How to Use the Engagement vs. Pipeline R

Why Use the Engagement vs. Pipeline Dashboard?

The Engagement vs. Pipeline dashboard allows companies to view attribution data and engagement data in a single view. Historically, marketers have struggled to evaluate a campaign's effectiveness. While we are huge fans of attribution, leading indicators should be included when evaluating a campaign.


First of all, marketers can't wait three months for a lead to convert to pipeline or nine months for revenue to be closed to evaluate a campaign. Attribution builds over time, while campaigns should be regularly evaluated. Leading indicators are a great place to start. They show whether the right companies and people are engaging with a campaign.

Secondly, not all campaigns are intended for the bottom of the funnel. Less established companies, in particular, need to run campaigns to build awareness. Those campaigns may lead to additional engagement, but they take time. Engagement data helps us see whether or not the campaigns are having the intended impact.

Where To Start

While it's tempting to either look at all of your data at once or a very narrow window of time, it's important to understand the question you're trying to answer. This will determine which filters, scoring model, and attribution model you use.

Which Attribution Model Should I Use?

Generally speaking, multi-touch attribution models make the most sense when comparing data to engagement models. Why? Engagement models don't filter out events, whereas single-touch attribution models only apply their weight to a single touchpoint. Which multi-touch model you use depends on your organization.

The following is loose advice for organizations that have not landed on a model yet:

  • If you have a chain-based model in place and a large data set, we recommend using chain-based.
  • If your sales team regularly indicates the primary contact on an opportunity and you believe the first and middle touches should get more weight, use the W-shaped model.
  • If you have less confidence in primary contacts being used reliably or correctly or aren't sure if any one touch should be weighed more than others, use the Even-weighted model.

Which Engagement Model Should I Use?

In general, engagement models should reflect roughly the time that it takes for a prospect to move from the first touch to opportunity creation. Touches become less meaningful the further out you get from the date of engagement.

The campaign chart will still show you which campaigns had the most volume of engagements regardless of timeframe, so the engagement model is only useful for looking at how engagement is changing over time and identifying which prospects might be converted into an opportunity today.

Which other filters should I be aware of?

When evaluating a set of campaigns, consider qualities that may influence buyer behavior. For example:

  • Do net new prospects behave differently than existing customers? If yes, then you may want to apply an Account Type or Opportunity Type filter - although a list would be more effective. You can learn more about list building here.
  • Does buyer behavior potentially differ by region?
  • Does buyer behavior differ by company industry?

If you don't know the answers to these questions, this is a great opportunity to use these filters and get more familiar with your buyer base!

Dashboard Definitions

Top Campaigns - Detail Table

Top Campaigns Attribution and Engagement

Campaign Name: Name of the Campaign as it appears in your CRM, Marketing Automation Platform, or Ad Platform

Campaign Type: This indicates the tactic (often referred to in CRM or MAP systems as the "Channel" or "Program") used in the Campaign.

Score: The weighted score according to the engagement model you selected. This can show which campaigns are "hot" or recently active.

Revenue Sourced: The sum of dollars attributed prior to opportunity creation associated with the campaign using the attribution model selected.

Revenue Influenced: The sum of dollars attributed after opportunity creation associated with the campaign using the attribution model selected.

Average Influence: The percentage of responses from this campaign receiving attribution compared to the total pool of responses.

Companies Influenced: Number of companies that responded to that particular campaign.

Touches: Number of campaign responses.

Source System: The data source of the response or touch.

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