How to choose the right text analytics software
From open-end survey responses and tracking studies to social media posts and online reviews, organizations have access to a plethora of customer feedback. But what’s the difference between spending hours reading through a hodgepodge of written responses and having actionable insights at your fingertips?
A quality text analytics software. With a tool powered by modern data science, trained by experts, built for in-depth analyses, and designed to implement improvements, text analytics tools can empower organizations to quickly assess what consumers are saying and shape their processes in response.
Here are some key components of a quality text analytics software.
1. A well-trained natural language understanding technology
Natural language understanding (NLU) leverages the latest in technological capabilities to quickly clean, dissect, and classify comments. This includes identifying and assigning sentiment, emotion, topics, and intent. NLU enables rapid consumption of large quantities of consumer feedback with minimal human intervention.
“Very unprofessional. Gave me the run around. Hung up on me 3 times. Transferred me twice to other departments and was already closed. Forced me to pay money I did not owe. Requested security light - still haven't got it after 2 scheduled times.”
NLU technology may assign the following to the above comment:
- Sentiment = Negative
- Emotions = Anger, Disgust
- Topics = Security lights, Missed appointments, Hung up on, Customer service, Staff interactions, Multiple attempts
When it comes to NLU, not all systems are created equal. Systems that have been trained over time on industry-specific terminology can detect nuances in consumer expressions and properly classify comments based on relevant models. Advanced systems will also remove or automate labor-intensive work that improves output quality, including:
- Translating responses to a preferred language
- Correcting improper spelling and grammar
- Extracting personal data, such as phone numbers and email addresses
Producing cleaner, more standardized inputs allows these technologies to better uncover risks and opportunities, but an NLU system will only be as good as the experts who train it. Quality outputs result from subject matter experts spending hours to refine the system based on industry knowledge.
2. Multiple ways to discover value in your verbatims
Text analytics software that only allows you to look at consumer comments from one angle limits your ability to make sense of the text. Whether you have open-end responses to a single-question survey or results from an ongoing Net Promoter Score (NPS) survey, the analysis you need may vary by looking at:
- Automatic- or artificial intelligence-based topics
- Pre-built industry topic models
- Sentiment and emotion
- Questions or suggestions
- Cries for help, safety issues, legal concerns
- Trends
Analyze sentiment by topic
Quickly identify sentiment associated with common topics among datasets in the Bellomy Text Analytics tool. Analyzing sentiment by topics shows the number of comments that mention each topic and the distribution of positive and negative comments related to that topic.
Analyze sentiment over time
Visualize patterns or spikes in positive or negative sentiment among datasets using the Bellomy Text Analytics trending capabilities.
What’s important is that you are quickly able to discover the main drivers of satisfaction or dissatisfaction. From understanding the relationship between NPS score and topics to discovering challenges related to a product, service, or ad campaign, a multifaceted tool can reveal a robust, well-rounded, and thorough understanding in a matter of minutes.
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3. Features that turn text analytics insights into action
Advancements in artificial intelligence technologies and the emergence of integrated discovery tools are making it much easier for organizations to embrace consumer feedback analysis and improve business processes and outcomes.
Text analytics can improve employee performance through identifying and closing the loop on customer complaints. Rather than leaving issues in the abyss of a help desk software, case management features help keep employees accountable.
Ensure that customer complaints are handled by delegating the right resources and tracking progress on the case from start to finish in the Bellomy Text Analytics tool.
The best text analytics software also helps organizations:
- Coach staff based on consumer input
- Make and measure action plans
- Track performance
- Detect employee soft skill strengths and weaknesses
- Improve service-level operations with automated alerts or reminders
While NLU and analysis offer a wealth of insight, pairing them with tools that empower employees to act on those findings furthers the reach and helps organizations better serve their customers.
4. Data visualization tools for enhanced reporting
Built-in reporting capabilities can not only streamline analysts’ workflows, but also help turn findings into compelling stories. From exporting data to creating various visualizations, text analytics software can help bring customer comments to life in a way that inspires and resonates with organizational leaders.
Present your data using a variety of visualizations available in the Bellomy Text Analytics tool.
Text analytics software is only as good as the analyst behind it
The symbiosis of natural language understanding, multi-faceted analysis, action-oriented tools, and analysts who can synthesize findings into action-oriented insights enables both reactive and proactive organizational growth. While modern text analytics software can reduce the need for data science expertise, thorough training paired with existing business knowledge is key.
Bellomy’s subject matter experts span a breadth of industries, allowing them to apply our Text Analytics Tool to industry-specific use cases and challenges. They’re also available and ready to equip clients with the capabilities, training, and ongoing support they may need to ensure they are optimizing each of the tool’s features..
- text analytics