What is Predictive Analytics
Predictive analytics uses large amounts of data to identify potential events and opportunities before they occur. The real value can be understood by learning its advantages and use cases in industries such as manufacturing, construction and many others. It makes use of predictive analysis methodologies with the help of techniques such as Artificial Intelligence, Machine Learning, Statistics and Data Modelling.
Many businesses find it difficult to predict future trends and patterns. Usage of these predictive analytics models not only help in revenue trend patterns but also forecast any potential issues before it occurs and develop an effective response.
Organizations are under constant pressure to speed up and improve decision making, which is growing more and more complex. Predictive analytics is aimed at making predictions about future outcomes based on historical data using modern analytical techniques like machine learning. With the help of predictive analytics tools and models, organizations can use past and current data to gain a competitive advantage, find new revenue opportunities, improve fraud detection, optimize processes and performance, increase asset utilization, improve production capacity and quality, improve collaboration and control, and reduce risks.
Predictive Analytics Use Cases Across Industries
Key industries such as Banking, Retail, eCommerce, Manufacturing, and Healthcare are using advanced analytics tools like predictive analytics to make the right decisions based on accurate data analysis. Here are some examples on how it is being used by various industries.
Manufacturing: Predictive analytics in manufacturing industry is used for inventory management and supply chain efficiency. It is highly accurate in predicting supply and demand fluctuations during the holiday season so that manufacturers can take advantage of sudden surges in demand. This ensures that manufacturers are never overstocked, understocked, or stuck with unsold goods, and even determine the optimal placement of products on your shelves.
Banking: Financial and banking industry takes help of machine learning techniques to identify target customers based on their behavior and design marketing strategies accordingly, it also helps them identify potential loan defaulters and high-risk customers.
Retail: Physical stores and eCommerce use predictive modelling of data to understand the website usage and customer behavior patterns. This helps in customer segmentation and devising strategies specific to the target segment to increase revenue and enhance overall customer experience.
Healthcare: Healthcare predictive analytics analyzes vast amounts of patient data related to health records, insurance claims, and administrative paperwork in search of patters to predict future outcomes.
Predictive Analytics Benefits
Predictive analytics enables businesses to analyze large amounts of data to identify potential events and opportunities before they occur. Understanding the key advantages of predictive analytics can push businesses takes steps towards identifying areas where it can be applied and creating an implementation strategy.
Improve Customer Retention
Businesses need to keep bringing in new customers to replace the ones that leave to avoid any loss in revenues. The cost of new customer acquisition is usually higher than retaining existing customers and therefore, it can be very expensive to acquire new customers. Predictive analytics can prevent churn and improve retention in your customer base by identifying signs of discontent among your customers, and predicting which customers or customer segments are most likely to leave. Companies can analyze this information and take the necessary actions to improve customer satisfaction and ensure that their revenues remain unaffected.
Identify Profitable Customers
It is important for marketers to identify the customers that spend the most, resulting in the most profits for their business over the long-term. This level of data insight is possible only through predictive analytics, allowing companies to optimize their marketing spend and focus their efforts on acquiring customers that will generate the most profits and eventually have the highest lifetime values.
Improve Customer Segmentation
Companies have diverse requirements and need to segment their customers based on criteria that matter the most to their business. Using predictive analytics, they can use their business data to focus on the right target audience, the right segments, and even entire markets that they didn’t realize existed.
Improve Decision Making
Besides identifying and defining the most profitable customers and segments, predictive analytics can also help you find the best way to communicate with your customers by analyzing all aspects of consumer behavior from buying patterns to social engagement and identifying the best times and right channels to connect with these customers.
Perform Predictive Maintenance
In asset-intensive industries, by using IoT sensors in combination with predictive analytics, companies can predict and plan for maintenance activities and expenses in advance. This is done by capturing and analyzing the data generated by the equipment and machinery, enabling you to control the costs associated with unnecessary preventive maintenance, avoid critical downtime, and extend the life of your assets.
Predict and Quantify Risks
Predictive analytics can forecast potential areas of risk by identifying trends and patterns in your data and make predictions on how these risks can affect your business. By combining these analytics with a clear risk management approach, companies can identify and prioritize the most critical risks, assess the potential impact, and decide on a course of action based on their severity.
Predict Demand and Optimize Pricing
Make accurate demand forecasts and avoid stocking inventory as it can be very expensive to store. On the other hand, stock-outs have an adverse impact on both revenue and customer sentiment. You can use predictive analytics to adjust pricing based on demand and offer targeted discounts, promotions, and segment-based pricing to target different consumers.
Predictive Analytics Best Practices
Organizations need to proceed with caution before embarking on predictive analytics initiatives. Here are three best practices which can help organizations in their journey.
Define Your Objectives
Predictive analytics is not a business objective in itself. It doesn’t necessarily refer to any particular technology, method, or value proposition. Rather, it is a technique employed by businesses to find value in their data. In a nutshell, predictive analytics uses machine learning data to learn from experience and predict future behavior and trends to drive better decisions.
Predictive analytics empowers your organization to optimize operations by predicting the most likely outcomes for your business. These predictions directly inform the action to take with each scenario or use case, e.g. marketing to those most likely to purchase, and identifying those most likely to commit fraud. It is therefore important to align your predictive analytics strategy with your business objectives and not just consider implementing it as to the end goal.
Build the Right Team
Don’t lead with analytics software selection – solution providers will tell you that their software offers the right features or the complete solution for all your problems. But the important thing is to identify the problems that require these solutions, like optimizing large-scale operations. The solution is just a new method that integrates machine learning data to solve real business problems. So, a predictive analytics tool is only a small part of what must be a holistic organizational process.
Rather than focusing on the analytics provider, deliver self-service capabilities and prepare your team to manage predictive analytics as an enterprise endeavor, and then allow them to select the most suitable analytics software during a later stage of the project.
Plan Your Deployment
The most common mistake that derails predictive analytics projects is to focus on machine learning before building a path towards successful deployment. These projects can be broken down into a series of steps that focus on how to deploy, the data to be analyzed, and the potential outcomes.
- Establish the business objective
- Define a specific prediction objective to serve the use of analytics in business
- Prepare the training data that machine learning will operate on
- Apply machine learning to generate the predictive model
- Deploy the model and integrate its predictions into existing operations
Note: This blog was originally posted in November 2020 and updated in September 2024.
