LinkedIn Conversation Ads Pipeline Payback

Project the revenue impact of LinkedIn Conversation Ads without building a full spreadsheet. Provide cost per send, response and SQL conversion rates, and the average pipeline value per SQL to see cost per lead, cost per SQL, attributed pipeline, and pipeline-to-spend multiples using a $10,000 test budget.

Average amount LinkedIn charges for each delivered conversation ad.
Percent of sent conversations that convert into leads or form fills.
Share of leads that progress to sales-qualified opportunities.
Pipeline amount you attribute to each SQL once sales accepts the opportunity.

Outputs assume constant conversion rates across the spend range and do not account for frequency fatigue, bidding changes, or downstream qualification filters.

Examples

  • $0.65 cost, 8% response, 35% SQL rate, $42,000 deal size ⇒ A $10,000 pilot funds 15,384.62 sends, drives 1,230.77 leads, and yields 430.77 SQLs. Cost per SQL is $23.21, generating $18,092,307.69 in pipeline—a 1,809.23x multiple on spend.
  • $0.90 cost, 5% response, 25% SQL rate, $28,000 deal size ⇒ The same budget buys 11,111.11 sends, 555.56 leads, and 138.89 SQLs. Cost per SQL rises to $72.00 while pipeline totals $3,888,888.89, or a 388.89x multiple.

FAQ

Can I change the test budget?

Multiply the outputs proportionally to model larger or smaller spends. For example, halving the budget to $5,000 halves sends, leads, SQLs, and pipeline while keeping unit costs constant.

How do I factor close rates?

Multiply the projected pipeline by your win rate outside the calculator to estimate closed revenue and true ROI.

Does this include sales cycle length?

No. Pair the pipeline multiple with your average sales cycle to understand how quickly spend returns as booked revenue.

What if I run message ads or lead gen forms instead?

Use the same structure with conversion rates from message ads or lead gen forms to compare efficiency against conversation ads.

Additional Information

  • Metrics assume a fixed $10,000 spend so you can benchmark scenarios consistently.
  • Pipeline value multiplies projected SQLs by your average deal size to show potential revenue influence.
  • Cost per lead and cost per SQL are reported even when conversion rates are low, highlighting efficiency gaps.
  • If conversion inputs yield zero leads or SQLs, the calculator flags that the scenario will not produce pipeline.

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