This dashboard directly addresses the alignment of marketing efforts with sales outcomes and overall revenue generation. In essence, this dashboard allows RevOps to move beyond just marketing metrics and directly connect LinkedIn activities to the health and efficiency of the entire revenue engine, identifying bottlenecks and opportunities for optimization across Marketing and Sales. Here are some key RevOps questions this data can help answer:

  1. Marketing’s Contribution to the Revenue Funnel:
    1. How much pipeline is LinkedIn marketing generating? (Directly answered by “Attributed Pipeline”)
    2. What is the ROI of our LinkedIn ad spend in terms of potential sales opportunities (pipeline)? (Answered by “Pipeline ROI”)
    3. Is our LinkedIn marketing contributing to actual closed-won deals (bookings)? (Answered by “Attributed Bookings” and “Bookings ROI”)
    4. What is the overall efficiency of our LinkedIn investment in driving revenue? (Answered by “Bookings ROI” and “Cost to Acquire Revenue”)
  2. Channel Effectiveness & Optimization:
    1. Which specific companies are we spending money on, and are those companies yielding pipeline or bookings? (Answered by “Lift Analysis” and “Attribution Detail by <Group by>”)
    2. Are there certain companies or segments where our LinkedIn spend is highly effective (high Pipeline/Bookings ROI)? (Highlighted by “Statistical Significance” and high ROI values in the detail widgets)
    3. Where should we strategically reallocate LinkedIn ad spend to maximize revenue impact? (By identifying high-ROI companies and potentially reducing spend on low-ROI ones)
    4. How does our LinkedIn ad spend translate into initial engagement (clicks) and what is the cost efficiency of that engagement? (Answered by “Clicks” and “CPC”)
  3. Performance Trends & Forecasting:
    1. Are there seasonal patterns or trends in our LinkedIn spend and attributed pipeline over time? (Answered by “LinkedIn Spend & Attribution Over Time”)
    2. How does our monthly LinkedIn investment correlate with the pipeline generated in that period, or subsequent periods (considering sales cycle)? (Requires analysis of “LinkedIn Spend & Attribution Over Time”)
    3. Are we pacing towards our pipeline and booking goals based on current LinkedIn performance? (Helps inform revenue forecasting by understanding a key channel’s contribution)
    4. Sales & Marketing Alignment & Handoff:
    5. How effectively are sales converting opportunities that had a LinkedIn marketing touchpoint? (Answered by “Attributed Opp Win Rate” for relevant companies)
    6. Why are Attributed Bookings so low or zero for LinkedIn overall, despite significant pipeline generation? (This is a critical RevOps question that could point to issues with lead qualification, sales follow-up, sales enablement, or even the attribution model itself)
    7. Are the leads/opportunities influenced by LinkedIn campaigns high quality? (Implicitly addressed by win rates and booking values – low win rates or $0 bookings could indicate quality issues).

KPI Metrics:

  1. Ad Spend – this widget shows you the total spend with LinkedIn during the defined time period
  2. Clicks – this shows the number of LinkedIn Ad Clicks during the defined time period
  3. Avg. CPC – this shows the average Cost per Click during the defined time period
  4. Attributed Pipeline – this shows the amount of Pipeline generated with a LinkedIn touch during the defined time period
  5. Attributed Pipeline ROI – this shows the Return on Investment as it relates to Pipeline in regards to LinkedIn Spend
  6. Attributed Bookings – this shows the amount of Bookings created that had a LinkedIn touch during the defined time period
  7. Attributed Booking ROI – this shows the Return on Investment as it relates to Bookings generated in regards to LinkedIn Spend

LinkedIn Spend & Attribution Over Time

This widget allows us to visualize:

  • How our LinkedIn ad budget was allocated month-over-month within the specified date range?
  • When and how much sales pipeline was generated as a direct result of our LinkedIn activities?
  • The relationship between our monthly investment and the pipeline generated, helping to identify periods of stronger or weaker performance.

Lift Analysis by Company Name

This widget allows for a dynamic Group by selection. You can choose either Company Name or Campaign Name.

This widget helps us pinpoint:

  • Which specific Companies are responding most effectively or which Campaigns are most effective as it relates to our LinkedIn advertising in terms of generating sales pipeline.
  • Which of those responses are strong enough to be considered a reliable outcome (i.e., not just a fluke).

Here’s a breakdown of what it specifically does:

  1. Calculates Cost per Company / Campaign: It first aggregates the LinkedIn ad spend associated with each specific Company / Campaign. This is derived from LinkedIn ad data matched with our internal company records.
  2. Calculates Attributed Pipeline per Company / Campaign: It then attributes pipeline value to each Company / Campaign based on the ‘Even-Weighted’ attribution model, similar to the overall “Attributed Pipeline” widget, but segmented down to the company level.
  3. Determines Pipeline ROI per Company / Campaign: For each Company / Campaign, it calculates the Pipeline ROI by dividing the attributed pipeline by the associated Ad Cost. A Pipeline ROI of 1 means we got $1 in pipeline for every $1 spent.
  4. Assesses Statistical Significance: This is the “lift” part. Instead of just showing the ROI, it goes a step further to determine if that ROI is genuinely impactful or if it could simply be due to random chance.
    • It calculates the average Pipeline ROI and the standard deviation across all Companies / Campaigns that received LinkedIn ad spend within the reporting period.
    • It then compares each Company’s / Campaign’s individual Pipeline ROI to this overall average.
    • If a Company’s / Campaign’s Pipeline ROI deviates significantly from the average (specifically, if the absolute difference between its ROI and the average ROI, divided by the standard deviation, is greater than a threshold of 0.84), it is flagged as having “Statistical Significance: Yes.” This means we can be reasonably confident that the observed ROI for that Company / Campaign is a real effect of our LinkedIn spend, rather than just random variation.

LinkedIn – Attribution Detail by Company Name

This widget helps us identify:

  • Which specific Companies are engaging with our LinkedIn Ads or which Campaigns are leading to Sales Opportunities and Revenue.
  • The efficiency of our spend on those individual Companies or Campaigns (ROI).
  • Which Companies are “winning” more often after engaging with our LinkedIn content.

This level of detail is invaluable for optimizing ABM strategies, understanding the customer journey at an account level, and demonstrating the direct impact of LinkedIn advertising on specific high-value targets.

*This widget allows for a dynamic Group by selection of either Company Name or Campaign.

  1. <Company Name> Group by: This column would display the Company / Campaign Name for which the attribution details are being presented. It identifies the specific company that engaged with LinkedIn campaigns.
  2. Cost: This represents the total advertising spend (in USD) on LinkedIn Campaigns associated with this particular Company during the specified reporting period.
  3. Attributed Opps: This is the total count of unique Sales Opportunities that had at least one touchpoint attributed to LinkedIn Campaigns involving this Company.
  4. Attributed Won Opps: This indicates the number of unique Sales Opportunities that were successfully closed as “won” and had at least one touchpoint attributed to LinkedIn Campaigns involving this Company.
  5. Attributed Pipeline: This is the monetary value of potential Revenue (Pipeline) attributed to LinkedIn interactions from Campaigns involving this Company, calculated using the selected Attribution Model.
  6. Pipeline ROI: This metric shows the Return on Investment for Pipeline generated. It’s calculated by dividing the Attributed Pipeline by the Cost for this company. A value of 1.00x means that for every dollar spent, one dollar of Pipeline was generated.
  7. Attributed Bookings: This represents the monetary value of actual closed-won deals (bookings) attributed to LinkedIn touchpoints from Campaigns involving this Company, based on the selected Attribution Model.
  8. Bookings ROI: This metric indicates the Return on Investment for actual Bookings. It’s calculated by dividing the Attributed Bookings by the Cost for this company. A value of 1.00x means that for every dollar spent, one dollar of Bookings was generated.
  9. Cost to Acquire Revenue: This measures the cost incurred to generate one unit of Attributed Revenue (Bookings) for this Company. It’s calculated by dividing the Cost by the Attributed Bookings. If there are no Attributed Bookings, this value will be 0.
  10. Attributed Opp Win Rate: This is the conversion rate of Attributed Opportunities into closed-won deals for this Company. It’s calculated by dividing the Attributed Won Opps by the Attributed Opps.