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Case Study: Fashion

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: Fashion
Period of analysis: 1 Year
Total analysed mentions: 45.000
Sources: Twitter, Reddit, Tumbrl, News, Blogs, Forums, Instagram, Facebook

Sentiment Analysis & Social NPS

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

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 Weekly View allows to see how the NPS associated to the Brand and its competitors varies with time, revealing possible interesting trends.

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.

Satisfaction Analysis

By means of the Satisfaction Analysis it is possible to deeply analyse the real satisfaction of many clients (or potential ones) with respect to certain brands or specific products.

The General View shows the estimated aggregate percentage of satisfaction for each level on the whole period of analysis.
The satisfaction rate varies from 1 to 4, where 1 indicates “No satisfaction” while 4 states “High satisfaction”.
The Weekly View instead allows to see how the satisfaction levels vary with time, revealing possible interesting trends.

It is usually of interest to analyse deeper the different paths using a qualitative peak-and-trough analysis. This specific analysis helps to reveal the underlying reasons related to high or low satisfaction of your clients.

Cross-Topic Analysis

By means of the Cross-Topic Analysis it is possible to understand how different analysis relate one each other. This is performed through the computation of joint frequency tables and conditional probability tables.

For instance, the cross-topic analysis allows to understand how each trending topic is related to the sentiment. The Sentiment X Topic table represents the percentange of occurence (or joint frequency) of each analysed topic with respect to the three level of sentiment (positive, neutral and negative). It is also possible to estimate the probability of a document or post of being negative, neutral or positive given the fact that it speaks about a certain topic. This is done by means of the conditional probability tables, or using the Polar Plot.

The cross-topic analysis is not limited to “cross” trending topics with sentiment. It allows to combine together any analysis you are interested in. It could be valuable to see the interaction between the topics and the level of satisfaction, that is to study the relationship between the topics discovery and the satisfaction analysis. The Topic X Satisfaction and the Satisfaction | Topic tables allow exactly to study their joint frequencies and their conditional probabilities.

Color Analysis

By means of the Color Analysis it is possible to deeply analyse the real preferences of many clients (or potential ones) with respect to specific color of the company’s products.

The General View shows the estimated aggregate percentage of mentions for each color on the whole period of analysis.

Moreover, by means of the cross-topic analysis it is possible to combine together the results of different analysis: for instance, the satisfaction and the color analysis. This allow to see what are the colors that really satisfy or not the (potential) customers.

Finally, it is also possible to build up a quantitative index that summarises in a single value the color’s preferences found within the textual information. The index is created using the frequencies of appearance of each color, weighting conveniently by the satisfaction rate. This is the Color’s Preference Index, that is a particular type of Web Opinion Index (WOI).

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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