AI in CX, Quality Management and Contact Center Solutions: Why the Contact Center is Here to Stay

By Chrissy Calabrese, Vice President of Product Marketing at Playvox

AI in CX, Quality Management and Contact Center Solutions: Why the Contact Center is Here to Stay

To say that artificial intelligence (AI) has been written about frequently over the past year would be an understatement. There is probably one area where there are more expectations for AI than in customer experience. For many organizations, particularly in the telecom arena, contact centers are seen as cost centers, so those operating contact centers or holding support centers under their P&L are always seeking new ways to streamline costs in their business.

While recent studies show the expected growth and impact of AI over the next decade, there’s also potential for job creation. Most experts agree that customer service jobs will be augmented and automated, not replaced. Many believe that by automating mundane tasks, we’ll be more productive, efficient, and AI will enable businesses to provide better customer experiences with more self-service options and help fix employee burnout.

No matter what side you are on, the promise of AI is significant. In this article, we’ll discuss the history of AI being used in a contact center, how AI tools are utilized today, and the future of AI in improving CX.

AI in the Contact Centers: Looking Back to Look Ahead

Before we jump in and look at AI in customer experience today, it’s key to reflect and understand how AI has been previously used.

Workforce Management Solutions

Anyone who has worked in a service center for a long time is familiar with the teams of people, number of spreadsheets, and the hundreds of hours that have traditionally gone into creating schedules. There’s the initial analysis of determining when customer interactions come in (the hours when it’s the busiest), another analysis of which team members are available or not (noting special circumstances, when agents need to leave or any other schedule limitations), and then matching all this together to optimize schedules to deliver the agreed-upon service level agreements (SLAs) for response and wait times, and first contact resolution (FCR).

One of the early ways that AI was leveraged was to optimize all the previous steps via a modern workforce management solution (WFM). WFM solutions automate the entire process outlined above and consider the most accurate real-time data to produce a schedule for your agents that accounts for hours and number of people needed, and the differences in agent availability — to create customer experiences in accordance with agreed-upon SLAs. This process is all done using AI.

Chatbots

Another tool that’s been in use for a few years is chatbots. These AI-powered virtual assistants can provide immediate responses to customer queries, offer product recommendations, and even help troubleshoot. As chatbots continue to evolve, they can handle multiple customer conversations simultaneously, help reduce wait times, improve overall customer satisfaction, and transform CX at the contact center.

 Personalized Customer Experiences

Personalized customer experiences aren’t new, nor is the ability of AI to create these kinds of experiences. A few examples include:

• Using AI to provide relevant article recommendations on a website that relates to an article the visitor has already downloaded. In this case, a database saves information about a customer’s online identity and what they might have searched for or previously downloaded.
• Matching the caller ID with a record in Salesforce: Through robust integrations with Salesforce, this type of AI lets a contact center agent greet a customer by name as their phone number “pops” in the agent’s CRM when the contact calls. This type of AI has been in use for many years, and although it might seem basic, it’s still in action in the typical contact center.

Whisper Technology

Whisper technology is when an AI-type assistant “listens” to an agent’s interactions and suggests the right resource or answer to an agent via a “whisper” into their technology. Several years ago, this also could have been a supervisor remotely monitoring the call and/or and suggesting something to improve the interaction. The long-standing problem with this type of AI is that when you send an agent too many queues or suggestions, the agent either ignores or turns off the technology altogether. This is like help desk articles that might pop up, crowding an agent’s screen, and making it difficult for them to do their job due to the constant distractions of “helpful” technology.

AI in Customer Experience: A Look Ahead

AI in the contact center isn’t a new concept. From AI-driven WFM to chatbots and personalization, AI is already being used. But in the near and long term, there are many opportunities to leverage AI in customer experience.

• Sentiment Analysis: Sentiment analysisis understanding the “feeling” the customer had when a particular interaction ended. This is whether the customer interaction was rated positive, negative, or neutral by the words the customer used in their text-based interaction. Positive customer sentiment and experiences can lead to increased brand loyalty and spending. A recent study by Deloitte showed that customers are willing to spend 140% more after having a positive customer experience.
• Automated Quality Management (AQM): Quality Management  is the process of scoring a statistically sizable number of interactions by each agent and is usually monthly by a team lead, supervisor, or quality analyst. It uses a scorecard and may score things such as greeting and closing, resolution of customer’s issue or problem, speed of responsiveness, and anything else that can add or take away from optimal customer experiences. This process is automated, enabling you to see what drives customer interactions and underlying customer sentiment, and lets AI assist your analysts with a more efficient scoring and feedback process. Further, by spending more time fixing problems than simply identifying them, you can improve customer experience.
• WFM Powered by Your Agents: The WFM solutions of the future will lean more on agents’ wants and needs, while also aligning with customer experience expectations. While systems of the past focus on determining SLAs needs, and then staffing and scheduling to match this, future solutions will determine the SLAs and optimize for agent satisfaction – offering more flexibility and assisting with shift swaps to improve agent engagement and motivation.

The Future of AI In Customer Experiences

Companies are opportunistic about what the future of AI holds — and there is no reason why they shouldn’t. AI can help lower costs, improve efficiency, and customer experiences. As you look to incorporate AI into your support center, remember that an AI solution alone won’t magically fix any issues or problems you may have today. It’s important to align with a trusted partner who can be with you for the long term to help you experiment, leverage, and implement AI to improve your overall operations without disruption. The future of customer experience will likely be using more AI tooling but used deliberately and thoughtfully.

Links:

Website: www.playvox.com

LinkedIn: https://www.linkedin.com/company/playvox

X: @playvoxcx