AI: Igniting Contact Center Customer and Employee Experiences
In this blog
Throughout the past few decades, contact centers have dabbled in artificial intelligence (AI). In many cases, initiatives were kicked off without a well-developed strategy, leaving customers with frustrations. I personally felt this frustration when I called my cable company and:
- I was transferred to four different departments.
- Each person I spoke to asked me the same verification questions before even getting to the reason for my call.
However, my experience with my internet provider was the complete opposite:
- Customer verification and reason for calling are all part of the Natural Language IVR, so when my call reaches an agent, that agent knows who I am and why I am calling.
- The experience far surpasses that of the cable company and is a huge reason why I am now a streaming customer.
After years of trial and error, AI is no longer just a buzzword – as a matter of fact, many now refer to it as the key driver of the fourth industrial revolution. As a former director of the contact center, I know how frustrating it is for agents to have to start from scratch with a customer when they should have all applicable information at their fingertips and be empowered to focus on the situation.
AI is becoming infused across contact center solutions to truly transform customer and employee experiences. AI can help make sense of and fully leverage the overwhelming amount of data that is collected in the contact center environment to bring a new level of intelligence to your contact center platform. AI acts as a catalyst to accelerate the benefits derived from traditional contact center solutions such as self-service IVR, interaction routing, analytics, and robotic process automation.
"Customer service is consistently 1 of the top 3 AI-driven use cases across all industries." – HBR, Forbes, Deloitte, MIT Tech Review
Where is AI headed in the contact center space?
"In 2031, conversational AI chatbots and virtual assistants will handle 30% of interactions that would have otherwise been handled by a human agent, up from 2% in 2022. By 2026, 15% of contact center end-user deployments will have active professional services engagements (associated with conversational AI) in place." - Gartner
Do not get left behind the competition, let's explore some critical contact center AI use cases with benefit highlights and recommendations for driving these use cases through to implementation.
CX & EX AI use case examples
Conversational self-service
Convert kludgy, long touch-tone menus (press 1, 2, 3…) to naturally flowing, conversational bots that can provide more effective, faster self-service leading to greater customer satisfaction and increased agent call deflection. Partial deflection can also reduce live agent call duration and cost-per-interaction. The resulting data generated also acts as a critical input to your predictive analytics engine to enhance future customer interactions/journeys.
AI-enhanced chatbots
Create a 24x7 chatbot agent. Deflect live/agent chat volume. Analyze previous interaction data and real-time activity details (e.g., pages visited, amount of time spent on the current page, specific mouse clicks) to provide the best responses, pro-active SMS messages and more based on results of predictive analytics.
Advanced speech analytics
Use AI to gain insight into customer emotions to customize customer/employee journeys in real time and to identify actionable improvements. Accurately interpret customer intents and sentiments. Monitor customer/employee experiences, identify hot spots, and use resulting key metrics to recommend improvements using speech-to-text and text-to-speech capabilities.
Predictive analytics
Compare call recordings to historical records to predict customer behavior, analyze actual behavior, and predict outcomes. Gather and interpret details about website and mobile app activity to determine the next best actions to improve proactive and digital engagement.
Predictive routing
Evaluate historical data and compare it to data associated with potential target queues/agents to determine the routing path that can deliver the best outcome for the caller based on effective customer/agent matching.
Agent assist (live guidance)/conversation intelligence/knowledge content management
Deliver customer empathy by transcribing agent calls in real-time and dynamically guiding agents with the most appropriate knowledge content and recommending the best responses to provide to callers. Analyze transcripts to expose tribal knowledge, and document and streamline the most effective decision-making processes.
Business decision support
Introduce a purely data-driven approach to operational decision-making. Generate "what if" scenarios to determine the most optimal business model.
AI-driven coaching/automated quality management/workforce management
"Listen" to agent conversations to identify training opportunities and workforce management strategies to better accommodate agent needs. Prompting next action to agents in real-time to guide calls to faster resolution, improved customer experience, and in some cases, additional revenue. Utilize the power of speech-to-text to automatically score interactions based on specific criteria and provide more targeted feedback and online training opportunities to your contact center agents in real time.
Contact center robotic process automation (RPA)
AI can exponentially power and continually teach automation solutions to handle more complex and advanced tasks than in the past. AI can automate all or part of an interaction regardless of channel – partial automation can be extremely beneficial to reduce transaction time and improve CX and EX. For example, automate and standardize call wrap-ups, interaction summaries, and required application form filling.
The impact of AI in the contact center on EX and CX
The benefits of leveraging AI to dramatically improve contact center customer and employee experiences are boundless. Customer effort can be sharply reduced by introducing natural language and predictive routing capabilities to simplify customer journeys while reducing cost per interaction.
Over recent, Covid-19 crisis years, the fast-evolving remote work, and shortages of agent staff due to the "Great Resignation" have driven the need for increased contact center operational efficiency and improved agent experiences. Leverage AI-powered automation to allow live agents the time to focus on the "right" goals like issue resolution and customer satisfaction. When interactions need to be routed into the contact center, AI can automatically provide "super agents" with the knowledge sources, suggested responses, next-best actions, and scripting needed to tackle the more complex interactions, increasing first-contact resolution while making the agent role more fulfilling.
Applications like intelligent/contextual screen-pops, automatic call summarization, and form-filling improve efficiency and reduce errors, leading to better experiences across the board. AI can help to shift contact center volume to digital channels. Data has shown that agent turnover rates are much lower in digital teams than in teams that manage traditional channels (e.g., voice, and email). Digital interactions are also less costly to your organization. Provide real-time, automated quality feedback to your agents to improve performance and reduce training costs.
From a more holistic perspective, make use of critical KPI data already being collected across your Contact Center(s) by introducing AI solutions that analyze even unstructured data and offer up business model recommendations. For example, use AI to analyze historical interactions to look for trends that lead to customer attrition and then develop plans to course correct, increasing revenue. The possibilities seem endless.
"78 percent of organizations believed AI and machine learning
would improve employee retention and job satisfaction
according to one study, which also found that 31 percent
of organizations that are using AI and machine learning to
improve their business processes achieve at least a 10x
improvement in their KPIs." - CallMiner
Best practices/approach to contact center AI
Develop an AI strategy and roadmap for your contact center(s). The most important step is determining where to start with AI. Start small but think big. Continually analyze and expand. Engage with stakeholders across your organization to identify and prioritize the right AI use cases. Remember that AI and omnichannel strategy (allowing customers to interact on their channel of choice) alignment is critical. Proper sequencing of AI initiatives needs to be determined and incorporated into the AI strategy. For example, perhaps begin by inserting natural language voice and chatbots in low-risk interaction flows and expanding from there. Prioritization and logically sequenced delivery will help secure buy-in as you expand. Ensure that leadership is continually aligned as the strategy evolves.
Follow critical preparation steps:
- Identify critical data sources and knowledge content that will be incorporated into your AI applications.
- Focus on accuracy, maintainability, and consistency (e.g., developing and leveraging FAQ content that can be referenced by BOTs across all interaction channels). Make AI education a priority (e.g., capabilities, limitations, risks).
- Prepare your staff. An effective contact center AI strategy will also require resource realignment considerations. Contact Center interaction subject matter experts (e.g., agents, quality analysts) will be needed to help in the development process. The introduction of BOTs will reduce the volume of basic inquiries typically handled by entry-level agents. Required developer and administrator skill sets will change based on the solutions chosen. The need for internal technical resources to collaborate across teams will be required – for example, Contact Center tools experts will need to work together with data analytics, reporting, and science teams.
- Most importantly, be realistic in your approach. Professional services resources with niche skills (e.g., data analytics, AI, conversational design) are required to be successful. The technology is still maturing. There are hundreds of conversational AI vendors. Which solution integrates best with your core contact center platform? If your organization is considering a Contact Center platform overhaul such as migrating from an aged on-premises solution to a cloud solution, it will be very important to evaluate the AI core features and integration capabilities of the target state platforms being considered. The solutions landscape can seem fragmented and confusing to customers.
Our contact center advisory services team at WWT has an average contact center tenure of over 25 years. Contact us now to learn more about how we can help you achieve your AI, CX, and EX objectives.