The central question that every organization in the contact center business asks is how to improve the customer experience while lowering costs. The best way to achieve such a seamless customer experience without investing too much money or effort is to achieve large-scale digital transformation in contact centers.
This digital transformation includes the adoption of emerging cloud technologies, including artificial intelligence (AI), machine learning (ML), chatbots, robotic process automation (RPA), and the Internet of Things (IoT), which can activate additional services like email, text, chat, and direct website interface, and ensure a quick shift from multi-channel to omnichannel centers in which all channels are more integrated and the user information is transferred seamlessly from channel to channel, making customer service more consistent.
The key difference between a multi-channel contact center and an omnichannel center is a consistent brand experience regardless of the technology or communication channels the customers choose.
Cloud-based emerging technologies are revolutionizing the contact center space by eliminating the need for human agents in some cases, especially in repetitive, mundane tasks of answering frequently-asked questions or entering data, and enhancing the experience of employees in contact centers, enabling them to spend more time on design, strategy, and implementation.
This post will explore some key emerging cloud-based technologies in contact centers.
Artificial Intelligence (AI)
At its core, AI is a collection of technologies that, when used together, can automate tasks that require a lot of time and resources to complete manually. These innovations can be divided into two categories: those that enhance cognition (the capacity to understand and solve problems) and perception (the capacity to recognize). Both are crucial for the operation of AI. Cognition without perception is like trying to drive while having a clear view of the road but not knowing what a road is.
Technologies that recognize different forms of communication, such as natural language processing and image recognition, fall under the perception group. Machine learning, a buzzword at the heart of AI, is classified under the cognition group. There are numerous varieties of machine learning. Unlike in the past, when programmers had to write explicit instructions into computers to carry out tasks, machine learning enables the software to pick up new skills from examples.
This could be used in contact centers to feed a data set of millions of questions mapped to millions of “good” answers from prior phone calls rather than having scripted responses for every question a customer might ask. At some point, the software could respond to 30% or more of customer inquiries and generate responses without explicit input from a programmer.
AI has many exhilarating advantages. In addition to operating continuously, it performs tasks more accurately, freeing up human agents to switch to work requiring a more human touch. AI has the potential to improve human performance in addition to freeing up human time. Most AI users know there are still many scenarios in which interactions are too complex for AI, and a customer needs to be transferred to a human. Even if an agent takes over the interaction, AI can still be helpful. By listening in on the call or even using predictive software to foresee what a customer needs based on their customer history, AI can recommend the best course of action for agents.
The dashboard of an agent would then display suggested phrases. By transferring customers to agents through predictive routing who have relevant experience or personalities similar to the customer, AI could improve an agent’s chances of success. Even better, AI might analyze callers’ emotions and give agents immediate coaching and feedback so they can advance more quickly. The training, workforce management, and recruiting costs that are a problem for today’s contact centers would also be reduced by this real-time coaching for the organizations. An average call center service agent costs $4000 or more to hire and another $4800 or more to train, costs that quickly rise with staff attrition. Notably, the attrition rate for contact center employees is 30-45%, more than twice the 15% national average.
Despite the obvious advantages, AI has drawbacks as well. The most significant of these deficiencies is a lack of general knowledge. Although artificial intelligence (AI) can become extremely good at a specific task, such as translating languages from users in different countries, this does not imply that it is good at related tasks, such as comprehending slang in various languages. Customer resentment may result from this widespread misconception. AI is also not a technology that can be bought from a vendor and immediately put to use. Each organization’s needs must be met by AI, which necessitates extensive backend programming and time.
As a result, incorporating AI requires a sizable resource investment. For context, the least complex chatbot version runs about $30,000, while the most complex versions cost up to $250,000. These sums are merely approximations for a prototype. Costs will rise if the prototype is scaled up for the entire organization.
Other problems emerge when humans and AI collaborate in the workplace. One is that AI sometimes makes decisions that are difficult to understand. Humans depend on reason. Agents can explain why they tried to diffuse an angry customer’s phone call by offering a discount on a different product. Contrarily, AI’s decisions frequently defy easy explanations, which can be challenging to accept.
Furthermore, figuring out why the AI erred when it did might be challenging. Lastly, since machine learning, which gives AI cognitive abilities, depends on sufficient data, bad data can make AI make mistakes. Consider a scenario in which the Department of Commerce transferred decision-making authority for approving small business loans from humans to AI by feeding the AI information from human loan decisions. The AI would also be biased against the elderly if those humans were.
Robotic Process Automation (RPA)
RPA is software that performs computerized tasks as humans would. Because it concentrates on tasks that do not require cognition and dynamism, unlike AI, which must be able to adapt to various situations, it differs from AI. RPA software is based on rules and performs best with structured data. RPA is advantageous for businesses with a high volume of repetitive processes, such as data entry or invoice processing, because it uses structured data. RPA software is regarded as “lightweight” IT because it resides in the presentation layer of software, the graphical user interface that humans use, making it simpler to implement than more complex emerging technologies like AI that call for IT department intervention. With subject matter knowledge, employees involved in business operations can start automating processes within a few weeks of training without any programming experience.
The best way to demonstrate the advantages of RPA is with examples. Imagine a worker copying inspection numbers into a spreadsheet while sitting in the FSIS hallways and various documents. She must access the database of her business to complete this task. There, she clicks on the file for the factory that was recently inspected, scrolls to find the inspection number, finds it, copies it, and then pastes it into the spreadsheet. It will take her at least one workday if not more, to complete the process for more than 1000 inspections. Imagine that a camera watched her screen the entire time she worked, capturing every keystroke and click. It could precisely imitate her movements the following day. She can now work on more interesting projects, like assessing the inspection forms to see if they can be improved because she is no longer required to complete this monotonous task. According to estimates, businesses can anticipate a 50% reduction in discrete and specific processing time and related labor time through adopting RPA. RPA offers a safety valve for employees and businesses to get time back and free employees to do more creative work in an era where the volume of work, especially paperwork, keeps growing.
RPA relieves humans of tedious, time-consuming tasks, allowing them to work more quickly. Humans are prone to error as they progress through repetitive tasks because of fatigue or boredom. RPA software is always alert and capable of precise and reliable movement. The equivalent of two to five humans can complete structured tasks using one license of RPA software. RPA can reduce errors in highly regulated business processes where errors can be expensive, like in the finance or accounting departments. RPA can eliminate the risk of human error in manual tasks when properly coded and tested.
RPA has flaws, just like AI. RPA’s inability to manage exceptions to the structured rules used to create it is one of its drawbacks. Talking customers through their annoyance with defective products or handling orders incorrectly will always be human tasks. Furthermore, even though RPA minimizes human error in repetitive tasks, human error can still occur during RPA setup. A human error in the RPA design could have catastrophic results for the organization. Finally, RPA cannot perform the majority of automation tasks that AI can. The less likely RPA will be helpful, the more complicated the project is.
Internet of Things (IoT)
The Internet of Things (IoT) is the concept of information transfer from sensor-based devices that can connect to broadband. IoT has spread across the country because broadband internet is widely available, and more devices than ever have sensor capabilities and can connect to the Internet. IoT impacts many industries, including contact centers, ranging from smart thermostats in homes that can be controlled remotely by a mobile device to sensors that can track the location of livestock.
IoT will significantly impact how customer service is provided in the future, even though its applications in the contact center are not as obvious as those of AI or RPA. IoT will notably shift customer service from being reactive to being more proactive. Devices can notify the manufacturer before a problem arises because they are intelligent and connected. Without the customer even being aware of a problem, a contact center representative would speak with them or resolve it remotely.
Humans will still be required, as with other new technologies. The main difficulty will likely be the complexity of customer inquiries that require human agents. Therefore, it will be necessary to train agents to be more technically proficient and able to respond to challenging questions. Expert agents will be common in an IoT-driven future economy. The number of things connected to the Internet will reach 50 billion by 2023, and over the following ten years, IoT will generate $19 trillion in profits and cost savings.