Quo Vadis Green Shoot AdTech Interviews With John Joe Smith, Founder and CEO of WasteNot

Today we have the great pleasure of speaking with John Joe Smith, Founder and CEO of Wastenot. WasteNot is the first platform focused solely on making omnichannel, realtime ad suppression strategies something marketers can activate, measure and test themselves at scale.

Tom: For the uninitiated, what are ad exclusions, why do you consider them to be so important, and why are they more relevant now?

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 target 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 the 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 that they haven’t been able to because of a lack of usable solutions that would allow them to activate suppression strategies.

Tom: What are some of the most common suppression audiences every advertiser should be thinking about?

John Joe: I’ll start with 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 (eg. 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), and 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 and that adds 0 incremental value. Within that paradigm there’s basically two camps of audiences advertisers should look to avoid. 

1. Those already likely to convert through organic channels or direct traffic, who advertisers should suppress to avoid budget cannibalization.
2. Those 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 the brands using us based on their specific advertising strategy and walk them through activating and measuring the impact of each as, again, it’s not something most advertisers have traditionally focused on or have a well defined strategy. We’ll focus on the “Crawls” since you asked for the most common examples.

Let’s 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’ssome text
  • completed a purchase in the past 30-90 (or ever) depending on the brands 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:some text
  • 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

Group 2 Might include

• Don’t serve ads to users who have requested refunds in the past 30 days, or more than 2 refunds all time.
• 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 lost” or “churned customer” in your CRM.

Tom: What’s the actual impact of efficient suppression?

John Joe: If you think about the basic arithmetic of ROAS and how large the pond most brands are fishing to find customers is, especially in the era of signal deprecation, avoiding wasted ad spend is a force multiplier for all other optimization strategies in that it’s shrinking the pool by eliminating the unqualified users and increasing the concentration of potentially valuable users. For example. 

• Pretend you’re spending $10,000 on an Instagram Campaign. 
• 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 (the average we see spent on these groups is 30-40%, reasons for which are highlighted in the section below). 
• Many people might stop at the calculation and say “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”.

But 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, you start to understand the force multiplier that efficient suppression actually is:

• $2,500 of the budget is being wasted, meaning your effective budget is actually $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 just on par with the rest of your non-wasted ad spend (which it will), you’d see a 33% increase in ROAS and 35% increase in CVR without changing anything else about targeting, pacing, creatives, etc.
• That means the opportunity cost of ineffective suppression here is $10,000, (should have grossed the advertiser $40,000 at a 4:1 ROAS instead of $30,000).
• Meaning 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 reallocating ad spend away from those already likely to buy (“Camp 1” in the examples above) to avoid cannibalizing organic channels which will decrease CAC, the cost of ineffective suppression across all channels grows even more! 

TLDR here is that even a conservative example demonstrates how the cost of ineffective suppression is 6X what most advertisers think it is. That’s what we mean when we say omnichannel, real time suppression strategies are a “force multiplier” for all other ad optimizations and the most effective targeting tactic for improving overall growth.

Tom: If that’s the negative impact of not doing this, how do you measure the value of doing this right?

John Joe: Right now, our primary KPI focuses on the amount of previous 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 and that is now allocated to other users (see a screenshot from our “Analytics” page below).

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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.

Tom: OK, if it’s so impactful, why isn’t this something the industry has kind of shunned aside, as you said, and not something more advertisers, agencies, ad platforms etc. are focusing on?

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 really any 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 incentivized all parties to go after the lowest hanging fruit and attract the most likely buyers to convert through their channel. There wasn’t this broader concern about overall performance, incrementality and marketing efficiency for the brand that we see taking hold now as a result of marketers being asked to do more with less. 

One of themes at your AdTech Economic Forum was that 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 waking up to this as is evident in the backlash to the “AI-optimized” whole platform campaign types like Advantage+ and PMax. With ML-driven targeting and AI-driven automations like these, the new mandate for advertisers is going to be creating the right guardrails like dynamic suppression strategies that keep these platforms honest and drive them to be as beneficial to their brands’ overall growth as possible.

Secondly, there’s a patchwork of legacy “sorta solutions” for suppression, which are all ineffective, especially in the age of identity deprecation, omnichannel user experiences and platform fragmentation or unusable / unscalable for marketers. These include relying 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. But in the tests we’ve run, these have only been able to track 10-20% of recent customers, and are totally incapable of identifying users across devices. This also only allows advertisers to suppress users based on purchase or website visit events, and offers no support for suppressing users based on first-party data like email opens, loyalty status, mobile app opens, offline purchases, etc.

There’s also the old workflow of manually compiling suppression lists, then uploading them to the ad platforms and applying them to individual campaigns / ad groups. But this is equally 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 are able to activate them across all campaigns, which highlights the issue of scalability and usability for marketers that we’re trying to solve for.

And I think both of these themes are enabled by the lack of awareness of the negative impact and opportunity cost of not optimizing suppression strategies, which we’re working to help advertisers understand better.