How smart retailers protect profit: Reducing waste in paid media

Paid media is one of retail’s biggest and most expensive sources of waste, especially during the holidays when costs surge and customer attention is harder to earn. In peak season, every inefficiency compounds: you retarget customers who’ve already purchased, your audiences shrink because of low match rates, and platforms optimize against incomplete or missing signals.

When your data foundation isn’t ready for the holidays, you’re not just wasting ad spend, you’re giving competitors an edge.

The retailers who win the holidays are the ones who reduce waste by improving their data signals, not just their media strategies. A Composable CDP, or Customer Data Platform, enables that by activating customer data directly from the warehouse, so every campaign benefits from more complete identity and clean signals.

In this blog, we’ll explore the most common sources of waste in retail paid media, and how a Composable CDP reduces them.

Waste 1: You keep advertising to customers who already purchased

Suppressing recent purchasers should be simple, but only if your ad platforms can reliably recognize a customer across devices, browsers, and channels – including online to offline. Without that ability, your ad platforms may not know who’s purchased. The result is you keep targeting them with ads, wasting spend and skewing performance metrics.

Real-world scenario:

A customer views a pair of shoes on your website from their phone without logging in, and they leave without purchasing, so you target them with ads for that product. Later, they sign in on their phone to buy the shoes. Because those sessions don’t connect, ad platforms keep showing them ads, thinking they’re still browsing.

Waste 2: Low match rates shrink your reach and weaken lookalike audiences

To advertise to your customers, platforms like Meta and Google must be able to match your records to profiles in their systems. When they can’t, your match rates fall and your lookalike seed list weakens, reducing the reach of your campaigns. Even the best campaigns underperform when match rates are low.

Real-world scenario:

You upload 50,000 high-value customers to Meta, but only 25,000 match because Meta couldn’t match the identifiers on your list (like email, phone number, name, date of birth, and zip code) to known Meta profiles. That’s half of your best buyers missing from your targeting. And your lookalike model is trained on a smaller, less accurate dataset.

Waste 3: Incomplete conversion tracking limits optimization

Ad platforms have advanced AI systems that optimize based on the conversions they can see and match. If identifiers aren’t strong enough to link a purchase to an ad interaction, platforms lose critical learning signals. They bid less intelligently, optimize toward the wrong users, and require more budget to find the right ones.

Real-world scenario:

A customer views a Meta ad and later buys a pair of shoes on your website using their email address. You send the purchase event and hashed email to Meta so it can inform attribution and train Meta’s AI systems, but if the email they used at checkout is different from the one tied to their Meta account, Meta can’t match the purchase to a known user. As a result, the platform can’t attribute the ad the customer saw to the conversion, and its AI systems lose a valuable signal that would have improved targeting, bidding, creative optimization, and budget allocation.

The composable fix

The key to better suppression, higher match rates, and more efficient ad optimization is strong identity resolution, or the ability to tie actions back to a single customer across devices, channels, and sessions. When marketers can reliably stitch behavior together, they can suppress purchasers accurately, personalize campaigns with confidence, and feed platforms the signals they need to optimize spend.

A Composable CDP enables this by stitching profiles together from the full breadth of data in the data warehouse, including from offline transactions. Some platforms, including Hightouch, then pair deterministic identity with probabilistic identity to increase matches. Deterministic methods use strict and clean identifiers like verified email, verified phone, or loyalty ID, while probabilistic methods use looser signals, like fuzzy email matches, inconsistently formatted addresses, nickname variations, etc., to link transactions and profiles even when exact identifiers are incorrect or missing.

With the right foundation in place to resolve more profiles, marketers waste less budget retargeting customers who already purchased, even if they bought offline or browsed logged out. Match rates rise and lookalike audiences grow. And ad platforms see a fuller picture of performance, optimize more intelligently, and drive lower CAC as a result.

And with Hightouch’s Match Booster layered on top, first-party data can be further enhanced through best-in-class identity providers. The outcome is even higher match rates, more scalable audiences, and more efficient spend through better optimization signals.

The bottom line

The retailers who protect holiday profit aren’t necessarily the ones with the biggest budgets. They’re the ones with the cleanest data.

When identity is unified, match rates rise and conversion signals flow cleanly across channels, making every dollar work harder. Platforms optimize better, audiences expand, and CAC drops.

The holidays magnify every weakness in a retailer’s data foundation, but they also magnify every strength. Fix the waste in acquisition now, and you don’t just protect seasonal profit, you build a performance engine that keeps paying off long after peak season ends.

And the impact speaks for itself:

WeightWatchers increased match rates across TikTok, Google, and Meta by as much as 155%, drove 52% more new-to-brand members, and improved LTV-based ROAS by 24% simply by sending richer first-party signals.
Chalhoub Group cut CAC by 30%, improved ROAS and CTR across every paid channel, and now drives 40% of Meta revenue and 45% of Snapchat revenue using Hightouch audiences.
bol.com boosted audience reach by 109% and lifted CTR by 33% after replacing pixel-based targeting with durable first-party audiences.

If you want help improving suppressions, boosting match rates, and strengthening conversion data, request a demo to learn more about how Hightouch can help.

If you found this helpful, you may also want to explore the other two posts in this series:

Together, they outline the full picture of where retailers lose (and gain) the most margin, and how a modern, Composable CDP approach helps you enter next holiday season with a foundation built to win.