Every added $1,000 in media came back as about $17,800 in pipeline.
A B2B brand was running a single-channel search program with no way to connect spend to sales pipeline. I rebuilt it into a full-funnel program across six-plus platforms, then tested the link between spend and pipeline the way a data science team would.
6.5x
Growth in monthly media investment over the engagement
2x
Monthly pipeline value, from under $1.6M to over $2.8M
0.88
Correlation between monthly spend and pipeline value (p < 0.001)
$17,800
Added pipeline value per $1,000 in added monthly spend
01
The challenge
The brand sells directly to other companies. Deals are high-value and close through a considered, multi-touch sales process. When I took over, spend sat almost entirely in one channel, across product lines with very different buyers and sales cycles.
02
Why platform conversions weren’t enough
Platform-reported conversions are easy to inflate, especially on social, where multi-day click windows take credit for activity they didn’t drive. So the bar was set higher: tie spend to CRM-tracked sales pipeline every month, and prove that more investment meant more business, not just more traffic.
03
A funnel where every channel has a job
Instead of pouring more budget into the one channel already running, I rebuilt the program around the full B2B buying journey and matched each stage to the platform best suited to it.
Awareness
Cold audiences
LinkedIn brand awareness
Pinterest with native, editorial creative
Connected TV
Consideration
Prospecting
Non-brand search on Google and Microsoft
LinkedIn job title and firmographic targeting
Meta interest and lookalike stacking
Conversion
Remarketing
Brand search to capture the demand the funnel creates
Cross-channel retargeting
Talk to sales and request-a-quote offers
04
What I built inside each layer
1
Search, done properly
Microsoft Ads came in at a meaningfully cheaper cost per click than Google for the same B2B traffic, so it became a real second engine. A recurring search term review cuts consumer-intent, local retail, and competitor waste before it scales.
2
Audiences built from customer history
The client’s own customers, ranked by lifetime value, defined four buyer personas: operations lead, founder, HR leader, and finance approver. LinkedIn and Meta lookalikes are seeded by product line, not one generic list.
3
Creative mapped to the funnel
Cold audiences get a low-commitment free consultation. Warm, high-intent audiences get request a quote and talk to sales. Each platform gets its own spec so the brand stays consistent and still feels native.
4
LinkedIn as the reach engine
LinkedIn became the most efficient reach channel in the mix, delivering a far larger share of impressions than its share of spend.
5
Early tests on new channels
I piloted AI-powered ad platforms early and stood up a second product line from scratch, correcting course when early keywords leaned toward consumer language instead of how real business buyers search.
05
Spend and pipeline climbed together
Monthly media spend
All platforms combined
~$16K
Apr '25
May '25
Jun '25
Jul '25
Aug '25
Sep '25
Oct '25
Nov '25
Dec '25
Jan '26
Feb '26
Mar '26
Apr '26
May '26
~$104K
Jun '26
Monthly sales pipeline value created
~$1.5M
Apr '25
May '25
Jun '25
Jul '25
Aug '25
Sep '25
Oct '25
Nov '25
Dec '25
Jan '26
Feb '26
Mar '26
Apr '26
May '26
~$3.0M
Jun '26
Both charts start at zero. Spend from the ad platforms; pipeline value from CRM-tracked opportunities, April 2025 to June 2026.
Monthly media investment scaled about 6.5x, from a roughly $16K single-channel test budget to a fully diversified program at over $100K a month.
Monthly pipeline value roughly doubled over the same stretch, from under $1.6M to over $2.8M in new qualified opportunity value.
The program grew from one channel to six-plus platforms across search, professional social, visual discovery, and connected TV, each with its own job in the funnel.
06
Proof, not a chart that happens to go up
Pearson correlation between monthly media spend and pipeline value, 15 months
Spend → total pipeline
Same month · R² 77% · Highly significant
r = 0.88
Spend → pipeline next month
One-month lag · R² 58% · Significant
r = 0.76
Spend → existing business pipeline
Same month · R² 52% · Significant
r = 0.72
Spend → new business pipeline
Same month · R² 37% · Significant
r = 0.61
r runs from 0 (no relationship) to 1 (perfect relationship). R² is the share of the swing in pipeline that spend explains. Significance tested with linear regression.
The spend-to-pipeline relationship came in at r = 0.88, p < 0.001. Spend explains about 77% of the month-to-month swing in pipeline.
It held after accounting for both numbers trending up over the same period, so it isn’t two lines that just happen to rise together.
It held again with a one-month lag, matching the real time it takes a new lead to become a qualified opportunity.
The regression gives leadership a planning number: every added $1,000 in monthly spend is associated with about $17,800 in added pipeline.
07
How I run every engagement
1
Build the full funnel
Put each platform where buyers actually spend attention, with one clear job per stage.
2
Measure what leadership measures
Pipeline and revenue from the CRM, not the platform metric that’s easiest to make look good.
3
Test it like a finance team would
Correlation, regression, trend controls, and lag tests before anyone calls it a trend.
4
Turn the result into a budget model
A number like $17,800 per $1,000 makes the next budget conversation a plan, not a hope.
“Leadership doesn’t need more green arrows on a dashboard. They need a number they can plan a budget around.”
Bradley Zeller, Zeller Media
Client details are anonymized. Spend comes from the ad platforms and pipeline value from the client’s CRM, April 2025 to June 2026. Statistics from Pearson correlation and linear regression across 15 monthly observations.
