Case Study: Retail

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: Retail
Period of analysis: 1 Year
Total analysed mentions: 4.000
Sources: Twitter, Reddit, Tumbrl, News, Blogs, Forums, Reviews

Sentiment Analysis

By means of the Sentiment Analysis it is possible to interpret peolpe feelings from complex textual data. This way the sentiment is translated to quantitative information that generate useful business insights.

The General View shows the estimated aggregate level of sentiment of the whole period of analysis. The positive sentiment has a higher percentage than the negative, implying a  good perception, on average on the web, of the Brand.

The Weekly View instead allows to see how the sentiment varies with time, revealing possible interesting trends.

It is usually of interest to analyse deeper the different sentiment paths using a qualitative peak-and-trough analysis. This specific analysis helps to reveal the underlying reasons related to positive and negative opinions.

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 1 is the most appreciated whith respect to the others. Moreover, the NPS of Brand 4 is also the only one to be negative.

It is also possible to extract a temporal view which shows how the NPS varies with time, revealing possible trends.

Purchase Propensity Analysis

By means of the Purchase Propensity analysis it is possible to deeply analyse the real interest of many potential customers with respect to certain brands or specific products. This way people opinions are translated to quantitative information that generate useful business insights.

The General View shows the estimated aggregate percentage of purchase propensity for each level on the whole period of analysis.

The purchase propensity rate varies from 1 to 4, where 1 indicates “No propensity” while 4 states “High propensity”.

The Weekly View instead allows to see how the propensity levels vary with time, revealing possible interesting trends.

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.


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