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Unstructured data in emails, call center transcripts, and social media conversations are a treasure trove for understanding customer experience. One of the ways to organize and understand this unstructured data is to perform a text analysis. The process involves using a combination of statistical and linguistic approaches to analyze the data and derive insights.
Businesses with considerable IT expertise and budget might want to develop technologies in-house to perform this function. However, for most (especially small and midsize), these resources could be scarce, making in-house development tough.
Off-the-shelf text analysis software provides a viable route for the latter category. The option is also suitable for businesses that want a plug-and-play tool to analyze customer interactions.
In this buyers guide, we’ll explain what text analysis solutions do, their common features, and other considerations to help businesses make informed purchase decisions.
Here’s what we’ll cover:
- What is text analysis software for customer experience?
- Common features of text analysis software for customer experience
- What type of buyer are you?
- Key considerations
What is text analysis software for customer experience?
Text analysis software is a tool that helps mine insights from unstructured business data that are scattered across various tools and platforms. This provides the opportunity to leverage interactions between businesses and clients to understand customer experience, grievances, and recommendations and then act upon them.
Common features of text analysis software for customer experience
Text analysis software helps derive insights from unstructured textual data. Below are the key features of the software.
|Data extraction||Extract textual data from various channels—social media, online chat, self-service knowledge base platforms, emails, CRM systems, etc.—that are repositories of customer interactions, by manually importing data or via API integrations.|
|Topic clustering||Automatically classify or allow users to configure aggregator terms, to group similar content into hierarchical and searchable categories.|
|Sentiment analysis||Determine the sentiment of the content author (the customer in this case) by using scoring mechanisms that classify content as positive, negative, or neutral.|
|Reporting dashboard||Derive insights in the form of summary reports of customer sentiments or by visualizing findings in the form of charts and graphs.|
What type of buyer are you?
The right type of text analysis software depends on your unique requirements coupled with the expertise level of your in-house IT team. Below are the two buyer categories based on these factors.
- Small and midsize businesses: These buyers want an application that solves immediate business requirements for social media monitoring, call center transcript analysis, website experience analysis, etc. The suitable applications in this case are cloud-based, plug-and-play text analysis solutions available in monthly/annual subscription plans.
- Large enterprises: These buyers want a feature-rich data analysis platform with predictive analytics and advanced natural language processing capabilities. The platform should also offer a deep level of integration with a wide range of enterprise tools and support a range of complex use cases such as the ability to analyze and derive insights from customer journeys across multiple channels.
Before buying a text analysis tool, businesses should carefully analyze its capabilities and check out alternatives such as open source platforms or outsourced data analytics services.
- Additional capabilities: Optical character recognition, speech-to-text conversion, handwriting recognition, etc. are rapidly evolving into key business requirements. Figure out whether investing in a solution that offers such additional capabilities will make ROI-sense in the long-term, even if you don’t have an immediate need for them.
- Open source platforms: Do your text analysis requirements exceed what off-the-shelf tools support? Do you have an IT team with specialized domain knowledge of text analysis processes? If it’s a yes for one or both of them, consider open source tools such as Apache OpenNLP, Natural Language Toolkit (NLTK), and General Architecture for Text Engineering (GATE).
- Outsourcing: Does your business have the required budget but lacks an in-house IT staff with domain expertise? The best option in such a scenario is to obtain the services of a third-party data analytics service provider.
Note: The applications selected in this article are examples to show a feature in context and are not intended as endorsements or recommendations. They have been obtained from sources believed to be reliable at the time of publication.