Contact centers represent the tip of the spear when it comes to customer interface. However, these centers frequently fail to deliver satisfactory service and responsiveness.
To address this challenge, contact centers have leveraged solutions like interactive voice response (IVR) solutions, which require callers to navigate voice prompts and menus to determine their needs. But customers frequently view IVRs as obstacles, with many callers simply bypassing the menus and pressing “0” to speak to a live agent. This phenomenon not only renders IVRs useless, it also results in service-agent overload, with live agents flooded with calls about simple topics, eroding contact-center responsiveness and efficiency.
Today, a new set of solutions has arrived on the scene: artificial intelligence (AI) algorithms that can automate and improve the efficiency of conducting individual tasks in contact centers—from routing customer inquiries, to transcribing and translating conversations, to determining callers’ emotional states.
Contact centers and other customer-service organizations that are just commencing their AI deployments are leveraging standalone algorithms to achieve immediate productivity gains. These beginner-level AI users now are employing algorithms separately, with employees taking the results of individual AI analyses and manually advancing them through workflows. For example, call-center workers can review audio transcriptions, search for relevant keywords and then route the results to the correct salespeople.
Customer-service operations use individual algorithms for a range of other tasks, including:
- Ingesting data from social media, email, phone calls and other sources
- Creating a 360-degree view of a customer at the time of interaction
- Generating automated responses for key conversation topics
- Conducting sentiment analysis of calls
- Translating conversations
- Performing analytics of call-center content
- Estimating customers’ intentions
- Recognizing customers’ voices and faces
- Automated creation of support tickets.
While delivering major benefits, the standalone approach to using algorithms is far from optimal, often squandering AI’s efficiency gains by tying processes to slow and inefficient manual workflows.
Customer service operations with more experience using AI have learned to employ algorithms in combination to deliver unprecedented efficiency and superior customer service. Those organizations that have reached more advanced levels of AI maturity are chaining together multiple algorithms to automate entire business processes, rather than simply performing individual tasks. By combining separate AI engines into seamless processes, these organizations are transforming entire call-center workflows, rather than just enhancing individual tasks.
For example, such solutions that can perform tasks like:
- Automatic routing of customer-service calls using transcription and sentiment-analysis algorithms. When a sentiment-analysis algorithm detects a caller is expressing negative feelings, the system automatically alerts the relevant manager via text message. This allows managers to respond immediately as customer-service issues arise, without requiring workers to manually advance the issue through the hierarchy.
- Transcribing customer calls, detecting any customer questions and automatically finding responses. When inquiries are spotted, the system automatically passes the questions to a Google search API and obtains the top-five results for the topics, saving call-center workers enormous amounts of time and effort chasing down answers.
- Analyzing the details and content of calls using transcription and speech recognition algorithms to automatically create support tickets and forward them to the appropriate individual—instead of requiring a worker to manually review or manually run robots, prepare the tickets and send them to the right staff member.
These are just some of the ways that a holistic AI solution can transform data-center workflows.
Larger organizations maintain software development teams capable of developing sophisticated automation solutions that can tie together various AI algorithms into cohesive call-center workflow systems. However, this is impractical for smaller organizations.
Today, new approaches have arrived that allow contact centers to leverage the power of multiple, ready-to-deploy AI models in a single automation solution without employing extensive coding expertise or resources. These approaches, such as Veritone’s Automate Studio, provide a low-code solution to workflow design that empowers technical and business teams to develop and deploy AI-powered business processes at scale within a matter of days. Such approaches employ an intuitive drag-and-drop interface to easily create workflow steps for content ingestion, enrichment and integration on a digital canvas, without requiring in-depth coding skills or AI expertise. These solutions can generate standalone AI engine workflows, but also can generate flows called by legacy call center applications or business process automation (BPA) and robotic process automation (RPA) solutions used for call-center process automation.