The key to effective deployment of AI in SaaS contact center solutions is natural language processing (NLP). NLP helps improve workplace efficiency and decrease human capital costs. For inbound calling, NLP helps intelligently route callers to the agents with the right skill set for the issues they are calling about. Intelligent routing produces faster resolution by matching agents and surfacing the right scripts.
NLP dramatically enhances interactive voice response (IVR) by recognizing a wide variety of responses rather than just a few phrase options. Studies have shown that most customers calling into a contact center are unhappy with typical IVR responses. More than 60% of consumers say typical IVR creates a poor experience. NLP solves this problem by giving consumers more options without forcing them into limited options.
NLP is being widely used in the industry for both inbound and outbound calls to make processes more efficient and create a more user-friendly customer experience.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) allows the software to understand human speech as it is written or spoken. NLP uses a rules-based approach to parse words based on defined linguistic rules that can be customized to specific industries and organizations.
Machine learning helps the software get better as it collects more data and has continuing interactions with callers. Over time, the software will create additional rules based on the recognition of speech patterns and outcomes.
Human speech can be complex, especially when you add in regional differences. NLP works by breaking down speech and written words in multiple ways, such as:
Examining the syntax helps uncover the words and phrases consumers are using to determine the proper course of action. Analyzing the semantics can decipher the intent and the meaning behind the words.
Understanding the sentiment involved in a contact can be a significant component of customer experience and retention. AI-enhanced natural language processing can tell if a customer is angry or frustrated. These calls may be prioritized, routed to retention specialists, or flagged for intervention.
The right contact center software can track and match contacts to previous interactions. Agents know in real-time who they are dealing with and what level of frustration customers are displaying. This can help prepare agents better and adjust their tone as needed. AI and NLP can also produce different scripts or resource materials for agents based on customer sentiment.
The Benefits of NLP to a Contact Center
For a contact center, there are multiple benefits that serve both customers and agents in a variety of ways.
The typical way customers engage with NLP is when they reach a contact center’s auto-attendant. The auto-attendant interprets the speech. When this happens, NLP deciphers what customers are inquiring about and can determine the best approach — whether it can be handled with an automated or self-serve approach, requires a contact center agent, or demands a specialist.
Self-Serve or Automated Options
For example, someone calling the contact center for banking might be inquiring about their account balance. Directing these calls to an automated system that provides the information relieves the burden of customer support teams to handle low-level inquiries. NLP recognizes these requests whether someone asks for an account balance, asks how much is in my checking account, or can I withdraw $100 from my savings account?
Speed of response is one of the most important variables in the customer experience. A survey by HubSpot shows that 90% of customers expect an immediate response to their inquiries. The faster you can resolve their issue, the higher your CSAT and NPS scores are likely to be.
Providing self-service options not only saves agents time, but also provides customers with quicker resolutions to their queries.
While the majority of customers still want to connect to a human to handle issues, a growing number of people are starting to prefer automated and self-serve options. 40% of consumers said they don’t care whether they talk to a chatbot or a human, for example, as long as they are getting the answers or help they need. Natural language processing helps get them what they need faster.
Intelligent routing can direct calls to agents with the requisite level of experience necessary to answer queries. By interpreting consumer intent using natural language, it can not only get the call to the best available agent to handle the inquiry, but it can also produce the script and available options for the agent.
Both features lead to faster resolution and reduce customer frustrations, such as wait times while agents search for the information they need.
Team Member Productivity
In a tight job market with high turnover, productivity is more essential than ever. Organizations using natural language processing also see significant productivity gains. Self-serve options take the burden off of team members to handle routine queries and frees up their time to handle the contacts that require human interaction.
Because agents have the information they need when they answer the call, they can also provide faster and better service. This reduces the investigation time and can move to resolution more quickly, which improves productivity.
Consumers are using multiple channels to contact companies more than ever before. Some customers will reach out across multiple channels or switch between channels, which can create a challenge with the right contact center software from omnichannel solution providers.
55% of customers say they expect consistency across channels.
With the right contact center software and call center dashboard, agents can also offer omnichannel support with the same efficiency. NLP handles queries in the same manner whether they arrive by voice, text, email, chat, or social media channels.
Since labor costs are one of the biggest expenses in any contact center, efficiency in handling contacts translates right to the bottom line. By creating a more efficient workflow and process, you can lower operating costs without compromising on service quality.
When you can resolve customer support issues without engaging an agent, everyone benefits. Self-service options using NLP work 24/7 and don’t take breaks or call in sick. Research shows that auto-attendants, chatbots, and other self-serve options can save companies as much as 30% of their customer support budgets and speed up response times by as much as 80% for routine questions.
NLP and AI also improve reporting efficiency. With intelligent routing and relevant information provided to agents, it allows for a better way to track agent efficiency metrics for similar queries.
NLP can also help with customer retention in several ways. Faster resolution is a hallmark of providing a positive customer experience. The longer consumers have to wait on hold or to receive a response and resolution to an email or test, the more CSAT scores will suffer.
Sentiment analysis also allows organizations to study consumer churn rates by analyzing customer experiences during interactions. With this data, organizations can more quickly recognize the warning signs that lead to customer churn, so agents can address them more effectively.
This data can help more accurately predict customer churn based on interactions and help agents lower customer complaint levels.
NLP can also help with business data analytics in ways that basic feedback forms can’t. Rather than limit customer feedback to a few options, you can use open-ended questions to evaluate performance and let NLP decipher the meaning.
Natural Language Processing Alleviates Customer Frustrations
One of the biggest customer frustrations is time on hold. You already know it’s one of your most important contact center metrics to monitor. Research shows that after just two minutes, customers are likely to disconnect. More than a third of those customers never reach out again.
NLP and AI speed everything up to help process and resolve customer contacts more quickly. For example:
- Natural language processing can capture data like names and addresses quickly.
- NLP can recognize different languages and route calls to native-speaking agents.
- NLP can provide direct access to self-serve options to improve first contact resolution
Studies show that more than two-thirds of customer churn is avoidable if customer service contacts are resolved efficiently during the first interaction. NLP helps make this possible in the most efficient manner.
How Contact Centers Use Natural Language Processing
Contact centers are deploying natural language processing as a core component of their customer support. NLP is being integrated cross-channel to provide a consistent customer experience.
Perhaps the biggest benefit is being able to resolve contacts with specific information. For example, when a consumer calls their health insurance carrier and hears “Press 6” for benefits, they’ll still need to go through several intermediate steps or wait on hold for an agent when what they really wanted to know was the co-pay for an in-network provider. With NLP, an automated system can let them know their copay is $50 without having to sit through multiple IVR menu options or require an agent to look up the information.
Here are some of the most common ways contact centers are using NLP to support their customers.
- Auto attendants
- Agent support
- Record data analysis
- Sentiment analysis
- Speech-to-text applications
Auto-attendants using NLP can quickly understand why customers are reaching out and provide a positive experience by getting them to the right place promptly — whether it’s an automated option or a live agent.
Customers often explain their concerns or inquiries in their own particular vocabulary. NLP goes beyond IVR to allow customers to express their needs without being forced to adopt a shortlist of choices. Nothing is more frustrating to consumers than getting trapped in an “IVR loop” where none of the options quite hit what they need.
IVR systems work well if contacts stick to certain words and phrases. IVR struggles if words or phrases are not used in the proper sequence. Grammar and syntax errors are among the biggest reasons for mis-recognition. NLP enlarges the vocabulary and listens for key phrases that indicate intent based on previous engagements from other contacts.
Statistical modeling also allows NLP to use open-ended questions rather than yes/no or numerical options for more accurate routing.
Intelligent routing produces several ways to support agents. Team members can see in real-time what consumers are inquiring about while contact center software can provide the relevant information necessary to help improve call resolution.
Once consumers express their reasons for making contact, NLP software rapidly provides the most likely solution to their inquiry. NLP reduces agent search time and speeds up call resolution.
During calls, NLP can “listen” and help suggest appropriate resources to help support agents as well. As more data is gathered and analyzed over time, machine learning algorithms will continue to optimize resources to enhance and augment agent interactions.
Many contact centers are also using NLP and AI to surface upsell or cross-sell opportunities.
Call Record Data Analysis
NLP can also be used for call record data analysis. Rather than spot-checking calls for agents, your contact center software can deploy natural language processing to sort through large numbers of calls, texts, and customer interactions.
Not only can this help you to assess agent performance, but this data can also help make your NLP more efficient. Contact analysis can analyze how customers speak, make connections from specific words and phrases to intentions, and even discern emotion. These patterns can help make your contact center software more efficient as the pool of data grows.
It can also help you create more personalized customer experiences.
Sentiment analysis helps understand the emotions and feelings behind the words. There’s a big difference between someone politely asking for help and someone screaming HELP! When agents understand the sentiment, they can be better prepared to match consumer tone and deal with callers effectively.
Sentiment analysis in NLP software can also discern intent from customer feedback, whether recorded or written online. Consumers may use words such as fast service, easy, awesome, or great to describe an interaction. These are words that indicate a positive emotional response. This information becomes data to help the software learn what actions lead to positive customer experiences. It can be used to predict consumer behavior, tailor training and best practices, and evaluate agent performance.
Consumers are becoming more comfortable using speed-to-text applications in their everyday life. Nearly every smartphone now has voice recognition and voice assistants. Vehicles incorporate voice commands to place calls, adjust the temperature, or tune radios. In homes, IoT devices such as thermostats, light bulbs, TV remotes, and other devices are now being controlled through voice commands.
Overall, 36% of US broadband homes now own a smart home device besides a smartphone. When customers interact with your contact center auto-attendant, they expect the same (or better) level of sophistication.
Speech-to-text listens and translates the information consumers provide to help agents understand customer queries. It also works the other way by translating agent speech into text responses to speed up interactions and reduce typing.
Speech-to-text functionality can also create a written record of customer and agent interactions.
Innovations from AI
AI has created an incredible amount of call center innovation in the past few years and has transformed the call center space. Using natural language processing, machine learning, AI delivers an abundance of benefits.
- Responsiveness: Delivering preemptive customer service by predicting customers’ needs based on past interactions and current concerns
- Consistency: Your AI contact center solutions are unaffected by emotions, difficult conversations, or agent preferences. AI provides the optimal solution based on data from previous contacts.
- Speed: AI delivers resolutions and opportunities faster than human agents can by quickly sorting through every available option to find the best solution. Real-time insights enhance the customer experience and allow queries to be handled more efficiently.
- Personalization: By instantly matching callers with customer data, AI can personalize the customer experience.
- Scalability: No matter how efficient contact center agents are, they can only handle so many customer contacts at any one time. AI is scalable to handle large numbers of callers.
- Training: AI only needs to be trained once and then will continue to improve over time. In an industry with significant turnover, this can be a huge advantage. Also, AI can be used to see data patterns that can be used for agent training to improve performance.
With the right contact center software solutions, such as Aceyus, you can transform your contact center and upgrade customer experiences.
Aceyus provides omnichannel contact center solutions that work across a myriad of platforms. With AI, NLP, and machine learning, Aceyus provides real-time analytics that improve customer interactions and elevate the customer experience.
With robust data analysis and reporting, you can provide greater agent support, reduce response time, improve time to resolution and gain new insights into the customer journey. Aceuys learns from previous experiences to anticipate customer needs and create a faster, more intuitive interface for customers and agents.