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.
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 Meetings Booked by Chat Playbook metric measures how many meetings were scheduled as a result of utilizing specific chat playbooks.
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 Dismisses % by Chat Playbook metric measures the effectiveness of chat playbook messages in engaging website visitors by tracking the percentage of times they are dismissed without action.
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 Clicks by Email Playbook metric measures the total number of clicks on links in marketing emails sent through the Drift platform.
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.
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.