Predictive pattern analysis uses data, statistical algorithms, and automated learn to help predict outcomes based on that information as well as AI learning. In the end, you will be able to receive an assessment on what could happen for your software into the future – at least the best prediction that can be made at the current time.
Predictive pattern analysis has been utilized for decades, though with agile analytics development becoming more and more popular, it continues to grow and change thanks to more volume, steadier streams of software, and cheaper computers. Add all of that to a more competitive environment, and the need for predictive pattern analysis continues to grow – as does the demand for what it needs to do.
Why Is Predictive Pattern Analysis Important?
Your business can use predictive pattern analysis to solve difficult problems while researching solutions into the future. Some of the most common uses for this type of analysis include:
Marketing: Using predictive patterns, you can determine how customers will respond to the purchases or services that they use, learning about remarketing opportunities that help you to attract and keep customers.
Security Risks: In our world, one must consistently be aware of all of the attacks coming against software. Cybersecurity is the pinnacle of risk for most businesses, so predictive pattern analysis can help you to spot potential vulnerabilities before they even emerge. The same can be said for fraud – helping you to look at your actions to spot any problems. In turn, this will eliminate risks.
Who Uses Predictive Pattern Analysis?
One of the most common things one hears with predictive pattern analysis is that “I don’t really need to use this,” or “That isn’t used in my field.” But this isn’t! There are many industries and businesses that can benefit from it. Some of these include:
- Financial Services
- Utility Companies
Anyone that uses agile methodology or applications to run their businesses will benefit from it in some way.
How Predictive Pattern Analysis Works
Predictive pattern analysis uses information that you have given it as well as known results to build a model that can predict what will happen with the input of new data. Some of the things you can use to input values include revenue, problem times, traffic, or expenses.
There are two basic types of predictive pattern analysis – Classification models and regression models.
Classification models look at your customer base, for example, and predict whether that person is likely to become a repeat customer or whether he is a one-time only guest. Regression models, on the other hand, predict a number about customers, for example how much that customer will spend month over month.
In general, regression models tend to be more popular because they deal in numbers, which most businesses prefer. You can use this information to make decisions, and see what happens if you make changes small or large.
So how does it really work?
To start, you have to have a question in mind: what do you want to know based on the information that you already have? When you have this question, you will be able to do the rest of the work. Then, you will need to get that data. There are many different ways to track that data, from pulling it from reports, gathering it from a certain point. How much information you have will likely change daily, so you may want to put someone in charge of getting all of that information together in an organized, cohesive way.
Then, you can begin the analysis. You will need some help to do this from software that can put the results into plain English for you. THEN you can start to implement changes using the information that you have gathered.
Predictive pattern analysis requires teams to work together to gather information, feed it into the system, and then put that information to good use. It can be difficult to understand at first, but those who use it will see immense benefits.