Clicks by Chat Playbook is a metric that measures the number of clicks on suggested responses or actions within a chatbot conversation, indicating user engagement and effectiveness of the chatbot's pre-programmed responses.
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 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.
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 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 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.
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.