It has been more than two decades since call centers revolutionized the customer support system for various industries. While some static call scripts and one-size-fits-all strategies may remain unchanged in business outsourcing, technology has played a crucial role in drastically altering the way call centers function.
Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data include various sources like the marketing campaign the customer has viewed multiple times, the type of transaction completed recently, or what a prospective customer looked for in their latest search. Of many technologies that call centers may have leveraged, artificial intelligence is one. Artificial Intelligence in call centers has allowed explosive growth owing to widely available cloud services and machine learning tools.
There are two major types of AI in BPOs right now. The first can evaluate vast volumes of data and provide just-in-time insights for the agents to improve their performance on a call. This AI delivers the correct information during a call and ensures the agent or executive is on track with the right information when the caller asks for it.
The second type of AI in business outsourcing is conversational AI. It analyses the speech of both the executive and the caller to identify emotions and, ultimately, intent. Conversational AI is mainly used to forecast the impact of a conversation based on vocal ticks, emotional state, and overall engagement level of both the caller and the agent. The sentiment analysis provides valuable real-time feedback on the emotional state of both the customer and agent and intervenes whenever required.
Ultimately, businesses want to create a personalized, positive experience for customers. And we are all well aware that when it comes to providing a good experience, the credits are always measured in sales, whereas a bad experience can have lasting effects. So how does AI change any of it? Here’s the answer.
AI automatically captures data, routes calls to the suitable agent based on the input and mood from the analyzed data, and creates a profile for future reference that can be used in the call center and other business areas.
Call center AI provides in-depth analysis of individual calls making it easier for the managers and quality control executives to make decisions. Each call is measured and compared against performance benchmarks to provide a clear picture with an actionable insight of where the agent is performing and lacking.
AI is directly integrated with call center service workstations for agents providing immediate insight into the data being captured, the probable outcome of the call, and much more. The result is faster response times, a higher call resolution rate, and happier and motivated call center agents who are now empowered with tools to help them perform better.
It is no news that call centers strive to provide a seamless and easy experience to customers since they constantly have to face the risk of losing out to a competitor. This report from a survey conducted by American Express found out that prospective or existing customers have bailed from a current purchase because of a poor service experience. To ensure that does not happen, call centers have turned to AI and machine learning solutions to help them take the following best action, turn leads into customers, increase retention rates, propensity product purchase, and much more.
Business Process Outsourcing or BPO firms tend to work a lot around data. That is why data-driven call centers look forward to implementing AI solutions to improve customer experience. Here are a few practices for data-driven call centers with AI and big data to enhance selling via a good customer experience.
One of the significant advantages AI brings to call center finances is saving on human resources costs. Call routing through AI helps get the right customer to the right representative, taking into account the reason for the call and lifetime value and call complexity.
A good number of call centers also opt to use skills-based call routing techniques to respond to a promotion. Say, for example, if one team is striving for promotion A and the other for B, AI can quickly identify and analyze the call and route it to the appropriate team. Layering in AI to skills-based call routing also ensures that the customer arrives at the right agent who can guide them in the best possible way.
Traditional call monitoring cannot pull data from multiple sources in real-time. For example, the performance of call center executives or agents is currently monitored according to human observation. Calls are listened to and analyzed by a small number of ‘human’ managers who may or may not have their own biases that could impact evaluations. The analysis can be inaccurate, directly moving the scoring of agents.
If you have ever called a customer support center of an e-commerce firm or any business, you’d be well aware of how they inform you before the issue that the call may get recorded for quality purposes. This indicates that agents receive feedback and support only after the ring, and there is probably no automation or real-time assistance implemented to guide the agent. What gets even more annoying for the customer is that they may have to speak to several agents or keep dialing digits without receiving any fruitful outcome — the result- poor feedback for the agent.
AI improves this situation by performing those essential functions like monitoring, analysis, and support in real-time. Much of this function is performed by its branch, machine learning. ML services help in analyzing the mood and response of both the customer and agent on call. When used as feedback, the data enables the agents to respond more effectively, making them both happier, as compared to previous experience.
While the main objective of most call centers has constantly been improving customer satisfaction, with time, BPOs have also come to realize that it’s equally important to take care of the agents as well. Each agent communicates differently and appeals to a different segment of customers. Some agents can handle negative comments well, and some do not. Firms must look at the personality profiles of the agents and other professional factors like sales numbers and call handle time to match them with preferable customers. But that would be too much of a task, probably having them hire someone to do this job alone.
AI can assist here. Once the call has been routed and data produced, firms can ask agents who made a particular sale to handle those calls where customers look for a solution hidden in the said product.
Using data to get clarity in context is crucial in a call center. Context makes the process easier for the customer as they don’t need to repeat or spend a lot of time on the call explaining their problem or question. Moreover, context is also one of the key elements for call routing.
AI can actively analyze data from a customer call to predict when someone is likely to get annoyed and facilitate a change before that happens. This is done by capturing data points related to vocal characteristics, previous call history, nature of the concern, and the customer service agent’s response. Predictive analytics can help understand the problem a customer may have, thereby eliminating long call durations, waiting time for customers, and so on.
Thanks to the digital age, online reviews available on Google or e-commerce sites for a product has a lot of influence on buying trends. Customers tend to use a chatbot before calling customer support. However, while chatbots and automated phone systems can guide customers to answer simple questions that may not need a human agent to handle, chatbots are considered the front line in the customer service ecosystem.
Advancements in machine learning have improved how NLP (Natural Language Processing) is being implemented across industries. To make chatbots leverage conversational text, AI-powered assistants are now being introduced to respond better to customers’ prompts, just the way Google voice assistant, Alexa, or Siri are used.
The AI-powered call center is nothing new, especially since digital transformation started turning tables for businesses. The largest companies have been integrating these tools for several years and realizing their substantial benefits. The Coronavirus pandemic has only accelerated the implementation of AI in other call centers.
AI and ML have had the most profound impact in the past two years, not by replacing humans but by supporting them. Firms that had it implemented before the world went into a global lockdown made the most out of it during the pandemic. And for those that could not, have outsourced the service implementation to an artificial intelligence and machine learning consulting firm. The reason being that though nothing can replace the personal touch of a human voice, technology can empower them to do it more efficiently.
What was once a centralized process that allowed close monitoring of call center agents has now been decentralized, with service agents and quality staff working remotely from home? AI is helping bridge the productivity gap, efficiency by providing real-time support for both customer service agents and managers who need to monitor and evaluate call quality.
Any industry one can think of is weathering the storm right now. From travel industry customer support executives helping upset customers process cancellations to the IT and software company representatives seeing a sudden surge in demand for their services, the E-commerce industry seems to have seen an exponential rise in demand for AI-powered chatbots owing to the switch in online shopping.
AI and ML are transforming the call center, pushing boundaries for experience in customer service calls. This wave of AI affecting call centers is perhaps the best opportunity for CXOs to be early adopters of cutting-edge technology take takes their call centers to the forefront of the AI-powered revolution.
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