For SaaS and subscription apps, a campaign may appear successful as Google or Meta dashboards fill with installs, signups and trial activations. On paper, the algorithm seems to work and cost per lead (CPL) or acquisition (CPA) falls.
Subscription businesses face a paradox: an advertising event may be technically real but have no commercial value. Modern ad fraud can do more than consume budget; it can also distort the signals used by Smart Bidding.
The subscription paradox: the gap between metrics and value
In traditional ecommerce models, the impact of advertising is almost immediate. The user clicks, enters the site and purchases. In the SaaS world and apps, however, the real value is spread over time and develops along a chain of events.
This time and economic distance creates a huge hole between the fast metric (the event traced immediately by the platform) and the real value (the turnover that enters the company). And it is exactly in this gray space that insinuate fraud, automatons/bots and other unsuccessful traffic.
Although platforms claim to use automatic systems and artificial intelligence to filter these attacks, native filters do not solve the problem of the quality of the out as next: an account may exceed the basic controls of Google but prove totally destructive for campaign learning and its subsequent segmentations.
How ad fraud undermines Smart Bidding
Advertising platforms work in pure optimization. Google App Campaigns allows you to optimize for installations, in-app actions or conversion value. The same official documentation differentiates the campaigns oriented to the pure volume of installations from those focused on deep actions. But the fundamental problem lies in the intrinsic operation of Smart Bidding:
The campaign optimizes the fastest and most available event (e.g. click, signup or trial activation).
Bot, emulators and abuse systems generate in mass those early events.
The platform interprets these artificial conversions as signs of absolute success.
The algorithm moves the budget, stringing the circle around sources, audiences and locations similar to those that generated abusive trials.
So, the risk is to use fake and non-monitoring users as positive examples to train the advertising algorithm.
Five forms of abuse in subscription services
To protect advertising signals, first identify the forms of fraud and abuse that distort a SaaS funnel.
1. Click fraud and invalid traffic
It represents the classic advertising fraud, which includes bots, clicks repeated by competitors, accidental clicks or incentive traffic, which rode the budget before the product interacts.
2. Mobile app install fraud
In the world of mobile applications, fraud takes on more complex contours, widely documented by players such as AppsFlyer and Adjust:
Bots, automations and emulators simulate installations from scratch.
Artificially inflates the ICC.
Networks send avalanches of false clicks in an attempt to intercept a random installation.
Rub merit and budget with organic channels or campaigns.
A malware on the device intercepts the installation and enters an instant click before opening.
It employs real and genuine installations to a fraudulent advertising partner.
Hackers simulate cryptographic events directly from the SDK without real application.
Generate installations and in-app ghost events.
Hundreds of real phones are moved mass by operators or scripts to simulate human interactions.
They inflate metrics of campaign and intent. Very difficult to detect.
In addition to the wasted budget, marketing data is polluted, forcing teams to waste operational resources to clean up reports.
3. Fake registrations and free trial abuse
The Stripe platform defines the free trial abuse as the repeated use of free trials without any intention of converting to paying user. It is perpetrated through multi-accounting, disposable emails, rotated identities, residential proxies and logging bots capable of bypassing standard verifications.
In addition to bridging analytics and funnel metrics, modern AI-related SaaS or cloud infrastructure, this abuse translates into very heavy computing costs and API calls. In addition, creating fake accounts can serve as infrastructure for subsequent attacks: trial farming, payment fraud and abuse of referral systems or free credits.
4. Subscription fraud and payment abuse
Fraud also moves to the payment gateway. Paddle highlights how recurring models are structurally exposed to attacks due to the remote, repetitive and often anonymous nature of transactions. Card testing (bots testing lists of stolen cards on checkout forms), takeover accounts, chargeback and so-called fraud friendly (real users who contest legitimate transactions) dirty the last step of the funnel. Therefore, registering an “active subscription” in the tracking panel is not synonymous with value if the event is followed by a refund or a penalty of chargeback.
5. Hidden WebViews: invisible traffic
Fraud is not limited to rudimentary bots, but exploits structured networks. Research conducted by Satori has exposed colossal operations such as SlopAds (224 infected apps and over 38 million downloads). These infrastructures used hidden WebViews and sophisticated coding techniques within seemingly legitimate Android applications, generating hundreds of millions of daily bid requests and simulating advertising activities and conversions entirely invisible to the real user.
The need for a new approach
In apps and SaaS, the easiest metrics to track are often the easiest to manipulate: installs, signups, free-trial activations and first app opens.
Benchmark data shows the volatility of these events. RevenueCat's 2026 report, based on the analysis of over 115.000 apps and 16 billion dollars of revenue, highlights deep discrepancies in the trial universe: longer trial periods (between 17 and 32 days) record conversion rates at a charge much higher than the short 3-day trials. At the same time, the data of Adapty (collected on 16,000 apps) show that 90% of the trials leave the very day of the installation and that weekly subscriptions generate very high trial start odds, but with equally vertical dropout rates.
The trial is a quick metric, but it is not intrinsically a quality metric. It generates a fake optimism in the marketing dashboard long before the business can actually know whether that profile will generate retention or if it will remain active. For this reason, it is recommended to trace in a granular way every single phase of the funnel, passing every recurring renewal to the advertising system, since the real revenue is built more slowly.
A three layer mitigation strategy
To solve the problem at the root you can not rely on the only standard controls of advertising platforms. An integrated defence strategy is needed at the same time on three fronts:
Upstream traffic analysis – real-time analysis of IP addresses, datacenters, suspicious ASNs, residential proxies and abusive VPNs to block non-genuine carriers before interacting with the software. Control includes device fingerprinting, user agent and monitoring sessions and behavior.
Validation of funnel events – intersection of technical data with real product usage telemetry. A user who activates a trial without performing in-app actions or with a logical order of incoherent events must be declassified, following a multilevel logic based on behavioral controls, temporary emails and payment patterns.
Education of advertising platforms – return of a clean signal to Google and Meta. Using features such as Conversion Adjustments and importing offline conversions, you can correct or reset the status and economic value of initial conversions as a result of later commercial anomalies, such as chargeback, refunds or spam.
Protect your campaigns now: try Adefence for free
Adefence's infrastructure is located exactly at the centre of this defensive ecosystem, orchestrating technical and business data to protect advertising campaigns.
Thanks to the IVT Protection Modules and Signal Engineering, Adefence intercepts the initial raw conversion, analyzes the technical integrity and enriches it with the actual usage and payment data from internal systems and CRM. This process guarantees the maximum protection of the Smart Bidding, implementing a definitive passage from the apparent volume to the net economic value.
Adefence’s final goal is to block unsuccessful traffic and purify the algorithmic signal that drives the company’s growth.
Questions and answers
Why are free trial periods a target for fraud?
A free trial lowers the barrier to entry: an email address may be enough, sometimes without a credit card. This makes it easier for bots, click farms and bad actors to generate fake signups counted as conversions, despite no intention of becoming paying customers.
What is the distance between metric conversion and real value in subscription services?
For SaaS businesses, trial activation is easy to measure, but value appears only when a user becomes a paying subscriber and remains a customer. Optimizing for the number of trials alone also rewards signups that never generate revenue.
How does the fraud sabotage the Smart Bidding in subscription services?
If the offer algorithm receives conversion signals based on trial fasulli or abusive, it learns to search for other traffic with the same characteristics, i.e. other users who are not likely to pay. The result is a spiral in which the budget is increasingly directed towards low-quality signals.
What are the most common forms of trial abuse?
Common examples include one person creating multiple accounts to repeat a free trial, bots inflating signup counts, competitors abusing trials, users with no intention of paying and fraud involving stolen payment details.
How does Adefence help protect SaaS and subscription models?
Adefence analyzes user behavior after trial activation and connects the later conversion to a paid subscription. Campaigns can then optimize for users who create lasting value instead of fake signups or trials unlikely to become paying customers.
How long can you tell if a trial was used abusively?
Typically a few weeks, enough time because most real trials convert into paying subscriptions or are naturally abandoned. Expecting too little risks trading for abuse simple undecided users, while waiting too long slows the update of signals to the offer algorithm. The right period also depends on the duration of your trial: the longer the trial period, the longer it takes before you can safely judge whether that registration has generated real value or not.
How does a user who is abusing the trial stand out?
Not always, and distinguishing them is the most delicate part. An undecided user can still behave as a genuine customer: explore the features, return several times to the product, maybe involve a colleague before deciding. Those who abuse the trial, instead, tend to show more mechanical patterns: accounts created in series from the same device or network, minimum or null use of the product, or trial reactivated immediately after the expiry of a precedent with slightly different data. Watching behavior as a whole, not a single indicator, helps make this distinction with more safety.
Can competitors generate false trials to damage my campaigns?
Yes, it can happen, even if it requires an investment of time or money from the competitor. Creating multiple accounts, perhaps with the help of automated services, to generate fake trials that never convert to subscription is an indirect way to make your campaigns appear less efficient than they really are. It is a less widespread practice of pure fraud, but not impossible, especially in very competitive SaaS sectors where the cost of an abusive trial is however low compared to the potential damage that can cause the competitor's optimization data.
Just request a credit card in the trial phase to block abuse?
Many SaaS platforms already require a credit card to activate the trial, although then nothing is charged until the end of the trial period. This discourages some of the simplest abuses, but does not eliminate them: there are services that generate disposable prepaid cards to overcome this kind of control. Requesting the card remains a useful filter, but it must be considered a first level of protection, to be added to a more detailed monitoring of behavior after activation, not a definitive system alone.