Introduction
Many businesses use predictive analytics as a core part of their business strategy. Predictive analytics helps them make the best out of their customer data, such as buying patterns, customer preferences, and other buyer data, to gain insights into the customer’s future purchases.
Organizations use predictive analytics to make informed business decisions, even for internal monitoring purposes. For example, a Deloitte report highlights that 44% of organizations use predictive analytics to gather information on the working patterns of their workforce. These patterns help the organization optimize resource utilization and better manage the workforce.
Understanding customer needs
Predictive analytics can help businesses with insightful data, such as predicting the potential sales of a new product, discerning its popularity given the current market dynamics, and analyzing how to best promote the product or service and emerge as a winner.
Predictive analytics also enables businesses to identify future demands in a niche and explore opportunities for new products or services. It helps them cater to the customer’s precise needs and deliver value with each touchpoint the business has with its audience.
Risk mitigation
One of the most useful applications of predictive analytics is risk management. Unaddressed or underappreciated risks may lead to operational malfunctions and subsequent costs to the business.
Predictive analytics help businesses identify potential risks and offer ways to avoid or mitigate them. It enables enterprises to analyze and assess possible exposures such as the following:
- The uncertainty of entering a new geography or market for business expansion
- Financial factors related to new investment in a product or service
- Develop root cause analysis capabilities to investigate catastrophic events and reverse engineer RCAs to avoid risks
Predictive analytics benefits the banking and finance sectors, helping them create an accurate picture of their customers, and assess credit risks and liabilities. As a result, it helps them make better lending decisions.
Cost optimization
One of the biggest priorities for any business is to optimize its costs across all departments.
Predictive analytics can play a role in cost optimization. For example, imagine that a business needs to decide whether or not it should proceed with an R&D expense for a revolutionary new product—something that carries a significant investment. Companies can feed the latest trends and market data into their predictive analytics framework to assess the need for such products. Augmenting the results of this analysis with social listening can help the business observe and predict its potential demand.
Based on this information, a business can easily chart a demand profile to either justify the R&D investment or cancel or alter the initiative. In similar ways, companies can reduce various costs across the organization.
Improvement in operational efficiency
Predictive analytics platforms are widely used in the supply chain arm of businesses. However, managing inventory and synchronizing it with production and manufacturing is a delicate process. For everything to work in tandem successfully is a big challenge for companies. Predictive analytics helps maintain lean philosophies throughout the supply chain, manufacturing, and inventory.
Predictive analytics highlights the potential demand over time so businesses can plan material acquisitions and schedule production. In turn, this helps reduce material wastage, holding costs for overstocked inventories, and align post-production activities to accelerate operations.
Fraud detection
Today’s financial institutions have digitized most processes and operations, leaving a data trail for all transactions. These institutions follow specific protocols and procedures for everything in the operational flow. This creates a set of use patterns from which predictive analytics can identify and extrapolate fraudulent activities, non-standard transactions, or criminal behavior. This revolutionizes how financial institutions function and reduces fraud activities.
Industry-specific benefits of predictive analytics
Predictive analytics directly benefit various industries in the following ways:
- Retail: Personalize product recommendations to customers.
- Health: Predict a possible infection outbreak by analyzing historical trends and identifying their progression.
- Sports: Predict team performances and alter game plans accordingly.
- Weather: Deliver more accurate weather forecasts.
- Energy: Determine maintenance needs of energy equipment that can lead to fewer power outages.
- Internet of Things: Predict equipment health and MTBF in facilities when you enable communication between devices.
Challenges in predictive analytics
While predictive analytics has many benefits, it takes effort to achieve them. Businesses will have to navigate various challenges before they reap positive results.
Expertise
Building predictive analytics models requires coding expertise in modern languages such as Python. In addition, businesses need to hire data scientists to work with the extensive project lists requiring iterations for each predictive model update. A high demand for these skills and a low supply of skilled coders make it challenging to build predictive analytics models in-house.
Integration and Compatibility
Predictive analytics models are usually standalone and customized for individual businesses. Integrating standalone models into existing workflows and implementation is challenging, and adoption takes time. It takes longer for the tool to “learn” your enterprise data and generate usable insights. These are essential considerations that can affect the time it takes to bring a system online and its success rate.
Conclusion
Predictive analytics is an advanced business solution that helps with strong, data-backed decision-making. The phenomenal benefits of predictive analytics are cost optimization, fraud detection, and improved internal efficiencies in an organization.
If your business is ready to implement a predictive analytics solution, you can lean on PreludeSys’ expertise to build a predictive analytics framework quickly. For more information, visit PreludeSys.
