The New Best Practices for Contact Center Workforce Engagement Management

Contact center workforce engagement management (WEM) practices have evolved rapidly in recent years.

The chief catalyst? Advancements in AI.

Indeed, AI is reshaping how organizations manage their workforces, optimize operations, and deliver superior customer experiences.

Recognizing this, CX Today reached out to five experts to understand how the two primary areas of WEM - workforce management (WFM) and quality assurance (QA) - are evolving.

These industry specialists are:

• Vincent Paquet, Chief Product Officer at Dialpad
• Tim Richter, Senior Director of Product Marketing at Five9
• Trudy Cannon, Senior Director, Go-To-Market Strategy, for Verint
• Matthias Goehler, Zendesk CTO EMEA
• Richard Lea, Senior Consultant at Calabrio

Below, the panelists share their best practices across these remits of WEM and beyond.

New Best Practices For Workforce Management (WFM)

Forecasting for the Impact of AI

Paquet: Contact centers are adapting their WFM strategies by forecasting contact volumes based on how many tickets will be triaged/resolved by AI.

Additionally, they're adding scheduling flexibility for agents to handle escalations from bots and implementing comparative performance metrics across humans and AI.

To help, planners are leveraging AI themselves.

Moreover, by using AI forecasting, they are more accurately identifying outliers in volumes and predicting resources required for customer-facing and back-office tasks.

Lastly, planners are utilizing AI-based, real-time analysts to surface volume/capacity changes and recommend the right course of action.

Continual Re-Training

Richter: It’s critical to ensure planners are continually retrained to fully utilize the latest AI-based forecasting and scheduling tools.

It may not be a new best practice, but it’s more relevant now than ever, given the large number of contact centers migrating from legacy premise-based solutions and spreadsheets to the cloud.

Workforce planners accustomed to running legacy WFM solutions may not yet have the knowledge or experience to operationalize the latest cloud-based, AI-enabled WFM solutions.

For one customer, we (Five9) noted that workforce planners took three days to create a schedule in their legacy WFM solution for a short time, even though they recently deployed a modern cloud WFM application.

The planners were simply set in their ways and not adequately trained.

With the new Five9 WFM solution, they got that three days down to 75 minutes.

Forecasting for Customer Journey Trends

Cannon: One emerging best practice is recognizing the impact of CX Automation on WFM, emphasizing the need to forecast for interactions that start in and span multiple channels.

Now that organizations are embracing AI and automation, customer journeys have become even more dynamic.

This makes it difficult to forecast customer interactions, which was already challenging to predict.

As such, WFM forecasting and scheduling must be agile and flexible. Organizations should monitor shifting customer journey trends so they can scale capacity up or down to meet customer needs.

Personalized Employee Schedules

Goehler: AI-powered WFM goes beyond simply offering flexibility. It analyzes employee data to align tasks and projects with their strengths and interests, improving engagement and job satisfaction.

At the same time, it helps organizations manage resources and reduce costs by assigning the right people to the right tasks at the right time.

Many companies have successfully adopted this approach to achieve cost savings and improved workforce satisfaction.

A key advancement in WFM is the integration of AI to create personalized employee schedules.

These schedules consider individual preferences, skills, availability, and career goals while optimizing workforce utilization across brands, countries, languages, and support tiers.

Another important aspect of AI in WFM is its ability to adapt schedules in real time, responding to changes in availability or workload fluctuations without compromising efficiency.

Overall, it prioritizes both employee empowerment and business outcomes.

New Best Practices For Quality Assurance (QA)

AI Automated Scorecards

Paquet: AI allows graders and supervisors to dramatically increase the number of calls they can score in QA.

Without AI, the number of calls graded is routinely in the sub-five percent range.

AI-Assisted scorecards will suggest some answers to the scorecards but will rely on a human grader to confirm the score.

This saves time as graders can simply click on the suggested score and be brought automatically to the relevant part of the transcript to validate the AI-suggested score.

Think Beyond Agent Performance

Richter: It’s essential to look for quality management to deliver insights beyond opportunities to improve agent soft or hard skills.

In particular, consider how quality management can help discover and correct operational bottlenecks and identify automation opportunities.

One healthcare insurer used quality management to uncover that agents spent, on average, 30 seconds at the front end of calls just to authenticate members.

As a result, they automated the authentication process and instantly shaved 30 seconds off agent handle time.

Soft and hard skills remain core to quality management, but quality management can (and should!) do more.

Continuously Evolve Quality Scorecards

Cannon: Organizations can’t achieve their quality objectives with manual checkboxes and static forms.

The biggest issue with a static form is that contact centers assume the questions and criteria align with how they want the customer experience to evolve.

However, customer journeys are evolving so quickly that service teams can be caught "flat-footed" trying to measure performance against criteria one step behind.

How does a contact center listen to ensure every interaction meets our desired standard? Automation. The caveat is not everything is going to be automated, and that is not the goal.

The goal is to introduce automation to provide the scale and visibility necessary to guide coaching – both automated real-time guidance and via supervisor engagement.

Automation frees supervisors to have the time for mentoring and coaching.