Marketing Lead Pipeline Dashboard Overview (version 2022.2)

Overview

The purpose of this Marketing Lead Gen Dashboard is to develop a turn-key report that helps clients align website activity and marketing in one place. The audience for this report is someone who needs to understand how marketing is affecting their lead collection at a high level.

Dashboards

This dashboard includes the following dashboards:

Pipeline Summary:

1. How much am I spending in order to gain a lead/form fill?
2. What is the volume of a metric (impressions, interactions, etc) needed to gain a lead/form fill?
3. Are my overall number of leads/form fills trending upwards?
4. What channel/data source/campaign is contributing the most to leads/form fills?

Forecast Summary:

1. How much should I budget for in order to achieve X amount of leads/form fills?
2. Other iterations of this question can be asked. “If I spend X amount, how many leads/form fills should I expect?
3. As of today (mid-flight), am I on pace to achieve my goal?

Supported BI Tools

The Marketing Lead Dashboard is available in Tableau and Power BI.

Data

This dashboard includes the following data sources:

• Act-On
• Apple Search Ads
• Bing Ads
• Bronto
• Campaign Monitor
• Digital Media via Google Sheet
• El Toro
• Emfluence
• Exact Target
• Facebook Ads / Social
• Google Ads
• Google Analytics
• Google Campaign Manager
• Google Search Console
• Hubspot
• IBM Watson
• Instagram Ads / Social
• LinkedIn Ads / Social
• Mailchimp
• MNTN (formerly known as SteelHouse)
• ON24
• Pardot
• Pinterest Ads
• Reddit Ads
• Salesforce
• Snapchat Ads
• TikTok Ads
• Traditional Media via Google Sheet
• Twitter Ads / Social
• YouTube Analytics

This dashboard is built on the following dataset:

v_oneview_pipeline_lead_gen

Tracking Requirements:

ChannelMix Conversion Tracking implementation is required for this dashboard. Read more in the ChannelMix Conversion Tracking - Onboarding article.

Visualizing the data:

These aforementioned data streams are what powers the dashboard. Below is an illustration of how these different data points make up the pipeline summary.