Learn about predictive analytics and what it can do for you
Your business has mountains of data at its disposal. Putting this data to good use can improve forecasting, marketing, customer service, and other processes. Predictive analytics is a great way to use the latest data science to forecast future activity, behavior, and trends.
Predictive analytics has been around for decades, but recent developments in software modeling have expanded its potential for improving operations, reducing risks, and expanding areas of customer service and marketing.
While it might sound futuristic, predictive analytics is not machine learning, though it does use machine learning. In today’s competitive environment, predictive analytics can help organizations of all kinds to improve their products, services, efficiency, and customer relationships.
In this article, we explore what predictive analytics is, its benefits, and how you can use predictive analytics to make your business better, more competitive, and more efficient.
This post will discuss:
- What predictive analytics is
- Who uses predictive analytics
- Types of predictive modeling
- Benefits of predictive modeling
What is predictive analytics?
Predictive analytics is a type of advanced analytics that uses statistics and machine modeling to make predictions about future performance. It uses this technology to look at your historical data and identify patterns, trends, and relationships. The ultimate goal of predictive analytics is to mobilize your data to forecast future outcomes.
Predictive analytics is your business’s closest thing to a magic crystal ball. You need every advantage you can get to be competitive in a tightening business environment. Predictive analytics help you create future-proof risk assessments, marketing strategies, financial forecasts, and operational efficiencies.
While it isn’t infallible, predictive analytics is a data-driven approach to business that equips you with the right tools to make proactive, strategic decisions in a tricky environment.
Who is using predictive analytics?
Predictive analytics is incredibly useful for any organization that collects a lot of data. This tool is popular in a range of industries, from finance to manufacturing. Here are just a few industry use cases for predictive analytics.
Finance and banking
Predictive analytics helps financial companies and banks forecast financial trends both in the economy at large and within their own businesses. It’s common for the financial sector to use predictive analytics for:
- Minimizing risks and the potential for losses from bad debts
- Analyzing customer data to increase customer retention
- Doing algorithmic trading based on price movements and trends
Since 51% of organizations experienced fraud in the past two years, it’s also common for financial institutions to use predictive analytics to spot fraud. This technology identifies unusual patterns and flags anomalies that could indicate fraud, helping banks respond to illegal activity more quickly.
Predictive analytics is a type of advanced analytics that uses statistics and machine modeling to make predictions about future performance.
Marketing requires a deep understanding of your target audience. Marketers use predictive analytics to significantly enhance their customer interactions. Predictive analytics helps marketers:
- Target audiences based on behavior.
- Predict customer lifetime value (CLV) and other important metrics.
- Optimize email, advertising, and content marketing performance.
Healthcare organizations have an abundance of patient data in their systems. Predictive analytics helps providers quickly sift through this information to improve patient care, reduce costs, and even create a better employee experience.
For example, healthcare companies use predictive analytics to optimize staff scheduling for high-demand times of the year and create personalized discharge plans to reduce readmission rates.
HR and staffing professionals use predictive analytics to make data-driven decisions.
This technology can predict which job candidates will be top performers based on their skills and qualifications. It can even use historical data to identify which factors contribute the most to high employee performance, helping HR create more useful training programs and performance assessments.
In manufacturing, predictive analytics forecasts equipment failures and reduces downtime through preventive maintenance. In supply chain management, predictive analytics forecasts demand — making it easier for companies to manage their inventory. It even accounts for factors like supplier reliability, weather patterns, and geopolitical issues.
Predictive analytics is a valuable tool for a number of industries, thanks in large part to its flexibility. A business can use several types of predictive models to customize predictive analytics to its exact needs.
Types of predictive modeling
Predictive analytics is a collection of different technologies, including artificial intelligence, data mining, machine learning, predictive modeling, and statistics.
There are two types of predictive analytics — classification and regression.
Classification models are a type of predictive analytics that can predict a categorical outcome, like if an email is spam or not. Regression models, on the other hand, forecast continuous outcomes, like predicting the cost of a house based on its location, size, and number of rooms. The type of predictive analytics a business uses depends on the data it’s processing and the insights it needs to glean.
Whether you’re running a classification or regression model, you can choose from these different types of predictive models.
1. Decision trees
Decision trees are a machine learning algorithm for classification and regression predictive models. They have a tree-like structure with different features and rules to visualize different outcomes.
In predictive modeling, decision trees take your data and make a decision based on different data attributes. Data trees are easy to read, so they’re a popular option for visualizing a decision-making process.
Linear regression and logistic regression are two common types of predictive analytics. With linear regression, you predict a continuous outcome variable based on predictor variables. For example, linear regression can predict house prices based on size, location, and number of rooms. This model gives you an easy way to quantify the impact of each variable.
Logistic regression is better for predicting the likelihood of one of two outcomes. For example, logistic regression can determine whether a patient does or doesn’t have a certain disease.
3. Neural networks
Neural networks are a type of machine learning that mimics how the human brain processes information. They receive data, process it, and make predictions based on that data.
In predictive modeling, neural networks apply different weights to the data you input. They’re more complex than decision trees, making them ideal for processing a lot of unstructured data.
4. Cluster models
Cluster models are a type of machine learning that groups objects together. They’re great for identifying patterns and relationships without any predefined labels. They don’t predict outcomes, but they do reveal patterns in your data.
Clustering your data can enhance your predictive model’s overall performance. For example, clustering reveals segments in your customer data that you can use to build more accurate prediction models. Cluster models are also ideal for image categorization, recommendation engines, and anomaly detection.
5. Times series modeling
Time series models analyze variables that change over time. They collect data at regular intervals and focus on the chronological order of the data.
For predictive modeling, time series models are ideal for predicting seasonality and trends — so time series modeling is very helpful for analyzing climate patterns or daily stock prices.
Benefits of predictive modeling
Predictive modeling is no longer a tool reserved just for enterprise companies. As long as you collect enough data, you can use predictive modeling to be more competitive in your industry. Predictive modeling also comes with many other benefits, from security to enhanced decision-making.
1. Enhanced security
Predictive modeling is great at identifying patterns and anomalies in your data. For example, this technology can analyze network traffic to identify potential cybersecurity threats. Whether you’re concerned about fraud or breaches, predictive analytics helps you spot threats quickly to improve security significantly.
2. Reduced risk
Predictive analytics can process past data related to risks, as well as analyze your current data for any troublesome patterns. By anticipating future scenarios, you can mitigate potential risks before they’re even a problem. This leads to greater compliance and cost savings over time.
3. Greater efficiency
Predictive analytics makes your business more proactive. Instead of taking a reactive approach to everything, you can plan ahead, streamline your processes, and improve efficiency. For example, manufacturers use predictive analytics to prevent expensive equipment failures. Retailers use it to prevent stock-outs at their brick-and-mortar stores. With fewer disruptions, you can run a better business.
4. Better decision-making
Predictive analytics gives you data-driven predictions. Instead of making guesstimates about your business’s performance, you can trust that you’re making informed, objective decisions. From strategic planning to product development, predictive analytics gives you the competitive advantage of data-driven insights.
Shaping tomorrow with the power of predictive analytics
Predictive analytics improves your value proposition, helps you compete in a tightening business market, creates stronger customer relationships, and makes your processes more efficient.
When you’re ready to start using predictive analytics, explore tools that make it easy to embrace predictive modeling across your business. If you want to create actionable insights from your existing data, explore Adobe Analytics.
Adobe Analytics turns real-time data into real-time insights. Backed by Adobe Sensei, Analytics uses AI to deliver predictive insights based on the full scope of your data.