Data Architecture

Retailers use demand forecasting with region-specific features to optimize product selection, increase sales, and reduce inventory losses. Calligo’s solutions use safe-harbor data anonymization methods to extract maximal value from customer data while maintaining privacy and avoiding legal risks.

Customer segmentation helps retailers market more effectively, improve product selection, lower risks, and understand expansion opportunities. Calligo’s machine learning solutions use customer data, external data, and domain expertise to create valuable customer groupings that inform various business areas.

Calligo provides data solutions to optimize various aspects of healthcare operations. Its services include improving the STAR rating of healthcare providers through prescriptive solutions, using predictive models and time-series analysis to prepare for a health crisis, optimizing staff scheduling to reduce labor costs while meeting patient needs, and minimizing inpatient surgical costs while maximizing revenue. Calligo also offers solutions to improve supply chain logistics to reduce the risk of inadequate supplies and reduce patient wait time through predictive models and optimization.

Moreover, Calligo offers solutions to reduce readmission rates and improve ER admittance, which can increase revenue in shared-cost models and improve the STAR rating. These services rely on predictive models, Monte-Carlo simulations, and machine learning models to analyze data and provide actionable insights to healthcare providers. With Calligo’s advanced data solutions, healthcare providers can make informed decisions to improve patient care, reduce costs, and optimize their operations for better outcomes.

Calligo provides transportation and logistics solutions that use predictive models, time-series analysis, Monte-Carlo simulations, and optimization to solve complex problems such as price optimization, cost predictions, and mileage optimization. These solutions help businesses maximize profit margin and reduce costs related to fuel, labor, and maintenance.

By utilizing machine learning algorithms, Calligo provides data-driven decisions that consider fuel costs, labor costs, shipping demand, and the availability of other opportunities. Predictive models and optimization are also used to minimize mileage driven and maximize profitability per truckload through mileage optimization and opportunistic loading.

Calligo provides data-driven solutions to optimize different aspects of telecom operations such as call center staff scheduling, market penetration analysis, store location optimization, service interruption detection, and customer segmentation. The use of predictive models and optimization leads to reduced costs, increased revenue, and higher customer satisfaction.

By leveraging machine learning solutions, Calligo can efficiently meet customer needs and optimize labor as call center needs change. Understanding market penetration using predictive models and time-series analysis helps identify high-potential markets that yield the best return on investment.

Calligo is a data science company that helps non-profit organizations improve their decision-making process. They use predictive models, time-series analysis, and economic modeling to forecast the value of investments and marketing impact. Their machine learning solutions use data from new markets, employee and resource data, to predict outcomes, helping organizations choose the most advantageous new markets to pursue.

Calligo also uses predictive modeling to determine the best targets for fundraising efforts, to allocate labor efficiently, and to find the most efficient routes and methods for product shipping. Finally, they use optimization, predictive models, anomaly detection, and collaborative filtering to match service providers with service recipients, reducing risk, and increasing the number of services rendered.

Calligo provides machine learning solutions for the financial industry, including fraud detection, automated data extraction, customer segmentation, risk assessment, and product recommendations. Fraud detection uses clustering, anomaly detection, and time-series analysis, while OCR algorithms facilitate automated data entry. Customer segmentation uses clustering and collaborative filtering.

Risk assessment and underwriting involve predictive models and time-series analysis, while Calligo helps capture the best return on marketing investments using predictive models, A/B testing, and customer segmentation. Predictive models, collaborative filtering, and time-series analysis are also used to generate additional revenue through product recommendations and execute targeted marketing for reduced costs and increased revenue.