As the director of Research and Development for PriceSpider, I’m always looking for new ways to apply technology in order to add more value into our business. My team is constantly experimenting with the latest technologies to find new and innovative ways to help our clients. The thing about technology is that it’s constantly changing and providing new and exciting ways to dive into data. One of the newer tech areas for PriceSpider is “machine learning.” This is now a critical capability that allows our system to provide the most advanced and accurate data for our clients. So, what exactly is machine learning?
What is Machine Learning?
Machine learning, on a basic level, is essentially a way to get a computer to look at a bunch of examples and just “sort everything out” on its own for us. Of course, there’s a lot more to it than that, but that’s the main idea.
When a human looks at something, it’s easy for us to define it. Is this item a chair? A dog? We know right away! We don’t get chairs and dogs confused with each other. We know all of this because we have experience. We’ve seen thousands of examples of chairs and thousands of examples of dogs. We know what we’re looking for. Machine learning gives us a way to apply these same kinds of “perceptions” to our data, but with a computer instead of a human. And of course, the advantage is that a computer can go through vast volumes of data over and over again, without ever getting tired!
In the product space, imagine instead of creating and maintaining thousands upon thousands of human-authored rules (or code) to classify every product and seller combination on the web (as is the traditional approach), you were able to train a computer to do the same thing. The machine learning approach is to provide examples of the specific items you’d like to define and label, and then letting the program determine what new items fit within each of those categories by using the parameters of the examples you’ve already provided. Then, the program makes the decision on its own and also adds that new item to its bank of examples. This means that the program learns and grows with each label it creates. Of course, we can pop in and correct the program if needed, providing even more data for it to use moving forward and advance its understanding of the labels.
How does PriceSpider use machine learning to help clients?
PriceSpider has applied machine learning to its proprietary “spidering” technology, which aggregates valuable data on behalf of our clients. The ultimate goal is to remove the guesswork and possibility of human error with a fully automated solution. From the crawl, our system labels different pieces of information and groups the findings into insightful and actionable data points.
From the information collected, brands can make sure that their products are being correctly presented by retailers, gain insight into their customers and their purchasing habits, review sales performance and even monitor ratings and reviews. With machine learning, we can gain predictive analytics to anticipate future product performance. We can even collect and review data points from our client’s competitors to gain incredible knowledge about the overall product space. All of this and more is possible because of machine learning. Plus, it all happens with exceptional accuracy and speed. Brands can use this wealth of knowledge collected to make important strategic business decisions.
What’s next?
Machine learning has come a long way since its inception. Programs have become more and more sophisticated and because there are so many examples available on the web, machine learning is more precise every day. In fact, you don’t need training in machine learning to utilize its power. Programs like ours allow brands to harness the information collected through the power of machine learning with easily digestible reports and actionable data sets.
As machine learning grows, I imagine we’ll see even more detailed and advanced programs, including huge strides in artificial intelligence. Machine learning offers many different opportunities for advancement in an array of disciplines and fields. I’m excited to see where it goes, how we can utilize it for the benefit of PriceSpider’s clients and what new technological advancements emerge in the future.
Authored by Dave Templin, Director of Research and Development for PriceSpider
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