In today’s rapidly-changing environment, financial services firms are experiencing the benefits that groundbreaking technologies like AI, ML and NLP offer. Between improving efficiencies, creating significant cost savings and bettering the customer experience, financial services firms now realize that natural language processing models, in particular, can improve how they gather customer analytics.
The increased application of NLP, a fast-growing sub-segment of AI, is helping firms become more inclusive and make informed decisions. As the space becomes more competitive and customers seek better and more personalized service offerings, financial services firms will see the need to modernize how they do business and make decisions.
5 Ways NLP is used for customer analytics in the financial services industry
Though there is an increasing amount of data available on consumers, a large portion of this information is in raw text. Thus, gaining access to this information and processing and analyzing it is not a simple task. NLP, or natural language processing, can augment human-machine interactions, extract meaning from text data, and more. Today, financial services firms can integrate NLP and customer analytics to gain previously-inaccessible insights.
1. Analyzing Data with a Human Approach
One of the main benefits of using NLP for customer analytics is its ability to detect intent, tone, and emotion from text and unstructured data with accuracy using sentiment and relevant analysis. This benefit has significant implications for financial services firms and their customer analytics, as it can help automate time-intensive processes without omitting human input and needs.
NLP uses speech-recognition models that take text data and break it down into individual parts, like words or sentences. Then, it compares these elements to text that the model has previously analyzed to provide context and meaning. With these powerful capabilities, NLP can be used to process loan applications and insurance claims, power chatbots, manage and detect risk, and more.
Using NLP, financial services firms get more out of AI models than just using them for logic and rule-based tasks. Instead, this sub-segment of AI can analyze documents, interpret customer messages, and extract meaning from text data like emails, applications, or various other mediums. Combining NLP and customer analytics, firms can efficiently analyze both structured and unstructured data and have a more meaningful and comprehensive view of all aspects of their clients.
Across the financial services industry, firms are seeing increased demand among clients for more personalized services tailored to their specific needs. While historically, a one-size-fits-all or tiered offering approach was the standard in the industry, this isn’t the case anymore. To improve the overall customer experience, financial services firms are taking advantage of the wealth of customer data available and applying that to their own service offerings and customer analytics.
Today, with more customer data available than ever before, financial institutions can access information about clients’ personal details based on their online activity. Therefore, NLP allows firms to take advantage of this data and extract meaningful insights from it to target customers with personalized and custom offerings.
While this would have been impossible before, firms no longer need to rely on staff manually combing through a customer’s online interactions to gain insights into their lives and preferences. Instead, firms can use NLP and customer analytics to analyze large amounts of text and unstructured data in a short amount of time. This keeps firms up-to-date with clients’ lives and ensures that they’re being offered personalized services that are relevant to them.
3. Customer feedback analysis
Financial services firms can improve their service offerings by responding to customer feedback in a timely manner, though manually combing through reviews and visiting comparison websites isn’t a sustainable or efficient practice. Keeping customers happy and responding to valid critiques are key to staying relevant in the field today, though it can be a tedious and time-consuming task when done by hand.
With NLP, firms can more easily analyze customer feedback and sentiment about their business, all without having to read or filter through each individual review. Since NLP can extract insight from text data, this makes it the perfect tool for firms to keep track of customer feedback, determine whether it’s positive or negative, and to what degree. This allows organizations to monitor reviews in real-time and flag the most important or time-sensitive comments, provide timely feedback, and ignore filler or irrelevant reviews.
Improving market research with customer analytics NLP
Staying up-to-date with the latest in the field is crucial today as competition in the space ramps up and new and innovative firms emerge each day. Financial services firms need to stay current on the latest happenings and updates in the industry to take advantage of new markets and keep an eye on what competitors are doing.
With the constant flow of news stories and company filings released at all times, firms need a way to have around-the-clock monitoring of the latest in the industry to stay relevant and competitive in today’s business environment. NLP is a powerful tool in this aspect, as it can help firms monitor sentiment and flag the most important news stories of the day. This provides the firms with meaningful insights and allows them to perform competitor and market research accurately and efficiently.
Improving customer service and experiences
Overall, NLP can help financial services firms improve the customer experience and how they gather customer analytics. NLP enhances human-machine interactions and can be applied to improve the customer journey for all. Powering chatbots and allowing for quicker response times, NLP can easily conduct sentiment analysis to determine what type of assistance the customer needs.
With NLP, when a customer contacts the institution with an issue or concern, their problems can be addressed in real-time and resolved with a personalized solution. In the past, they would have to call and wait for an available representative who would then scan through their history with the firm and gather relevant data about their account before they could work on a resolution.
While customer service departments haven’t traditionally been early adopters of technology, firms are beginning to see the power of NLP and customer analytics. Through NLP, firms can provide faster and more personalized customer service, aggregate customer feedback, and free up employees for more impactful tasks.
As AI continues to revolutionize the financial services industry, firms will begin to see how powerful NLP can be within their organizations. With a wide range of possible applications, NLP can help firms gain better customer analytics, enhance market and competitor research, perform customer review analysis quickly and efficiently, and make better business decisions. Looking ahead, the use cases of NLP within the financial services industry will continue to expand as the technology evolves and becomes more sophisticated.
How No-Code NLP Empowers Better and Faster Customer Analytics
The financial services space will continue to be impacted by the rapid pace of technological advancements, and firms will find new and effective ways to implement AI into their organizations. Today, AI can make companies’ workflows more efficient and improve the overall customer experience without sacrificing the human-like aspects, which is possible through NLP.
For any financial service firm, implementing AI into their business may seem like a daunting and expensive task. With a no-code NLP solution, like the one offered by Accern, all firms can seamlessly integrate AI into their existing workflows by choosing from the wide range of ready-made use cases. To see how no-code AI can empower better and faster customer analytics in your firm, start your free trial today.