Your business has access to countless data sources with feedback from your clients, customers, employees, vendors, and more. This unstructured data holds the key to achieving your customer experience goals, but analyzing it properly requires specialized solutions. Text analytics technology introduces an automated approach to analyzing and visualizing unstructured text data for qualitative measurements.
Imagine gaining actionable insights from every social media post, email, chat message, trouble ticket, and survey. Text analytics allows your business to learn more about what people are talking about, thinking, and feeling when engaging with your products and services.
The Differences Between Text Analytics, Text Mining, and Natural Language Processing
To fully understand text analytics, you also need to know about text mining and natural language processing. Text mining processes large sets of unstructured data in order to extract information from it. Without this tool, you would have to manually work on screening written inputs and determining whether it’s quality data. Once this data is extracted into structured data, you have a form that’s capable of going through analytics to look for valuable insights. Text analytics can create reports, identify significant patterns, and offer other ways that businesses can make data-driven decisions.
Natural language processing is a key technology used by text mining and text analytics. It’s a type of artificial intelligence that is capable of processing human language into a form that is usable by computers. The end user doesn’t need to know specific keywords or syntax to make their request understandable by the machine on the other end. Instead, natural language processing steps in to make that happen. This technology uses a model so that it learns based on the data fed into it. Its accuracy and relevance of its insights improve over time. This is a type of machine learning process.
How Text Analytics Works
The text analytics process starts by gathering large text data sets. Depending on the scope of your project and the resources that you have available, you may pull from social media comments, website text, books, structured surveys, ad hoc feedback, or call logs. You may work with a single set of data or look at multiple aggregated resources.
The text analytics solution may have text mining features set up so that it begins to sort this data. In some cases, you would use two or more solutions to generate the extracted data sets needed to find valuable information. Some examples of what happens during this part of the process include breaking down the sentence, tokenizing the text, and configuring the language.
The natural language processing feature in the software can manipulate the data in many ways, such as tagging it, grouping it, or setting it up in other taxonomies. After the basic, low-level processing is completed, the text analytics tool can move to the next step.
A common usage of this technology is to perform sentiment analysis on a given set of data. The software can find out whether customers are unhappy or happy, the topics that they feel passionate about, and notable input about the customer experience. It parses the syntax and context of the text to determine the real message behind it. Systems that stop at the text mining stage of this equation could end up with only surface-level insights, as human language is complex and may not have the same sentiment that a literal interpretation of the words provides.
By working with data sets that are far too large to process manually, your business is able to gain important research information that can drive your marketing strategies, customer service policies, budget allocation, product development, and countless other operations.
The majority of this process is automated, so you simply need to step in at the beginning to feed the data sources to the system and configure the learning models, and then at the end to build on the text analytics’ interpretation of this information.
Capabilities of Text Analytics
Each text analytics tool has its own set of capabilities, but here are a number of features that you’ll commonly find in leading solutions on the market:
- Verbatim text analysis: The system can take verbatim comments as-is for processing. Since the context of comments is so crucial to understanding the overall emotion behind it, working with the verbatim text is essential.
- Custom dashboards and reports: Your organization may need many types of data visualization, depending on the use case. Being able to customize dashboards and reports allows them to look at the data in many ways.
- Natural language processing ruleset customization: You need a way to tell natural language processing what you’re looking for, the priorities for a report, the data sources it should pull from, and the learning model to use. Customizing the ruleset allows you to choose the option that best fits your business and the particular use case that you’re applying it to.
- Works with structured and unstructured data: While some text analytics solutions are limited to unstructured text data only, others are capable of also working with structured text data. Using a solution that works with both cuts down on the number of solutions you need to gain useful insight from written data.
- Automatic translation: Companies with a global presence, or those in markets that are multilingual, may need to translate comments. The system can detect the language being written and then translate it into a given language.
- API connectors: Text analytics solutions with API connectors streamline the process for accessing data in other software.
- Data import and export: Native integration with popular third-party solutions is helpful for interoperability.
- Data scrubbing: Remove personally-identifying information, curse words, and other information from the text before it’s analyzed.
- Real-time and dynamic results: You can act quickly to identify issues with your customer experience and other parts of your operation when you have real-time data available.
- Granular analysis: Drill down into the insights that are most relevant to your needs. You cut through the signal to noise ratio.
- Comprehensive automation: A substantial part of text analytics solutions should be automated. The sheer volume of text data available for the typical business exceeds manual processing capabilities.
The Benefits of Text Analytics
Text analytics delivers many advantages to your organization, as it’s a critical part of extracting value from the unstructured data sets that you’re otherwise unable to process.
Work with Verbatim Comments in Many Types of Media or Language
You are not constrained to a particular format or media type with text analytics. This compatibility cuts down on the amount of pre-processing you need to do with your data before inputting it into the system.
Improve Experiences for Customers, Employees, and Other Stakeholders
Your customer experience, employee engagement, and other areas will have changing needs over time. You can continually find ways to improve in this by taking the direct, verbatim feedback and discovering the trends in the data.
Increase Your Company’s Revenue
Happy customers stick with your company and make repeated purchases, while highly engaged employees are loyal and turnover rates go down. When you can stay on top of the areas that are most important to these parties, you put your business in a position where it can enjoy sustained long-term growth.
Gain Better Control Over Your Costs
Your budgeting is informed by the text analytics data. The areas that need the greatest investment are targeted, while those that have less of an impact on operations have a lower priority.
Boost Efficiency of Working with Unstructured Data
One of the biggest time-sinks of working with unstructured data is putting it in a form that standard analytics systems can work with. Text analytics can take the data as-is without conversion or other tedious manual tasks.
Make More Data-driven Decisions
Valuable insights from text data and verbatim comments give you another source of data for your decision-making process. You can take a deeper dive into why people scored your company the way that they did on a survey or learn about the reception of new products and services.
Act Quickly on New Opportunities
Your market can change drastically overnight, often in response to new technology, innovations, or ongoing feedback. Text analytics gives you access to this information so you can discover new opportunities that lend a competitive advantage. An example of acting on this information is suggestions for use cases that you haven’t considered before. If customers talk about using your product in unexpected ways, you can create resources to support that use case, develop products specifically suited for that part of the market, or expand horizontally into a new market.
Use Cases for Text Analytics Software
Many companies have limited insights into their customer experience as they rely on surveys that lack open-ended questions. While these data sources are useful, they often lack information about why someone chose a particular response. Empowering your organization through open-ended questions opens up more communication channels between you and your customers. They can go into detail about their recent experiences and what they think about your company. Here are several everyday use cases for text analytics software.
Researching the Voice of the Customer
It’s hard to get a more accurate idea of your customer’s wants, needs, desires, and pain points than using their direct feedback. Text analytics supercharges VoC programs and allows the team to focus on the strategic side of working with the information.
Gaining Greater Understanding of Net Promoter Scores
Net Promoter Score surveys are an excellent tool for gauging satisfaction, engagement, and other metrics. However, it’s challenging to identify the specific areas that influence that score. By pairing your Net Promoter Score surveys with open-ended survey questions, you have another piece of the customer experience puzzle.
Identifying Problem Areas in Customer Satisfaction Surveys
Where does your customer experience fall short? Look at the topics that get brought up most often in negative responses so you can see where customer expectations are not being realized. In some cases, you can fix the problems. In other areas, you may find that your sales team is overselling product features, that marketing is setting the wrong expectations, or that product availability is a point of frustration.
Getting Feedback on New Concepts
If you’re not sure whether your audience will be receptive to a new product, service, or feature, gauge their input before making large-scale investments. You can quickly gather and analyze this feedback with text analytics, allowing you to maintain forward momentum on fast-paced research and development projects.
Discovering Employee Engagement Levels
Many organizations focus solely on the customer-facing side of their overall experience, but employee engagement is a significant factor. Companies that have constant turnover, toxic work environments, insufficient pay and benefits, inadequate backend systems, and other problems will see this reflected in customer satisfaction scores. Your customers might not know why their experience is sub-par, but investigating the data further can point to issues stemming from poor employee engagement.
Testing the Effectiveness of Advertising
You can discover whether people are neutral, negative, or positive towards ad creatives and ad copy. This evaluation allows you to fine-tune the assets you use for your campaigns and expands on metrics that are typically tracked in ad and marketing software.
Types of Text Analytics Tools
You have several categories of text analytics solutions you can choose for your organization. The right choice depends on how you want to use the solution, the types of specialists that you have available, your budget, and the kind of text that you’re working with.
- Barebones APIs: If you have a strong development and data science team available and the need to build a fully customized solution, you can start with barebones APIs that include the text analytics features required. While this option gives you the most flexibility, it’s also the most resource-intensive of them all.
- Text Mining and Coding: This type of solution processes verbatim comments to extract the most critical information from it. It’s important to note that this category doesn’t include analytics. It sorts, categorizes, and labels text, among other functions.
- Text Analytics: Text analytics solutions focus on surfacing insights from text, and allowing you to manipulate the data as needed to understand it better.
- Data Visualization: These solutions take the data from text analytics and present it in a variety of visualizations. This reporting tool can help non-technical users learn more about the data and use it to inform their decisions.
- All-in-one Tools: If you don’t want to piece multiple solutions together, all-in-one tools are available that include text mining, coding, analytics, visualizations, and other useful features. This comprehensive system is a good choice for those starting out in text analytics, as well as those that have plenty of experience. A good system accommodates both of those end-user groups to support business growth over a long-term period.
The volume of written text is only going to increase over time. If you don’t have a system in place to do something with this data, you’re losing out on insights that can be game-changers for your organization. Text analytics tools are essential for businesses that want to stay on top of customer experience.
If you are looking for a text analysis solution, contact us at Ascribe to learn about CX Inspector, our fourth generation text analytics solution which can analyze large amounts of verbatim comments quickly, and Illustrator, our interactive visualization tool which can create customized dashboards and reports.