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Plug-and-Play IoT Solution in the Cloud

Market basket analysis is used to get insights in sales and profit margin by categories and locations. It is used behind the scenes for the recommendation systems used in many brick-and-mortar and online retailers. The learned association rules indicate the combinations of items that are often purchased together.

Credit Scoring

It is key for the financial institution to predict risks that are related to their loan portfolio to keep related losses at the required level. It is key for the financial institution to be able based on the information about the applicant to predict what size of probable loss can be generated by the applicant. BellaDati Advanced IoT Framework performs probabilistic credit scoring for loan applicants using its machine learning studio.

Telemetric Sensor Monitoring

This video demonstrates how BellaDati IoT Analytical framework collects real time data from hitch sensors that are subsequently used to improve driving style of truck drivers and to decrease damages of cargo. BellaDati analytics helps to increase the safety of cargo, decrease related costs as for example diesel consumption, truck amortization and cargo damages.

Customer Segmentation

BellaDati Advanced Analytics/ Platform will help you to segment customers to set up efficient marketing strategy to define for each customer segment to bundle the offer, at the right price, on the right place, with the right message.

Market Basket Analysis

Market basket analysis is used to get insights in sales and profit margin by categories and locations. It is used behind the scenes for the recommendation systems used in many brick-and-mortar and online retailers. The learned association rules indicate the combinations of items that are often purchased together.

Data Cleaning

High-quality data drives good business decisions, increase in revenue and saves cots. Cleaning of data is expensive, it may reach up to 40% of total project costs. Some organizations willingly leave out 20 or 30 percent of data in their analytics because bits are missing. In effect, they accept blatant inaccuracy as normal. Sweeping this multi-billion dollar challenge under the carpet is fundamentally misguided and creates shaky foundation on which to build a data-driven actions for companies or government.

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