Challenges

1 / Effective segmentation. Accurately segmenting clients to pinpoint the most profitable groups in the tech industry.

2 / Customer retention. Developing strategies to minimize client turnover and strengthen retention.

3 / Data analysis. Analyzing extensive customer data to discern preferences and trends in tech solutions.

4 / Predictive modeling. Constructing models to forecast future client value and tech needs based on current data.

Goals

1/ Identify the most profitable customer segments to tailor technology solutions and marketing strategies more effectively

2/ Develop models reflecting the common attributes of significant clients for future targeting

Results

By segmenting customer transaction data based on brand occurrences, the bank achieved a 15% increase in customer engagement.

The creation of heat maps to track payment card usage geographically led to a 20% improvement in targeted marketing efforts.

The launch of co-branded bank cards, informed by popular brands in transaction data, resulted in a 25% increase in new card sign-ups and a 30% rise in transaction volumes with these cards.

Selling depersonalized transaction data to retailers opened a new revenue stream, enhancing the bank’s profitability by 5%.

Challenges

1 / Data integration. Seamlessly integrating various data sources into a unified data lake.

2 / Transaction monitoring. Managing and analyzing the vast number of daily transactions.

3 / User behavior analysis. Understanding client actions and payment details across multiple channels.

4 / Real-time analysis. Ensuring the timely and effective analysis of incoming data for instant decision-making.

5 / Fraud detection. Identifying and preventing suspicious transactions and patterns of money theft.

Goals

1/ Analyze transaction history, client actions, and payment details for insights

2/ Set up predictive analytics

3/ Enhance the monitoring department’s efficiency with an intuitive interface for rule generation and editing

Results

1/ Our service advanced algorithms that enabled instantaneous insights, allowing for real-time fraud detection.

2/ The implementation of predictive analytics within our service allowed for a 90% confidence level in forecasting customer trends, utilizing sophisticated data modeling.

3/ Rounding loss fraud identification. Detected a unique fraud scheme involving multiple small transactions in different currencies, exploiting rounding differences.

4/ Predicted customer financial trends with a 90% confidence level, greatly enhancing foresight in fraud prevention.

Challenges

1 / Customer segmentation. Effectively using data to segment customers for targeted cross-selling and up-selling.

2 / Optimizing channels for clients. Understanding and optimizing customer interactions across diverse channels.

3 / Customer loyalty. Ensuring high levels of customer satisfaction and loyalty in a competitive market.

Goals

1/ Successfully conduct customer behavior analysis

2/ Use data-driven strategies to improve customer satisfaction and trust

3/ Predict customer departures and create targeted marketing campaigns.

Results

1/ A significant 20% increase in customer loyalty scores, as well as an improvement of 10 points in the Net Promoter Score (NPS). These results stemmed from the analysis and segmentation of the client base.

2/ A 20% increase in customer usage of web and mobile banking channels, achieved through the analysis of the elimination of user barriers.

3/ Development of a model reflecting the common traits of the most profitable customers from past years, so the bank could focus special retention programs on them, enhancing customer value.

4/ Segmentation and development of “customer profiles”. 

These profiles identified groups of customers with preferences for certain banking services, allowing for targeted and efficient advertising and marketing activities.