Introduction
Once you have a few weeks worth of data syncs, you're ready to take your reporting to the next level.
It's worth starting with the basics if you have yet to gather the necessary dataset or want to start with the fundamentals.
Advanced Signal-Based Reporting
To get started, try building a pivot table in different orientations to track the relationship between deal creation and LinkedIn impressions. This will allow you to spot trends in engagement before and after a deal is created.
Example 1: Deal Creation vs. Impressions Over Time
Chart Type: Pivot Table
Sources: Companies (primary), Deals, Signals
Filters: Signal Type = LinkedIn Ad Impression, Pipeline = New Business, Deal Create Date = This Quarter (can adjust as needed)
Rows: Deal Create Date (Monthly), Company Name
Columns: Signal Date (Monthly)
Values: Sum of LinkedIn Ad Impressions (Last 7 Days)
This report gives you a clear view of how engagement correlates with deal creation, helping to identify pre- and post-engagement trends.
Example 2: Deal Count vs. Impressions
Chart Type: Pivot Table
Sources: Companies (primary), Deals, Signals
Filters: Signal Type = LinkedIn Ad Impression, Pipeline = New Business, Deal Create Date = This Quarter (can adjust as needed)
Rows: Deal Create Date (Monthly)
Columns: Signal Date (Monthly)
Values: Deal Count and Sum of LinkedIn Ad Impressions (Last 7 Days)
Once you’ve set up this initial test, try applying similar logic across various signal types to explore how different signals (or combinations) are influencing deals.
Week-over-Week Impressions by Company
Next, you can measure the consistency of impressions among your target accounts by tracking weekly data. Below are two report examples:
The first report shows the total number of companies with LinkedIn impressions each week and the average number of impressions per company.
The second report narrows this down to companies with more than 25 impressions per week.
This can be particularly valuable when analyzing the reach of your LinkedIn ads on specific segments, so you can identify which companies are receiving consistent engagement.
Example: Weekly LinkedIn Impressions
Chart Type: Combination (Bar & Line)
Sources: Companies (primary), Signals
Filters: Signal Type = LinkedIn Ad Impression, Ad Impressions Property > [X] (or remove for first chart)
X-Axis: Signal Date (Weekly)
Y-Axis 1: Count of Companies
Y-Axis 2: Average of LinkedIn Ad Impressions (Last 7 Days)
Breakdown: Apply filters like account tier, company size, or industry for deeper insights
Basic Reach Tracking
For a simpler approach, use the report below to track LinkedIn impressions at a high level on a monthly basis. This report shows companies with a threshold of LinkedIn impressions and any associated deals. Again, an average of impressions can be applied here to smooth out any anomalies.
Example: Monthly LinkedIn Impressions & Deals
Chart Type: Bar Chart
Sources: Companies (primary), Deals, Signals
Filters: Signal Type = LinkedIn Ad Impression, Impressions > [Threshold]
Y-Axis: Count of Companies and Deal Count
X-Axis: Signal Date (Monthly)
Conclusion
Ultimately, the goal of these reports is to understand how to maintain consistent, impactful engagement that influences your pipeline in the long term. By tracking signal-based activity in this way, you'll be able to pinpoint the ideal level of engagement and impressions needed for each account or segment to drive measurable results.