Data analysis and modeling
Data analysis outsourcing service
This service is for clients who have built up a store of data but don't know how to analyze it or use it effectively. Using a variety of [R] data aggregation, analysis and modeling techniques applied to both financial and non-financial data (such as research and surveying results), we identify pertinent trends and produce meaningful solution-oriented findings.
![Finance](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/zaimukankeibunseki.jpg)
Finance
- Performance forecasting based on historical data
- Statistical survey to identify optimal pricing of rental charges
![Manufacturing](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/seizoukeibunseki.jpg)
Manufacturing
- Statistical analysis to identify bottleneck processes
- Statistics-based quality control studies
![Predictive modeling](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/yosokumodel.jpg)
Predictive modeling
- Demand forecasting
- Predicting the probability of a tax audit
![Marketing](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/marketingdata.jpg)
Marketing
- Identifying key motivators and factors in raw survey and questionnaire data
- Forecasting customer numbers and sales figures based on customer attributes
- Efficacy analysis of multiple advertising media formats
![Client base](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/kokyakubunseki.jpg)
Client base
- Clustering customer trends
- Identifying key correlations between customer attributes and purchasing patterns
![人事データ分析](https://silom-p.com/wp-content/themes/lightning/assets/images/pc/data/jinjidata.jpg)
HR
- Boosting employee retention rates through predictive modelling of employee departures
- Identifying key factors in historical data on employee departures
Main methodologies
Coefficient of correlation, time series analysis, cross-tabulation, multiple regression analysis, logistic regression analysis, decision tree analysis, random decision forests, cluster analysis, factor analysis, principal component analysis, correspondence analysis, covariance structure analysis