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.
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 Replied by Email 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.
Total Conversations is a metric that measures the number of chat or messaging interactions between customers and a brand or business on Drift's platform.
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.
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 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 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.
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.