Proving Ad Impact in 2026
B2B Growth

Stacking Growth (The B2B Go-to-Market Podcast)

Proving Ad Impact in 2026

Stacking Growth (The B2B Go-to-Market Podcast)Nov 25, 2025

AI Summary

Matt Sciannella and Keith Putnam-Delaney discuss how B2B marketers can prove ad impact in 2026 by moving beyond saturated LinkedIn and Google channels into B2C platforms like Meta, YouTube, Reddit, and TikTok, and by using server‑side events and conversion APIs to close mobile‑desktop measurement gaps. They emphasize that traditional attribution is insufficient and showcase lift and incrementality testing methods—holdout groups, geographic splits, and audience‑based experiments—to demonstrate true pipeline and revenue lift. The conversation also covers unbundling ABM with flexible audience‑targeting tools, CPM efficiency comparisons, and the use of share‑of‑search as a leading indicator, offering practical frameworks for growth and finance teams.

Why It Matters

Proving true ad incrementality equips B2B teams to justify spend, optimize CPMs, and align with finance, accelerating growth in saturated markets.

Episode Description

This roundtable explores how B2B teams can use modern demand strategies, B2C channels, and incrementality testing to prove true ad impact in 2026. The conversation highlights omni-channel expansion beyond LinkedIn, data-driven measurement, and practical ways to validate lift across pipeline and revenue.

Speakers and Roles

Matt Sciannella – Host and practitioner running paid media for multiple B2B clients; shares real client use cases, lift results, and practical frameworks for measurement and experimentation.

Keith Putnam-Delaney – CEO of Primer; former Dropbox growth leader; expert in B2B expansion into B2C channels, audience targeting, mobile–desktop measurement problems, match rates, and lift testing.

Authority: Both speakers bring hands-on experience running B2B paid programs at scale and deep insight into attribution limits, ABM constraints, and cross-channel growth strategies.

Topics Covered

Rising costs and saturation in traditional B2B channels (LinkedIn, Google).

Why B2B brands must expand into B2C channels like Meta, YouTube, Reddit, TikTok.

Mobile vs. desktop measurement gaps and cross-device limitations.

Signal loss, attribution decay, and the need for server-side events.

How to validate true impact using lift tests and incrementality.

CPM efficiency comparisons across channels.

ABM unbundling and alternatives to large, monolithic ABM platforms.

Using holdout groups, geographic lift, and omnichannel testing strategies.

Real client examples showing lift in inbound, share of search, and revenue.

How audience targeting tools unlock TAM expansion outside LinkedIn.

Questions This Video Helps Answer

How do B2B marketers prove real ad impact without relying on last-touch attribution?

How can brands expand beyond LinkedIn and still target ICP buyers effectively?

What causes demand generation inefficiency and how do you fix it?

How do mobile–desktop and cross-device gaps distort performance data?

What is the right way to design lift tests or incrementality experiments?

How can small TAM companies still scale using B2C channels?

What alternative ABM workflows exist beyond large enterprise platforms?

How should B2B teams interpret rising CPMs and shrinking reach?

Jobs, Roles, and Responsibilities Mentioned

B2B growth marketing

Growth teams

Sales operations managers

Revenue operations roles

VPs of Marketing

Regional sales directors

Medical device surgeons (ICP example)

Marketing, sales, finance

Infosec teams

PLG teams

Field marketing

Outbound sales teams

Key Takeaways

Attribution alone cannot prove channel value; lift tests reveal true incrementality.

B2B audiences exist far beyond LinkedIn, and CPM efficiency is often dramatically higher on Meta, Reddit, and YouTube.

Mobile-heavy consumption breaks MTA models; server-side signals and conversion APIs are now essential.

ABM can be unbundled using smaller, more flexible tools and alternative data sources.

Expanding TAM and using audience targeting unlocks more reach and stronger pipeline outcomes.

Share of search is a powerful leading indicator for demand creation impact.

Omnichannel experimentation paired with structured test design improves confidence with finance and executive teams.

Frameworks and Concepts Mentioned

Incrementality testing

Holdout groups

Channel-based lift tests

Geographic lift tests

Account list split testing

Leading vs. lagging indicators

Share of search analysis

Server-side conversion APIs (CAPI)

Cross-device measurement

Audience match rates

ABM unbundling

CPM efficiency analysis

Show Notes

This roundtable explores how B2B teams can use modern demand strategies, B2C channels, and incrementality testing to prove true ad impact in 2026. The conversation highlights omni-channel expansion beyond LinkedIn, data-driven measurement, and practical ways to validate lift across pipeline and revenue.

Speakers and Roles

Matt Sciannella – Host and practitioner running paid media for multiple B2B clients; shares real client use cases, lift results, and practical frameworks for measurement and experimentation.

Keith Putnam-Delaney – CEO of Primer; former Dropbox growth leader; expert in B2B expansion into B2C channels, audience targeting, mobile–desktop measurement problems, match rates, and lift testing.

Authority: Both speakers bring hands-on experience running B2B paid programs at scale and deep insight into attribution limits, ABM constraints, and cross-channel growth strategies.

Topics Covered

  • Rising costs and saturation in traditional B2B channels (LinkedIn, Google).

  • Why B2B brands must expand into B2C channels like Meta, YouTube, Reddit, TikTok.

  • Mobile vs. desktop measurement gaps and cross-device limitations.

  • Signal loss, attribution decay, and the need for server-side events.

  • How to validate true impact using lift tests and incrementality.

  • CPM efficiency comparisons across channels.

  • ABM unbundling and alternatives to large, monolithic ABM platforms.

  • Using holdout groups, geographic lift, and omnichannel testing strategies.

  • Real client examples showing lift in inbound, share of search, and revenue.

  • How audience targeting tools unlock TAM expansion outside LinkedIn.

Questions This Video Helps Answer

  • How do B2B marketers prove real ad impact without relying on last-touch attribution?

  • How can brands expand beyond LinkedIn and still target ICP buyers effectively?

  • What causes demand generation inefficiency and how do you fix it?

  • How do mobile–desktop and cross-device gaps distort performance data?

  • What is the right way to design lift tests or incrementality experiments?

  • How can small TAM companies still scale using B2C channels?

  • What alternative ABM workflows exist beyond large enterprise platforms?

  • How should B2B teams interpret rising CPMs and shrinking reach?

Jobs, Roles, and Responsibilities Mentioned

  • B2B growth marketing

  • Growth teams

  • Sales operations managers

  • Revenue operations roles

  • VPs of Marketing

  • Regional sales directors

  • Medical device surgeons (ICP example)

  • Marketing, sales, finance

  • Infosec teams

  • PLG teams

  • Field marketing

  • Outbound sales teams

Key Takeaways

  • Attribution alone cannot prove channel value; lift tests reveal true incrementality.

  • B2B audiences exist far beyond LinkedIn, and CPM efficiency is often dramatically higher on Meta, Reddit, and YouTube.

  • Mobile-heavy consumption breaks MTA models; server-side signals and conversion APIs are now essential.

  • ABM can be unbundled using smaller, more flexible tools and alternative data sources.

  • Expanding TAM and using audience targeting unlocks more reach and stronger pipeline outcomes.

  • Share of search is a powerful leading indicator for demand creation impact.

  • Omnichannel experimentation paired with structured test design improves confidence with finance and executive teams.

Frameworks and Concepts Mentioned

  • Incrementality testing

  • Holdout groups

  • Channel-based lift tests

  • Geographic lift tests

  • Account list split testing

  • Leading vs. lagging indicators

  • Share of search analysis

  • Server-side conversion APIs (CAPI)

  • Cross-device measurement

  • Audience match rates

  • ABM unbundling

  • CPM efficiency analysis

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