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Predicting the Future Based on Voice of the Customer (VoC)
Hitachi’s Sentiment Analysis Service provides
a highly accurate visual representation of customers’ opinions and sentiments
about a company or a product, based on analyses of text data.
The service can analyze the data from a variety of media, such as social media,
customer reviews, mass media including newspapers and television,
and business data from questionnaires and call centers.
And then supporting future planning activities like sales forecasting.
This service uses AI technology to analyze a variety of text data from social media, customer reviews, and business data. The service classifies the results into three major emotional categories (positive, negative, and neutral). The text data can then be further broken down into more detailed sentiments (joy, happiness, love, etc.). The service can also classify text into different categories (related to vehicles, food, sports, etc.).
Hitachi provides unique filtering and tagging technology to improve data visualization of the analysis results.
To use sentiment data for your business, you must be able to quickly and easily find the data you want. To achieve this, this service's dashboard has three features to help you notice important points. First, the dashboard displays analysis data in various graphs (pie chart, timeline, word ranking, etc.). Second, the dashboard can filter analysis data not only by keywords and dates, but also by emotions and topics categorized by AI. Finally, the dashboard can also filter analysis data by tags set by users.
Through the machine learning of highly relevant words and technical terms contained in collected data, the AI automatically updates the refining conditions used for the filtering dictionary. This makes it possible to maintain and improve the accuracy of search refinement without additional maintenance work.
Hitachi's Sentiment Analysis Service is available in a default service package or customized systems to meet your needs.
Data from Twitter and Pantip (Thai only) is supported for analysis in the default plan.
Contribute to Your Business with a Range of Approaches
Product planning to comprehensively meet market needs
Perform sentiment analysis of data that includes both business data, such as product functions and performance, and information from social media. Based on customer complaints about the product, this analysis clarifies areas in need of improvement, which allows you to identify customer needs. This, in turn, enables you to perform product planning in a way that comprehensively meets market needs, thereby contributing to your efforts to increase sales and reduce lost opportunities.
Implementation of prompt measures against risks, such as social media flaming and recall
Perform sentiment analysis of data that includes both business data (such as product specifications and information about the state of business when the product was released) and information from social media. This enables you to detect negative feelings about the product before they spread, and to identify words that indicate risks. This helps you to improve your brand image by promptly implementing measures against the risk, such as preventing accidents that may be caused by defects in the product or announcing a recall.
Effective promotional activities
Perform sentiment analysis of data to monitor customers opinion from social media. Based on the customers opinion about your promotion, you can make an effective improvement for your promotion. Effective promotion activities increases your social media account followers in your business. This will make your account more attractive.