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.
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 Clicks by Signature Playbook using Databox, follow these steps:
The Median Time to Close metric measures the average amount of time it takes for a sales team to close a deal and determine the time frame for effective sales activities.
Sent by Chat Playbook refers to the number of times a particular playbook has been initiated by a user to start a conversation with a visitor on the website or app using the Drift chat feature.
Emails Captured by Chat Playbook is a metric that tracks the total number of unique email addresses collected through automated chat conversations within a specified playbook.
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 Opens by Email Playbook metric measures the number of times a visitor opens an email sent through the Drift email playbook feature. It lets you track the effectiveness of your email campaigns and identify opportunities for improvement.
The Unsubscriptions by Email Playbook metric measures the number of recipients who unsubscribe from your email marketing communications following the implementation of an email playbook.
Meetings by Signature Playbook metric tracks the number of meetings scheduled through a specific Drift playbook, indicating the playbook's effectiveness in generating leads and guiding prospects towards conversions.
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.