Businesses today have evolved from being solely brick and mortar stores to extending their offerings across multiple digital channels. With the increase in internet and smart device penetration, digital channels have become the primary touchpoint for customers to interact with brands. This distancing from in-person brick and mortar stores might seem like customer connections have diminished, however with the omnichannel and ubiquitous digital presence, businesses are now interacting with customers more than ever. This online relationship with customers has also enabled the brands to track and measure each and every interaction in the realm of cyberspace, thus more accurately predicting and delivering what their customers want.
The abundance of customer data might sound like a big boon, but it also poses a particular problem to be solved – access and analysis at scale. With more than 80% of the customer data unstructured, semi-structured, and textual in nature, it’s quite challenging to process and extract insights compared to structured data. However, text analytics presents itself to be an apt solution to overcoming this problem. In this blog post, we explore how text analytics helps customer experience (CX) and marketing teams attract, engage, and satisfy customers while staying ahead of the competition.
What is Text Analytics?
Text Analytics/ text mining is an Artificial Intelligence (AI) technology that leverages Natural Language Processing (NLP) algorithms to convert unstructured and semi-structured textual data into normalized structured data that’s suitable for analysis.
Source: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications - Book
Use cases in which customer experience and marketing teams leverage
Text analytics has a wide range of applications and can be applied anywhere where unstructured textual data exists. The following are some of the key use cases where text analytics comes in handy.
Text analytics for marketing teams
It is quintessential for the marketing teams to understand the customers deeply in order to acquire, engage, and delight customers. Marketing teams also need to be aware of the customer pain points, ways customers perceive the product or service, and crucial factors influencing their buying decisions. In order to access customer insights, marketing teams conventionally rely on methods such as surveys, interviews, and feedback. However, these methods are prone to bias and error that makes them inefficient. To overcome this, the application of text analytics enables marketing teams to access customer conversations from text-based data sources such as social media, discussion forums, and blogs, and transform them into useful insights to boost customer satisfaction and revenue generation.
Early customer trend detection
With the rise of social media and other digital mediums, customers share more of their daily lives online. This abundance of customer data can help businesses identify emerging trends. Uncovering these emerging trends provides brands new opportunities and an edge against their competitors. Text analytics serves to ingest a large volume of online data and identifies customer trends or emerging brands that could become a threat to the business.
Voice of the customer report
The voice of customer report provides a comprehensive, yet granular, view of customer sentiment and emotion related to a brand, product, or service. These insights help in improving customer experiences, increase revenue and drive customer engagement. However, tracking the triggers that influence customer opinions about brands, products, or services across various touchpoints is challenging. Text analytics facilitates the analysis of customer conversations and feedback from various online sources and serves them up as useful data and insights at scale. These insights guide the marketing team to strategize campaigns for better output and engagement.
Text Analytics for Customer Care Teams
Customer experience teams are often inundated with high volumes of customer communications on any given day. With a magnitude of textual data involved, CX teams often leverage text analytics to automate repetitive tasks and identify areas to improve Net Promoter Score (NPS) and other customer satisfaction KPIs.
Conversation analysis
The internet has become the predominant medium for customers to communicate with businesses via multiple modes such as emails, support tickets, social media, and chat. These customer conversations provide a wealth of insights for understanding customer needs and contributing to business growth. Applying text analytics enables us to analyze customer interactions and communications at scale to find data-driven insights that highlight critical customer issues and improve customer experience.
Automated ticket prioritization and routing
Customers expect and value timely, effective, and personalized customer service interactions. However, businesses are falling short of resources and efficient workflows in place to handle customer queries efficiently. Managing a high volume of customer queries can be challenging as manually tagging tickets and routing them through the right human agent is a time-consuming process prone to errors and may also create bottlenecks and delayed response times. However, text analytics can automatically categorize the support ticket and assign it to the most suitable pool of agents. In addition, with sentiment analysis, the systems can determine the urgency of resolution based on the tone of the customer communication and prioritize the tickets.
SetuServ offers a variety of text analytics solutions or companies that want to support growth while maintaining a strong customer connection. Our methodology combines Artificial Intelligence (AI) & Human Intelligence (HI) to provide accurate insights. SetuServ’s Voice of Consumer Insights and Signals (VOCIS) platform serves a majority of industries with offerings across departments For much more complex and industry-specific solutions, SetuServ offers custom AI solutions based on the specific customer use cases.