How To Improve Customer Satisfaction with Financial Services Using AI Sentiment Analysis
The financial services industry has changed significantly over the years in response to the growing demands of the new generation of customers. For many organizations, there has been a dedicated focus on creating more accessible and convenient financial services to customers while ensuring they remain adaptive to the changing needs of those they support.
As organizations scale, however, the complexities involved with recognizing the independent needs of customers while extracting needed data to make better decisions to improve their financial services offerings can be hard to navigate. As a result, financial services companies are now looking at AI-driven and machine learning technologies to enhance their operational efficiencies to assist in this effort.
Customer Satisfaction in Financial Services – Making Customers Happy
In today’s modern digital landscape, a financial services company’s success is defined by its ability to attain, but most importantly, retain its customers. Alternative digitized financial solutions and growing disruptions due to highly remote business operations have made customer retention more complicated than it ever has been for financial institutions. The impact of higher than average customer churn can be devastating for businesses if left unchecked.
A common reason for high client churn is poor customer service and a lack of insight into the client’s needs. In many cases, this has little to do with not caring about addressing customers’ needs and more to do with a lack of data to drive better decision-making. Financial institutions need to prioritize their customer experiences, whether over the phone, through email, or using mobile applications to serve customer’s needs. Recognizing customer pain points, preferences, feedback, and other relevant metrics to compete with other competitive businesses and services.
What Is AI Sentiment Analysis?
AI sentiment analysis uses machine-driven technologies, including natural language processing, biometrics, and computational linguistics, to better understand human behavior when purchasing or interacting with products and services. When deployed in organizations, AI sentiment analysis can collect and analyze large amounts of information to provide valuable insights relevant to the likes and dislikes of customers and the overall value they place on their brand engagements.
How Does AI Sentiment Analysis Work?
The primary working component of AI sentiment analysis is Natural Language Processing (NLP), a machine learning technology that applies syntactic techniques to understand human language better. Using a combination of lemmatization, speech tagging, and tokenization, sentiment analysis helps to classify large amounts of text by different emotions. These emotions are then collected and tracked across large blocks of text in real-time to give readers a sense of the overall feeling associated with all types of interactions.
Leveraging AI Sentiment Analysis in Customer Interactions
Sentiment analysis can be applied in various aspects of the business when processing large amounts of data for valuable data insights. However, when it comes to customer interactions and evaluating overall satisfaction rates, few technologies offer the best return on investment than AI sentiment analysis.
Using AI sentiment analysis, organizations can analyze all touchpoints with a customer thoroughly. This is incredibly useful for financial organizations looking for ways to differentiate their services from the competition while ensuring they continue to meet the needs of their customers. Below are some of the areas where to apply AI sentiment analysis and how it benefits financial services organizations:
Phone Call Interactions
Voiced-based communications are still an essential part of providing active support in financial services organizations. However, just because a call is over the phone instead of through digital correspondence doesn’t mean organizations still can’t benefit from AI sentiment analysis. For example, organizations can utilize voice-to-text solutions to transcribe dialogues from support agents, identifying analyzed emotions from the dialogue using voice-connect ticket management solutions and voice-driven caller prompts.
Sentiment analysis can also help identify a client’s overall satisfaction or frustration levels before engaging with a live support agent. This helps organizations prioritize their live chat cues when initiated through chatbots, ensuring their support cases are effectively escalated and managed as sensitively as needed.
Email support cases are excellent sources of feedback for any financial services organization. When combined with feedback surveys and quality assurance reviews, organizations can utilize sentiment analysis to create an accurate picture of how customers feel about their interactions with support representatives. While sometimes questionnaires and satisfaction surveys can be filled out incorrectly, sentiment analysis gathers its emotional context from the dialogue itself, giving a much more accurate representation of the client’s overall happiness.
As newer, self-serve financial technologies and solutions become available to the general public, financial organizations need to do what they can to ensure they differentiate their services. AI sentiment analysis gives the organizations the highly accurate data they need to ensure they provide the level of service their customers expect while giving them the transparency necessary to make changes to their approach as required. If you’re looking to uncover and address inefficiencies in your customer experience processes click the button below to register for our Zendesk Efficiency review!