AI with Predictive Analytics can drive innovation across multiple industries. Predictive analytics is not limited to a single niche; its use cases and potential implementations are found across industries and verticals.
There are few use cases mentioned below that combine AI with Predictive Analytics to improve efficiencies and improve customer service
In the banking sector, predictive analytics are increasingly being applied to detect possible fraud by observing and analyzing the most prevalent operational trends concerning transactions, trades, and payments. Predictive models work to explain the hidden patterns of data in both structured data (transactions) and unstructured data (reviews, emails, and forum entries). Few banks have monitoring systems in place that regularly scan data for lead generation, identify opportunities for cross-selling and upselling, customer retention, and customer relationship management.
Predictive analytics is a new word in agriculture with the immense untapped capacity to enhance agronomic opportunities, such as product decisions, product amounts, and decision making profitability. Algorithms can be easily implemented from historical and future data based on the measured field to field, acre to acre, and within acre variation to affect processes and harvesting.
Retail companies function in a highly competitive environment to boost consumer retention rates, anticipate and prevent customer turnover. It reduces customer acquisition costs and tailor marketing strategies to raise revenues. All of this can be tackled with more in-depth, data-driven insights gathered from consumer data from phones, social media, shops, e-commerce sites, and more purchases.
Churn management is a matter of concern in the telecommunications industry. In the telecom industry, predictive algorithms are used to formulate methods for reducing or preventing churn. Telecom businesses use Automated Customer Relationship Management (ACRM), Fraud Reduction, Bad Debt Reduction, Demand Optimization, and Call Centre Optimization predictive analytics.
Predictive analytics has considerable untapped potential in the field of personalized learning experiences for the education sector. Educators can identify patterns of learning behaviors for students and customize the academic expertise and course schedules they would be most interested in learning.
When data such as consumer behavioral habits, financial wellbeing is identified in advance, the reliability of claim management improves dramatically. It will streamline the process that has traditionally taken weeks and even months to help reduce risks for the claims team. It also allows insurers to evaluate their insurance processes based on historical data and to make informed decisions to maximize performance.
Machine downtime could cost thousands of dollars an hour for businesses. This makes predictive analytics so crucial for the manufacturing industry to predict downtime when a computer goes down to schedule hours or days before downtime for preventive maintenance.