Using Data Science to Grow Your Business: 3 Key Areas to Consider | 7wData
data science can have incredible benefits for your business but it’s important to understand that it’s a solution to a problem, not a way to find the problem. It means that if your company has a lot of data that you don’t quite know what to do with, you need to figure out what you are trying to improve or change before hiring a team of data scientists. Data scientists analyze data to find insights but it’s the job of product managers and business leaders to tell them what to look for.
There are a lot of ways how data scientists can bring value to your company, if you are in the process of figuring out exactly how data science can benefit your business, you can consider the following ways of using data science:
Let’s take a closer look at these three areas.
Data science is able to help you bring a better product to your target market in two main ways: you can either customize a product or a service to make it more personal, or you can provide a totally new experience with the product or service.
Today Machine Learning looks most attractive for businesses in terms of generating real value and enabling breakthrough innovation. There are three main types of machine learning algorithms – unsupervised, supervised, and reinforcement learning. We are going to focus on the first two and provide you with real-world examples of how these algorithms can benefit your product.
The discussion about supervised and Unsupervised learning can get complex and technical at times but essentially Supervised learning is about predicting an outcome, while Unsupervised learning is about identifying a pattern. Both of them can help you deliver better products to your customers by understanding them better.
Unsupervised learning allows you to capture your customers’ preferences, and use the data to anticipate their needs and behaviors in the future. The most common examples of unsupervised learning are Amazon’s recommendations based on what other customers also bought, and Spotify’s recommendations for your playlist based on the songs you’ve already liked or added. To build this type of recommendations data scientists solve a clustering problem, grouping similar users together to form homogeneous clusters.
Supervised learning is typically used for predicting customer behavior. By solving a classification problem machine learning engineers may help you identify satisfied and unsatisfied customers andpredict churn. By solving arecommendation problemdata scientists are trying to guess things that your customers may be interested in. By solving a ranking problem data scientists help users find the right thing faster when they search.
Supervised learning is also used to enable features such as face recognition,image classification, speech recognition. These features revolutionize customer experience and make tech products more intuitive to use, like telling virtual assistants to schedule a meeting instead of accessing scheduling software to find a time, create an event, and type the details.
The great thing about machine learning is that it is especially helpful in optimizing user engagement and retention, both of which are critical success factors for your product.
There are many other possible applications of machine learning that remain to be seen. Have an idea but need some help implementing it?Contact usand we will help.
With data science and predictive analytics, in particular, you can predict important metrics and trends for your business which improves your ability to serve your customers or otherwise compete in the market. Most importantly, predictive analytics provide you with the ability to detect issues that can negatively impact your business before they happen or spread.
Predictive analytics is not a new field, but where and how you can apply it has grown rapidly thanks to recent advancements in technology.