Detection of Next Best Actions for Greater Automation
Our Next Best Action Detection feature makes post-call work easier and more automated. Reduce your LMD and the cost of post-call processing.
In-depth Semantic Analysis of Call Content
Our advanced transcription and semantic analysis capabilities, powered by Large Language Model (LLM) technologies and other AI methodologies, offer a comprehensive understanding of call content.
This empowers us to generate insightful summaries, evaluate customer satisfaction, and facilitate interaction and post-call processing with unparalleled accuracy.
Identification of Actions Taken During Calls
Our analysis provides a clear overview of the actions completed during a call, identifying whether they were performed by the agent, the customer, or jointly, like scheduling an appointment.
This insight allows for a detailed review of the call’s progression, pinpointing any overlooked tasks by the agent or confirming the completion of all necessary actions. Our technology also enables the correlation of customer satisfaction with specific actions executed or omitted during the call.
Recognition of Pending Actions Post-Call
Beyond acknowledging completed actions, our technology is adept at identifying pending tasks that need to be addressed after the call, as discussed during the conversation.
Whether it’s sending an email, making a callback, or any other task mentioned, Diabolocom AI seamlessly provides access to these tasks, detailing the task title, a brief description, and the identified responsible party, whether it be the customer, the agent, or both.
Enhancing Efficiency with Post-Call Automation
The insights derived from our analysis are integrated into our post-call scenarios, enabling a wide range of automation possibilities.
This can include logging customer satisfaction data into your CRM system or addressing pending actions with our AI’s help, potentially automating tasks directly from the scenario.
Leverage the combined strength of our AI and IVR technology to significantly reduce the time spent on post-call activities and elevate your Customer Satisfaction Score (CSAT) through improved efficiency.
With Diabolocom's Artificial Intelligence solution
decrease in time required to gather customer context
cost savings on post-call processing
decrease in customer churn rate
Frequently Asked Questions
Do I need to set any parameters to detect actions?
No, there are no special settings required for action detection. The system is designed to automatically recognize the actions evoked during the call.
In what form can we recover the remaining next best actions detected on the call?
Detected next best actions are available in two forms: as a variable directly integrated into the call scenario, and in JSON format, for easy integration with other systems.
What types of actions are recognized?
The system is able to recognize any type of action mentioned orally during the call without any specific restrictions.
Does it work in all languages?
Yes, the system works in most languages, offering great flexibility of use across different international markets.
How can I automate the actions that need to be taken following the call?
To automate detected next best actions, you can use a “Check Attribute” block in your scenario. This block automatically applies the scenario branch corresponding to the detected action.
What are the advantages of applying this detection?
Automated next best action detection significantly reduces ACW, improving operational efficiency and customer satisfaction.
Is it possible to just get the information without automatically performing the tasks?
Yes, it is possible to configure the system to provide information on detected next best actions only, without executing them automatically.
Can I send this information to a third-party system, like a BI tool?
Absolutely, thanks to the use of web service blocks and other blocks available for the post-call scenario, you can easily transmit detected information to a third-party system or BI tool for further analysis and processing.
