Learn More About Text Analysis Software
What is Text Analysis Software?
Text analysis software helps businesses analyze their text data using natural language understanding, which is a subset of natural language processing. Because of the unstructured nature of text data, these analytics solutions take text as an input and provide some form of labels, tags, or insights as an input. In the age of digital transformation, businesses are embracing the need to understand company data like never before.
Text analysis software, also known as text mining software or text analytics software, has become an important tool for nearly every business over the past decade. A more recent aspect of analytics and business intelligence is the need to understand not just structured data, but unstructured data as well. Unstructured data, such as text data, can be mined for meaning to inform business decisions.
Text mining initiatives can help businesses ultimately better understand textual data sets. Being able to pull out actionable insights from numerical data housed in ERP systems, CRM software, or accounting software is one thing, but being able to gain insights from unstructured data sources is invaluable. Without dedicated software for this task, businesses must either spend significant time and resources on building natural language understanding models or haphazardly investigating the data.
What Types of Text Analysis Software Exist?
Many types of text analysis solutions share overlapping functionality, while simultaneously catering to different user personas like data analysts and financial analysts, or providing unique services.
Some solutions may offer self-service features so that the average employee can assemble their charts and graphs from big data sets. Others, however, require more significant support from IT or data analysts.
Self-service text analysis tools
Self-service text analysis tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for tagging text, prebuilt templates for querying data, and other tools for data discovery. Similar to analytics platforms, organizations use these tools to build interactive dashboards for discovering actionable insights.
For example, a customer service business leader might use this type of software to analyze thousands of customer emails to discover trends, such as sentiment and the choice of words they used. This analysis can inform how customer service agents respond to customers to achieve desired outcomes.
Traditional text analysis tools
As opposed to self-service options, some text analysis solutions are geared towards data professionals, such as data analysts and data scientists. They can use this software to train and deploy algorithms, as it assists them in tagging their data. Data scientists can use these tools to ingest text data, such as social media, call center transcriptions, news sources, and reviews, and to build and improve applications, achieving goals such as improving fraud detection and conducting sentiment analysis.
What are the Common Features of Text Analysis Software?
Many capabilities of text analysis software can help users pull business-critical insights from text data.
Language identification: Text analytics solutions provide users with the ability to understand which language the text was written in. This can be beneficial when determining where a social media post came from or when a business has offices in multiple countries.
Part of speech tagging: Once the language is identified, text analysis software can tag each word with a part of speech, signifying if the word is a noun, verb, adjective, and so on.
Syntax parsing: Syntax parsing is very similar to part of speech tagging, but instead of understanding each word, it helps break down how a sentence was constructed and why.
Entity recognition: Text analytics solutions can help determine not just parts of speech but actual entities. For example, the part of speech may be a noun, but text analytics will break down whether that noun is a person or a place.
Keyphrase extraction: Another major feature of text mining and text analytics is keyphrase extraction, which allows users to determine patterns and themes within the text. These tools can pull out those common themes for the user.
Sentiment analysis: All of the above features can be relevant for sentiment analysis. Text analysis tools can offer up sentiment analysis scores, determining if the text is positive, negative, happy, sad, or neutral, among many other classifications. With the sentiment determined, businesses can decide how they want to act or interact with this data. For example, if a software company sees that all of their negative reviews are mentioning one particular feature, it might be a good idea to examine the state or viability of that feature.
What are the Benefits of Text Analysis Software?
The reason to use text analysis software is rather straightforward—users need to analyze text—but there are many reasons behind why a business may want to perform text mining and analysis. It all boils down to better understanding and utilizing company data to impact business processes and the bottom line. It should be used to increase efficiency and productivity and to optimize processes that could be working better.
Sentiment understanding: Businesses are always trying to gauge customer satisfaction, and text analytics is an easy way to do so. Many different text data sources can provide customer sentiments, such as social media, emails from customers, phone transcripts, customer reviews, and others. If a company can understand their shortcomings or where they are excelling with customers, they can better support and manage those customers. Ultimately this can lead to increased revenue.
Employee satisfaction: Similarly to better understanding customers, businesses can improve employee engagement and satisfaction by using text analysis. While businesses shouldn’t necessarily spy on their employees, they can figure out employee sentiment and satisfaction based on surveys, emails, or phone transcripts. This can help businesses ensure that they are promoting the right company culture and providing a healthy and happy place to work.
Survey analysis: Text analysis is very often used when companies are running surveys. These surveys may be intended for customers or employees but can also relate to market research. Being able to quickly pull insights verbatim from survey responses can provide a unique perspective and insight that businesses may not be able to obtain through multiple-choice questions.
Document classification: An easy use case for text analysis software is document classification. Businesses often need to organize existing documents; by pulling out sentiment and themes, it can be much easier to bucket documents, such as invoices and contracts.
Who Uses Text Analysis Software?
The typical user of text analytics is the same person who is tasked with using analytics and business intelligence solutions—a data analyst or data scientist. These users are trained in developing analytical and machine learning models used to pull out actionable insights from data. Data scientists are also tasked with deriving a business narrative from data, and text data is no different. If the text analytics product is of the self-service variety, less technical business users, such as operations, customer service, and finance teams can benefit from the technology to dig into their text data and derive insights.
Data analysts: Depending on the complexity of the software, analysts may be required. They can help set up the requisite tagging of the text data and dashboards for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data.
Operations and supply chain teams: A company’s supply chain frequently has many touchpoints, and as a result, many data points. Everything from invoices to shipping information can be analyzed with this software. Therefore, employees working in operations and supply chain teams can use text analysis software to gain a better understanding of their departments and the text data that is generated, such as from ERP systems. These applications track everything from accounting to supply chain and distribution. By inputting supply chain data into this software, supply chain managers can optimize several processes to save time and resources.
Finance teams: Finance teams leverage text analysis software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as accounting software, employees such as chief financial officers (CFOs) can see how well the business is performing. For example, they can analyze free-text data in expense reports to discover trends in the data. With this knowledge, they can determine the biggest spenders and spending categories and put a plan in place to curb spending, if desired.
Sales and marketing teams: Sales teams also seek to improve financial metrics and can benefit tremendously from being more data driven. They can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. Through the analysis of survey data, business leaders can find out the most effective way to sell products.
For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, these tools allow marketing teams to track the performance of those campaigns in one central location. Marketers can learn about how their audience is responding to their messages using sentiment analysis. In addition, they can evaluate their ad copy by tagging and classifying it to better understand what drives conversions.
Consultants: Businesses do not always have the luxury to build, develop, and optimize their analytics solutions. Some businesses opt to employ external consultants, such as business intelligence (BI) consulting providers. These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields.
Customer service teams: Customer service teams are faced with a challenge. They are frequently inundated with a flurry of customer concerns, whether that be via text, voice, or mail. Although agents can respond to each comment and concern individually, it is beneficial to have a proper understanding of trends, including the sentiment of messages, the types of complaints, and more. Using text analysis software, businesses can equip their agents with tools to help them respond to messages in a targeted manner, depending on factors such as sentiment and key phrases.
What are the Alternatives to Text Analysis Software?
Alternatives to text analysis software can replace this type of software, either partially or completely:
Feedback analytics software: Text analysis software is an all-purpose solution built to analyze any text data. Businesses looking to focus on feedback text, such as from surveys, review sites, social media, and customer service tools, can leverage feedback analytics software to achieve this goal. This software enables businesses to consolidate and analyze their customer feedback within a single platform.
Software Related to Text Analysis Software
Related solutions that can be used together with text analysis software include:
Data warehouse software: Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.
Data preparation software: A key software necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using text analysis tools. Some text analysis platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.
Analytics platforms: Analytics platforms might include some limited text analysis features, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.
Stream analytics software: When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with the internet of things (IoT) data, which people usually want to analyze in real time.
Predictive analytics software: Broad-purpose text analysis software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution.
Challenges with Text Analysis Software
Software solutions can come with their own set of challenges.
Need for skilled employees: The main issue with text analysis software is that, despite the tool pulling information surrounding text data, it still requires a human to go that extra mile and determine what the data means. Without context, sentiment analysis, phrase tagging, and pulling themes or patterns from a text can only inform a user so much. An analyst will need to interpret that data and decipher the business implications of it.
This is much more easily tackled with text analysis software because of the ability to visualize the data in an organized manner, but it still requires interpretation nonetheless. Some text analytics tools may offer a certain level of predictive analytics and provide users with suggestions or recommendations based on the data, but more often than not, human intervention is necessary.
Data preparation: Another potential concern is preparing the data to be ingested by the text analysis tool. The data needs to be stored properly, whether that is in a database or data warehouse and may require IT or a dedicated admin to ensure the text analytics tool can consume the data. The beauty of text analysis software is that it doesn’t always require the neatness of structured data. Unstructured data does not need to follow a columnar approach that structured data often requires.
User adoption: It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.
Which Companies Should Buy Text Analysis Software?
As it has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and industries should consider some sort of analytics solution, such as text analysis to make sense of that data and begin to make data-driven decisions. Here are some illustrative examples of how textual analysis can be used in several industries:
Financial services: Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes.
Healthcare: Within the space of healthcare, bad data practices might have dire or even deadly consequences. Text analysis software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter.
Retail: Retail organizations, whether they’re B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With Text analysis software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions.
How to Buy Text Analysis Software
Requirements Gathering (RFI/RFP) for Text Analysis Software
If a company is just starting out on its analytics journey, G2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2.com is a great place to sort and filter reviews based on these criteria, along with many more. The variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs and their future needs as they accumulate more data.
To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a text analysis software.
Compare Text Analysis Software Products
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Selection of Text Analysis Software
Choose a selection team
As text analysis software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes, facts, and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.
Negotiation
Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
What Does Text Analysis Software Cost?
Businesses decide to deploy text analysis software to derive some degree of a return on investment (ROI).
Return on Investment (ROI)
As businesses look to recoup the funds they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.
Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate their gains from their use of the text analysis software.
Implementation of Text Analysis Software
How is Text Analysis Software Implemented?
Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether it’s an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
Who is Responsible for Text Analysis Software Implementation?
It may require a lot of people, or even teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together its data and begin the journey of analytics, starting with proper data preparation and management.
Text Analysis Software Trends
Data literacy
Business data is no longer locked up in silos. With text analysis solutions, more users across a business can find, access, and analyze this data. In addition, artificial intelligence (AI) software such as natural language processing (NLP) software help make searching through and for data easier and more powerful, providing more accurate results. Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from all their various sources.
Shift to the cloud
The move from on-premises data analytics to the cloud has been underway for several years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons like time to insights. The move away from on-premises infrastructure has helped many companies enable data to work anywhere one has access to the cloud—anywhere with internet access.
Deep learning
The main trend related to text analysis software is deep learning, but more specifically, natural language processing. As AI technology continues to advance, deep learning and NLP become more precise and effective when performing actions such as text analysis. This means that users need to do less digging through text, and instead, the insights are given to them. This is extremely beneficial, because, despite the comprehensive features that text analysis software provides, analysts are still required to dig through the data and determine the insights themselves. The next step, which NLP is contributing to, is to have the software provide actionable insights without the need to dig through the text data.
