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, behaviour and trends.

Predictive analytics has been around for decades, but recent developments in software modelling 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 organisations 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 modelling
• Benefits of predictive modelling

What is predictive analytics?

Predictive analytics is a type of advanced analytics that uses statistics and machine modelling 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 mobilise 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 to 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 organisation 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:

• Minimising risks and the potential for losses from bad debts
• Analysing customer data to increase customer retention
• Doing algorithmic trading based on price movements and trends

Since 51% of organisations 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.

Marketing

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 behaviour.
• Predict customer lifetime value (CLV) and other important metrics.
• Optimise email, advertising and content marketing performance.

Healthcare

Healthcare organisations 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 optimise staff scheduling for high-demand times of the year and create personalised discharge plans to reduce readmission rates.

Human resources

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 programmes and performance assessments.

Manufacturing

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 customise predictive analytics to its exact needs.

Types of predictive modelling

Predictive analytics is a collection of different technologies, including artificial intelligence, data mining, machine learning, predictive modelling 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 visualise different outcomes.

In predictive modelling, 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 visualising a decision-making process.

2. Regression

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 modelling, 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 categorisation, recommendation engines and anomaly detection.

5. Times series modelling

Time series models analyse variables that change over time. They collect data at regular intervals and focus on the chronological order of the data.

For predictive modelling, time series models are ideal for predicting seasonality and trends — so time series modelling is very helpful for analysing climate patterns or daily stock prices.

Benefits of predictive modelling

Predictive modelling is no longer a tool reserved just for enterprise companies. As long as you collect enough data, you can use predictive modelling to be more competitive in your industry. Predictive modelling also comes with many other benefits, from security to enhanced decision-making.

1. Enhanced security

Predictive modelling is great at identifying patterns and anomalies in your data. For example, this technology can analyse 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 analyse 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 shops. 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 to 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 modelling 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.

Request an Adobe Analytics demo or watch the overview video to learn more.