Knowing how to harness and leverage big data helps brands build a competitive edge and improve overall performance.
One of the more popular uses of big data is in predictive analytics which is the use of historical information to forecast future events. Different industries around the United States are utilizing predictive analytics to assess customer behavior, improve marketing campaigns, increase sales, detect fraud, or enhance cybersecurity.
Learn how predictive analytics works and how different industries can use it to their benefit.
Predictive analytics was first used by scientists and mathematicians in the 1940s to assess the behavior of atoms, resulting in the invention of the atomic bomb. Today, thousands of organizations employ predictive analytics to increase their bottom line and gain a competitive advantage.
So, what caused predictive analytics to become so popular? Some explanations include-
- The rise of big data and the increased usage of data to produce valuable insights
- More advanced computers and business intelligence tools
- Intuitive, user-friendly predictive analytics software
- Economic difficulties and a growing need for a competitive advantage
Businesses all over the world use predictive models to forecast trends, prevent churn, assess risk, target customers, and improve their overall performance.
Common predictive analytic uses include-
1. Detects Fraud
Predictive analytics helps detect patterns and prevent criminal behavior. It is employed to monitor real-time online activity and determine whether there is unusual behavior, persistent threats, or vulnerabilities that may lead to fraud.
2. Optimize Marketing Campaigns
Analytics is used to predict customer behavior or assess purchasing patterns. Armed with this information, marketers can design customized campaigns to target key audiences. Businesses use predictive analytics to attract new prospects and retain current customers.
3. Improve Operational Efficiency
Different industries use predictive models to forecast inventory and plan, allowing organizations to be more efficient with their time and resources.
4. Reduces Risk
Current data or behavior is analyzed to assess the possibility of future risk. This helps insurance agencies, financial services, and collectors prevent fraud or mitigate situations that may put them at risk.
A growing number of industries use predictive analytics, including-
Banking & Financial Services
Banks utilize predictive analytics to detect and reduce the likelihood of debit or credit card fraud. It is also employed to assess credit risk and ensure credit cards are only distributed to qualified customers.
Predictive analytics is utilized to analyze the effectiveness of promotions and determine which products are best suited for consumers. It is also used to analyze customer behavior and provide a complete picture of a customer base.
Oil, Gas & Utilities
The energy sector employs predictive analytics to assess the failure rate of the equipment, forecast future resource needs, and mitigate safety risks.
Government & The Public Sector
Government agencies utilize predictive analytics to forecast population trends and enhance cybersecurity. Other public sector entities use it to prevent fraud and improve their overall performance.
Health Insurance Industry
Health insurance companies utilize predictive analytics to detect fraudulent claims and prevent the industry from wasting money on unnecessary payouts. It is also used to identify patients most at risk for chronic disease, and then, determine which treatment is best suited for them.
Manufacturers implement predictive analytics to identify which factors lead to production failures. It is also used to improve service resources, optimize distribution methods, and understand warranty claims.
Predictive analytics takes proven historical data to create a model, which determines the probability of a future event occurring. Rather than explaining what event happened or why an event happened, predictive modeling discerns what will probably happen in the future given past events.
There are a variety of techniques utilized to display and explain the data found within classification models and regression models. Classification models utilize historical data to predict class membership or the group that a variable belongs to. Regression models use historical information to predict a number or amount.
The most popular predictive modeling techniques are-
Decision trees are classification models that split data into categories in a tree-like fashion. Decision trees use a flowchart-like structure to show an observation or decision about an item and the consequences or results of that observation/decision. Each internal node represents an item/idea, the branch represents an observation about that item, and the leaves reflect the consequences of the observation.
Here's an example of a decision tree utilized to show the pros and cons of hosting the company Christmas party on the company boat-
Internal Node/Item- Location of Company Christmas Party
Branch- Company Boat
Leaf 1- People May Feel Seasick
Leaf 2- Boat Can Only Hold 100 People
Leaf 3- Boat Provides a Unique and Different Experience
The purpose of this model is to find the variable within the data that splits the data into logical groups that are most different and unique. Decision trees are popular because they are easy to interpret.
Regression analyses are used to show the correlation between an independent and dependent variable. The independent variable acts as a predictor and the dependent variable represent a target or result. It helps find patterns in large data sets and determines how much specific factors influence another variable.
Regression models are typically depicted as line graphs to show trends over a period of time. Line graphs are helpful for time series modeling as the eye is naturally inclined to interpret upward and downward linear patterns.
Here's a regression model that utilizes revenue data by month. The month is the independent variable and the revenue amount is the dependent variable-
Topic- Revenue by Month
January- $ 10,000
February- $ 8,000
March- $ 9,500
April- $ 11,000
May- $ 11,500
June- $ 12,000
A regression model in the form of a line graph would show a revenue dip between January and February, with a rise in revenue from March to June.
Neural networks model complex, non-linear relationships in data. Mostly useful for confirming findings from decision trees and regression analyses, they employ pattern recognition and some Artificial Intelligence processes to find discoveries. Neural network techniques are helpful when no formula shows a relationship between inputs and outputs.
The more data an organization collects, the harder it can be to find patterns in that data. Neural networks utilize AI to look at large amounts of data and find patterns that are not obvious to the average person.
Utilize the following steps to get started on predictive analytics modeling-
Find a Problem to Solve
Identify what needs to be predicted and what the company will do once they have those predictions. Consider how the insights found in the predictions will lead to better business decisions.
Choose data sources that will provide the information needed to make predictions. Data can be collected from call center notes, weblogs, customer databases, third-party sources, or even social media sites. Find someone with data management experience to cleanse and prepare the data for analysis.
Build a Predictive Model
Utilizing an effective predictive software system will make it easier to builds analytical models. A data analyst can also help refine models and sort through data sets to find important information that is useful for predictive modeling.
Take a Team Approach
Find analysts and team members who know how to prepare data for analysis and build effective models. Employ the IT department to ensure the company has the right analytics infrastructure for model building.