Software for text analytics has reached a new level of sophistication, giving you powerful tools to improve your customer experience, satisfaction and loyalty. Most text analytics technologies are primarily based on an underlying set of techniques termed natural language processing (NLP).
There are two other complementary text processing methods, which are equally relevant and can be especially helpful in handling very large volumes of feedback data. One is machine learning, an artificial-intelligence approach that learns how to categorize and interpret text from samples previously coded. The other is semi-automated coding, an auto-assisted method that organizes the work intelligently and optimizes human decision-making.
You can now start to build highly effective technological solutions to integrate feedback using varied customer insight initiatives and customer feedback channels. With a multi-technology approach, you can apply the right tool, or combination of tools, to the project at hand and thus deliver an efficient and effective solution.
The technology behind NLP, or rules-based text analytics, uses lexicons or dictionaries alongside a series of deterministic rules to bring together responses that appear to share similar content, to identify topics or to identify sentiment such as positivity or negativity. NLP is particularly well suited to discovery of meaning or sentiment in large data sets, when used as a query or interrogation tool, as well as in developing taxonomy – an approach some describe as text mining.
Machine learning is an artificial-intelligence, or “learning metaphor” approach. The program learns how to categorize and interpret text automatically from a sample of manually coded training examples. As more examples are subsequently provided, including any that arise from quality-control checks, overall accuracy improves. Machine learning is especially well suited to large-scale repetitive tasks and can run as an automated process, once trained, with minimal intervention.
Auto-assisted methods organize the work intelligently and optimize human decision-making in classifying customer comments by using powerful web-based searches and “fuzzy” matching within an overall organizing structure.
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