Table of Content
1. Predictive Analytics
2. The Applications of Predictive Analytics for Financial Services and Banking Industries
3. Personalized Approach to Customers
4. How B2Metric Helps Businesses in the Finance and Fintech Industries
5. Conclusion
Predictive analytics is advanced analytics that uses past data integrated with data mining techniques, machine learning, and statistical models to predict future outcomes. Predictive analytics is working well in various industries. With the ability to forecast future spending, organizations can adjust their spending. This enables organizations to reduce spending, find customer patterns in datasets, and determine risks and opportunities while maintaining a constant revenue stream.
Predicting the return on investment and effectiveness of marketing measures is the first application of predictive analytics in businesses. Who spent months developing a campaign and wants it to fail? This technology will help you succeed sooner or later.
This option works by extrapolating past campaign data into the future. This software can provide insights into KPIs such as sales, churn rate, conversion rate, and other metrics.
Businesses collect data from both online and offline channels to reach these insights. To gain deeper insights from these datasets, marketing, growth, and data teams use various techniques, including Marketing and Product Analytics Tools.
Gain meaningful insights: Segment your customer database to classify your customers based on their differences.
Let Existing, and Potential Customers Retain Target customer segments with relevant offers by analyzing customer interests, past purchases, and profiles.
Improve Conversion Rates: It enables businesses to measure customer value creation and improve conversion rates using effective and efficient customer retention approaches.
The Applications of Predictive Analytics for Financial Services and Banking Industries
The demand for big data in the fintech field is increasing. Additionally, big data in the financial technology industry leads to open banking. Almost every successful financial and banking organization relies on automated predictive analytics to run their business as it strengthens and simplifies data to help companies to be profitable.
Personalized Approach to Customers
Financial companies are entirely dependent on their customers. They are their most valuable asset. According to his McKinsey & Company Report for 2018, personalized experiences have increased his fintech industry revenue by 15%. Personalization improves performance and improves customer outcomes. Fast-growing companies earn 40% more income from personalization than slower-growing companies. Also,71% of consumers expect personalized business interactions, and 76% feel frustrated when they don't.
Big data analytics in the finance and fintech sectors allow companies to take a personalized approach when handling individual customer cases. Banking apps, in particular, rely on big data to gather information about customers' characteristics and behavioral features and deliver highly customized notifications. Financial institutions collect information from multiple channels, including mobile apps and websites.
Personalization matters more than ever before
The surge in online interactions since the pandemic outbreak has raised expectations. Consumers have become exposed to the personalization practices of e-commerce executives, raising the bar for everyone else. Consumers have seen personalization as the default interaction standard from the web to mobile to face-to-face interactions. So, predicting customer behavior is crucial for companies not to lose customers.
A comprehensive approach to data-driven growth and personalization1
- Opportunity Identification: Business growth across the customer life cycle, where to focus, how to start.
- Quick activation and Optimization at scale: Making AI-Based decisions to provide for activation among all channels and touchpoints.
- Martech and Data Utilization: Technical capabilities help companies solve customer use cases rapidly.
Fraud Detection
Real-time analytics help organizations see the nuances of user behavior, attitudes, and fraud detection. AL-based technology can analyze variances based on user transaction history and public data such as social media. For example, fintech platforms automatically block transactions, large cash withdrawals, or access from unusual locations until the customer confirms their behavior.
Risk Management
Forecasting and risk management are not new concepts for financial institutions that have always considered a risk when approving loans and making other decisions. The difference is that risk analysis no longer needs to be done manually. This helps reduce errors and biases while freeing up critical resources. Big data analytics ensures better risk assessment by integrating data from different sources. In particular, it helps identify and anticipate risks that could harm your business.
- Which Bond accounts will have their balances reset,
- It is predicted how long the account will remain,
- Customer segmentation and CRM infrastructure in the bank,
- Customer campaign management, Smart scoring, and Instant bidding,
- Estimating customer churn and Setting cross-selling.
How B2Metric Helps Businesses in the Finance and Fintech Industries
B2Metric serves as an augmented analytics solution that is a unifying layer of a platform that orchestrates customer journeys across all interactions. B2Metric AI/ML-based customer journey analytics solutions can be implemented and adapted seamlessly if your company has different communication channels such as Mobile apps, digital media, agency channels, etc. So, we can apply all of the similar or other scenarios. To exemplify, we can ingest the customer events and all touch points into the B2METRIC IQ Customer Journey Analytics platform via web or mobile applications to gain valuable insights and actions from raw data. Then, marketing & product teams at the company can follow all customer journeys. It is more pivotal for your business because before applying and sending such campaigns to your customers, the executives should know which campaign should be applied for different segments to make the campaign cost-effective. Thus, by using the B2METRIC IQ platform, your business will make an understanding of return-of-investments and budget optimizations.
The demonstration will include the following use cases for Finance and Fintech industries;
- 360 degrees Customer Journey Analytics,
- Customer Propensity Prediction,
- Customer Micro-Segmentation and Churn Prediction,
- Online and digital Customer Retention Prediction,
- Campaign Optimization with Intelligent CRM,
- B2B & B2C Credit Risk Scoring,
- Banking Customer Next Best Actions,
- Customer Propensity Predictions.
Conclusion
Predictive analytics is a difficult-to-adapt but impressive technique that can efficiently predict consumer behavior and help businesses maximize their return on investment when fully embedded in the right marketing strategies.
Read here for an overview of B2Metric IQ and how it can benefit your business.
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