Data must be understood as an asset allowing your organization to be on the head-start at the moment of offering your products or services.
Data analysis is the process that aims to highlight useful information in the data, to suggest conclusions, and to support the decision making of a business or organization.
With the different types of analysis we are able to:
- Better understand the business and the industry.
- Formulate the right questions.
- Model data in order to understand past and current business performance, so future behavior can be predicted. We do this by applying data mining and simulation techniques, and by finding rules and patterns hidden in your data.
- Understand the best and the worst-case scenarios.
- By applying modeling tools and techniques, we are able to isolate the causes for a specific event that took place, and detect further business opportunities in the areas of highest priority for the business.
Our broad experience in business and data allow us to work in two directions:
- We formulate the right questions and then find the data suitable for answering them.
- We explore your data in a comprehensive way to find the business opportunities hidden in it.
As an example, we can list the following applications:
- Análisis de recompra
- Customer Segmentation and RFM modeling
- Customer acquisition and retention optimization
- Up-sell and Cross-sell models
- Product and basket association
- Product recommendation techniques
- Merchandizing and Pricing optimization
- Shipping costs optimization
- Discount codes optimization
- Product discoverability
- Customer lifetime value and Customer Profitability
- Cohort analysis
- ROI and attribution modeling
- Fraud analysis
- Anomalies detection
A preliminary step to analysis is to get the available data, audit them, clean them and finally consolidate them. If some of these data are not available, we propose the best implementation techniques to start collecting them as soon as possible.
To fulfill these services we rely on the best-of-class tools, such as:
- R (#rstats)
- Python
- Knime, Rapidminer, SPSS, Clementine
- From spreadsheet or Excel to a data product (Shiny, Dash, etc)
