The Hands-free agent experience

Demonstrating the power of Alvaria Automate and Natural Language Processing (NLP)

Often more time an agent spends conversing with a customer is not in verbal conversation, but typing in notes, recording the outcome of questions and repetitive copying of data between different desktop applications or systems. It is not common for calls that have an actual call time of 60 seconds, but the actual handle time exceeding 700 seconds, nearly 1100% increase in time spent largely typing.

Alvaria Automation solution working in conjunction with Real-time voice analytics partners such as CallMiner can remove that need by automatically inserting summary notes to the relevant application in real-time, whilst also validating the information shared, capturing commitments that otherwise are often missed and offering next best action.

Alvaria Automate Supervised can completely revolutionise the way agent behaviour removing the need for non-productive typing enabling your agent to spend the time focused on serving your customer, but with the knowledge all the relevant data is captured and recorded

Female callcenter agent talking to a customer 2_edited.jpg

Key Benefits

Supports all media channels both voice and text
Industry leading NLP transcription and categorisation
Automated capture of key notes within the conversation based on categorisation
Automatically complete forms and web pages based on the conversation in real-time.
Identifies automictically that the agent has correctly communicated key questions, regulatory information and statements within target time frames, and linked to previous parts of the conversation

Automated prompting when statements and questions are missed, or simply when information provided does not match that previously known, recorded or said.
Automatically copy and paste information between systems, whilst redacting from agents view
Open up key information, next step action or event application records based on the conversation
See the results and benefits of the automation through the web based analytics tools.
Significantly reduce your AHT to more closely resemble the true ACT.

Alvaria Advanced Analytics with Automate

Industry leading real-time audio and text categorisation tightly integrated with micro-flow robotic desktop automation

1. Contact transcription and categorisation

Automated transcription of a contact in real-time, with the use of advanced machine learning algorythmns to identify categories of information that the business is interested in. The example shown is based on Alvaria's partner CallMiner technology, but Alvaria integrates with other vendore technology such as AWS, Microsoft or even Alvaria's own NLP technology.

2. Identify conversation categorisations

Identified categorisations of information trigger relevent Alvaria Automate micro-workflow via a secure Rest API. Alert categories can be for example:

Specific Statements were said by either agent or customer, ie DOB
Specific values returned, such as DOB or reference ids
Confirmation that certain pirces of information or actions have been discussed and confirmation that the customer has understood.

3. No-code micro-workflow development

Alvaria Automate micro-flows are created in a web-based tool which requires no knowledge of coding to utilise. A micro-flow allows business rules to be applied to the inputted alert and either transform that data, access or interact with backend platforms or execute an action on the relevent agent desktop clients.

4. Desktop Automation - Data entry and summarisation

The Alvaria robotic client, a part of the Alvaria Automate service, site silently on a users desktop waiting for a micro-flow trigger or a user interaction event that meeting a business criteria.

Examples of what the robotic client can do are:

Pre-fil data fields with one or more clients.
Automatically transform data to needed format of the application or suplement it with additional data from the backend platforms.
Auto-pop "Next Best Action" alerts to high-light identified issues, such as when an agent hasn't discussed a key issue, or a prompt what to do when the customer mentions a key fact.
Automatically populate requests for data dependent requests such as a request to call them back at a specific date or committment to send information in x weeks time. This can also be validated against Workforce Management to ensure callback committments can be honoured.
Automatically summarised the call collecting all of the relevent key points and writing this information into the post-call summary box of the relevent application.

5. Desktop Automation - Data validation

The robotic client can also validate data entry made by the user to ensure it is valid and also aligns with known information. i.e. The customer indicates they only have two active loans, but known system information about the customer indictates they have three