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 reliably forecast trends and make accurate future predictions.
Top Benefits of Predictive Analytics
- 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
- Reduce risks
Predictive Analytics Use Cases
Predictive analytics enables businesses to analyze large amounts of data to identify potential events and opportunities before they occur. The real value of predictive analytics can be understood by learning the major use cases that they support and dive into those use cases for the applicable industries.
1. 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.
2. 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 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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 common predictive analytics mistakes that organizations make and the best practices to help you avoid them.
1. 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 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 predictive analytics as to the end goal.
2. Build the Right Team
Don’t lead with software selection – analytics 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 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.
3. 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. Predictive analytics projects can be broken down into a series of steps that focus on how to deploy predictive analytics, what you need to predict, and what data you need to predict it.
- Establish the business objective
- Define a specific prediction objective to serve the business objective
- 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