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.
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 Dismisses by Chat Playbook using Databox, follow these steps:
This dashboard tracks the traffic levels of visitors using chat for assistance, along with the overall performance of Drift Campaigns.
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 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.
The Contacts metric in Drift tracks the number of unique people who have engaged with your chatbot or been contacted by a team member, providing insight into customer interactions.
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 Replies by Email Playbook metric measures the percentage of automated email replies sent by Drift that receive a response from the recipient, indicating engagement and potential interest.
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.
Opens by Signature Playbook metric measures how many times signature playbooks were opened by website visitors, providing insight into the effectiveness of personalized messaging.
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.