In our industry, granularity comes from data.
Lots and lots and lots of data. It can seem overwhelming, and many times it is. That’s why machine learning is so interesting to us. It’s a way to connect the dots in an easier way.
What is machine learning?
Machine learning is a technology where massive amounts of data can be correlated, using large libraries which have already been built and are in the public domain. Generally speaking, the core idea is to collect a lot of data, build a model and make better decisions by using more data with feedback from the model.
So we had this client…
Here’s a great example on how it works. One of our clients was capturing a lot of data on how customers were using their website. So we took that data and figured out how to create a more seamless and efficient experience. Once we’d built the model, we could then train it to map the customer’s characteristics, which helped them make real-time decisions as users interface with their website.
Training with data.
The machine learning model does nothing but consume data. This provides a way to take massive amounts of information, get answers back, and constantly improve. To train the model, you have to feed it data and then use several different techniques to build meaningful correlations. Over time, as the model is continuously retrained, it just gets smarter and smarter as it learns.
Say someone wanted to buy concert tickets.
They’ve gone through all the steps on the ticket website, and all they want to do is just get their seats and be done with it. Machine learning gives you the ability to determine, with greater accuracy, whether someone on your site is human or not. If your machine learning model has been constantly fed the right data, and trained in the right way, the customer doesn’t have to prove they’re human (for example, via Captcha). The less roadblocks between your customer and what they want, the better.
Read the social landscape.
Machine learning goes even further than you may realize. Azure now provides sentiment analysis as a service that can determine positivity or negativity associated with social media content. This then helps them figure out the relevant data points. And when the model continuously learns and relearns, you can get more accurate readings of the social media landscape.
Say you’re watching CNN’s social feeds.
In the past, media analysis was based on a number of times someone was mentioned. Now you can train the model to take the source data and incorporate data points along the way, which can give you a more specific idea of context and sentiment. Instead of knowing how many people were talking about the President, you can now tell, with a greater degree of accuracy, how many were speaking favorably, and how many were not.
The potential is huge.
Machine learning’s potential uses run the gamut of business. Machine learning allows us to map indicators of positive or negative talk about you on the Internet. In a marketing space, you can use machine learning to get a more detailed understanding of customer interactions. You can use it to hone in on aspects of pricing that may reveal different ways to package and sell what you offer. Problems that once seemed impossible can now be solved.
Machine learning is one of the most exciting new developments in the technological landscape, and we’re excited to share it not only with our clients, but with readers like you as well.
