BKT Bank x B2Metric Success Story
The roots of Banka Kombëtare Tregtare can be found on November 29th 1925, when the first branch was established in the oldest Albanian financial institution in Durres.
The largest and oldest bank in Albania, BKT, was founded in 1993 by Banka Tregtare Shqiptare and Banka Kombtare e Shqipris.
The privatization process of BKT was completed in 2000 and on June 30th 2009, Alk Holding became the sole shareholder of BKT.
Challenge Overview
Analyzing Vast Customer Data for Informed Decision-Making
To draw accurate predictions and insights from the sheer volume and diversity of data, including customer demographics and bank account information, a meticulous approach is required. Identifying patterns and trends that can guide strategic decision-making requires thorough investigation.
Complexities in Predictive Analytics
Predictive analytics is complex when big banking data is involved. It is difficult to extract meaningful information from such a vast dataset, ensuring accurate predictions based on customer behavior, financial patterns, and demographics. It is often difficult to navigate the complexities of predictive modeling using traditional methods of analysis.
Inability to Define Different Types of Churn
One of the main problems is that BKT Bank needs different definitions of churn, but they were having difficulty interpreting the big banking data effectively. To accommodate different churn definitions, there was a need for different predictive models.
The Solution: B2Metric Customer Journey Analytics
Customer Churn Prediction
Using a variety of machine learning models, BKT Bank developed a comprehensive solution to predict customer churn. Customer data was carefully examined to ensure a robust approach to prediction. Several rigorous validations of the model were conducted, guaranteeing high accuracy and reliability.
Adaptive Learning
Using an adaptive learning approach, the model remained continuously up-to-date as new data accumulated, ensuring that predictions of customer churn were accurate regardless of changes to data or customer behavior.
Customer Micro-Segmentation
Aktif Bank's marketing and growth teams were empowered by B2Metric customer micro-segmentation, which was meticulously crafted by our solution. Detailed reports provided invaluable insights into each micro-customer segment, making it easy to analyze and optimize growing marketing budgets.
Optimizing Marketing Budgets
Through the use of micro customer segments, Aktif Bank was able to optimize marketing budgets and achieve customer satisfaction and loyalty through targeted marketing strategies.
Data Preparation and Detailed Analysis
The NPL scoring model was meticulously cleaned from dirty data from past loan applications, ensuring accuracy and reliability. A detailed analysis of customer information collected during loan applications further refined the predictive capabilities of the model.
Optimized Loan Application Approvals
With B2Metric NLP Scoring, the strategic use of past loan application data and demographic characteristics allowed the bank to optimize its loan approval process, ensuring resources were allocated efficiently.
Benefits
27% Customer Churn Reduction
By leveraging the insights gained from accurate B2Metric ML-Based churn predictions, BKT Bank implemented proactive measures to reduce customer churn. The result was a notable 27% reduction in churn, showcasing the effectiveness of the preventative actions taken. BKT Bank can maintain customer relationships now as well as save substantial amounts of money due to this reduction.
Challenges in Banking
Navigating the complexities of customer engagement, retention, and marketing spend optimization in the fintech industry.
The Solution: B2Metric Adaptive Learning for Fintech
To tackle these challenges, Papara leveraged B2Metric's advanced machine
learning models, designed to optimize fintech marketing strategies.
Key Features of B2Metric’s
Analytics Solution for Airlines
In a world where passenger expectations are constantly rising,
how can airlines stay ahead?
FAQ
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The Challenge Overview
Accurate Analysis of User Segments:
Pokus must accurately analyze user segments to gain insights into their preferences, behavior patterns, and engagement levels. This involves segmenting users based on factors such as activity frequency, preferred training categories, session duration, and interaction with the app.
Without precise segmentation, it becomes challenging to tailor marketing strategies and content to meet the specific needs of each group.