Request-to-Pay: A New Driver for Innovation in…
Contemporary use cases of AI and its potential for value creation and preservation in client services.
Contact centers have had to rapidly adapt to meet the challenges and shifts caused by the COVID-19 pandemic. These include remote work, maintaining proper data security, and facing significant spikes in inbound call and chat volumes. Extended wait times experienced by customers, due to surges in call volumes, have been unpredictable and strenuous to business operations. This is due to the limitations of remote staff numbers and limited technology available to service inquiries.
To keep up with the increasing volume trends, contact centers initially viewed hiring more personnel as the obvious solution. However, this may not be feasible for many businesses from either a logistics or financial standpoint, especially given the challenges of hiring in the current environment. This is where advanced Artificial Intelligence (AI) comes in.
Fundamental to customer service is providing excellent support to clients. Considering the contact center is often the first point of outreach, ensuring this encounter meets quality expectations is of utmost importance. Given this, it’s understandable that incorporating chatbots into the exchange could cause skepticism. However, with the advancements of modern AI software, such as the Amelia platform, concerns regarding user interactivity have been rapidly alleviated.
With the implementation of advanced AI in the contact center function, human-like dialogue with chatbots and virtual agents is now more of a reality than ever, and clients no longer need to wait on hold to interact with an organization.
The COVID-19 pandemic has had a lasting impact on contact centers, both in the ways they operate as well as in the shifting needs of the clients they serve. As with many industries, contact centers have had their fair share of challenges to overcome with regard to remote work.
Contact center annual turnover is currently in the 30-45% range, which, in a remote work environment, presents difficulties in hiring and properly training new agents in a timely manner. It’s important to note this isn’t a temporary issue as 73% of professionals in the contact center space believe that training will continue to stay virtual even in the years following the pandemic.
It is estimated that over 90% of the global contact center workforce has moved to remote working during the pandemic. The transition to a work-from-home service delivery model has been especially difficult for off-shore agents, who are often situated in lesser developed economies and may not have the necessary technologies and applications to perform at the same level as their onshore counterparts.
This presents substantial difficulties in facilitating effective client support and ensuring employee morale. Furthermore, unpredictable developments in federal and local COVID restrictions across regions of operation present difficulties in planning and allocating resources in an effective manner.
As previously mentioned, the pandemic has resulted in exponential increases in call volumes at client services centers across the globe. While contact centers in the travel and medical spaces are often top-of-mind when it comes to areas most impacted by surges in volumes, nearly every sector that contact centers support has been affected.
The financial services industry is still reeling from the effects of the pandemic. The digital transformation of operational teams, and the ways that these organizations conduct business, have been forever changed. During the early phases of the pandemic, many contact centers saw an almost 300% increase in calls compared to their business-as-usual environment.
Alongside volumes, KPIs (key performance indicators) commonly used to assess and appraise operational performance have also been impacted greatly, primarily due to the spikes observed in inbound client activity. Average Handle Time (AHT)—a metric typically observed in the range of 3-6 minutes—has been observed at 10 minutes or greater. Abandoned rates have increased substantially as well.
The resulting impact is that Average Wait Times and Service Level Standards have increased significantly which are not characteristic of the business-as-usual operational environment. Higher transfer rates—another impact of the pandemic, which can often be traced to “bubble staff” with minimal support experience who are assisting with high call and chat volumes—have led to both longer Average Wait and Average Handle Times.
In an attempt to provide first-contact resolution during the pandemic, contact centers have moved toward automation techniques such as chatbots, intelligent virtual agents, AI and natural language processing. These technologies are helping to better map the customer experience and assist in driving conversations with minimal human intervention.
This has been achieved with much greater effectiveness than previously observed, as technologies such as AI have rapidly developed to meet current business requirements. The observed reduction in calls, emails and agent-based chats dur to AI has had a phenomenal impact on contact center operations and staffing requirements. Natural language processing is being used to dynamically react to the customer’s input, driving conversation forward and only diverting clients to a human agent if deemed necessary.
An Omni-Channel customer experience is a highly effective mode of customer relationship management that has become important in the wake of the pandemic. Omni-channel digital tools and utilities enable businesses to communicate more efficiently by updating client communication channels in real-time. Using omnichannel, conversations can be seamlessly transferred between a variety of communication channels, including text, chat, and voice. Thus facilitating a more streamlined customer service strategy, with impactful first-contact resolution.
Enabling an omnichannel customer journey allows interactions to be synchronized across digital and analog channels, making it simple and straightforward for agents to review conversation history before assisting a client. Clients can also choose their preferred way of service, a characteristic of a client services model that has proven to result in improved customer satisfaction and a more personalized experience.
Traditional, multi-channel contact services centers can lead to customers having to repeat themselves when forced to move to a different channel. The highly tangible impact of omnichannel communication is that all previous interactions are stored digitally on the cloud and synchronized cross-platform, ultimately resulting in faster resolutions. This is due to the ability of any agent in the organization to queue this information up efficiently when the customer’s inquiry is transferred cross-platform (often pertaining to an escalation, etc.) and is something that AI can facilitate easily within the contact center environment.
Enhanced self-service through AI can generate significant cost savings within the contact center environment, as well as improve agent occupancy levels through driving down call volumes. Clients will note a more curated and fluid user experience, as they can interact via their preferred channel with the service center and continue that conversation in other channels with rapid ease if required to resolve their inquiry.
Utilizing AI, the contact center agent’s responsibility shifts to partly that of an SME (subject matter expert), as AI can handle simple activities that do not require human intervention. AI works best when operating in an environment where answers are primarily binary in nature or information can be queued accordingly to address client concerns.
The key benefit of AI in the contact center environment is that the software can deliver critical information in relation to customer concerns that may arise overnight, during holidays, and at other times when staffing can be cost more.
Integrating AI features, such as predictive contact center call and message routing, can facilitate the effective transfer of clients depending on select criteria. These include age and income level, whether they have high or low spending patterns, or automating the classification of the inquiry type and client sentiment. This pairs them with the best agent/SME who can resolve their inquiry the first time.
AI can also be utilized to analyze customer sentiment through speech recognition and natural language processing during the call in real-time, as well as provide enhanced statistics and insights after the call. AI can detect sentiment in customer communication and provide intelligent analytics outlining whether the client was satisfied with the call. This can enable the evaluation of contact center agents for performance review purposes, as well as develop future use cases for AI client services functions.
While agents are actively assisting customers via phone, chat, and other channels, AI can operate in the background, scouring the agent’s knowledge base for content and answers to the customer's concern. The agent can rapidly tend to the client’s inquiry while the AI agent develops chat transcripts and queues up critical information to address their questions.
With the agent analyzing the results of the knowledgebase search in real-time, they can decide which information integrates best in the current case and can most effectively solve the customer’s issue. The agent can then communicate this content to the customer, resulting in a highly satisfactory, first call resolution service. Particularly efficient in the COVID era of high call volumes and extended wait times.
AI can actively sort and filter information before presenting it to the agent in a manner that is easily digestible for both parties involved in the information exchange. This can facilitate more effective communication and mitigate any breaks in communication between the agent and the customer. Once the AI agent is trained via “first line of defense” exchanges with customers before routing to an agent in an escalation, the software can more easily interact with customers and handle a more significant degree of communication than when first taken 'out of the box'.
AI can be easily implemented to streamline the verification process, traditionally confirmed within an environment where PII (personal identifiable information) and private client details will be discussed during the call. AI can utilize voice biometrics to analyze the client’s voice or other credentials in order to determine if the caller is actually who they say they are.
Using AI for the client verification process can strip minutes off calls requiring pre-verification, resulting in increased agent occupancy (available time during the day) and can allow agents to focus on more value-add activities, such as servicing high-value client inquiries. AI can also handle password reset inquiries, streamlining this process and, similarly to credentials verification automation, allowing agents who typically tend to these types of inquiries to focus on higher-value customer inquiries and issue resolution.
AI applications, once adequately deployed, can enable agents to automate manual workflow processes. Specific triggers which are set up by the agents and management for standard processes, such as CRM usage can be deployed, and applications can be launched automatically by the AI application once the agent performs specific actions on their computer.
Amelia is a market-leading Conversational AI platform that has proven to enhance the customer experience within the contact center domain. Amelia’s impact can be observed in many financial services organizations on Wall Street and across the globe, and the software’s effectiveness is only limited by its ability to continue to learn and develop based on its active deployment in day-to-day contact center operations.
Amelia has been shown to effectively address customer inquiries without the need for agents, effectively minimizing call volume and reducing wait time for customers, leading to more efficient, and less costly, interactions, enhancing first contact resolution and client satisfaction.
Amelia can enhance agents’ service to clients through targeted escalation from Amelia virtual agents to the correct support staff who can handle the client inquiry once it has been passed on. Even after the issue is escalated to a human, Amelia can be configured to continue to monitor the conversation and help guide the agent in their responses using industry best practices. This can help train new agents and has proven to be more impactful in the provision of first-call resolution. This ensures more satisfied customers as well as fewer callbacks.
Amelia can also work alongside agents to actively gather and provide pertinent information or launch programs. While the agent speaks with the customer to address their concerns, Amelia can trigger enterprise systems and contact center databases (providing agents with data and information on a separate screen or chat, while an agent is engaged with a customer), allowing for highly satisfactory and seamless client interactions.
Through Amelia’s virtual agent capabilities and customer conversation flow, enhancements around new use cases, insights into abandoned rates escalations reduction and overall improvements to existing AI use cases within the contact center environment can be easily achieved and documented. While the agent converses with the client, these advanced analytics can provide additional recommendations so the AI is further trained to fully understand the scope of customer concerns and can even point out additional training needs for less-tenured agents. Data from interactions can also be used to better train dispatch and routing algorithms to enhance efficiency in future interactions.
A large U.S. regional bank wanted to heighten customer engagement through its contact centers, reduce high-volume pressures on customer contact agents, as well as allowing bankers to focus on providing unique and personalized services to customers across their business.
After previously using a traditional IVR system to direct and respond to customer inquiries, the bank sought to improve and accelerate customer experiences by hiring Amelia to provide human-like communication and collaboration with the banks’ contact center agents.
Today, the AI engages 100% of incoming calls and now has the ability to scale and resolve about 50% of incoming calls into their contact center, including end-to-end automation of approximately 100,000 calls per month. Since June 2020, Amelia has allowed the bank to provide automated self-service to more than two million customer calls. All of this has had a major impact on the bank’s operations and customer service levels, and has allowed the bank to focus personnel on higher-value activities, now that Amelia is handling so many first-touch customer interactions.
A Spain-based financial services company wanted to transform its internal and customer-facing technology, understanding that traditional banking was becoming less and less popular in relation to digital and mobile banking. The leadership team at the bank wanted to see whether Conversational AI could help improve both internal and external processes which would then free employees to focus on more valuable and unique tasks. Within a year, Amelia Conversational AI was conducting almost 50,000 active conversations per month, with resolution and intent recognition rates of 90% each. Amelia recently surpassed 1 million total conversations, far above initial expectations.
A multinational telecommunication company located in Spain implemented Conversational AI as a voice-based customer service agent to handle all calls received to hotlines, which handle approximately 72 million calls in total. As a result, the company developed 28 specific skills it wanted the conversational AI to master, including 18 end-to-end automation skills and 10 skills that would require Conversational AI to route a call to an appropriate human agent. Since the initial roll out, the company has expanded the number of use cases in deployment from the original 28 up to 75.
Today, Conversational AI handles 100% of the company’s mobile call volume and it recognizes customer intent correctly on 97% of calls. Furthermore, customer abandonment rates on conversational AI-led calls have decreased 24% from the initial week of deployment and customer satisfaction in calls managed by Conversational AI are at the same level as those handled by human representatives.
While it’s clear that industry uncertainty has driven high volumes throughout the pandemic and altered the KPIs by which performance is evaluated, the AI-driven and automated contact center organizational model is now considered essential in effectively serving clients. By channeling client inquiries through digital channels and harnessing the power of AI, management can improve key performance metrics and bring value to customer interactions in ways that were unheard of prior to the pandemic.
Sia Partners has observed that COVID-19 has forced substantial evolution in the ways in which contact center personnel are managed and deployed within the high-volume environment. We have also noted key areas of potential improvement from a technology standpoint. Contact center teams continue to grow and adapt in the wake of the pandemic. The ability to analyze customer sentiment, divert “low value” call volumes away from valuable agent resources, and more closely align staff to customer concerns most pertinent to their individual skillsets, have proven critical in achieving operational equilibrium, all capabilities that AI can afford the modern, technologically-forward contact center team.