Livyoung Realtech is a data analytics company that works with companies from more than 30 industries to integrate, combine, and analyze diverse data kinds from multiple data sources to meet their most specific departmental and organizational needs. Some basic types of analytics are as follow:
- Tracking a company’s earnings, costs, and profitability.
- Analysis of profitability and management of financial performance.
- Setting a budget, creating long-term company plans.
- Anticipating and controlling financial risk.
- Predictive modelling and study of consumer behaviour.
- Segmenting customers to create specialized sales and marketing efforts.
- Offers for personalised upselling and cross-selling to increase customer lifetime value.
- Risk management for managing customer churn and attrition.
- Examination of customer sentiment.
Sales and product analytics
- Analytics for sales channels.
- To create pricing strategies, use pricing analytics.
- The detection and forecasting of sales trends.
- Analysing the performance of a product.
- Seeing how customers engage with a product to spot the problems that cause churn.
- Doing benchmarking against rivals.
- Monitoring and tracking of assets in real-time.
- Establishing asset maintenance plans, predictive and preventative maintenance.
- Preparing asset purchases.
- Analytics of asset use, planning, and scheduling of asset replacement/disposal/modernization plans.
- Monitoring and analysis of departmental and employee performance.
- Examination of employee satisfaction and experience.
- Management and optimization of the retention plan for employees.
- Analysis and improvement of employee hiring strategy.
- Cost analysis of labor.
Supply chain analytics
- Anticipating and planning for customer demand, as well as identifying demand drivers.
- Evaluation and monitoring of supplier performance.
- Route optimization using predictions.
- Deciding on the ideal stock level to satisfy demand and avoid stockouts, inventory planning, and management.
- Recognising patterns and trends throughout the supply chain for improved risk management of the supply chain.
Transportation and logistics
- The examination of inbound goods, customer delivery schedules, vehicle availability, and employee shift schedules is used to plan and optimize operational capacity.
- Analytics for predicting car maintenance (failure prediction, recommendation of maintenance actions, etc.).
- Forecasting the demand for cars.
- Estimating the ideal fuel requirements by studying driving habits.
- IoT data analytics for secure cargo delivery (data on temperature, humidity, etc. of the cargo; data on driver behaviour; data on vehicle condition, etc.).
- Analysis and improvement of overall equipment efficacy.
- Quality improvement of the manufacturing process.
- Scheduling for equipment upkeep.
- Forecasting and management of energy use.
- Root-cause investigation of production loss.
- Monitoring of patient health status; condition-based alerts.
- Optimization of patient care.
- Risk evaluation of the patient and recommendations for an individual care plan.
- Determining trends and patterns in a patient’s condition that need a doctor’s attention through proactive care
- Detection of fraud in health insurance.
- Prediction of the workload for medical staff and optimization of shift work.
- Optimising the use of equipment and space in the clinic.
- Analyzing the operation of retail businesses and tracking their sales and profits.
- Forecasting and analysis of demand.
- OptimiZing inventory at multiple levels.
- Planning and optimizing assortment and merchandising.
- Data-driven suggestions for the best kind of product promotion.
Our Solutions – from Automation to Advanced Data Analytics
Data integration and data warehousing
- Design and implementation of extract, transform, load (ETL) or extract, load, transform (ELT).
- Implementation of data governance (security, quality, availability, etc.).
- Design and deployment of data warehouses and data marts.
- Setting up and supporting a big data infrastructure.
- Management of big data security and quality.
- Acquisition, analysis, and reporting of big data.
- Management and preparation of data.
- Machine learning (ML) model development and optimization, including deep learning
- Modelling for data mining development and optimization.
- Artificial intelligence (AI) systems design and implementation.
- Creation of software for image analysis.
- Design and deployment of the infrastructure for business intelligence and data analytics.
- Analytics querying and reporting on a sporadic and regular basis.
- User interface using natural language.
- Using an interactive dashboard.
- Both bespoke and ready-made images.
- Various visualization methods (symbol maps, line charts, bar charts, pie charts, etc.)