With continued market uncertainty, lingering inflation, and heightened customer expectations, business leaders are laser-focused on improving or, more accurately, energizing the overall customer experience (CX), as well as enhancing the customer support experience. In particular, eliminating points of friction in the customer journey is top priority.
That’s because key contact center metrics, like handle time, number of transfers, and number of contacts, shape the overall CX. When companies are able to proactively improve these metrics, they stand to benefit when it comes to lowering both cost to serve and customer turnover, and strengthening customer loyalty.
At the same time, there’s been a rush of powerful new solutions backed by artificial intelligence (AI) and machine learning (ML) designed to help in these areas. That’s creating pressure for businesses to dive headfirst into investing in AI and ML technologies to drive customer outcomes.
To be successful, however, leaders need to take a thoughtful approach and carefully consider their options. They need to understand the key use cases for deploying these technologies in the contact center in order to constructively channel their power. They also need to think about the kinds of CX challenges they’re currently unable to address and determine whether or not AI can help.
Ultimately, executives need to have clear business objectives in mind for using AI and ML technologies, whether that’s to reduce the cost to serve, improve agent efficiency, or enhance the quality assurance (QA) process through automation.
The Contact Center Maturity Curve
At the bottom left of the contact center maturity curve, there are companies that are doing little to nothing to understand or analyze their CX. They’re not monitoring any of their customer conversations to learn how to improve contact center agent training or products, services, and policies to enhance the overall CX.
Slightly further along in the journey are companies that are still using old school methods for assessing contact center operations. They’re using manual QA to evaluate a random sampling of calls per agent per month and are having their agents select the reason for calls, resulting in limited analysis and insights.
At the middle of the maturity curve are companies using surveys to collect customer insights. This is an important start, but the results usually reflect the experience of a minority of customers, typically highly satisfied or dissatisfied customers.
…executives need to have clear business objectives in mind for using AI and ML technologies…
At the top of the contact center maturity curve are the most advanced companies: those that are monitoring and analyzing 100% of the conversations their customers are having with their team across touchpoints, including emails, support tickets, live chat, phone calls, SMS, and social channels.
These businesses are able to do this at scale by using AI and ML models. These tools minimize the need for humans to manually read and tag every conversation. They instantly transcribe and draw insights from phone conversations, video, text, and both structured data (such as drop-down choices in a menu) and unstructured data (blocks of open-ended text, such as that in a social media mention).
Key Use Cases for AI and ML in the Contact Center
Tools like AI and ML can support several key use cases in contact center operations.
1. Reducing time to unlock insights and value
These technologies work faster and are more scalable than requiring humans to manually review conversations. AI can make it possible to automatically assess every contact center interaction and instantaneously categorize the reason for contact and sentiment of conversation.
Being able to understand the context of all customer interactions enables organizations to save time on completing manual and time-consuming tasks, understand customer needs faster, and implement changes to improve CXs in the moment.
2. Uncovering emerging or previously unknown issues not yet on a company’s radar
At the start of the COVID-19 pandemic, companies weren’t necessarily prepared for the kinds of questions customers would have around safety precautions, such as cleaning protocols or mask requirements.
For companies with AI tools in place to analyze contact center conversations, it quickly became apparent that these were the types of issues customers were concerned about.
But as the economy shifts, and other factors influence the concerns of today’s customers, businesses using AI can catch wind of these kinds of trends in the moment and react more nimbly than competitors without such timely insights.
3. Detecting customer conversations that might otherwise be overlooked
Companies have their own way of referring to their products, services, and employees, but customers don’t necessarily use the same terminology. They might use acronyms, abbreviations, misspellings, or different words to refer to the same things.
When companies initially set up search queries to analyze their contact center conversations, these typically focus on the words and phrases the company uses. AI can be used to find the correlation between different concepts and words to make sure companies are capturing all customer conversations related to a specific topic, even when consumers use new or different terms.
4. Creating connections with customers
Better connecting with customers is important for customer service. When customers feel a connection to a company, they feel a higher sense of loyalty.
As discussed earlier, AI can be used to automatically analyze the emotion of any conversation, and companies can use these insights to detect behavioral patterns across segments.
This knowledge empowers them to understand how people of different age groups, stages of life, regions, races, and genders feel when interacting with the contact center. They can then adapt agent training accordingly so they can better handle certain situations, especially as sensitive topics arise.
For instance, financial services or healthcare companies may use AI to track and detect moments of customer embarrassment or surprise when it comes to paying bills or overdraft fees. They can then provide the appropriate coaching to agents to use the best wording to evoke less embarrassment or surprise to optimize for the customer’s comfort.
5. Personalizing the CX
By using AI to collect, aggregate, and analyze customer interactions across touchpoints — from digital browsing activity to customer feedback and transactional data — brands can establish a clear picture of customer behaviors and interests.
They can then use these insights to personalize content and experiences for customers across channels to serve up information that’s relevant to the customer and their individual journey.
6. Streamlining customer service recovery
Using AI to extract customer insights from across interactions and touchpoints can help brands understand why customers are reaching out to customer support in the first place, so that teams can provide better service.
These insights can be used to improve customer support queue management and can be shared with agents to keep them in the loop about who their customers are and why they’re reaching out. This helps agents to communicate with customers with greater knowledge and empathy.
AI can also be leveraged to provide agents with automatically generated recommended next-best-actions to take based on a customer’s history with the company.
7. Proactively closing the loop with customers
AI modeling can be used to predict signs of customer churn. For instance, if a customer calls repeatedly without getting their issue addressed or exhibits high levels of frustration during a contact center interaction, companies can set up alerts to notify supervisors or a dedicated team to intervene.
A member of the dedicated team or a supervisor can then proactively reach out to the customer and provide a resolution for the issue, offer a special promotion, or give the customer the chance to voice their frustrations. Interventions that can drive retention and loyalty.
The Role of Human Analysis Alongside AI and ML
While AI and ML can help companies save time, these technologies won’t eliminate the need for human involvement. In fact, human intelligence and experiences are crucial to the successful adoption of these tools.
That’s why savvy organizations take a hybrid approach, relying on professionals to train these models to work more accurately and responsively, as well as to monitor and respond to things that truly matter to the business.
Humans bring important business context to the table that AI and ML tools simply don’t offer insight into, such as knowledge of changing corporate goals or learnings from strategic planning sessions.
Companies need to know what outcomes they hope to achieve…
For instance, retail staff might ensure these technologies are set up to flag critical conversations about Black Friday leading up to, during, and immediately following the event.
As another example, models powered by AI can be set up to track and create alerts when customers use language that signals an emergency, such as a natural disaster, so that the right folks can be looped in immediately to take swift action.
While a tool might be used to route conversations that include words like “dying” or “fire” to the right team members, human oversight is necessary too.
That’s because people can use these very same words in a completely different context to describe something that’s positive, funny, or great. People might also use these words hyperbolically to describe a poor CX.
If that’s the case, contact center professionals reviewing these alerts can make sure they are de-escalated (if they’re not being used in response to a true crisis) and get routed to the right individuals, such as to a customer service recovery team member instead of a disaster response team member.
AI is getting smarter, and as it becomes more sophisticated, companies will be able to automate more tasks, meaning less human intervention will be needed.
As it stands today, however, human interpretation is almost always needed to tweak what AI is monitoring, flagging, and reacting to, to enable these models to take action on the right things.
Before diving headfirst into the world of AI, it’s important that contact center leaders have firm business objectives in mind. Companies need to know what outcomes they hope to achieve and what pressing questions they want answers to, rather than simply implementing a product just because everyone else is doing it.
It’s also important to keep in mind that change management is key to successfully adopting any new technology within the contact center. Managers must bring agents on board with not only using a new solution but adapting to changing team processes, including reporting, metrics, and performance evaluations as a result of rolling out the new tool.