The Open % by Email Playbook metric measures the percentage of email recipients who opened a specific email sequence or playbook.
With Databox you can track all your metrics from various data sources in one place.
Used to show comparisons between values.
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 Open % by Email Playbook using Databox, follow these steps:
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.
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.
Click % by Chat Playbook measures how many times a chatbot suggestion was clicked compared to the total number of suggestions shown. It indicates the effectiveness of chatbot suggestions in guiding website visitors towards a desired outcome/metric.
The Conversations by Signature Playbook metric measures the number of live chat conversations initiated by a specific Signature Playbook within a given time frame.
Opens by Signature Playbook metric measures how many times signature playbooks were opened by website visitors, providing insight into the effectiveness of personalized messaging.
The Clicks by Signature Playbook metric measures the number of clicks on CTA's in marketing emails that were sent using a specific email signature template.
Click % by Signature Playbook measures the percentage of clicks on a particular signature playbook in comparison to the total number of plays it received. It helps to analyze the effectiveness of the signature playbook in driving clicks and optimizing it for better results.
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.