AWS has added “Agentless” voice dialing to its Amazon Connect Outbound Campaigns solution.
The feature allows contact centers to send high-volume, personalized voice notifications without involving live agents.
Those notifications may include appointment reminders, upcoming delivery notifications, and perhaps even post-contact follow-ups.
Moreover, service teams may use "Agentless" voice to build and kickstart customer journeys across multiple channels.
For instance, an automotive company could send an automated voice reminder to a customer that their MOT is due soon. Then, they could offer the option to pass the customer through to a voicebot, allowing them to schedule an appointment with the garage autonomously.
AWS has embedded capabilities from Amazon Pinpoint – AWS’s marketing communications service – to make such omnichannel use cases possible.
Yet, there are many more conceivable applications of "agentless" voice. Some start by leveraging the existing machine learning capabilities within Outbound Campaigns...
How Machine Learning Maximizes the Efficiency of Agentless Voice
When service teams lead outbound voice campaigns, agents spend lots of time listening to answer machine messages and busy signals on outbound calls.
The machine learning (ML) capabilities already within Outbound Campaigns enable answering machine detection to distinguish between these features and live customers.
As a result, agents only connect to live customers, driving up their efficiency.
With the rise of Agentless voice, this feature may appear less relevant. Yet, it can still support the development of mature automated outbound campaigns.
Indeed, by having a feature that identifies live customer pickups or voicemails, contact centers can customize their contact strategy accordingly.
For example, if the ML detects a live person, the Agentless voice capability can present options for them to select – like a voicebot transfer to kickstart a customer journey.