What Can Predictive Analytics Do for Your Business

Predictive analytics can help your business dig deep into your past data and combine it with current trends in predicting future results allowing your business to adapt changes and stay competitive.

Nowadays, more business across all sectors are utilizing the value of predictive analytics which isn't new but to use data to forecast future outcomes in all parts of their business. We'll go through how predictive analytics fits into the advanced analytics and how to apply it in your own business, as well as how to correctly build up a predictive analytics solution

Predictive Analytics

In simple words, predictive analytics is a type of data analytics solutions that uses machine learning to predict what will happen based on past data.

How Predictive Analytics is different from other types of Analytics

To get a better understanding how predictive analytics works for your business, first you need to understand that how other types of analytics work.

• Descriptive Analytics: By analyzing past data and finding new trends, descriptive analytics can tell you what happened previously. Most businesses that have gone far enough in their analytics journey are already using data analytics solutions to some extent

• Diagnostic Analytics: It enables businesses to understand the importance of data & why we should use it.  It allows improved decision-making and provides more predictive use cases as a result of understanding.

• Predictive Analytics: It makes use of past data as well as helps in predicting the future of your business according to the data gathered.

• Prescriptive Analytic: It takes a step further by advising you on automatic activities you may take to influence those outcomes. It's all about giving decision-makers the tools they need to make the best decisions possible based on projections.

What Are Some Predictive Analytics Use Cases?

By giving insights into future outcomes, predictive analytics may be extremely beneficial to any business. It enables business users to plan ahead of time, prevent missed opportunities, and make better-informed decisions in advance. Here are a few examples of how you can use predictive analytics in your company:

• Sales Prediction: The capacity to focus on opportunities, minimize mistakes, and establish connections is critical when it comes to sales prediction. Predictive analytics can look at historical data on purchase actions and combine it with trends like consumer behavior and weather patterns to estimate sales opportunities over time. Predictive analytics may also reveal the types of items and services that will be in high demand, allowing your company to capitalize on those sales opportunities.

• Marketing Analysis: Any organization's marketing department provides doors to new business while also maintaining existing partnerships. Data analytics services can help you learn how to perform both more effectively. By identifying signals of unhappiness, it can be utilized to anticipate and prevent client churn. It can be used to find sales opportunities and develop strategies to drive clients along the sales funnel. It can also be used to learn how your clients engage with your company so that you can make it easy for them.

• Product Service: The manufacturing business relies heavily on predicting servicing difficulties and keeping machinery from breaking down. The costs of a production halt can easily outweigh the cost of repair. Predictive analytics can use real-time data to precisely predict when a machine will break down, allowing the company to solve the issue before it becomes a major issue.

• Credit Risk and Fraud Reduction: Determining credit risk and detecting fraud is a high priority in the finance industry, as well as in the finance line of business. Predictive analytics can be used to understand possible danger areas from a variety of data points, allowing the company to make better decisions. It can also be used to detect and prevent fraudulent transactions by tracking and flagging transactions that deviate from the norm.

What Is the Process of Implementing Predictive Analytics?

Before you start using predictive analytics, you need to know why, what, and how you'll apply it in your business. To get started with predictive analytics, follow these six steps:

Make a list of your objectives and goals

It's pointless to build a predictive analytics model without first understanding why you're doing so. You'll need to do the following to identify your objectives and goals:

• Determine a problem to be solved. Starting with existing KPIs is a good place to start. Because you know what your aims are, and you probably have some good insight into what influences those targets.

• Identify what you're trying to predict and what you'll get out of it. Be precise about what options the business can make based on the projections that have been made.

Prepare your data by profiling it

Data preparation is the process of organizing your data, whereas data profiling is the process of determining what's in your data. As the number of data sources increases, it's more important to focus on data quality to ensure that the data you're utilizing in your predictive analytics model is reliable and capable of reaching your objectives and goals. To begin, you should do the following:

• Gather information that already exists. Data from transactional and operational systems, as well as third-party sources, is available. Whether it's a data lake or a data warehouse, pull data that's relevant to what you're trying to predict and combine it in one location. Data collection is the first step because you may have different systems that don't communicate with one another.

• To enable data modelling, organize your data in an useful way. You may have good data, but it isn't arranged in a way that allows you to view it. A data governance program can help you organize your information.

• Examine the data's accuracy. The outcome will be incorrect and unreliable if the data is not of high quality. To determine predictive capacity, look at summary statistics regarding goals and features to understand things like mean, variance, data normality, and so on.

Make a Data Model

Modeling your data allows you to build, train, and test a machine learning data model that can predict the likelihood of something happening or project particular numeric outcomes. Classification and regression models are the two most prevalent forms of data analytics services.

• A classification model categorizes or classes data based on what it learns from historical data and is commonly used to answer yes/no questions.

• When determining predictor strength, forecasting over time, or a cause-and-effect relationship, a regression model is used to find the best fit between predictor values and target values.

Validate Your Insights

You should validate the results after you've trained and tested your data model. Before deploying into operations, make sure you're happy with the findings, because a faulty model could break, or questionable results could lead to low adoption or confidence.

Develop and Implement Your Predictive Analytics Model

After the model has been validated, it is time to put it into action in a real-world setting. Implement your findings by incorporating them into applications or dashboards where they may be used right away.

Keep an eye on your predictive data model

It's critical to keep track of your predictive data model's performance once it's been implemented. Just because a data model is working now doesn't ensure it won't fail in the future due to unforeseen circumstances. Review your data model on a regular basis and make sure you have the flexibility to alter it on the fly as data changes. There are a number of strategies to successfully monitor your data, but two in particular should be used to maintain confidence, long-term adoption, and accuracy:

• Create a dashboard to track the progress of your expected and actual results. If you see that the results are diverging, it's a sign that something is wrong with your predictive data model, and you should make changes.

• Create a dashboard to track the results of business recommendations made with the predictive data model, as well as to compare business users who are following the recommendations with those who aren't. If there isn't a difference, you'll need to tweak your predictive data model or double-check that your end users are following the suggestions based on the forecasts.