Marketing to Sales (Leads) Pipeline Dashboard Overview (version 2022.2)

Overview

The purpose of this Marketing to Sales Pipeline Dashboard is to develop a turn-key report that helps clients align marketing and sales in one place. The audience for this report is someone who needs to understand how marketing is affecting their sales. Level 3 introduces CRM data. 

Dashboards

This report includes the following dashboards:

Pipeline Summary:

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

Forecast Summary:

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

Supported BI Tools

The Marketing to Sales Pipeline Dashboard is available in Tableau and Power BI.

Data

This dashboard includes the following data sources:

• Act-On
• Apple Search Ads
• Bing Ads
• 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.