The term “predictive analytics” is not new. But until recently, an organization’s ability to put Predictive models into production was limited by its ability to collect data, manage it and run advanced analytics.
We are now entering a fresh era of automation and ubiquity. Thanks to the proliferation of data and advances in computing power and algorithmic development. Businesses that take advantage of these technologies can leapfrog their competitors.
And as you know, there is a rise in cutthroat competition across all the industries, leaving no room for new businesses to grow. But with advanced technology applications, you can shake the market.
Hence, it is important to not only know what predictive analytics is but also how it works. Learn the fundamentals of data mining and predictive analytics, then read an overview of the predictive analytics process. On top of that, take a look at some industry examples using this technology in action.
So, adjust your glasses and take this short guide on predictive analytics, its process, and how industries use them. Let’s get started!
What is predictive analytics?
Predictive analytics is a component of data analytics that uses statistical techniques to make predictions about future events. It identifies patterns and trends in past data to better understand individuals’ or organizations’ future behavior. However, to carry out predictive analysis, you may need to apply machine learning and artificial intelligence algorithms to the data.
Thereby, you develop models that can predict future outcomes for you. Then you can use these models to make recommendations or decisions about how to best act in specific situations.
In other words, Predictive analytics is the practice of using data mining and machine learning techniques to make predictions about future events. It is a subset of data science and deep learning with an abundance of benefits for business intelligence applications.
Predictive analytics process: How it works
The predictive analytics process is the use of data, statistical algorithms, and machine learning techniques to recognize the likelihood of future effects based on historical data. But the goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.
The process utilizes data mining and predictive modeling to analyze current data to make predictions about the future. The predictive models are then used to identify trends and patterns and to perform other kinds of analysis that can help organizations make informed decisions.
For example, you can use it to predict who will be your customers, what products a customer might like; or how likely it is that a customer will default on a loan.
Simply, the goal of predictive analytics is to use past data to create models that will allow corporations to make better decisions in the future.
The predictive analytics process for businesses usually follows these steps:
- Define the business problem and objectives
- Data Collection and Preparation
- Data Modeling
- Prediction and Evaluation
Each step is important to the overall success of using predictive analytics for business purposes. Let’s take a closer look at each step.
Step 1: Define the business problem and objectives
Before starting any project with predictive analytics, organizations should have clearly defined goals and a plan for how they will measure success. The first step is to define the specific business problem that needs to be solved or the opportunity that needs to be exploited. Often it is difficult to identify the information needed or even where this information will come from. This requires gathering people from multiple functional areas of the organization who know what data exists within the company or can be obtained through third parties. Because predictive analytics projects typically take six months or longer to complete, it is vital to set clear expectations with key stakeholders at the start of a project so that their support doesn’t waver during the process.
Step 2: Collect and prepare data
The next phase involves collecting relevant data from different sources, either internally within an organization (such as customer databases or transaction logs) or data purchased from outside vendors. Or businesses can also use external sources, such as social media, news, or weather data.
The purpose is to obtain all the relevant data that will be used to make predictions. Often, data is messy and contains inaccuracies. Therefore, businesses must clean and prepare the data for modeling so that accurate predictions can be made.
Step:3 Data Modeling
Once the data is collected, it needs to be pre-processed to get it ready for analysis. In this step, the business will choose the model they want to use and the algorithm that will best suit their needs. This involves cleaning and formatting the data, and removing any irrelevant data.
Step 4: Prediction and Evaluation
In this step, the business will input their data, and the model will generate predictions. In other words, you will put the predictive model the business creates into production to test or get prospective analytics.
And when it delivers the results, the business then will evaluate if the predictions meet the accuracy and performance.
What are the advantages of predictive data analytics for Businesses?
Predictive analytics helps businesses analyze data and use statistics to predict future events. The process starts by gathering relevant data, analyzing it for patterns, creating a predictive model, and using the model to make forecasts. Furthermore, you can leverage it to forecast market opportunities, predictions of customer preferences, the likelihood of success, and estimated financial outcomes.
In business, predictive models use patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow the assessment of potential risk associated with a particular set of conditions. Thus, it guides decision-making for candidate transactions.
These predictive capabilities are used extensively across industries, including marketing, insurance, financial services, telecommunications, retailing, and health care. Let’s see how this statistical modeling process helps businesses decide about things like what products to sell or how to allocate resources.
Predictive analytics has proven its value in numerous financial services. For example;
- To predict stock prices, sales growth, gross margin, and other financial indicators;
- Helps banks, credit card companies, mortgage lenders, and other financial institutions to identify fraudulent transactions and offer their best rates to customers,
- To sell new financial products to the right customers.
Retail and consumer-facing industries
Retail, telecom, restaurants, and other consumer-facing businesses exploit predictive analytics tools and models to handle their customer relationships better. They use the past data of customer behavior to predict future trends and manage their business accordingly. For example;
- If customers are unhappy with their products or services;
- Whether they move to another brand;
- Forecasting demand to create unique marketing campaigns;
- Predict churn rate, customer lifetime value, and set other KIPs.
Predictive analytics helps Airlines to a significant extent by predicting insights such as;
- Collect and analyze flight data concerning each route distance and altitudes,
- Target right customers with optimizing the price in real-time;
- To estimate the amount of fuel needed for a flight.
Transport and logistics companies
The logistics industry employs predictive analytics in multiple ways to optimize supply chains and;
- Determine optimal shipping frequency;
- Predict quantity to meet the demand while minimizing costs;
- Find the fastest routes considering traffic congestion, distance, weather, and delivery points.
Predictive analytics software development solution
You see, predictive analytics provide major industries with insights to grow and prosper their businesses, maintaining good customer relationships. If you think your business needs a data-driven solution like this, you must opt for predictive analytics software development. But you must reflect upon your business goals and objectives to validate whether you need business analytics tools.
As another option, you can discuss your business needs with software development companies like us. We help startups and enterprises increase their strength with emerging technologies.
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