Customer suppression lists
TL;DR: TL;DR: Customer suppression lists fail when the same person uses more than one email address. Identity resolution links those fragmented accounts, so ad platforms stop showing ads to people who are already paying customers. The same technology catches voucher fraud by recognising returning customers who open fresh accounts.
When suppression lists work and when they do not
The table below maps the two core failure modes to their identity-resolution fix. All scenarios are drawn from the article body that follows.
| Scenario | Single-identifier suppression | Identity-resolution-backed suppression |
|---|---|---|
| Customer uses one email address | Works: the known email suppresses the ad | Works: same outcome, no change needed |
| Customer holds a second email (e.g. Gmail + Yahoo) | Fails: the second address is not suppressed; ads reach the existing customer | Resolved: both addresses are linked to the same person and both are suppressed |
| Returning customer opens a new account for a voucher | Fails: the new account looks like a genuine new customer | Caught: identity resolution matches the new account to the previous account in milliseconds |
| Data volume grows | No matching overhead | Requires a system built for scale and real-time ingestion to stay fast |
On this page
- When suppression lists work and when they do not
- Voucher Fraud
- Customer Suppression Lists
- Identity Resolution
- The Difficult with Identity Resolution
- Frequently asked questions
How many HelloFresh accounts do you have?
I am a regular, happy, and well-fed HelloFresh customer, but let’s just say that I have become a “new” customer more than once in the past. As a result, I probably have two or three different HelloFresh accounts under different email addresses.
This might seem like a non-issue, but for companies like HelloFresh, that are based on consumer subscriptions, these multiple accounts per customer can lead to them wasting millions of dollars in marketing costs.
Voucher Fraud
Firstly, there is the “new customer” voucher abuse. HelloFresh is pretty generous in giving free recipe boxes to new customers if they are introduced by an existing customer. Fast food delivery companies like Wolt or DeliveryHero, similarly will give generous vouchers to new customers to encourage that first purchase.
The downside of this generosity is that if I am an unscrupulous character, I will just keep on deleting their app, reinstalling it and making fresh accounts with new account details so I can use another new customer voucher. If I am determined enough, I might never have to pay for another pizza again.
Customer Suppression Lists
The bigger cost is arguably wasted marketing dollars. If we go back to my HelloFresh account(s) - I am currently a paying customer using my current, regular personal email address. However, my previous account was created using my previous email address, which I only use for social media sign-ups. You might not be surprised to learn that the former is a Gmail address, and the latter a Yahoo! email account.
HelloFresh will send my Gmail address to its advertising networks (e.g. Meta) and tell them not to bother showing me any HelloFresh adverts - the so-called “customer suppression list”. After all they have already won my custom, so why waste advertising dollars.
However, that other Steven with the Yahoo! Email address, who used to be a HelloFresh customer and left us - of course HelloFresh should spend money trying to win me back. So I see HelloFresh adverts on Facebook and Instagram. But of course that is a waste of money - I am already a customer - they just don’t know that the paying Steven Renwick with a Gmail address, is the same as that former customer Steven Renwick with a Yahoo! Email address.
Identity Resolution
This is where identity resolution comes in. Identity resolution is the process of determining that two separate identities in a dataset actually belong together - they are the same person. Using identity resolution technology, like Tilores, HelloFresh would be able to identify that Steven Renwick with a Gmail and Yahoo! Email address are the same person, and thus neither should see any adverts via Meta or Google.
When extrapolated to the millions of customers that a company like HelloFresh has, this can result in preventing millions of dollars being wasted on adverts to existing customers.
Similarly, identity resolution technology can help identify that the new customer that has just signed up and tried to use a new account voucher is actually a previous customer that has previously had an account and already used a voucher.
The Difficult with Identity Resolution
So you might be wondering what is so difficult about identity resolution? The issue is that to compare related, but non-identical data, you need to use fuzzy matching algorithms to allow for the fact that the two profiles might use non-exact data - i.e. I might have used “Steve” vs “Steven” as my first name, or I might have used a variation of the address - “Oxford Street” vs “Oxford St.”. I might be using a combination of fuzzy name matching, digital fingerprinting, and geolocation. For the particular use case, the right balance of precision vs recall must be found.
Then there comes the volume of data that needs to be compared - identity resolution is what’s known as a “quadratic problem” - which means the more data you have, the more comparisons that need to be made every time you ingest more data. The result is the system can get slower and more expensive to process as you grow. Especially for voucher fraud detection, it is essential that the entire identity resolution system can be checked and updated in milliseconds.
That’s why identity resolution systems that are built for scale and real-time data ingestion, such as Tilores, are essential for consumer-facing enterprises to make sense of their large volumes of customer data.
For a deeper look at how to measure matching accuracy, see precision and recall in entity resolution. If you are considering building your own system, this guide on building an identity resolution system covers the key architecture decisions. For the role identity resolution plays in data governance and MDM, see entity resolution technology for master data management.
See what resolved entity data does for your business — and your AI.
See it on your own data: book a demo for a walkthrough on your records, or get the evaluation build to try resolved entity data locally.
FAQ
What is a customer suppression list?
A customer suppression list is a set of identifiers, typically email addresses, that a brand sends to advertising networks like Meta or Google to prevent ads from reaching people who are already customers. The intent is to avoid wasting budget on audiences who have already converted.
Why do customer suppression lists fail to exclude all existing customers?
Suppression lists fail when the same person holds more than one email address. If a company uploads only the email linked to the active account, any prior email address the customer used remains invisible to the suppression system. That person continues to see retargeting ads even though they are a paying subscriber, wasting advertising spend.
How does identity resolution fix customer suppression lists?
Identity resolution is the process of determining that two separate identities in a dataset actually belong together. By matching a Gmail address to a Yahoo email address held by the same person, identity resolution technology lets a company add both identifiers to the suppression list and stop wasting ad spend on someone who is already a customer.
Can identity resolution also detect voucher fraud?
Yes. Identity resolution technology can identify that a new account attempting to use a new-customer voucher belongs to a person who has previously held an account and already used a voucher. Because identity resolution systems built for scale can be checked and updated in milliseconds, they can catch this abuse at the point of sign-up.
What makes identity resolution technically difficult to implement at scale?
Identity resolution is what is known as a quadratic problem: the more data you have, the more comparisons that need to be made every time you ingest more data. This means the system can get slower and more expensive to process as you grow. Fuzzy matching across names, addresses, and digital fingerprints adds further complexity, and the right balance of precision vs recall must be calibrated for each use case.
See what resolved entity data does for your business — and your AI.