The Average Time to Close (ATC) metric measures the average duration from initial contact to final resolution for a closed deal or opportunity, helping track and improve sales team efficiency.
With Databox you can track all your metrics from various data sources in one place.
Used to show a simple Metric or to draw attention to one key number.
Databox is a business analytics software that allows you to track and visualize your most important metrics from any data source in one centralized platform.
To track Average Time to Close using Databox, follow these steps:
Drift dashboard template which gives you insights about new and closed conversations, reply times by team members and more.
Total Conversations is a metric that measures the number of chat or messaging interactions between customers and a brand or business on Drift's platform.
MTFR is the duration between a customer's initial message and the first human response. It measures how quickly a company engages with its customers.
The Dismisses by Chat Playbook metric measures the number of times chat playbooks have been dismissed or closed by visitors during a conversation on the website or app. It helps evaluate the effectiveness of chat playbooks and identify areas for improvement.
The Sent by Email playbook metric tracks the number of chat invitations or links sent via email by the Drift playbook, helping to measure the effectiveness of email campaigns in driving chat engagement and conversions.
The Bounces by Email Playbook metric tracks the number or percentage of bounced emails sent through a specific playbook or automated sequence. This can help identify issues with email deliverability or the effectiveness of the playbook's messaging and targeting.
Opens by Signature Playbook metric measures how many times signature playbooks were opened by website visitors, providing insight into the effectiveness of personalized messaging.
Views by Signature Playbook metric tracks how many times a signature playbook has been viewed. It helps to evaluate the effectiveness of a playbook and make data-driven improvements.
CQL (Concept Drift Quantification) is a metric that measures the amount of change in the underlying concept of data over time, enabling drift detection in machine learning models.