Have you heard friends and prospects say that your calls are showing up as “Scam Likely” or another unfriendly call label?
As companies expand into high-volume, cold-calling-based sales methods, they often run into issues with call labels. This can be because the salespeople simply don’t understand how call labeling works and are engaging in legitimate sales activities that are mistaken for suspicious sales tactics.
Fortunately, it is possible to change how your phone calls show up to prospects. Because a “Scam Likely” message may negatively impact your business, carriers and call labeling applications have established ways for legitimate callers to change their labels.
Why Are Calls Labeled “Scam Likely”?
A “Scam Likely” message is a call label warning users that the call might be coming from a suspicious source. Users might see those messages because their phone carrier is trying to protect them, or because they have downloaded a third-party application to help label scam calls.
Carriers and application developers use a mix of call analytics and user feedback in order to determine what labels to apply to different callers. Once a caller receives a label, it can be difficult to change in the future. That’s why it’s important to understand the factors that can cause a carrier to apply a call label to your number in the first place.
One of the most important reasons you might be getting a “scam likely” label is negative feedback from users. While other factors might earn you a different negative label, carriers assign “scam likely” and “potential fraud” labels when they have evidence of malicious behavior.
One of the primary ways they can collect that evidence is through user feedback. When people receive calls they believe to be malicious, they can report them to their carrier and the FCC. With sufficient feedback of this kind, those organizations may decide to apply call labels that signal a specific intent, such as “scam likely.”
Unfortunately, legitimate callers are sometimes mislabeled due to false, negative feedback. Call labelers take all consumer feedback seriously, which opens the door for people to mischaracterize legitimate calls as fraudulent. The result is a system that appears overzealous in its labeling of “scam” calls.
If your business makes legitimate calls that may make people unhappy, it’s worth looking into how false, negative feedback may be impacting your call labels. Businesses that make calls reminding people to make payments, deliver automated messages, or ask for consumer surveys may be especially vulnerable to false, negative feedback. They should follow industry best practices to ensure they receive as few of these false reports as possible.
There are other factors, such as call volume, call duration, and call attestation, that can also contribute to a “scam likely” label. However, it’s difficult to find a caller’s intent from those analytics. It’s unlikely that analytics alone would result in a “potential fraud” or “scam likely” label.
Callers making a high volume of low-duration calls from a poor-quality dialer or VoIP provider are certainly at a higher risk of receiving a negative call label. These contributing factors can cause a less intent-specific label like “spam likely” by themselves. When combined with negative user reports, they can contribute to a much worse label, such as “potential fraud”.
How To Avoid “Scam Likely” and “Potential Fraud” Labels
AT&T is automatically blocking or labeling more than a billion calls per month. How do you make sure you aren’t one of them?
One of the best ways to make sure your numbers don’t receive especially negative call labels is with carefully written call scripts and solid training for callers. The best way to avoid complaints about your calls is to avoid pushy, aggressive messaging that consumers associate with scams.
A well-written script can help keep callers on track to use terminology that you have proven is more effective at building trust with the person you’re calling. That requires some trial and error, but it’s important for making sure that even new employees can make effective calls without building up consumer complaints.
At the same time, it’s important to train employees on the less tangible aspects of calling. Tone, accent, and attitude can all contribute to how the person you’re calling perceives your call. Without proper training, even a tried and tested call script can generate negative user feedback if read by improperly trained callers.
Report Improper Labels
No matter how well-prepared your callers are, they may face false reports. In certain industries, this can be more common due to consumer sentiment. If you know you work in an industry that experiences a high volume of false reports or suspect you might, it’s especially important to know how to dispute these false reports.
False reports can happen for a variety of reasons. Some consumers do not understand the criteria a call must meet in order to be reported, while others may register false reports maliciously. No matter the reason for the false report, the FCC and call carriers make it easy to dispute false reports.
In order to dispute a false report, you need to know where the person you called filed their report. Often, people place reports with their call carriers. You can find the carrier a certain phone number uses with BatchData, and register your appeal to a person’s report against you with the carrier.
Some people also use call blocking and labeling applications and may file negative feedback with them. Unfortunately, these apps can be more opaque, so it’s difficult to know whether your number is being blocked or labeled by one. While you can look into each application’s appeals process, that can be more time-consuming, with no promise of success.
Calls are labeled “scam likely” when they follow patterns of behavior associated with scammers and receive high volumes of negative user feedback.
Callers should receive specialized training to avoid negative user feedback.
If you receive false, negative feedback, you can dispute the report with carriers and call labeling applications.