Businesses looking to tackle mounting challenges in the global marketplace have turned to technology in ever-increasing numbers to level the playing field.
The emergence of machine-learning technology has played a major role in improving organisational efficiency in a variety of ways. Artificial intelligence offers up a wealth of productivity-enhancing features fit for use in organisations of all sizes, but applying it to each business’s use case reveals the unique functions it serves to be governed by the industry and niche it is employed in.
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Call centres can leverage several autonomous and semi-autonomous AI functions to streamline internal processes. Offering AI-enhanced processes to call centre agents does more than make their work easier; it enhances the user experience through improved speed in reasoning and augmented accuracy. Accenture predicts AI will increase business productivity by over 35% before 2040 in the US alone.
Investments in AI in the call centre industry are on the rise. A report from MarketsandMarkets estimates that the call centre AI market will grow to US$2.8-billion by 2024, an increase from $800 million in 2019. What’s more, Gartner predicts that 50% of enterprises will spend more annually on chatbot development compared to traditional mobile app development by 2021. “In the ‘post-app era’, chatbots will become the face of AI and bots will transform the way apps are built,” Gartner explains. “Traditional apps, which are downloaded from a store to a mobile device, will become just one of many options for customers.”
The potential for AI in the call centre extends far beyond AI-driven chatbots. Call centre agents equipped with AI tools can leverage their strengths without losing a sense of their objectives and service standards. However, for AI to deliver the fullest benefits to employees, organisations and the customers they serve, the way users and AI mesh in completing tasks must be examined closely.
Expertise and efficiency – How AI’s development stacks up to a human’s
Expertise among call centre agents plays a major role in determining how long customer issues take to be resolved.
When call centre teams are divided into specialists, it can help simplify the handling of niche requests and streamline issue-elevation processes. However, through the use of AI, the process can be greatly improved, allowing agents to adopt a more generalised, self-contained, assistive approach that better serves customers.
The differences in how expertise and efficiency manifest among human agents and AI solutions show just how much the latter can help streamline the duties of the former.
Human expertise and efficiency
Expertise is best defined by the results it yields. An expert is an individual who delivers results in their chosen field that few others are capable of repeating.
Expert call centre agents working without the aid of artificial intelligence rely on innate skills that are flexible enough to be applied to each of the unique facets of their jobs. These can largely be boiled down to the following:
- Identifying patterns: Where an average agent applies principles learnt in training when confronted with predefined customer issues, an expert agent can build on this foundation by connecting the dots between established protocol and new approaches — all while adhering to service standards.
- Specialisation: Both AI and humans tend toward specialisation, but the human equivalent has more leeway. Call centre agents who are adept at solving a specific set of issues may still prove effective at solving loosely related problems as well.
- Excellent memory: An expert customer service rep leverages a well-developed memory of relevant rules of engagement with customers, regulatory guidelines, customer history and more to effectively handle their job.
AI approaches the use of memory much differently, focusing on a more narrowly defined data set than a human expert commands.
AI expertise and efficiency
Artificial intelligence builds expertise in each function primarily through focused repetition. The main mechanisms that make this process possible are the algorithms employed to enable its improvement.
Machines use pattern recognition to identify similarities in data sets. Once identified, the data is categorised for future use in identifying similarities in more diverse data sets. The algorithm that is used colours the results derived from the data, and different algorithms may prove greater or less effective at certain tasks.
Algorithms represent a deep and detailed topic in the field of AI that extends beyond the scope of this article, but we will touch on a few noteworthy examples below:
- Classification and regression trees: Algorithms that fit this classification follow a decision tree, a series of interconnected true or false questions to determine the class that data belongs to (classification) or a specific number (regression). These are usually weak predictors on their own, but can be grouped to improve their accuracy.
- K-Nearest Neighbours (K-NN): This algorithmic approach to machine learning initially stores observable characteristics termed “feature vectors” and class labels to better understand the task at hand and the data it needs to interpret.
- Naïve Bayes Classifier: This algorithm centres on classifying data. It works by ignoring correlations between data characteristics and, instead, interpreting them independently to decide where every bit of data belongs.
How humans can help AI
As call centre goals and industry trends lead to narrower niches, the importance of both human and AI skill sets has been heightened. However, although AI plays a supportive role for call centre agents, the agents themselves may also be important for further development of call centre AI.
Machine teaching
Calibrating machine learning algorithms usually involves feeding in large batches of data for them to learn from. This approach encourages machines to develop a baseline from which they can more effectively interpret data in real time. However, machines “taught” in this way may take significantly longer to become effective at the task they have been given. Enter so-called machine-teaching techniques.
Machine teaching involves progressively teaching concepts to machines in digestible, logical steps explained in an understandable format by a human agent who is already adept at the task. This approach cuts reliance on data significantly, allowing companies with very specific needs to successfully train their AI despite not having access to vast, relevant data stores fit for the purpose.
As Jennifer Langston explains in an article published by Microsoft, “In difficult and ambiguous reinforcement learning scenarios — where algorithms have trouble figuring out which of millions of possible actions it should take to master tasks in the physical world — machine teaching can dramatically shorten the time it takes an intelligent agent to find the solution.”
Identifying opportunities to leverage AI
A wide variety of industries have come to terms with AI’s potential to revolutionise the way businesses develop and adapt to shifting consumer trends. Many prominent companies have already seen profitable results from their use of the technology, but more AI spend appears to be looming on the horizon.
The greatest impediments to implementing AI within any organisation have proven thus far to be largely regulatory and procedural in nature — local legislation and integration woes make using AI more complicated than it needs to be.
Coupled with the issue of AI’s inherently opaque processing techniques, it can be difficult to work out where the technology can safely be applied without betraying consumer trust or crossing legal lines. Data used to train machine-learning algorithms needs to be sourced responsibly, highly trained AI needs to explain its own reasoning in detail and training must be handled continuously to ensure its accuracy.
Identifying where AI fits into your organisation’s efforts comes down to assessing your own internal operations and determining which ones are the least burdened by complex legalities, flexible enough to incorporate new methods and niche enough to benefit from AI tools with highly focused functions. In a call centre, areas that may satisfy these constraints include assistive services for agents, customer interaction endpoints and more.
Why AI cannot replace human communication
At present, AI alone is ill-suited to navigate the complex dynamics of human conversations entirely unsupervised. The best results are achieved when it is employed in a supportive fashion to pad interactions between customers and staff members. AI excels at assisting customers with simple problems, saving agents’ time for more complicated concerns. In fact, roughly 67% of consumers have already come to expect to use messaging apps (where AI is already commonly used) when talking to a business.
Creativity, empathy and spontaneous judgment are still beyond the scope of AI’s capabilities, leaving humans as the only option for handling tough situations with no pre-programmable solution defined. Despite such limitations, AI’s future is bright as is reflected by the degree to which private businesses have begun to invest in the technology in recent years.
Their optimism for this technology’s continued development and profitability may be due to its built-in potential for consistent improvement over time.
Which call centre functions can AI be entrusted with?
AI excels at both prediction and automation, but the way it handles these tasks differs from other software solutions with similar uses.
Prediction
AI bots can consistently draw from a customer’s full history with your organisation to offer more appropriate solutions for their current problems. AI’s predictive capabilities are not to be trifled with as it has been proven that well-trained algorithms can better predict outcomes than humans can.
“In three competitions with human teams, a machine made more accurate predictions than 615 of 906 human teams,” explains Olivia Goldhill in an article published by Quartz. “And while humans worked on their predictive algorithms for months, the machine took two to 12 hours to produce each of its competition entries.”
As a virtual assistant to a human agent, AI can also enrich human-to-human interactions by speeding up searches for relevant information as agents communicate with customers directly and provide next-step guidance based on its knowledgebase of interactions that were deemed to have positive customer outcomes.
Automation
Fielding common complaints and general calls is one area where AI can directly free up time for human agents to devote to more complicated issues.
Which call centre tasks are best handled by humans?
Some tasks are best left to humans to resolve correctly. Here are two examples:
- High-stress customer concerns: Customer queries that require excessive effort on their part to solve through interaction with your organisation have the potential to send them into the arms of a competitor. It is in such instances that the supportive role of AI becomes self-evident as such customers must be patched over to live human agents when AI has run out of options it can act on.
- Exceptions and uncommon scenarios: As is the case with high-stress customer queries, issues that are at all uncommon may demand the intervention of human agents to be solved to the customer’s satisfaction. Such scenarios might include customers experiencing vulnerabilities as a result of health, caregiver, financial or other hardships where empathy and flexibility on the part of an agent are required.
Getting the most out of AI integrations
AI is a prime contender for “most promising technological development” as far as modern businesses are concerned, but many organisations struggle with leveraging this emerging technology to the fullest. Getting the most out of AI solutions in a call centre context involves tackling the following:
Robotic process automation
AI alone is capable of intelligently automating several key processes within any call centre’s daily operations. However, by pairing AI with robotic process automation (RPA), it is possible to streamline many otherwise walled-off internal business processes.
RPA works by mimicking human actions within a strictly controlled environment — such as a specific application’s user interface. This allows certain repetitive processes to be handled in much the same way procedural “macros” (macroinstructions composed of individual steps) are often used, conserving time otherwise put towards completing such tasks manually. This process does involve “teaching” RPA solutions how to go about completing a given task, but unlike AI, they are incapable of learning further through trial and error.
Together with AI’s decision-making capabilities, RPA can empower call centre agents with dynamic dialling functionality, speech to text input and more. In fast-paced customer service-geared environments, this approach can relieve agents of the stress involved in dealing with multiple applications and systems at once while interacting with callers.
Clear implementation standards
Establishing clear standards for your company’s AI implementation can help support future development goals with a stable foundation. Pivotal decisions such as whether AI should be applied on client-facing tasks or only on internal processes should be made early on and documented to avoid needless confusion with additional integrations in the future.
Focused business goals
Although AI can serve many different purposes within a call centre, it may not be reasonable or even possible to deploy it in all the ways you intend to simultaneously. Try to specify what each of the business goals you need AI to achieve will be first and then progressively deploy the technology over time. A few common call centre business goals that AI could be applied to are the following:
- Personality profiling: Profiling customers to determine ideal call routing is a clear example of this. However, agents can also be profiled to further optimize internal routing processes.
- User activity monitoring: AI can be employed to keep track of interactions a customer has had with your organisation and the channels they have used to do so, building a clearer picture of their relationship with your company and their likelihood of continuing to do business with you.
- Contextual predictions: AI can interpret customer concerns with their history and unique preferences factored in from the start, allowing for predictions of their behaviour to be made and acted upon in real time.
Cyclical development
AI develops as it performs its duties, fortifying its capacity to perform said duties over time. Although this represents the biggest draw to using the technology in the first place, it also implies a culture of continuous development meant to keep the technology improving in the right direction.
AI use cases for call centres
AI has many potential uses in the demanding environment of a high-volume call centre, ranging from freeing up human resources to directly improving customer satisfaction. The following are a few key uses of AI that call centres can leverage:
Data capture/analysis
The use of AI allows call centres to capture spoken words as text to be used in more varied analytics settings.
Customer relationship management (CRM) systems benefit greatly from the inclusion of AI, leveraging the technology’s advanced categorisation abilities to further streamline critical business processes in sales, marketing and more.
When AI is paired with a CRM in a call centre context, callers’ full histories with your organisation can be assessed to provide deeper insights into their issues on the spot. These insights may serve to inform the AI of suitable suggestions to offer callers before they are routed to a live agent or even to inform agents while they are on a call.
Behavioural predictions
AI can be used to make accurate predictions regarding future customer behaviour by assessing both their current interaction with your organisation as well as their history of interactions simultaneously.
Interactive voice response
Interactive voice response (IVR) solutions provide customers with an immediate response from your call centre that can direct them to the most appropriate agent for their problems. This technology is especially useful for helping call centre staff accommodate high-volume call periods without resorting to manual call routing.
However, when paired with AI, it can do even more to improve the customer experience.
Thanks to AI’s ability to interpret fuzzy real-world data, callers can interact with an AI-enhanced IVR system conversationally without sacrificing efficiency. If the call needs to be routed to a live agent, the shift can be handled automatically without disturbing the caller.
This type of functionality has grown more mainstream with the rise in popularity of AI developments such as voice search on consumer devices, which closely mirrors AI-boosted IVR capabilities. Some 40% of adults are already accustomed to voice search (using it daily) and are likely to appreciate similar speed and accuracy in an IVR.
Self-service options
In much the same way that IVR stands to benefit from applied AI solutions, self-service channels, including chatbots and knowledgebases, can be enhanced with this technology.
Chatbots capable of providing relevant suggestions to customers quickly can drastically improve the customer experience. Such implementations can also be configured to escalate the conversation to a live agent as needed.
Speech analytics
Speech analytics leverage AI’s strengths to extract words from audio and categorise them according to meaning, context, individuals involved in the conversation and more.
CallMiner Eureka offers up relevant insights to agents in real time by interpreting speech during each call. Legal risk levels of individual calls can be assessed for proper countermeasures to be taken while the call is in progress. This real-time assessment cuts down on more labour-intensive forms of call monitoring without sacrificing effectiveness.
AI as a training aid in call centres
AI is not only applicable to agent-customer interactions in a call centre, but also to manager-agent processes as well.
As a training tool, AI allows for highly personalised suggestions to be made to agents as they improve at their jobs, reinforcing positive behaviour and outcomes across the board with less manual intervention than traditional approaches would otherwise warrant.
Timely recommendations
Call centre managers need to push the right recommendations to their agents at the right time to keep them on a growth trajectory. Unfortunately, this can be tough to do effectively with a large team and even more so as that team grows over time.
AI is a powerful tool for more effective coaching in a call centre, improving upon the traditional approach to such a challenging process in more ways than one.
Traditional approach
Where offering timely recommendations to agents is concerned, managers are usually left to listen in on calls and assess workforce management software to determine if and when certain agents may be struggling. Once they determine who is having trouble and what challenges are getting the best of them, they can provide one-on-one advice to steer them in the right direction.
AI approach
The AI-enhanced approach to the coaching process for call centre agents accomplishes what managers may struggle to do for a large team in a scalable fashion.
As an example, CallMiner Eureka helps coach new hires and experienced agents alike by assessing calls as they come in and delivering personalised recommendations to service reps in real-time.
This approach is inherently hands-on and keeps guidance for agents both appropriate and consistent as they grow into their roles.
Behaviour analysis
Both agent and customer behaviour play a pivotal role in the outcome of any given interaction. Coaching agents on their tone and that of the customers they must communicate with can prove to be complex without detailed information about each of their calls. Again, AI offers a powerful solution to this problem.
Traditional approach
Without AI, agents and especially new hires may struggle to accurately assess a caller’s behaviour. Customer service reps with highly developed emotional intelligence and listening skills may excel without need for much intervention, but others could face difficulty in gauging a caller’s frame of mind in time to preserve the customer experience.
AI approach
By assessing both word choice, acoustic qualities and call history, AI can help in predicting potential interaction outcomes for individual customers, yielding actionable insights as the call progresses. AI can also assess the agent’s communication style and offer suggestions for improvement.
For additional information on the use of speech analytics to enhance the customer experience, download our white paper, The CX Pro’s Guide to Speech Analytics.
While AI’s prominence in the call centre industry is expected to skyrocket in the coming years, call centres will continue to discover innovative ways to leverage AI to supplement human judgment and human intelligence to improve customer interactions and boost customer satisfaction.
- This post originally appeared on CallMiner’s blog
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