One of the most exciting announcements of #CX18, the Genesys annual customer conference held this year in Nashville, was the news of Genesys #Predictive #Routing planned for release this summer (2018) along with many other enhancements to the #PureConnect and #PureCloud platforms. Along with #Kate, #Blended #AI from #Genesys, the future looks bright for the #Genesys #PureConnect and #PureCloud roadmap.
Business are looking for cutting edge solutions that leverage all of their technology and data. Just keeping up means staying ahead.
When would you like that?
The future is now. To merely survive is not enough. The modern contact center and enterprise workforce will require the latest and greatest in technology to thrive. Everyone is exploring ways to bring AI into the business with the goal of increasing profitability and improving the customer experience. We all know “data is knowledge” and “knowledge is power”. A blended strategy will be key. Analytics and modeling along with the combination of AI bots and humans able to solve problems faster. The perfect amount of self-service combined with the perfect amount of the human touch.
What’s the best route?
We have a prediction about that. In many cases, your customer has already told you what they prefer. Are you listening? It’s in the data.
Inbound Call Routing
Static routing has been the main source of incoming call distribution for over 40 years. Now, we can harness the power of artificial intelligence (AI) and machine learning (ML) to analyze, understand, and effectively utilize our unstructured data. With that, we can build predictive models for forecasting trends and behaviors. Sounds a bit like #sci-fi, right? Well, it is reality. The times, they are changing.
Queue based routing was one of the first methods to get callers to a designated agent. In most cases, the queue is a single “skill” or singular subset of capabilities. It’s predefined and doesn’t use dynamic data to update the route. Example: For support, press 1. Bam, you’re waiting in the support queue and hoping for an agent. This method is outdated and leaves gaps in your communication. We can fill those gaps with meaningful interactions with data driven routing.
Skills based routing took QBR to the next level. Identifying multiple criteria, skills-based routing takes into consideration the required skills for a successful interaction, the agents who possess those skills, agent availability, and then routes the interaction to a qualified agent with available capacity to take the call. Better, yes. Efficient, not so much.
Predictive Routing provides a much greater understanding of the Voice of the Customer. Surveys are now giving way to analytics. Predictive Routing, an artificial intelligence (AI) machine learning (ML) analytics solution, leverages historical and real-time data to accurately match customers with the most appropriate resource. Customer activity from back office systems, such as CRM and case management, is used to boost business intelligence across all areas of sales, marketing, and services. Models are built from aggregated customer profiles based upon their favored communication channel, purchase history, service requests, transaction activity, and more. The more data, the better the prediction.
Click here for your copy of Best Practices for Contact Center Routing
Outbound Predictive Dialing
Contact center systems handle incoming phone calls, emails, and other channels, but they often lack an adequate solution for outbound calling. If you need to make proactive calls, call back customers, cold-call prospects, or make outbound notification calls you need an outbound dialer. Dialer software uses calling databases and sophisticated behaviors to manage hundreds to thousands of automated calls, with expert systems listening to the connections and routing good pickups to live sales agents, or callbacks for support queues, all in real time.
By implementing Interaction Dialer businesses are able to maximize the agent interaction time from 24% to as much as 83%. This is a drastic decrease in wasted agent time. How? By eliminating the requirement for the agent to deal with dial tones, busy signals, answering machines, no answers, and the like, we are able to increase agent talk time as well as operational efficiency. This also results in minimal wait times and silence for the customer as they are connected to agents.
How would you like that served?
Very good with a side of easy, please. When is the last time you called a taxi on the phone instead of ordering up an affordable easy ride with a just a few clicks of an app? The same desire for ease of operation holds true for all aspects of business. Genesys is making it easy with #Kate. Kate uses technology that is tailored to customers’ journeys and behaviors as well as your employees’ needs. Kate works seamlessly with live agents to deliver “Blended AI by Genesys”, offering exceptional automated and live customer experiences while running smart businesses.
Did you know human-assisted interactions cost $5 to $15 more per interaction than chat or voice channels while an automated self-service transaction can cost as little as $0.20 per interaction?*
Adopting self-service automation includes other benefits that are harder to quantify, yet equally important, such as:
- Faster service and delivery, including reduced call volumes and improved first contact resolution
- Improved agent and customer satisfaction
- Better customer experience.
The bottom line, by assigning the right work to the right people at the right time, we can:
- Increase Customer Satisfaction
- Increase employee efficiency
- Decrease operating costs
- Reduce Handle Times
- Improve First Call Resolution
- Increase Net Promoter Scores
- Boost business profitability
Simplifying and automating this technology to solve these complex business challenges is where AVDS comes in. We have a vision for the future and a proven track record of success. We strive to create a partnership with our customers. Your best interest is our main concern. Give us a call, we want to hear your story.
*Based on Genesys Research using input and insights from multiple sources, including Dimension Data and other research analysts.