When companies use big data to analyze their customers’ habits and preferences, it’s often the structured, quantitative kind that is considered. Customer sales data and contact center KPIs such as the Net Promoter Score may provide valuable information, but such numbers reveal little about your customers’ feelings and expectations. To provide exceptional customer service, it is necessary to go further and examine unstructured data with the same level of attention. Indeed, it is unstructured data that contains such insights: All the smaller, often textual bits of data contained in interactions between your company and the customer, or even between customers. Using tools such as natural language processing and speech and text analytics, you can learn how customers actually feel about your brand. Here are five sources of unstructured data your company should mine for priceless information about your customers.
Customer data can be collected from everywhere and plays a crucial role in business decision-making. It is the foundation that allows for the identification and understanding of customer preferences, expectations, and disappointments. In other words, it enables the understanding of the customer’s voice, a key process in customer satisfaction. To achieve this, brands analyze a significant volume of data, both structured – coming from CRM systems – and unstructured. Unstructured data refers to data that is not organized in a predefined form or stored in traditional relational databases. It provides valuable information about customers, as well as market trends. This data can come from various sources such as emails, social media messages, blog comments, audio and video recordings, etc. Here are the 5 unstructured data sources that your company must explore to extract maximum value.
According to Gartner, email remains the most commonly used digital customer service channel. According to Hubspot, email is the second most used customer service channel after the telephone, with 42% of customers preferring email as a means of communication. This upward trend is confirmed by the increase in the volume of emails received by companies every year. According to a study conducted by Radicati Group, the number of emails sent and received per day reached 320 billion by the end of 2021. Undoubtedly, many of your customers reach out on this channel for service. A close analysis of their email exchanges with your agents may reveal common concerns about your brand and illustrate how effectively your employees handle these issues as they arise.
From Twitter to Facebook, social media channels are very popular—especially among millennial customers. Whether people are contacting you for service on these channels or talking among themselves about your brand, using social listening tools is an essential way to better understand your company’s reputation. With such tools, you can find keywords that come up during social conversations and determine if there are customer pain points that need to be addressed.
The telephone is still the most preferred channel for customers when it comes to contacting customer service. Telephone calls are therefore an important source of data for businesses. For this reason, it is necessary to review call transcripts during training sessions with contact center agents. But if you haven’t taken the time to analyze these interactions closely, start doing it now. As voice remains an important contact channel for frustrated customers (many pick up the phone for difficult cases), be sure to determine how agents can improve their interpersonal skills for better outcomes.
Like call transcripts, chat transcripts are excellent records of customers’ feelings and reasons for contact. However, without the human touch of the voice channel, live chat can be challenging for agents who need to master the art of conversation while still delivering timely service. Analyze this channel to learn whether agents are getting to the point quickly, if customers are well informed when the agent needs a pause to find information, and if cases are ultimately resolved to the customer’s satisfaction.
Social media channels are not the only places online where customers may be talking about you. Brands often overlook blogs and review websites where people are usually very blunt about their feelings. Personal blogs with a large following (such as those managed by influencers) as well as consumer advocate sites may reveal tons of insights about customers’ experiences with your brand. Similarly, review websites can outline exactly which products and services your customers love—and which ones need more work.
Analyzing emails can be challenging because they are often written in informal language and may contain grammar and spelling errors. To effectively analyze emails, you can use natural language processing (NLP) tools that can extract relevant information and identify key themes. NLP techniques include named entity recognition, text classification, and sentiment analysis.
Analyzing your emails can reveal customer trends and help you better understand the needs and desires of your customers, leading to improved customer experience and increased sales.
Due to the informal nature of social media comments, tracking and managing them effectively can be challenging. However, by using social media monitoring tools, you can collect and analyze these comments to understand customer preferences and needs.
Once these data are collected, it is important to clean and structure them to make them actionable. Use text analysis tools to remove irrelevant information and extract relevant insights. You can also use data visualization tools to make the collected information more understandable.
Social media comments can provide valuable insights into brand perception, market trends, product issues, and customer expectations. You can use this information to enhance your customer service by identifying gaps and improvement opportunities.
Data from call transcriptions represent a significant volume for businesses that use the telephone as a customer service channel. To facilitate the task of your representatives, you can use speech recognition tools to convert call recordings into text, allowing them to analyze conversations with customers and extract valuable information.
Call transcriptions can provide insights into customer preferences, expectations, and recurring issues. They can also be used to train customer service agents by identifying best practices and errors to avoid.
Live chats, chatbots, and other forms of online chat are increasingly popular among customers for communication with businesses. To analyze and process data from written exchanges more quickly, you can use transcription tools to convert chat conversations into text, enabling analysis of customer interactions and extraction of valuable information.
To gather this data, it is important to know where to look. Identify relevant sites for your business, such as expert blogs in your industry, discussion forums, consumer review sites, etc. You can use monitoring tools to collect data from these sites.
Once the data is collected, clean and structure it to make it actionable. Use text analysis tools to remove irrelevant information and extract relevant insights.
The choice between data mining and NLP for analyzing unstructured data depends on several factors, including the types of data you have, your analysis goals, and the level of complexity you want to achieve.
Data mining is a method that involves extracting knowledge from structured or semi-structured data. This method uses algorithms to identify patterns, trends, and relationships in the data. Data mining can be useful for analyzing quantitative data, such as sales data, website traffic data, marketing data, etc.
On the other hand, Natural Language Processing (NLP) is a technique that uses artificial intelligence to understand and analyze natural language used in unstructured data, such as emails, social media, chats, blogs, etc. NLP employs algorithms to identify entities, relationships, and sentiments in the text and translate the text into actionable data. NLP can be useful for analyzing qualitative data, such as customer feedback, user opinions, product reviews, etc.
In many cases, the choice between data mining and NLP will depend on the data you have and the goals of your analysis:
However, it is important to note that both methods can be used together for a more comprehensive analysis, providing a broader understanding of unstructured data and the valuable insights it contains. The patterns and trends identified through data mining can guide deeper analysis of unstructured data using NLP tools. For example, you can use data mining to identify trends in quantitative data and NLP to understand customer opinions and sentiments in qualitative data.
So when you are scouring the Internet for comments about your brand, be sure to consider this valuable data.Structured big data can reveal key information about your brand’s practices and the numbers behind your successes and setbacks. By analyzing unstructured data as well, you can further gain a greater understanding of what your customers want and adopt a customer-centric attitude.
