What Are The Various Use Cases For Predictive Analytics?

Organizations undergo various functions in order to ensure smooth and hassle-free functioning. They have to make the right & quick decisions, implement competitive strategies, understand the market dynamics, analyze the competitors, and many more. This puts much pressure on the organizations and may hamper the routine functioning instead of improving it. To avoid such adverse events in the business, the need and importance of Predictive Analysis come into the picture.

Predictive Analytics enables organizations to analyze large amounts of data to identify opportunities and potential events before they occur. The actual worth of Predictive Analytics can be understood by its use cases.

1. Improve Customer Retention

Organizations need to attract new customers to replace those who may leave to avoid any loss in revenue. The cost of acquiring new customers is usually higher than retaining existing customers, and thus, it can be expensive to acquire new customers. However, with the right set of competitive strategies and a keen understanding of the buyer’s persona, you can reduce the cost of acquiring new customers with the help of Predictive Analytics. With the aid of Predictive Analytics, you’ll know when a customer will leave your brand and start working in favor of new customer acquisitions.

2. Identify Profitable Customers

Marketers need to identify the target audience that spends more. This helps businesses earn more profits and grow in the long run. This information can only be gained with the help of Predictive Analytics, which allows organizations to optimize their overall marketing spending and channel their efforts on acquiring customers that help in generate maximum profits and earn the highest lifetime value.

3. Improve Customer Segmentation

Businesses have multiple demands and needs in order to segment their customers based on various criteria that are essential for the business. Leveraging the power of Predictive Analytics, businesses can use the data to primarily focus on the right target audience, increase revenues, and ultimately take the business to reach new heights.

4. Predict & Quantify Risks

Predictive analytics can identify potential risk areas by identifying patterns and trends in your data and making predictions on how these risks can affect your business. By integrating these analytics with a transparent risk management approach, organizations can identify and prioritize the most critical risks, assess the probate impact, and decide on a course of action based on their intensity and severity.

5. Perform Predictive Maintenance

Combining Predictive Analytics and IoT in asset-intensive industries, organizations can predict and plan for all maintenance activities and expenses in advance. It is usually done by capturing and analyzing the data generated by the machinery and equipment, allowing you to reduce unnecessary preventive maintenance costs, extend your assets’ lives, and avoid critical downtime.

6. Improve Decision Making

Besides identifying and defining the most profitable consumers 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 channels to connect with these customers.

7. Predict Demand & Optimize Pricing

Making accurate demand forecasts and avoiding piling up inventory as it can be expensive to store. On the other hand, stock-outs have a negative impact on customer sentiment and revenue. You can leverage Predictive Analytics to adjust pricing based on demand and offer promotions, discounts, and segment-based Pricing to target different consumers.