Drift
Sent by Email 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.
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 Sent by Email Playbook using Databox, follow these steps:
Goals
Scorecards
Metric Digest
Metric Builder
Data Calculations
Performance ScreenThis dashboard tracks the traffic levels of visitors using chat for assistance, along with the overall performance of Drift Campaigns.
Drift
New Conversations is a metric that tracks the number of new chats or conversations initiated by website visitors or customers within a certain timeframe.
Drift
The Average Time to Close (ATC) metric measures the average duration from initial contact to final resolution for a closed deal or opportunity, helping track and improve sales team efficiency.
Drift
Conversations by Chat Playbook measures the number of chat conversations initiated by users interacting with specific chat playbooks on a website or messaging platform.
Drift
The Meetings Booked by Chat Playbook metric measures how many meetings were scheduled as a result of utilizing specific chat playbooks.
Drift
The Open % by Email Playbook metric measures the percentage of email recipients who opened a specific email sequence or playbook.
Drift
Opens by Signature Playbook metric measures how many times signature playbooks were opened by website visitors, providing insight into the effectiveness of personalized messaging.
Drift
Open % by Signature Playbook is a metric that measures the number of times signature playbooks are opened by visitors on a website, indicating interest and engagement levels.
Drift
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.
