Case Study: Finance

The text mining techniques allows to capture and understand people thoughts, opinions and preferences on social networks and web with respect to a certain discussed topic.

In particular, a vertical analysis focuses on a specific market sector of interest, on a certain brand and its competitors, or on desired products of that industry. All the tools are provided to understand the business deeper from a web perspective.

Project Details

Sector: Finance
Period of analysis: 1 Year
Total analysed mentions: 400.000
Sources: Twitter, Reddit, Tumbrl, News, Blogs, Forums, Instagram

Social NPS Analysis

The Social NPS (Net Promoter Score) is an index that varies between -100 and +100, where the 0 represents the turning point from negative to positive perception. This indicator allows to compare and rank the feelings related to certain brands or products.

The Ranking tab allows to easily compare the Social NPS associated to the Brand and its competitors (or different products) and to directly rank them according to the social perception.
It is clear, in this example, that Brand 3 is the most appreciated whith respect to the others. Moreover, the NPS of Brand 2 is also the only one to be negative.

The Daily View instead shows how the NPS varies with time, revealing possible trends. In particular, in this example associated with some digital payment methods, Line Pay is the only one to be appreciated along the whole period.
It is usually of interest to analyse deeper the NPS trends using a qualitative peak-and-trough analysis. This specific analysis helps to reveal the underlying reasons related to highs and downs of the NPS.

Correlation & Lead-Lag Analysis

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.

Web Opinion Indexes: the new KPI

A Web Opinion Index (WOI) is a synthetic indicator that translates unstructured textual data into structured quantitative information that synthesizes the topic of interest. Essentially, web data are analysed and converted into numeric values by means of the artificial intelligence, and then normalized through well-known statistical techniques.

The result is an index that generates valuable business insights and is both easy to understand and to use. This new KPI, the WOI allows to monitor and compare the evolution of relevant topics.

WOI can be created for every desired sector, brand or products.


Recent Analysis