start forming patterns. Patterns that result in failure are flagged and not repeated. Patterns that end up in success are expanded and refined.
Machine Learning Evaluation
Just because the machine learning system discovered a pattern or made a prediction doesn't mean that this information is automatically accurate. You need a way to evaluate the results that your application comes up with, so you know that you have the right algorithms in place for your use case.
Think about the kid in high school who doesn't quite understand a concept in their math class. When they're doing their homework, they show the work involved in putting together an answer. However, problems with the equations that they're using to figure out that answer introduce some errors into the process.
The same thing happens when machine learning results don't get evaluated throughout the process. The system ends up with a lot of data that isn't the right answer, and it uses these bad data sources to draw more conclusions or create more predictions. Ultimately, you could end up with a lot of unusable results.
The evaluation process double checks all of the work before it throws everything off. Consider it a tutor for the machine learning system. The machine learning application expands its knowledge and makes a note of the errors that occurred during processing. It can then apply this information and continually improve its performance.
The Types of Learning in Machine Learning
The "learning" in machine learning comes in a few different types, depending on what you're trying to get out of your application. You may end up sticking to a single type or mix multiple together for a complex model that helps you improve your business.
Supervised learning is a configuration where you feed the system with examples similar to what it should see as it goes through the process. For example, when you look at a cookbook you see the recipe, which is the input, and the finished dish, which is the results. You can tell the machine learning system the food it could create with a given set of ingredients and instructions. The system then uses that common data set to explore other dishes it could create that fall within these parameters, such as "dinner."
How does this relate to your world? Think about how efficient you could make your mail system if you're able to feed it a set of variables and it generates multiple pre-written emails?
Unsupervised learning algorithms are the opposite. You don't give the machine learning system any examples of what you're looking for. You leave it to draw its own conclusions from the data it collects. You'll commonly use this configuration if you want to see whether it can pick up patterns that no one else could recognize. For example, if you see sales dips that appear random, but seem to be influenced by some sort of underlying pattern, you can send this data to your systems and discover hidden factors that impact your cash flow. The machine learning conclusion might be that the random dips were all directly related to the weather events, for example.
Finally, you have reinforcement learning. The machine learning application knows how to accomplish a particular goal, such as walking in a straight line. It then enters an environment with changing variables that require the program to adjust the basic process based on the feedback it receives. Think about an automated picking system in your warehouse that could adapt to environmental conditions, such as blocked paths or misplaced merchandise.
Why You Should Care About Machine Learning
As you can see, when machine learning systems or support are in place, our digital systems can be faster, more efficient and more effective. The potential for machine learning systems to manage complex operations smoothly, adjusting in real time to a range of variables is great. In a B2B sales and marketing world, the potential is also high. Just imagine these machine learning inspired concepts:
- Get a "Content Effectiveness" score given a set of documents, sales stage, and the customers profile
- Get automatic talking points based on the recent activity from your audience and the current news
- Get the most likely scenario of getting a sales deal closed given a set of variables
- Get content suggestions based on what content is closing deals and high in interactivity
- Predict what specific segments are buying products like yours and what the successfull pattern is