There are four main types of data analysis:
- Predictive analysis
- Prescriptive analysis
- Diagnostic analysis
- And descriptive analysis
This first category allows a company to detect trends or correlations. Without predictive modeling of the data collected, these correlations remain unnoticed.
In the context of advertising campaigns, predictive analysis or statistical analysis is ideal for determining various correlations. For example, it is possible to identify a correlation between a conversion rate on the one hand and a specific area of interest or a specific geographical area.
This second type of analysis will allow you to make the right choices. In other words, it is decision support. By drawing on a large volume of data from different data sources, advanced analysis allows, via Machine Learning, to answer questions asked by decision-makers.
A well conducted prescriptive analysis is then relevant to decide on the best action to take. It is therefore of real strategic importance.
Statistical analyses are, in the case of diagnostic analysis, generally used a posteriori. By using appropriate data analysis tools, the understanding of the information allows you to identify the causes of an event.
Through quantitative and qualitative data analysis, diagnostic analysis can be used to detect a problem before it occurs or to understand the data in a report.
Finally, descriptive analysis is perfect for producing comprehensive, qualitative reports as part of a business intelligence approach. Analyzing statistical data and displaying it in dashboards makes it easier to make decisions on specific issues.
This type of analysis is used for marketing reports. They allow, for example, to better identify an audience on social networks or any web platform.