Investments in contact center workforce optimization (WFO) solutions have increased exponentially in the last three years. Several reasons can be attributed to this shift including:
- The transcending role of the contact center as the centerpiece of customer experience (CX) and long-term profit centers with opportunities for cross-selling and upselling.
- As the home of the CX, contact centers meet with higher levels of frustration and heightened expectations from customers as they reach out to customer service agents only if they are dissatisfied with the self-service.
- Without meaningful employee engagement (a part of the overall employee experience), CX cannot be improved. Frost & Sullivan’s 2023 Contact Center IT Decision Makers Survey also confirmed that 60 percent of the IT decision makers deem investing in and developing employee experience (EX) to be a top goal for the enterprise, even though improving CX is important for them.
WFO tools are expected to offer capabilities to empower agents with higher speed, accuracy, and efficiency to improve customer satisfaction and promote agent well-being. SuccessKPI is one of the leading WFO vendors that is leveraging investments in diverse sets and subsets of AI technology including natural language understanding, natural language processing, automatic speech recognition, text-to-speech (TTS), and text processing, machine learning (ML) and deep learning (DL) to deliver on market needs.
Important use-cases of AI within WFO include:
- Agent onboarding and hiring: AI and ML help create an ideal agent profile and automate elements of the interviewing process such as the virtual interviewing, predictive analytics, and candidate assessments, along with stakeholder feedback to shortlist the best candidates. This reduces hiring time, hiring costs, and boosts quality and consistency.
Key performance benchmarks for new hires can be generated with the help of AI. Both agents and supervisors can keep a tab on individual performance against customized onboarding goals, and accurately assess coaching requirements.
- Workload management and scheduling: AI supports forecasting future labor needs based on historical data, reducing potential hiring costs. This includes automated scheduling that factors in complex country and regional labor or, scheduling multi-skill agents across multiple sites.
Supervisors can access data about agent paid time off, staffing level indicators, previously scheduled event conflicts, agent skillset, preference, availability, personal need, time-off requests, required breaks, shift rules and shift changes for better resource management. For instance, taking advantage of large language models (LLM), SuccessKPI’s AI Traffic Forecasting, predicts customer interaction volume, required staffing, shrinkage, occupancy target and staffing characteristics using AI/ML forecasting algorithms to boost workforce management.
In addition, agents’ well-being which is a huge theme when it comes to improving EX/agent experience can also be monitored based on their agent schedules and performance.
- Performance management and agent coaching: The use of generative AI, is providing AI summarization capabilities that leverage LLMs to summarize voice and text-based customer-agent interactions. The feature supports topic-based summary derivation for holistic interaction insights, speeding service delivery and accuracy and reducing costs.
Through speech and text analytics, it becomes easier to spot negative customer sentiments, uncover customer preferences, compliance issues, and indicators of expert agent performance. The insights can be leveraged to support agents in improving the quality and outcome of each customer interaction in real-time. In support of this, SuccessKPI offers a playbook builder that allows supervisors to drive alerts for agents to maintain regulatory requirements during customer interactions, positive behavior, and language that boosts agent performance and enables them to offer better CX.
Some vendors also offer training capabilities that combines AI-scored evaluations with a human-in-the-loop model helping quality management supervisors to train and refine keyword-based intent mapping to boost EX and CX. SuccessKPI also has a similar LLM model-based approach that allows supervisors to work with specific language models to score calls and revisit the scores on a day-to-day basis to adjust the relative accuracy of the machine.
In addition, agents can manage their daily activities and overall performance through holistic evaluations. Predictive NPS and evaluations provide visibility into agent interactions and allow for a highly accurate and holistic picture of their performance.
Frost Perspective: Future Ahead for WFO providers and customers
The contact center industry will continue to incorporate AI features across the WFO spectrum from forecasting and scheduling to advanced analytics for real-time assessment and actionable business insights. This accelerated adoption of maturing AI tech, such as ML and NLP, is being bolstered by the use of generative AI, further unlocking the tangible possibilities for rapid improvement in CX/EX.
As the trends reflect, business customers will prefer WFO providers offering strong AI-powered capabilities. Frost & Sullivan’s 2023 Cloud Survey also confirms that 43 percent of respondent organizations have already deployed generative AI solutions within customer service and support and 42 percent plan to do so in the next two years.
SuccessKPI’s recent announcement of its AI portfolio and roadmap provides a compelling reason for customers to learn more about this platform. The confidence and transparency this move reflects in revealing the future innovation path makes SuccessKPI a vendor to watch out for in 2024 and beyond.