The banking industry is always a strongly competitive one. It is highly responsive to political and economic conditions in domestic countries and all over the world. Banks are always under the risks, therefore a key strategy for them is continuous improvement of performance by reducing costs and increasing revenues.

On the other hand, the banking industry across the world has under gone significant changes in the way the business is conducted. With the recent implementation, greater acceptance and usage of electronic banking, the storing of transactional data has become easier and, at the same time, the volume of such data has grown enormously. It is beyond human capabilities to interpret this huge amount of raw data and to effectively transform the data into useful knowledge for the company. The tremendous amount of data that banks have been collecting over the years can greatly influence the success of data mining efforts.

By using data mining to analyze patterns and trends, bank executives can foresee, with increased accuracy, how customers will react to adjustments in interest rates, which customers will be likely to accept new product offers, which customers will be at a higher risk for defaulting on a loan, and how to make customer relationships more profitable. The banking industry is widely recognizing the importance of the information it has about its customers.

Biclast solutions will let your company to use relevant aspects of your customer base to facilitate decision making processes based on advanced analytics.

Biclast solutions is ready for the banking and finance industry to implement the following tasks:

• Churn prediction and customer retention
• Scoring
• Risk analysis
• Fraud and abuse detection
• Cross-selling prediction
• Customer segmentation
• Portfolio analyses
• Exploration of financial market conditions
• And others

Transportation, as a means for moving goods and people between different locations, is a vital element of modern society. Since the earliest days of the industrial revolution, transportation has facilitated economic development by moving materials, resources, products and people.

Transportation is also connected with problems, including safety and environmental pollution. Traffic accidents are not only costly in terms of loss of life, but also in economic cost through damaged property (vehicles and others), emergency services, loss of income, medical costs, rehabilitation costs, legal costs etc. Environmental pollution has costs through damage to our natural environment, as well as financial costs linked to health impact, global warming and others.

Big data analytics refers to the application in transportation the methods of data analysis, modelling, mining and visualization of large datasets for problems solving and to discover relevant and actionable decision drivers. Applying big data analytics will create more intelligent transportation solutions.

Biclast solutions is ready for the transportation and travelling industry to implement the following tasks:

• Customer analytics and loyalty marketing
• Price discrimination and profitability analysis
• Customer behavior modeling
• Cross-selling and up-selling prediction
• New products and campaigns personalization
• Online marketing optimization
• Predictive maintenance analytics

How much can companies in the telecommunications industry benefit from big data and advanced analytics? It’s a strategic question. Every operator is looking for new ways to increase profits during a time of stagnant growth in the industry. As market is very mature, competition is fierce, especially as OTTs move into core sources of revenue. In addition, despite the fact that customer acquisition costs are high and margins are slim, capital costs are tremendous. It all pushes operators to search, research and apply innovative ways of increase revenues, and reduce costs.

There are a lot of data mining solutions in telecom industry which commonly used and applied to reduce customer churn, decrease bad debt, improve marketing effectiveness and customer experiences, identify network faults.

Biclast solutions will let your company to use relevant aspects of your customer base for tailored marketing, to develop and assess targeted campaigns as well as to facilitate decision making processes based on supply.

Biclast solutions is ready for the telecommunication industry to implement the following tasks:

• Churn prediction and customer retention
• Customer segmentation and scoring
• Marketing campaign analysis
• Fraud detection and prevention
• Customer behavior modeling
• Subscriber analysis
• Customer profitability and lifetime value analysis
• Identify network faults
• Value-based network capacity planning
• And others

The hotels and hospitality sector serves millions of travellers every day, and each one of them checks in with their own set of expectations. Meeting those expectations is the key to getting people to return, and increasingly hotel and leisure operators are turning to advanced analytics solutions for clues about how to keep their customers happy.

Additionally, although their marketing departments would be dislike admit it, not all guests are equal in the eyes of hotel and leisure operators. Some will simply check in and check out with a minimum of fuss. Others will spend dozens or thousands of dollars on fine dining, entertainments, sports activities and spa treatments. Identifying those customers with a higher overall lifetime value to a particular business is hugely important in today’s market, but a customer’s lifetime value might not be empirically obvious from observing their behavior during one visit.

Hotel industry is the industry where effective use of analytics can change dramatically how business is run. Hospitality industry is another data rich industry that captures huge volumes of data of various types. However, for most hoteliers data remains an underused and underestimated asset. Biclast solutions will help to capture and use these assets to get competitive advantage.

Biclast solutions are ready for the hospitality industry to implement the following:

• Customer profitability and lifetime value analysis
• Customer analytics and loyalty marketing
• Price discrimination and profitability analysis
• Cross-selling and up-selling prediction
• New products and campaigns personalization
• Online marketing optimization
• Demand prediction and management
• Customer behavior modeling
• And others