In these customer-centric times, organizations are constantly dealing with one major concern – customer satisfaction. The success or failure of a business is directly proportional to how happy the customers are with the products and services offered.
Even though every organization aims at building a remarkable customer experience, gauging the customers’ reaction comes with its own challenges.
First, surveys and other feedback channels have to be designed. Second, each survey response, e-mail, chat, product review has to be manually sifted through, read carefully, responded to, and categorized for the company to consider. Just as tedious as it sounds, this entire process is not only be extremely time consuming but also requires a humongous workforce dedicated day in and out.
This is where process automation rushes to aid. Diminishing the need for highly skilled employees, technological solutions streamline complex, long-drawn processes and produce purposeful results. In the case of customer feedback management, there is a multitude of tools available to simplify the job. These tools include customer data platforms, VoC platforms, personalization engines, journey mapping tools amongst others.
However, CFM is not merely about gathering data from customers and ensuring their feedback is available for action. This feedback needs to be carefully analyzed for the organization to acquire any insight from it.
Here, text and speech analytics software present a solution. So, let us dig deeper into Text Analytics and the corresponding software.
On the business intelligence side of feedback management, analytics has a huge role to play. Drawing meaningful conclusions from gathered data and utilizing it to implement improvements to business procedures is what makes organizations climb the ladder of success. While analytics can be applied to almost any aspect of business operation, this article deals with text analytics. So, what is text analytics?
Text analytics is the process of gathering and analyzing written communication received from customers and using the analytical outcome to perform transformative actions.
“Text analytics software is a technological solution that automates text analytics. It turns existing raw, unstructured data into mathematical, visually representable, structured, actionable data and creates scope for an organization to monitor customer response continuously. It utilizes machine learning and natural language processing in addition to statistical algorithms to detect topics, sentiments, tones and trends in customer feedback.”
Like most other software solutions, text analytics tools also function on a few basic principles and microprocesses. What are these?
Once the data that needs to be analyzed is decided, channels are set up and the software is deployed, machine learning and natural language processing technology is set into action. Natural language processing employs semantic and syntactic clues to derive meaning from complex unstructured text. In progressive advancement, 4 analytical processes can be listed:
This is a comparatively primitive process that can be used for smaller datasets. It is a supervised method that requires a human to supply keywords and create categories. Its use cases are limited, but it forms the basis for more advanced technology.
This type of analysis categorizes feedback based on previous categories made by human analysts. It observes how text is categorized, which words are clubbed together and what they signify. This is a supervised method that utilizes machine learning to improve results over time but requires periodic intervention. If the training data is adequate and of sufficient quality, the accuracy of the categorization becomes exact.
This is an unsupervised, mathematical process that requires no human intervention to analyze raw data collected directly from the customers. It uses complex technology like word embedding, deep learning, sentiment analysis and probability statistics to sort data based on semantic understanding and syntactic structure. The analysis is based on topical categorization – this means it detects which words are used recurrently, where they appear in a text, what words they are accompanied by and finally what they mean when used in a certain context.
This type of analysis extracts phrases in addition to words for analysis. It depends more on the meaning of a feedback than keywords. The text analytics tool uses advanced NLP techniques and requires no training or prior categorization. It also allows various syntactic structures and language variations to be analyzed with ease. This allows organizations to collect and analyze open-ended customer feedback as compared to highly structured surveys.
Once the raw data (customer feedback) is categorized using one of the aforementioned processes, text analytics tools provide smart scoring based on sentiment analysis and intent detection. Raw data is converted into quantitative (mathematically represented) data and presented in graphical forms. These tools also highlight areas that require improvement, what the customers focus on, and why a product or service is received in a certain way. They also can recommend potential operational strategy.
Additionally, to understand how these processes benefit an organization, let us list advantages of Text Analytics –
1) Enormous amounts of data can be extracted, converted, categorized, and analyzed at whopping speed.
2) Most text analytics software is easily integrable and scalable. This means it can contribute to other business intelligence and customer experience management tools while expanding its reach as the company grows.
3) Data is represented visually in the form of graphs, charts and color-coded categories which makes it easily understandable.
4) The software performs analysis in near real-time as it is not constricted by time as human employees would be.
5) All insights presented, predictions made, and solutions offered are absolutely data driven.
Be that as it may, there are also a few cons of having an automated text analytics system in place –
1) If the dataset supplied is insufficient, the software may not learn linguistic nuances. This could hinder interpretation and the results would be underwhelming.
2) Supervised text analytics processes require constant human attention despite being automated to a certain extent.
3) In every dataset, some of the outputs are unactionable as humans simply cannot understand what the hidden meaning in them is.