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Data Mining Services
Data mining is used to retrieve valuable insights (like trends, dependencies, and hidden patterns) from huge and diverse data sets. Data mining outsourcing is the practice of engaging third-party vendors to perform data extraction and analysis tasks. Providing data mining outsourcing services since 2000, ScienceSoft has helped multiple companies derive value from their data without investing in corresponding in-house competencies.
Why Outsource Data Mining to ScienceSoft
- Data science and data analytics expertise since 1989.
- 11 years in big data.
- Image analysis expertise since 2013.
- 19 years of experience in rendering data warehouse services.
- ScienceSoft is a 3-Year Champion in The Americas’ Fastest-Growing Companies Rating by the Financial Times.
- ISO 9001 and ISO 27001 certified to assure the quality of the data mining services and the security of client data.
- Domain experience in 30+ industries, including healthcare, banking, lending, investment, insurance, retail, ecommerce, professional services, manufacturing, transportation and logistics, energy, telecommunications, and more.
How Our Data Mining Services Unfold
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Business analysis
- Defining business objectives to reach with data mining.
- Outlining exact questions, hypotheses, tasks to resolve with data mining.
- Determining issues with the existing data mining solution.
- Deciding on data mining KPIs.
2
Source data preparation
- Determining data mining sources and requirements for a data set.
- Data collection and transformation.
- Data quality management.
3
Data mining models creation
- Selecting optimal data mining methods to use.
- Defining the criteria for the future model/models evaluation (output quality KPIs, KPIs related to business outcomes).
- Creating data mining models.
- Running the developed data mining models on the prepared data sets.
4
Data mining models maintenance and tuning
- Continuous monitoring and tuning of data mining models for greater accuracy.
- Developing new data mining models (also, on new data sets) to address arising business needs.
5
Reporting and alerting
- Reports and dashboards with data mining results ready for business use.
- Alerting according to the predefined rules (e.g., pricing change detection).
6
Custom data mining applications (optional)
- Developing software with data mining capabilities for self-service use.
7
Quality assurance
- Evaluating data mining quality, including the quality of insights.
- Data mining quality evaluation reports.
- Testing software with data mining capabilities.
Our Data Science and Big Data Analytics Projects
Data Science Consulting for Electric Energy Consumption Analysis and Forecasting
ScienceSoft suggested high-level software architecture and provided detailed recommendations on creating machine learning models for electric energy consumption analysis and forecasting software, which would allow electric power companies to optimize their load management and price determination procedures.
Big Data Implementation for Advertising Channel Analysis in 10+ Countries
ScienceSoft implemented a big data analytics system, which allowed one of the top market research companies to carry out comprehensive advertising channel analysis for different markets.
Development of a Big Data Solution for IoT Pet Trackers
ScienceSoft delivered a scalable IoT data management solution that enables the processing of 30,000+ events per second from 1 million devices.
Data Science Implementation for Sales Analysis and Forecasting
ScienceSoft supported a leading FMCG manufacturer by delivering science-based sales forecasting and attainable sales targets.
Development of an Advanced Data Analysis for a Regulatory Authority
ScienceSoft developed a BI solution with a robust analytical module to conduct financial market analysis and determine development and capital raising strategies, market participant analysis to discover hidden relationships between entities, trigger fraud alerts, etc.
Data Sources We Cover with Data Mining Services
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Data Mining Use Cases
By industry
- Treatment effectiveness assessment.
- Recommendations on lifestyle changes.
- Personalized care planning.
- Proactive care (definition of trends and patterns in patient condition requiring a doctor’s attention).
- Fraud detection in healthcare insurance.
- Detecting fraudulent claims.
- Predictive insurance analytics, e.g., forecasting claims, liquidity leakages, policy renewals.
- Risk assessment based on customer historical data and behavior patterns.
- Customer segmentation for offer personalization.
- Root cause analysis, e.g., change drivers for certain insurance metrics.
- Analyzing historical and real-time portfolio performance data for investment planning and portfolio rebalancing.
- Identifying market trends, e.g., in stock prices, commodities.
- LLM-powered investment sentiment analysis.
- Predictive analytics, e.g., forecasting financial returns and possible risks.
- Stock planning.
- Equipment failure prediction.
- Prediction of defects or reduced yield.
- Operations cost optimization.
- Operational capacity planning .
- Vehicle supply allocation and vehicle dispatch management.
- Vehicle maintenance data mining for failure prediction.
- Dynamic route optimization.
- Fuel consumption forecasting.
- Market monitoring insights, e.g., supply and demand dynamics, mortgage interest rates.
- Property value forecasts.
- Property and lease management insights, e.g., occupancy, lease renewal analysis.
- Insights in customer-specific preferences.
- Property investment risk assessment.
- Client segmentation for efficient service and marketing personalization.
- Identifying project management success drivers and bottlenecks.
- Insights into resource allocation optimization.
- Employee performance evaluation.
- Forecasting project resources and timelines.
- Energy demand prediction.
- Insights for grid performance optimization.
- Identifying patterns in customer service usage data for efficient personalization.
- Predicting equipment maintenance needs.
- EUR, equipment maintenance, and production rates forecasts for the upstream sector.
- Insights into supply chain, energy consumption, and inventory management (for upstream and mid-stream sectors).
- Supply-demand forecasts, insights for refinery process efficiency, predicative maintenance insights for the downstream sector.
- Analyzing guest preferences and behavior for service personalization.
- Occupancy rate and resource forecasting.
- Insights into market conditions and competitor offering.
- Identifying student-specific factors for academic success or inefficiency.
- Insights into the effectiveness of learning patterns and curricula.
- Insights for learning personalization.
- Forecasting enrollment, drop-out, and retention rates.
- Insights for optimizing resource allocation.
- Segmenting audience based on viewing habits, demographics, and other parameters to develop tailored content recommendations.
- Audience sentiment analysis.
- Identifying popular and emerging trends, e.g., genres, formats.
- Predictive analytics., e.g., for content popularity forecasting.
By business area
- AP and AR trends identification, e.g., payment process bottlenecks, late-to-pay customers.
- Identification of liquidity, credit, investment, and other risks.
- Financial fraud detection.
- Predictive analytics, e.g., forecasting profitability, tax liabilities.
- Customer segmentation based on defined parameters, e.g., demographics, industry, preferences.
- Customer sentiment analysis based on data from communication logs, surveys, social media and online review data.
- Customer churn prediction and root causing.
- Customer lifetime value (CLV) forecasting.
- Sales performance prediction.
- Identification of up- and cross-selling opportunities.
- Lead scoring.
- Insights for sales process optimization.
Marketing
- Customer behavior and buying pattern analysis.
- Customer segmentation.
- Marketing campaign optimization.
- Sentiment analysis.
- Market trends evaluation.
- Competitor analysis and tracking.
- Pricing strategies and promotion tactics optimization.
HR
- Employee behavior and performance analysis.
- Employee retention prediction.
- Employee performance management.
- Recruitment management.
- HR policies evaluation.
- Employee engagement strategy optimization.
Benefits of Data Mining Outsourcing with ScienceSoft
Transparent service delivery based on KPIs
We deliver data mining services based on the agreed quality KPIs, which may include:
- Output quality KPIs:
- Interestingness metrics (e.g., how data mining model/pattern can become beneficial).
- Accuracy metrics.
- Reliability metrics (e.g., how a data mining model performs on different data sets).
- Missing alerts.
- KPIs related to business outcomes (e.g., conversion rate, sales, accounts receivable, etc.)
- Your satisfaction score defined with interviews and questioners.
To ensure data security on multiple levels, we employ:
- Highly secure cloud facilities for data storage and processing (Azure, AWS, Google Cloud).
- Secure data transfer methods (FTP and VPN) controlled via regular health checks.
- 24/7 in-house data security monitoring.
Technologies and Methods We Use for Data Mining
Data Mining Service Options We Offer
Regular data mining outsourcing
- Get valuable business insights out of large, heterogeneous and constantly changing data sets without hiring an in-house data mining team.
- Suitable for 6+ month- cooperation
* If you don’t want to manage large databases in-house, consider adding our DWH-as-a-service.
One-time data mining outsourcing
- Gain quick insight into pressing problems.
- Try how data mining solves a particular business problem.
Embrace Data Mining Capabilities
Outsourcing data mining services, you benefit from:
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Increased ROI from marketing campaigns due to accurate prediction of campaigns’ outcomes. |
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Optimized operational performance due to defining reasons behind operational bottlenecks. |
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Increased customer acquisition rate and decreased customer churn due to accurate targeting of well-defined customer segments. |
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Quick fraud detection due to outlier analysis. |
