Fake users are often generated through automated scripts or bots, and these fraudulent entities operate to interrupt marketing campaigns and distort performance data. Fake users do not even have the need to employ real human users or devices as most of the user activities are pre-programmed on a software. Essentially, they are faking the entire user journey and generating activities like installs, clicks, and ad interactions that never actually existed. While it is harder to maintain than other types of marketing fraud, if it remains undetected, fake users can scale limitlessly and be a detriment to the entire conversion funnel.
When fake users generate marketing data such as click or install data, it creates zero value as there is no real engagement. However, they are usually able to mimic a user’s entire user journey process and blend in with other data metrics. This makes it very difficult for marketers to pinpoint fake users and are prone to waste their valuable time analyzing fraudulent activities, thinking they are leads.
Once marketers collect user acquisition data that is mixed with fake users, this data becomes worthless and skewed, and marketers have to exhaust their budget paying for completely nonexistent interactions. From draining their resources to harming their reputation and credibility, fake users can take a business down and cause multiple pain points, all of which can become irrecoverable damage overtime.
In order to keep fake users from undermining their brand, marketers should enforce robust measures that can minimize the impact of fake users. Implementing anti-fraud technologies, tracking traffic sources to see if any user journeys are abnormally identical, or using MMPs like Airbridge to compare data reports may be a starting point to tightening the security. Yet, these solutions alone will not get rid of fake users, and the best marketers can do is to constantly keep an eye out for suspicious activity and prioritize this procedure.
