#91: Quo Vadis Green Shoot AdTech Interviews
With John Joe Smith, CEO Co-founder of WasteNot
Welcome back to Quo Vadis Green Shoot AdTech Interviews.
Before we get into it, make sure to sign up for what will be an incredibly enlightening webinar moderated by yours truly on October 30th. It’s about a publisher solution called “The Third Way” featuring Aditude’s Jared Siegal, John Shankman, and a special guest!
In this series, we’re stepping into the shoes of “AdTech Equalizers” — the green shoot companies changing how the advertising job gets done. We'll delve into their worldview and unveil new strategies, tools, and thinking that make advertisers better off.
Today we have the pleasure of speaking with John Joe Smith, Founder and CEO of WasteNot. WasteNot is the first platform focused solely on making omnichannel, real-time ad suppression strategies something marketers can activate, measure, and test themselves at scale.
If you didn’t already know, audience suppression is a really big deal!
Q1: For the uninitiated, what are ad exclusions, why do you consider them to be so important, and why are they more relevant now?
A: John Joe
Ad exclusions are the people, locations, placements, KW’s, and topics advertisers tell their ad platforms to AVOID, instead of those you tell them to proactively target. Throughout its history, the ad industry has spent 99% of its time focusing on finding the right combination of parameters to drive the best overall performance, while spending almost no time thinking about how to avoid ad buys that are a waste of budget.
I use the analogy of someone trying to improve their health by adding a bunch of healthy food to their diet, but not cutting out junk food, smoking, or drinking. There’s a shocking amount of ad spend that’s wasted on scenarios that marketers’ common sense tells them they’d like to avoid (eg. paying for branded search ads for existing customers or app install ads for active users, etc.), but they haven’t been able to because of a lack of usable solutions that would instead allow them to activate suppression strategies.
Q2: What are some of the most common suppression audiences every advertiser should be thinking about?
A: John Joe
I’ll start by saying we’re trying to shift the mindset away from “suppression audiences” which to me means a static list that needs to be updated every so often and loses value over time (e.g., everyone who completed a purchase from June 1 - June 30) to “suppression strategies” which means criteria that defines who is filtered from ad buys for specific campaigns or ad groups based on real-time user data (eg. don’t buy prospecting or brand awareness ads for people who have completed a purchase in the last 30 days). This is exactly what our platform allows marketers to do.
Overall, any suppression strategy’s aim should be to avoid paying for ads that won’t increase the likelihood of a conversion one way or the other. Within that paradigm there are basically two camps of audiences advertisers should look to avoid.
Those who are already likely to convert through organic channels or direct traffic, which advertisers should suppress to avoid budget cannibalization.
Those who are who are not going to convert no matter what, who advertisers should avoid to decrease their overall CAC and improve their marketing efficiency ratio.
Within both of these camps, we create a “Crawl, Walk, Run” guide for brands based on their specific advertising strategy and walk them through activating and measuring the impact of each. Again, it’s not something most advertisers have traditionally focused on or have a well-defined strategy for
So, let’s focus on the “Crawls” since you asked for the most common examples. We’ll start with group 1 — those already likely to convert.
Some of the most common “suppression strategies” we always have our brands set up on day one are:
Don’t serve brand awareness or prospecting ads to anyone who’s:
Completed a purchase in the past 30-90 days (or ever) depending on the brand’s AOV and purchase cycle
Regular website visitors
Social engagers
Loyalty customers
Users opening/clicking on your emails
(B2B example) Any individual who is a sales qualified lead in your CRM, or anyone at any organization that is at the “Demo” stage or later in your sales pipeline.
Don’t send remarketing ads to:
Users who completed a purchase recently
Loyalty customers
Users who have visited your website more than x times in the past y days
Users who have added items to their cart more than x times but completed less than 1 purchase
For Group 2, the strategies might look like:
Don’t serve ads to users who have requested refunds in the past 30 days, or more than 2 total refunds in the past.
Don’t serve ads to users who have marked your emails to spam.
Don’t serve ads to users with an open customer support ticket.
(B2B Example) Don’t serve ads to anyone at any organization that is marked "closed won", “closed lost” or “churned customer” in your CRM..
Q3: What’s the actual impact of efficient suppression?
A: John Joe
If you think about the basic arithmetic of ROAS and that large pond that most brands are fishing in to find customers, especially in the era of signal deprecation, avoiding wasted ad spend is a force multiplier for all other optimization strategies. Suppression shrinks the pool by eliminating the unqualified users and increases the concentration of potentially valuable users."
For example, imagine you’re spending $10,000 on an Instagram campaign. The 90-day ROAS is 3:1 ($30,000 in revenue), CPM is $10 (1,000 impressions), CVR is 2% (20 conversions). Assume 25% of those impressions are wasted on the user groups listed above, which is conservative given the average we see spent on these groups is 30%-40% or more. Many people might stop at the calculation and say,
“Ok, so I’m wasting $2,500 in ad spend. No big deal, let’s chalk that up to spillage, that’s less than 10% of the revenues from the campaign, digital advertising isn’t perfect.”
Wrong. If you consider the true performance of this campaign when discounting the wasted ad spend and the opportunity cost to the advertisers in terms of lost revenues that the diminished performance leads to, then you’re starting to understand the force multiplier that efficient suppression actually creates.
Now let’s revise our example.
$2,500 of the budget is being wasted, meaning your effective budget is $7,500, meaning the effective ROAS of this campaign (discounting waste) is actually 4:1 ($30,000/$7,500)
Your effective CVR is 2.7% (20 clicks from 75 non-wasted impressions).
If you were able to suppress that wasted spend and reallocate it toward a population that performs on par with the rest of your non-wasted ad spend (which they will be by default), you’d see a 33% increase in ROAS and 35% increase in CVR without changing anything else like targeting, pacing, creatives, etc.
That means the opportunity cost of ineffective suppression here is $10,000 because the advertiser should have grossed $40,000 at a 4:1 ROAS instead of $30,000.
This means the true cost of ineffective suppression goes from “only $2,500” to $12,500, which is 125% of the entire campaign budget.
Not to beat a dead horse, but when you consider that a large part of effective suppression strategies is about reallocating ad spend away from those already likely to buy (“Camp 1” in the examples above). Doing so avoids marketing cannibalization across organic channels and decreases CAC, so the cost of ineffective suppression across all channels grows even more.
Q4: If that’s the negative impact of not doing this, how do you measure the value of doing this right?
A: John Joe
Right now, our primary KPI focuses on the amount of previously wasted ad spend we’re now reallocating toward value-additive ad buys. We use a straightforward calculation based on the unique number of users that are being suppressed from any individual campaign or ad group multiplied by the CPM and frequency for that campaign to measure how much ad spend would have been wasted on those groups that are now allocated to other users.
We’re currently working on measuring incrementality like that outlined in the example above using some more sophisticated measurement partners to demonstrate the overall impact on revenue, ROAS, CAC, reach, and frequency that individual suppression strategies have.
Q5: OK, if it’s so impactful, why is this something the industry has kind of shunned aside and not something more advertisers, agencies, ad platforms etc. are focusing on?
A: John Joe
That’s the question we first tried to understand through a lot of research and potential customer interviews before deciding to build WasteNot, and two themes emerged.
First and foremost, I think there’s historically been an embedded disincentive for certain parties to not focus on suppression. In the days where last-click or other type of attribution was king, the ad platforms, agencies, and even many of the individual marketing managers were all judged on the performance of their channels in a silo. This behavior incentivized all parties to go after the lowest-hanging fruit and attract the most likely buyers to convert through their channel. A broader concern about overall performance was not in play like incrementality measurement and marketing efficiency that we see taking hold now as a result of marketers being asked to do more with less.
The same theme popped up at your conference — AdTech Economic Forum. I’m paraphrasing here but the notion was something like this:
The veil’s been lifted on how the ad platforms have been pretending to be highways for growth while in reality they have functioned as toll booths.
Brands are now waking up to this reality and are increasingly skeptical of “AI-optimized” whole platform campaign types like Advantage+ and PMax. With ML-driven targeting and AI-driven automation like these, the new mandate for advertisers is going to be about creating the right guardrails like dynamic suppression strategies to keep ad platforms honest and drive them toward mutually beneficial outcomes.
Secondly, there’s a patchwork of legacy “sorta solutions” for suppression, which are all ineffective, especially in the age of identity deprecation. These non-solutions rely on third-party cookies, device IDs, and conversion tags to tell the platforms who your existing customers are so they can be excluded via the ad platforms’ UIs. However, in the tests we’ve run, these non-solutions have only been able to track 10-20% of recent customers, and are totally incapable of identifying users across devices. That means advertisers can only suppress users based on purchase or website visit events, but there is no support for suppressing users based on first-party data like email opens, loyalty status, mobile app opens, offline purchases, etc.
Last but not least, when in doubt, advertising muscle memory will try to do something manually when automated solutions can do it infinitely better.
For example, outdated workflows of manually compiling suppression lists, then uploading them to the ad platforms and applying them to individual campaigns/ad groups. It’s totally ineffective due to lack of scalability, latency of data (where waste accrues between updates), and low match rates for the user lists uploaded.
Lunio put out a great study that found that 94% of advertisers use ad exclusions on at least one campaign, but only 24% of advertisers can activate them across all campaigns, which highlights the issue of scalability and usability for marketers which was we’re trying to solve.
Get In Touch With Wastenot: If you’d like to learn more about universal ad suppression, reach out to John Joe at johnjoe@wastenot.tech.
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