Detecting referral fraud and making sure your subscribers don’t win rewards by cheating is one of our most important responsibilities—and something we take very seriously!

Our anti-fraud measures ensure only high-quality referrals and is the reason most big newsletters go with us—we’re by far the most advanced in terms of anti-fraud for email referral programs.

SparkLoop comes with a sophisticated range of anti-fraud mechanisms to help you detect and prevent cheating in an automated, or mostly automated, way.

This keeps the quality of your list high, without requiring a lot of manual effort from you.

Anti-fraud levels

You can set your preferred level of anti-fraud detection by going to Settings > Anti-fraud.

There are 3 levels of anti-fraud security in SparkLoop: Flexible, Strong and Very Strong.

The majority of referral programs will be best served by the Flexible setting.

Larger newsletters, referral programs offering high-value or expensive rewards, may find the Strong setting necessary.

The Very Strong setting is our highest level of anti-fraud.

⚠️ We only recommend using the Very Strong setting if you have an audience that is extremely prone to cheating, or are using a reward or giveaway prize of extremely high value.

Very Strong anti-fraud protection will label everything we think could be fraud as “rejected.” That inevitably means some real referrals will be marked as rejected too.

For most of our customers, it's an easier and better experience to allow a small number of fraudulent referrals to get through, rather than blocking all fake referrals but having subscribers complaining that their friend signed up but wasn't counted as a referral.

How anti-fraud works

When a new referral is tracked, our algorithm will assess the fraud level in real-time.

Noticeably fraudulent referrals will immediately be rejected and marked as rejected referral, regardless of your anti-fraud level.

Fraudulent referrals include people who use email aliases (for example, [email protected] refers [email protected]) or disposable domains (temporary, one-time email addresses that don't require the creation of an account and that are often used by bots; e.g. mailinator.com).

When a referral is marked as "rejected," it will not count towards the advocate's reward points. And it won't be included in their referral count (or the RH_TOTREF custom field in your ESP).

When a referral is marked as “rejected,” SparkLoop will update their RH_ISREF custom field to “REJECTED”. This allows you to segment these subscribers in your ESP and easily remove or unsubscribe them.

Unverified referrals

Sometimes, it's not immediately obvious if a referral is fraudulent.

If that happens, our anti-fraud algorithms predict the likelihood of a referral being fraudulent based on multiple factors such as the referral's IP address, device, domain and other secret factors (AKA our "secret sauce").

Based on your selected anti-fraud level, SparkLoop may then mark a referral we aren't completely sure about as Unverified.

You can see all the referrals SparkLoop couldn't verify as genuine by going to the Unverified tab under the Subscribers page.

There, you can easily and quickly reject the referrals you think are fraudulent.

Manual and semi-manual reward approval

The most important concern when it comes to anti-fraud is making sure rewards—especially ones that have a production cost—can't be won by cheating.

To help you avoid giving rewards to cheating subscribers, you can set a custom reward approval level on a reward-by-reward basis.

There are 3 levels:

  • Automated: The reward will be approved automatically when a subscriber wins it. Ideal for digital/low-monetary rewards (e.g.: a free ebook, access to a secret newsletter).

  • Semi-manual: The reward requires manual approval in case of unverified referrals ONLY. When a subscriber wins this reward, we will check whether any of their referrals are unverified. If so, we will send you an email prompting you to manually approve (or reject) the reward. If the subscriber has only made verified referrals, the reward will be approved automatically.

  • Manual: The reward always requires manual confirmation. Ideal for expensive/high-monetary-value rewards that are won infrequently.

You can set the approval level of each reward in the "edit reward" modal.

Blocking and whitelisting

Every now and again, you might notice a subscriber who refers lots of obviously fake referrals, and you want to prevent them from attempting to make more referrals in the future.

To block advocates who are referring fraudulent email addresses:

Go to their profile > Click on Actions > Select Block.

Whitelisting is the opposite of blocking, that is, when you whitelist an advocate, SparkLoop will automatically confirm any reward they win even if they have unverified referrals.

You can use this feature to ensure that any referrals that your known fans and ambassadors bring in are approved automatically.

⚠️ You will still need to manually approve any rewards that require manual confirmation.

To whitelist an advocate:

Go to their profile > Click on Actions > Select Whitelist.

⚠️ You can unblock or remove advocates from the whitelist any time.

Rejecting individual referrals

There might be times when you want to retroactively reject verified referrals. This can happen, for example, when you notice a fraudulent referral has got away from our anti-fraud algorithm.

To manually reject referrals, follow these steps:

1. Go to the referrer's profile page and scroll down to the Referrals section.

2. Select all the referrals you want to reject by ticking the checkbox next to their email address.

3. Click on the Reject Referrals button in the top right corner.

4. In the modal window, click on Reject Referrals again to confirm the rejection.

When you reject referrals, SparkLoop will update the RH_ISREF custom field of the rejected referrals to “REJECTED” in your email platform. This allows you to segment these subscribers and easily remove them or unsubscribe them.

⚠️ Please note that rejecting these referrals will NOT remove rewards that have already been won.

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