WasteNot Exclusions Differentiation Overview - Cody Plofker / Jones Road Beauty

Why Exclusions Are Failing: The Hidden Challenges in Ad Suppression

Exclusions in ad platforms like Meta and Google are failing to effectively target new customers, resulting in significant wasted ad spend. Below, we summarize the root causes of this failure using real-world examples and documented technical limitations.

1. Incomplete, Inaccurate, and Latent Data

Incomplete Data: Most exclusion methods rely on partial data, missing vital customer interactions that could identify past buyers.
- Example: A Shopify aggregator discovered a major gap using Klaviyo's dynamic audiences for exclusions. Using Elevar to measure new customer acquisition from Meta campaigns with "full customer suppression" via Klaviyo's audiences, they found 40% of conversions still came from returning customers. Klaviyo explained that their Meta API integration only passes purchase data for customers originating from Meta campaigns. While Klaviyo tracks all engagement and purchase events (from email, organic, or other channels), it can't share this broader dataset with Meta—leading to a 40–60% undercount of true audience size.
- Why It Matters: In addition to massively undercounting groups, these lists lack unified IDs across data sources, resulting in poor match rates when uploaded to ad platforms.
Inaccurate and Latent Data from Website Audiences/Conversion Tags: Site tags (like Meta Pixel) and conversion tags fail to identify past customers up to 90% of the time because of the following reasons.
- Ad Blockers: 35–50% of users globally use ad blockers preventing tag tracking.
- Tag Failures/Improper Setup: 5–10% of tags fail from slow loading, misconfiguration, or script conflicts.
- Private Browsing and iOS 14/Privacy Restrictions: About 20% of users use private browsing, and 85% of iOS users opt out of tracking. Apple's Intelligent Tracking Prevention limits Safari cookie lifespans to 7 days.
- Expiry Windows: Meta Pixel cookies expire after 180 days, losing older customer tracking.
- Offline/Anonymous Conversions: In-store purchases and guest checkouts aren't tracked unless manually uploaded.
- Cross-Platform/Cross-Device Gaps: Customers converting through Meta ads may still see Google Ads because platforms don't share data, and privacy restrictions limit cross-device tracking.
- Impact: Our analysis shows site tags only exclude 10–15% of past customers across platforms, or 20–25% within Meta alone. This explains why 50% of ad spend still targets existing customers despite exclusions.

2. Ineffective or Restricted Exclusion Methods

Meta Limitations: Meta has increasingly restricted exclusion capabilities:
- Advantage+ Shopping Campaigns (ASC) don't permit exclusions, and even with a 0% existing customer budget, they still target warm audiences. DTC ecommerce brands report ASC campaigns deliver only 25% new visitors despite exclusion settings.
- Meta has removed the existing customer budget cap from its UI (though it's still available via API) and disabled targeting parameter exclusions for location, interest, and search terms.
Google Limitations: Key campaign types, especially Performance Max and Shopping Campaigns, don't allow direct exclusions. This forces marketers to use complex, often ineffective manual workarounds.
Impact: These restrictions lead platforms to prioritize easy conversions from existing customers to meet CPA targets, undermining top-of-funnel strategies.

3. Too Tedious, Too Technical, and Too Error-Prone

Manual Effort: Updating exclusion audiences across sources (Shopify, Klaviyo, CRM) is time-consuming. MacCoy Merkley calls it a "PITA" (https://x.com/MacCoyMerkley/status/1883242986223636586), especially since 40% of spend still reaches existing customers.
Tagging Complexity: Managing purchase event and product tags for exclusions is labor-intensive and too technical for many marketers, yielding minimal improvements due to data gaps.
Workaround Management: Platforms require various workarounds (API-only settings, bid adjustments) for exclusions, varying by campaign type and platform. Even with a complete playbook, implementing these manually is overly complex or impossible for most marketing teams. </aside>

How WasteNot Is Different: A Comprehensive, Automated, and Effective Solution

WasteNot addresses the root causes of exclusion failures—data gaps, restricted methods, and manual complexity—through complete data unification, automated execution of all possible exclusion strategies, and proprietary workarounds. Our approach and mechanisms make WasteNot uniquely effective, ensuring you can confidently exclude past customers and focus ad spend on net-new audiences.

1. Complete, Accurate, and Real-Time Data

Complete Data Unification: Unlike standard methods that rely on incomplete, inaccurate or latent data, WasteNot ingests every event and associated user ID from all your first-party data sources—Shopify, Klaviyo, Stripe, Hubspot, Snowflake, and more. We standardize these into a single, unified first-party user graph:
- Mechanism: Our real-time data connectors create a comprehensive identity graph with all available identifiers (e.g., emails, phone numbers) for each user, far surpassing single-source lists like Klaviyo's or Shopify’s dynamic audiences..
- Differentiation: By merging identities across all data sources, our profiles have more usable IDs (e.g., an email and mailing address from Shopify, a phone number from Klaviyo, a CC token from Strope), resulting in higher match rates when uploaded to ad platforms, ensuring more customers are accurately identified for exclusion.
Accurate and Real-Time: Our system processes data daily in real time, ensuring exclusion lists are up-to-date and reflect the latest customer interactions. This eliminates the latency of manual list uploads, which can be days or weeks out of date (e.g., Shopify audiences are only updated once per week at best, many less frequently).

2. Automated Execution of All Relevant Solutions

No Silver Bullet, Only Many Effective Bullets: There's no single solution for exclusions due to platform restrictions and campaign-specific nuances. Instead, WasteNot executes a combination of native solutions, API-only workflows, and proprietary workarounds tailored to each campaign's context.
Simple, Familiar UI: Our interface mimics the audience creation flow marketers know well (e.g., Klaviyo’s audience builder). You define exclusion criteria (e.g., past customers, recent website visitors, frequent email openers, combination of many) and apply them to specific campaigns or ad sets. Behind the scenes, we handle the complexity:
- Explicit Solutions:
  - For BAU (business-as-usual) campaigns, we apply exclusion audiences directly, ensuring platforms like Meta and Google exclude the specified users.
  - We enable settings like "only target new customers" where available, maximizing TOF focus.
- Explicit but Inaccessible Solutions:
  - Meta has deprecated features like targeting parameter exclusions and existing customer budget caps in the UI, but these remain functional via the Meta Graph API. We confirmed with Meta that they intend to keep these API calls active (though they wouldn't explain why). WasteNot leverages these API-only features to enforce additional exclusion tactics that aren't possible through the UI.
- Proprietary Workarounds:
  - Meta ASC Campaigns: For campaigns like ASC, which don't allow direct exclusions, WasteNot employs a multi-pronged approach. For example, if an advertiser were to indicate in our UI that they want to exclude past customers, recent website visitors, and frequent email openers/clickers from an ASC campaign, WasteNot’s system would:
    - Send simulated offline conversion events for these users to the Meta Conversions API (CAPI), backdating them to before the campaign's start date. This ensures Meta doesn't count them as "new customers" within view-through or post-click attribution windows.
    - Based on the campaign's type, conversion goal, and targeting settings, apply settings like "only target new customers" or an "existing customer budget cap" (only available via API now), forcing Meta to prioritize net-new audiences.
  - Google Standard Shopping Campaigns: Google doesn't allow direct exclusions for Standard Shopping Campaigns, but WasteNot circumvents this limitation:
    - We create a dynamic audience of users meeting your exclusion criteria (e.g., past buyers) using our data advantages listed above
    - We ensure all of the campaigns’s ad groups's targeting settings are set to "targeting" for audience segments (or update the setting if necessary).
    - We apply the WasteNot audience as a segment to the ad group and set a bid multiplier of -90% for this segment, effectively reducing ad exposure to these users to 0.
Differentiation: Unlike standard suppression methods that rely on a single tactic (e.g., uploading a customer list, clicking a radio button in campaign targeting settings, etc.), WasteNot combines multiple strategies—native, API-driven, and proprietary—to ensure exclusions are as effective as possible. Our simple, intuitive UI abstracts away the complexity of identifying and managing dozens of different campaign / ad set contexts and then activating the correct combinations of solutions to maximize exclusions effectiveness.

3. Proven Results with Minimal Effort

Measurable Impact: We prove our value using your own first-party data. During our free 30-day pilots, we demonstrate improvements in key metrics like new customer acquisition cost (nCAC) and marketing efficiency ratio (MER). For example, GOA Skincare saw a 51.3% decrease in first-time purchasers after activating WasteNot's dynamic exclusions, as shown in our recent pilot case study.
Ease of Use: WasteNot sets up in under 15 minutes with no engineering resources or ongoing maintenance required. This addresses the bandwidth constraints growth teams face, as noted in our Q&A with another brand. Unlike manual exclusion methods that are tedious and error-prone, WasteNot turns a complex problem into a quick, high-impact win that amplifies other optimization efforts without deprioritizing your team's initiatives.
Differentiation: We not only make exclusions effective but also make them accessible to marketers without technical expertise. Our focus on usability ensures you can activate nuanced exclusion strategies—across platforms, campaign types, and ad sets—without needing to understand the underlying API calls or workarounds.

4. FAQs

How Is This Different from What You're Already Suppressing?:
- Current suppression methods (e.g., exclusion lists, ASC settings) are failing because they rely on incomplete data and restricted platform capabilities. WasteNot's unified first-party data graph captures a broader set of customer interactions, ensuring more accurate identification of past customers (e.g., closing the 40–60% undercount gap seen with Klaviyo). We also execute a wider range of exclusion strategies, including API-only features and proprietary workarounds, to enforce exclusions where native methods fall short (e.g., ASC campaigns with “full exclusion” still burning 50% of spend on existing customers).
Mechanism Explained: When you define an exclusion audience in WasteNot (e.g., past customers), we:
1. Build a comprehensive audience using all your data sources, not just Meta-tracked events.
2. Apply this audience to your campaigns using a combination of direct exclusions, API-only settings, and workarounds (e.g., simulated conversions for ASC, bid adjustments for Google Shopping).
3. Update daily to ensure real-time accuracy, avoiding the latency of manual uploads. This multi-layered approach ensures exclusions actually work, addressing the root causes of failure you've experienced.

Why Choose WasteNot: Turning a Tough Problem into a High-Impact Win

WasteNot's sole focus is solving the exclusion problem that's costing brands like yours millions in wasted ad spend. We understand the frustration of seeing half of ad spend go to the audiences you’ve explicitly tried to avoid. Our solution is purpose-built to address this by combining more complete, real-time data, automated execution of all native and proprietary exclusion tactics, and an intuitive UI that empowers marketers to achieve true TOF targeting without the hassle. With proven results (see relevant case studies below for Health and Beauty) and a setup that takes under 20 minutes, WasteNot delivers measurable growth without straining your team's bandwidth. Let us help you turn ineffective exclusions into a competitive advantage—schedule a demo today to see how we can make your ad spend work harder for net-new customers.

Video Overview

https://www.loom.com/share/dae8c0b0a62248c49923b2895dcea5f1

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Relevant Case Studies for Skincare and Beauty

Goa Skincare

GOA Skin Care Case Study (2).pdf
Mirai Clinical

Mirai Clinical Case Study (current copy).pdf