By means of the Correlation Analysis it is possible to understand how KPIs correlates to the evolution of opinions, sentiments and the Social NPS. Moreover, once established their correlation, it is possible to verify how these time series data are influenced by each other.
The Correlation analysis shows how different time series relates to each others and estimates whether there exist possible linear (or non-linear) relationships among them.
For instance, it is possible to quickly visualize how proprietary data or business KPIs relates to Google Trend data, Social NPS, sentiment trends and topic trends. This way one can immediately exploits interesting positive and negative correlations.
Moreover, the Lead-Lag Analysis (also known as Leader-Follower Analysis) is an advanced and powerful method that allows to understand how different series relates temporally.
In particular, it is possible to see whether there exist anticipatory effects, that is to verify if a series can be identified as leader (implying it anticipate some trend) or as follower (meaning it is anticipated by some trend).
The graph shows that the imported series 3 anticipates the negative sentiment, while the positive sentiment follows the research about google trend 1.