Predictive analytics can serve as a powerful tool in business, allowing companies to make better decisions – driven by data – about everything from customer retention to product development. In addition to identifying potential problems before they occur, predictive analytics uses historical data to identify trends, automate tasks and more.
For example, if a retail company is worried about potential customer churn, they could use predictive analytics to identify customers who are at risk of leaving, then target them with specific marketing campaigns to encourage them to stay. This is just one of many scenarios in which predictive analytics acts as a compass, allowing companies to find true north and chart their path toward a better future.
So, what business challenges or opportunities are on the horizon? Companies may be able to use predictive analytics to find out.
Predictive Analytics Defined
Predictive analytics is the practice of using historical data to predict future outcomes. This advanced form of data science uses statistics, artificial intelligence, and machine learning techniques to identify patterns in data and make predictions about a wide variety of things, including customer behavior, product demand, quality control and more.
Building predictive analytics frameworks typically involves a few key steps: defining the problem to be solved, collecting and organizing data sets, choosing the right predictive model, and sharing key takeaways.
Common Uses of Predictive Analytics
In business, predictive analytics can be used to make predictions about a wide variety of things, providing insight into customer behavior, anticipating product demand, identifying opportunities for preventative maintenance, and so on. Some of the most common uses of predictive analytics include:
- Risk management: Predictive analytics can be used to identify and manage risks in a variety of areas, such as financial risk, operational risk, and compliance risk.
- Demand forecasting: Predictive analytics can forecast future demand for products and services, allowing businesses to optimize inventory levels, streamline supply chain, and avoid stockouts.
- Product development: Companies can leverage predictive analytics tools to identify opportunities for new products and predict their potential for success. This information can be used to make better product development decisions.
- Customer segmentation: Predictive analytics can be used to divide customers into groups based on shared attributes, shedding light on each step of the buying journey, and creating opportunities for companies to tailor content to unique audiences.
Common Types of Predictive Modeling
There are several modeling techniques used in predictive analytics. The type of predictive model that is most suited for the situation will depend on both the type of data and the desired outcome. For example, if a company wants to predict when a customer is likely to switch to another provider, a classification model would be better suited than a clustering model.
Though there are many modeling techniques that can be used for specific problems, let’s explore some of the most common types of predictive models.
Classification models are used to predict categorical outcomes. These models are trained on a dataset of historical data, allowing them to categorize data based on historical trends, predicting customers who are likely to churn, loan applicants who are likely to default, and so on.
One example of a classification model is a decision tree: an analytics methodology that uses data mining to separate data into subsets based on categories of input variables. This can give companies insight into the end user’s decision-making process, breaking down complex decisions into a series of simpler choices, which are visualized in a tree-shaped diagram. Each branch represents a potential choice, while each leaf on the branch represents the result of the decision.
Regression models are used to predict a continuous outcome, such as the price of a house or the number of sales a company will make. This makes regression models popular in statistical analysis, as they can easily pinpoint patterns in large datasets and determine the relationship between two or more of the variables.
Regression models work by analyzing a dataset of historical data, which includes the outcome of interest, such as the price of a house, and a set of features that are thought to be predictive of the outcome, such as the house’s size or location. Types of regression models include linear regression, polynomial regression, and time series models.
Clustering models are used to group data points together based on their similarities. The term “clustering” refers to the method of aggregating data based on shared attributes. Clustering models can be used for a variety of purposes, including customer segmentations and product recommendations. Types of clustering models range from basic algorithms, such as k-means clustering, to more complex algorithms, like hierarchical clustering.
Neural networks use machine learning techniques to analyze past and present data, forecasting future outcomes. This type of predictive analysis, which imitates the human brain, uses artificial intelligence to recognize complex or complicated patterns. This can allow neural networks to identify underlying relationships within a dataset that may not be discovered using other methods.
Benefits of Predictive Analytics
There are many potential benefits of using predictive analytics in business. With strategic data collection and the right predictive model, companies can increase efficiency, decrease costs and more. Though some uses of predictive analytics may vary by industry, in general, companies who invest in this form of advanced data analytics can expect to reap the rewards of:
- Improved decision making: Business decisions based on data are intentional and strategic. With the power of predictive analytics, business leaders can make confident, calculated decisions.
- Streamlined operations: Predictive analytics can be used to streamline workflows, forecast inventory, and manage resources, allowing businesses to operate more efficiently.
- Optimized marketing: Data can be used to recognize shifts in demographics, to identify customers at risk of churning, or to segment audiences to create personalized marketing campaigns.
- Easier fraud detection: Predictive analytics can easily detect patterns in data, identifying network abnormalities in real time and allowing cyber defense teams to take a proactive approach in tackling potential threats and vulnerabilities.
Predictive Analytics in Action
Predictive analytics can lead to transformative results. Adopters across industries are leveraging predictive models to make strategic decisions on how to set pricing strategies, how to manage supply chain, how to target customers, and more.
Though there are many ways predictive analytics can be deployed, here are a few examples to illustrate how this form of advanced analytics is currently being used in different industries
- Retail: Retailers are using predictive analytics to forecast future sales, predict customer demands, optimize inventory levels, and personalize marketing campaigns, segmenting audiences and providing them with offers that may appeal to their preferences based on historical data.
- Finance: Banks and other financial institutions use predictive analytics to assess credit risk, identify fraud and manage investments. For example, banks often use predictive analysis to predict which customers are most likely to default on a loan, allowing them to make better lending decisions.
- Healthcare: Healthcare organizations are using predictive analytics to improve patient care, manage costs and prevent fraud. For instance, some hospitals have begun using predictive analytics to identify chronically ill patients who might be at risk of developing complications, allowing them to provide preventative care.
- Manufacturing: Manufacturing companies use predictive analytics to optimize production processes, improve quality control and reduce costs. Manufacturers frequently use predictive analytics to predict when equipment is likely to fail, scheduling preventative maintenance to avoid unnecessary downtime in production.
- Education: Colleges and universities are beginning to use predictive analytics to manage student enrollment and retention. For example, a prediction algorithm may be able to use data from a student’s high school years to predict the type of support they will require. Predictive analysis in education can also be used to identify at-risk students, personalize learning experiences, and strategize fundraising efforts.
Across industries, predictive analysis acts as a catalyst for innovation, analyzing historical data and identifying patterns that can allow companies to make predictions about the future of their business. By investing in this form of analytics, organizations gain access to insights that can improve operational efficiency, reduce risk, and enhance the customer experience.
However, it’s important to note that predictive analytics is not a perfect science. The accuracy of predictions will depend on the quality of the data, and choosing the right type of model will require an understanding of machine learning algorithms, including which model is best suited for the task at hand, with decision trees, regression, and neural networks being among the most popular options.
To overcome these challenges while capitalizing on the benefits of predictive analytics, companies should consider placing their data in expert hands. At Stefanini, we combine industry expertise with analytics solutions designed to find the information hiding in your data. Contact us to learn more!