Making optimal use of your contact center workforce is one of the biggest challenges for any contact center leader. Balancing the needs of your company, your customers, and your agents is a time-consuming, stressful effort — especially if you’re doing it manually.
Compounding the issue is that unpredictable events — from unexpected sales growth or the impact of a global pandemic — often render what you know about historical patterns as useless.
At Playvox, we use artificial intelligence (AI) and machine learning to automate workforce management so it becomes a hands-off, less stressful part of your job. Contact center AI technology is a powerful tool that improves your contact center’s productivity and performance.
Artificial intelligence in contact centers isn’t a solution for the future — it’s happening right now. In fact, a recent study by Deloitte revealed that 79% of contact center leaders plan to invest in AI in the next two years.
What Is Workforce Management?
Workforce management is the practice of ensuring your contact center has the right number of agents with the right skill sets working at the right times to help your customers with their issues — all while managing service levels and costs.
Too few agents can mean extended wait times and frustrated customers. Too many agents on hand can mean wasted money and a restless team. If you’re not using a workforce management solution to help, you probably find it to be pretty tricky.
Is it time for you to implement a solution for workforce management? We’ve developed a checklist to help you decide, but in general, it depends on your goals around service levels, costs, schedule accuracy, and integration with other systems you use.
Why AI Matters in Workforce Management
The AI powering our workforce management solution automates what has traditionally required significant manual analysis and judgment. It takes the interpretation out of the equation and instead allows you to focus on solving problems and making targeted decisions. With digging through data off your plate, you can use the recommendations our contact center AI delivers to improve scheduling accuracy and reduce staffing costs.
These recommendations will help you make the most efficient use of your existing team so you’re not overstaffed or understaffed, and you won’t waste resources on onboarding and training unless bringing on new team members is truly necessary. Contact center AI technology also helps you better manage and schedule your hybrid workforce and navigate the current labor shortage.
Al also can use real-time data from your ticketing, chat, social media, and phone platforms to monitor schedule adherence and occupancy throughout the day — allowing you to move staff to the channels that need the most attention.
Factors in AI for Contact Centers
The AI powering Playvox Workforce Management uses a wide range of factors to create recommendations for balancing your staffing needs, including:
- Volume and agent activity
- Ticket, chat, and call data
- Arrival data
- Handling and interactions
- Previous behaviors
Impact of AI on your agents
Workforce management solutions have a big impact on the day-to-day lives of contact center agents. It is the first place they go to view their schedule, make shift swaps, and request time off. In addition to helping determine their schedule, using AI in contact center staffing helps you automate training, reduce backlog, and give agents the right combination of tasks to make the best use of their time at work. Attention to the quality of your agents’ work environment creates a happier team with less attrition.
How AI Affects Your Customer Relationships
Artificial intelligence and customer experience go hand in hand. First and foremost, ensuring you’ve adequately staffed for your forecasted call volumes means your customers spend less time getting their issues resolved. What can further improve their experience is paying close attention to call resolution — whether a customer’s issue is actually taken care of to their satisfaction in as few steps as possible.
One way Playvox Workforce Management handles this is by allowing an omnichannel approach to handling customer issues. If a ticket is created that needs further attention beyond the agent who takes the call, AI helps analyze the downstream impact of that situation and how to fully resolve the issue for the customer.
Using AI to Improve Contact Center Metrics
One benefit of AI workforce management solutions that’s often overlooked is how AI improves the measurement of contact center metrics.
Not only do you save time fine-tuning your scheduling and reacting to unexpected changes, but you can seamlessly map your workforce management efforts to the metrics your business uses to measure its success.
With contact center AI technology connected with your measurement of essential metrics, you’ll be able to demonstrate the impact of efficient workforce management on your results and even further fine-tune your scheduling.
Where Contact Center AI Is Headed in the Future
As Playvox continues to refine its AI and machine learning, we plan to offer you even more precision in managing your staff’s time. Look for:
- Shortcoming alerts to balance staffing with desired customer experience levels
- The ability to automatically detect growth rates, decline rights, and changes in behaviors
- Real-time, self-tuning re-forecasting that allows you to see and address problems before they turn into catastrophes.
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Don’t Manage Your Team with Outdated Technology
Keeping your staffing balanced with your costs is important — too important to rely on basic spreadsheets, “the way we’ve always done it,” or your gut.
Today’s contact centers demand to be run with reliable, easy-to-understand data that drives value instead of consuming your time and energy. AI and machine learning deliver that value and create more time for you to do work that matters more than juggling numbers.
How are you using technology to support your workforce management efforts? Tell us in the comments! Then, read how SeatGeek used Playvox Workforce Management to reduce forecasting and scheduling complexity by 10X.