Channel-agnostic marketing measurement tracks a campaign’s impact across every purchase path, not just where the ad ran. This matters because “channel” means three things, marketing, purchase, and order, and conflating them hides real cross-channel effects. Without that separation, a brand running ads on Meta can’t see the “echo” those ads create at its stores or website. This video explains how modeling these relationships reveals which channels actually deserve more budget.
Why Channel-Agnostic Marketing Measurement Starts With Fixing Vague Terminology
Channel-agnostic marketing measurement starts with fixing vague terminology, because the word “channel” alone can mean the marketing channel, the purchase channel, or the order channel, and treating them as one thing hides exactly the cross-channel relationships marketers most need to see. In this video, John Wallace, CEO of LiftLab, points out that marketing is full of words like “segment” and “campaign” that can mean almost anything depending on who’s using them, and “channel” is one of the worst offenders. Keeping the marketing channel, purchase channel, and order channel separate in his own thinking is what lets him see the cross-tab between them.
A channel-agnostic measurement framework has to onboard data from every platform a brand spends on, including offline media, and measure impact across every place a purchase can actually happen, a brand’s own site, a marketplace like Amazon, a retail partner like Walmart or Target, or an owned physical store. That’s what lets a brand answer a question like what a TikTok campaign’s “echo” is at the cash register, or whether media running on Amazon is also pulling people to the brand’s own website. Wallace explains that LiftLab uses models, essentially curves describing diminishing returns, to test whether changes in spend reliably explain changes in response, and to what degree, which is what turns a directional hunch into a fundable number.
In this video, you’ll learn:
Why standard marketing terminology often creates confusion and why precise definitions matter
The distinction between marketing, purchase, and order channels for better measurement
How to implement a channel-agnostic framework to track cross-channel impacts
Why measuring the “echo” of digital campaigns on physical store sales is essential
How predictive modeling identifies the relationship between ad spend and consumer response
TIME STAMPFull Video Transcript: Channel-Agnostic Marketing Measurement Explained
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In this video, LiftLab CEO John Wallace explains why channel-agnostic marketing measurement starts with separating marketing, purchase, and order channels, and how to measure the cross-channel “echo” between them.
[0:00 – 0:31] Defining Marketing Terminology
There are a few favorite words in marketing that can mean almost anything when you use them. Everyone has used the word segment to say all kinds of different things. It might be a segment of 100 people or a segment of a thousand people, or it might be a segment of the market. Another favorite term is campaign. Campaign could be something really small where you are spending $5 a day, or it could be a multi-channel campaign that is running across paid social, paid search, TV, print, and might be tens of millions of dollars, and they are very different. The other word that is a dirty word in marketing is channel.
[0:31 – 0:54] The Three Types of Channels
What do you mean by channel? What I have had to end up learning in my career is the prefix channel with: are we talking about the marketing channel, or are we talking about the purchase channel, or the order channel? I love keeping these separated in my mind. It is kind of, maybe I am one of those people where my red socks do not touch my white socks, or my black socks do not touch my white socks, but I need these to be separate in my mind, because what you actually need to be able to do is see the cross tab of those two.
[0:54 – 2:23] The Need for a Channel-Agnostic Measurement Framework
In order to do that, you need a measurement framework that is channel agnostic. It should be able to onboard marketing channel data from any of the platforms where you are spending, including some of your offline media, and it should be able to measure the impact of all of that media on a variety of purchase channels. I know a lot of measurement is designed to measure online sales, and of course, that is where a lot of the growth is, but it could be that you are also selling on someone else’s site. You could be working with a marketplace or on Amazon. A lot of our customers also have owned and operated stores, and they want to pull that data in, and they literally need to know: when I run this TikTok campaign, what is the echo at the cash register, as I like to call it? So a measurement framework that can do that is pretty important. The extension of this could be even bringing in data from other stores where you are selling. You may be able to acquire that data from transactions that are happening at Walmart or Target or something like that. It is completely agnostic. One of my favorites is: what if I am running media on Amazon but I want to see the impact on my own website? The impact is greater than zero. Not all of those people are going to transact on Amazon; some may end up coming to your site looking for a bargain. These things happen, and we want to be able to not ignore them; we want to quantify them. Or in the other direction, what if I am running a campaign on my own, maybe a prospecting campaign on Meta, that might have an impact on Amazon, or it might have an impact on my stores, or it might have an impact on my sales in someone else’s store? This cross tab of channel is an unlock for a lot of brands, and when we talk about having the most detailed marketing P&L, this is an exact example of that.
[2:23 – 3:14] Using Models to Quantify Marketing Impact
What we are doing when we say the word model, and we use this word synonymously with things like curves and diminishing returns, is to say: do changes in ad spend on Meta reliably explain changes in sales? We would assume that there is a stronger relationship between media that we buy from Meta and an impact on our website. It is easier for that media to get someone to open a tab than to get someone to jump in the car and drive down to the store, but the effect on the store is definitely not zero. I am just talking about these things directionally; that is the job of a curve or a model, is to say: as I increase spend, does it reliably explain changes in response? And if so, to what degree? With that information, you are in a much better position to properly quantify the investment, understand the economics of that marketing investment, and then continue to reward and fund the channels that are high performers.
Key Lessons: Building a Channel-Agnostic Marketing Measurement Framework
Channel categorization: Clearly separating marketing, purchase, and order channels is a critical prerequisite for accurate performance analysis.
Complete measurement: An effective measurement framework must incorporate data from all sources, including offline media and retail partners, to capture the complete consumer journey.
Quantifying impact: Every media investment has an impact greater than zero on different channels, and these relationships must be quantified rather than ignored.
Modeling performance: Utilizing curves and modeling allows brands to understand diminishing returns and ensure marketing dollars are allocated to the highest-performing channels.
