Many marketers still view ad fraud from a linear lens. They think bots are easy to spot, and platforms flag it. However, this assumption is no longer true.  

Over the years, advertising has transformed into a deeply interconnected, automated, and omnichannel ecosystem. Brands no longer run isolated campaigns. They operate across open web, apps, platforms, affiliates, influencers, CTV, and ecommerce media simultaneously.  

With this scale comes complexity, and with complexity comes a new class of ad fraud. One that hides deep inside the user journey, behaviour, blends into engagement, and surfaces only after real business impact has already been compromised. 

You know you need an answer when your campaigns don’t perform as you expect them to. Or maybe they do perform, but just on dashboards? 

Click fraud is no longer limited to just basic bot traffic. In 2026, it has evolved into more sophisticated threats that many traditional detection tools are not equipped to identify. 

Fraudsters now deploy AI-powered bots that simulate real user behaviour to evade behavioural detection. They operate across performance marketing ecosystems, including Google Search, display, GDN, Pmax, Meta, affiliate networks, app install campaigns, lead generation, and re-engagement campaigns. 

According to mFilterIt, 18% of global digital ad traffic was invalid in 2025. Moreover, with global digital ad spend projected to reach $866.2 billion in 2026 and $916 billion by 2027, the scale of fraud opportunity is growing at exactly the same rate as advertiser investment. 

Therefore, the traditional method of identifying fraudulent clicks is not enough. Marketers need a tool that has advanced specifications that can help them stay ahead of such evolving threats. 

But the question remains, “What exactly should marketers look for in a click fraud prevention tool in 2026?” 

We have simplified this search for you in this blog. 

It breaks down:

What features should an advertiser prioritize in a click fraud protection tool? 

How is mFilterIt different from standard ad fraud detection solutions? 

Why isn’t click-level protection enough for web and app campaigns? 

So, if you’re comparing solutions or preparing to invest, this is the clarity you need to make the right decision. 

Key Features to Look for in a Click Fraud Protection Software

The right click fraud protection tool is supposed to give you actionable insights, measurable improvements, and cross-channel protection. Here’s what to expect from a tool that actually solves your business problems: 

Proactive Click Validation

Click fraud operates in milliseconds. Your click fraud protection tool should have the capability to detect fraud proactively before it reaches your deep funnel or MMPs. Click validation ensures invalid traffic is flagged and filtered before it drains your ad budget. Here are some checks that a robust solution must perform:  

Click Repetition Behavior: Spamming on the same Device ID, click and impression injections, IP address repetitions, clusters, and spikes  

Malicious IPs / VPNs: VPNs, proxies, and data center traffic should be identified in real time  

Invalid Device Make-Model: Invalid devices detected via User Agent analysis  

Invalid Geo: Non-applicable geographies flagged via IP address checks 

Multi-Channel Compatibility Across Performance Campaigns

Fraud is not confined to one platform.It spreads across Google Ads, affiliate programs, Meta, DV360, mobile app networks, and even OEM and influencer traffic. Your protection tool should have omnichannel compatibility to work seamlessly across all environments to give you consolidated protection. 

Integrated platform coverage must include Google Search, Google Search Partners, GDN, DV360, Facebook Audience Network, FB.com, YouTube, Bing, affiliate and direct publisher networks.  

Full-Funnel Traffic Scoring from Click to Conversion

Since ad fraud doesn’t stop at click, the right click fraud solution doesn’t just analyze a single click. It evaluates the entire customer journey from impression to post-click behaviour, and scores each interaction based on engagement, path anomalies, and conversion likelihood. This helps identify suspicious traffic that may initially look normal.

Here’s what full-funnel validation covers:

Impressions: Ad visibility and post-bid checks for invalid inventory. 

Clicks: Real customer clicks vs. bot-generated traffic. 

Visits: Actual customer visits vs. bot-simulated sessions, scored by intent. 

Leads/Events: Genuine leads vs. malicious leads; form submissions validated against bot detection and geo-IP matching. 

Purchase/Sale: Organic sales vs. falsely attributed conversions.

Behavioural & Session-Based Analysis for GIVT & SIVT Detection

Basic filters cannot catch sophisticated click fraud. It needs a deeper context, including behavioural and session-level analysis. The industry-standard classification splits invalid traffic into two categories:  

General Invalid Traffic (GIVT): Traffic from known crawlers and bots behaving in obviously non-human ways, easier to detect.  

Sophisticated Invalid Traffic (SIVT): Advanced ad fraud techniques like advanced bots, click spam, fake attribution, cookie stuffing, ad pixel stuffing, domain spoofing, fake clicks, click injection, punched leads, reseller fraud, duplicate users, and device farms, require ML and behavioural analysis for detection. 

Therefore, the advanced ad fraud prevention solution must analyze session depth, scroll behaviour, dwell time, bounce rate, and other engagement signals to understand true user intent. This helps distinguish a curious customer from a bot. 

A few years ago, a globally recognised brand cut two-thirds of its annual online advertising budget around $100 million. What happened next revealed a shocking truth about the digital advertising industry.  

There was little to no drop in performance. Conversions held steady, demand didn’t collapse, and the business continued as usual. The reason wasn’t efficiency; it was ad fraud and the brand that showed the mirror to the world was uber. 

Fast forward to 2026, and the problem has only become more complex. 

Consider a fast-growing ecommerce brand with strong organic traffic and a well-run affiliate program. Revenue looks solid every month, but one odd trend appears: a mid-tier affiliate suddenly becomes the highest contributor, while trusted, high-quality partners stay flat. 

At first, it feels like a performance win. 

However, a closer look reveals the truth. 

Most of those “affiliate-driven” traffic was from users who were already interested to buy from the brand. At the last moment, the credit shifts to the affiliate — even though they didn’t bring in a new customer. To burst this bubble, focus on what really adds value. 

The BFSI sector runs some of the most high-stakes digital campaigns in advertising. With customer lifetime values stretching across years of loans, credit cards, investments, and insurance renewals, every lead matters enormously. But that’s precisely what makes BFSI such a lucrative target for fraud. 

Performance campaigns in BFSI are built around a simple promise: pay per lead, optimise for cost per acquisition. It’s efficient in theory. In practice, it’s where exploitation begins. Because a bad click isn’t just a bad click — it travels downstream, pollutes the funnel, and contaminates every stage below it. Fake clicks become fake leads. Fake leads inflate CAC. And inflated CAC quietly bleeds campaign budgets dry. 

This blog covers – 

Why BFSI Brands Are Prime Targets for Lead Fraud  

Average Bot Traffic on BFSI Campaigns   

The Invisible Journey of Lead Fraud in BFSI   

How Lead Fraud Impacts BFSI Campaigns   

Why Surface Level Detection Falls Short 

What Smarter Click Fraud Detection Looks Like in BFSI   

Conclusion 

Why BFSI Brands Are Prime Targets for Click Fraud

Few industries spend on digital ads as aggressively as banks, insurers, fintechs, and financial services. The reason is clear: customer value in BFSI is unusually high. Hence the cost running campaigns is higher. 

A single customer can generate returns for years through loans, credit cards, investments, insurance renewals, or financial subscriptions. 

Because of this, keywords like “instant loan,” “credit card instant approval,” “trading app,” and “term insurance” often carry some of the highest CPCs in digital advertising. 

For fraudsters, that creates a perfect setup. The higher the cost per lead, the more profitable fake leads become. 

Fraud networks know BFSI brands can’t slow acquisition cost, especially during festive seasons, loan drives, tax-filing months, or insurance renewals. That’s why BFSI campaigns often attract unusually high levels of invalid traffic, fake installs, and manipulated engagements across both web and app ecosystems. 

What makes it worse: today’s fraudulent activity rarely looks obviously fake. It blends into normal user behaviour, making detection far harder than before. 

The Scale of the Problem: Bot Traffic on BFSI Campaigns

On BFSI campaigns, bot and invalid traffic routinely accounts for a significant share of total traffic often sitting well into double digits.  

The above table reveals a far more serious reality for BFSI advertisers globally.  

Bot traffic for different BFSI campaigns ranged from 9% to 28% of total campaign traffic. It is not a minor inefficiency; it contributes to click fraud and later contributing to lead fraud, a massive setback for brands.

In BFSI, losing nearly one-fourth of campaign traffic to bots means brands are potentially pouring millions into fake engagement, distorted performance metrics, and audiences that never truly existed. 

The Invisible Journey: How Click Fraud Becomes Lead Fraud in BFSI

BFSI marketers are accountable for metrics that sit deep in the funnel: 

Cost per approved loan 

Cost per activated credit card 

Cost per funded account 

Cost per issued policy 

These aren’t click metrics. They are business outcomes. Which is exactly why click-level fraud gets missed for so long, it enters at the top and the damage only becomes legible at the bottom, by which point the cause is buried under months of campaign data. 

The journey looks like this: 

Impression → Fraudulent Click → Fake Lead → Inflated CAC → Low Conversion → Revenue Gap 

Three Ways the Funnel Gets Exploited

Fake Leads

Bots or traffic farms fill out lead forms with fabricated or recycled personal data. These submissions pass basic validation — name, phone, email format checks — but carry no intent. They exist only to trigger the publisher’s payout. 

Punched Leads

More deliberate than bot-generated fakes. Publishers or affiliates manually manufacture form fills, sometimes using real personal data sourced from other lists, to meet contractual lead volume targets. These are harder to filter because they look human. They fail at sales qualification, not at form submission. 

Attribution Fraud – Click Spamming, Click Injection, & Cookie Hijacking

These two tactics specifically target app-based BFSI campaigns and they operate differently. 

Click spamming is probabilistic A malicious app running silently on a device floods attribution systems with fake click signals across many publisher IDs. The bet: if a user eventually installs a banking or investment app organically withing the time period of attribution window, there is a fraudulent click already logged that will claim credit. The conversion happened legitimately. The payout goes to a fraudster.

Click injection is surgical. Malicious apps on a device monitor signal that announce when another app is downloading. The moment a fintech app begins installing, the malicious app fires a fake click, timed to arrive in the attribution window milliseconds before install completes. It hijacks attribution with near-perfect timing. Detecting it requires comparing click timestamps against install timestamps at a resolution most attribution setups don’t maintain.

Cookie Hijacking targets web-based journeys. Fraudsters manipulate, overwrite, or drop tracking cookies on a user’s browser shortly before a conversion occurs. When a customer later completes an application, account opening, or purchase, the attribution system incorrectly credits the fraudster’s affiliate, publisher, or traffic source instead of the channel that genuinely influenced the conversion. The user action is real, but the attribution trail has been tampered with, diverting marketing spend and performance credit away from legitimate partners. 

Digital ad fraud is growing at breakneck speed. This year, marketers are expected to lose over $114 billion to ad fraud collectively. The situation is scary, and the future seems bleak. However, many marketers and brands still successfully turn a blind eye to ad fraud, and they do it because of a lack of awareness. Awareness of the different types of ad fraud can help you be more vigilant and cautious towards the rising threats and help you save your ad budgets from getting wasted. With this blog, we hope to help you bring this awareness.

As part of the “Know the Difference series”, first, we will discuss two of the most common types of mobile ad fraud prevalent in the industry. These are click spamming and click injection. While both terms may seem similar, they follow different procedures to carry out ad fraud and steal your marketing budget.

Let’s look at them in more detail:

Know the difference: Click Spamming and Click Injection

What is Click Spamming?

Click spamming is a relatively primitive way to commit ad fraud. As the name suggests, click spamming is the act of generating a large number of fake clicks on an ad. This type of fraud mostly takes place on mobile apps but isn’t limited to them. In some cases, click spamming can also be observed on websites accessed through mobile devices.

In most cases, an unsuspecting user downloads an app laced with malware. In others, the websites visited by the users are operated by fraudsters. The malware allows fraudsters to click on ads without the user’s knowledge. This kind of fraud can take many forms. Some examples are:

The user never sees the ads, but they are live in the background, and the fraudsters are clicking on them. This is also known as click flooding.

Apps running in the background can generate clicks anytime or throughout the day (and night).

Common examples of such apps include launchers, battery-saving apps, and memory-cleaner apps.

Some fraudsters mask views as engagement by the user and get paid for that engagement. And in some cases, fraudsters may also send clicks from the device to different vendors to collect a payout.

While Google Play Store and Apple’s App Store have security measures to detect and ban apps loaded with malware, fraudsters have found innovative ways to bypass them. For instance, some apps download malware after the app has been downloaded on a user’s device by disguising it as an update for the app.

Impact of Click Spamming

The most apparent impact of click spamming is the wasted advertising budget. However, click spamming has a deeper, much more dangerous impact on advertisers- skewed advertising data. Because of click spamming, certain advertising platforms and apps (publishers) may deliver an impressive number of clicks on your ad. When this is reflected in the reporting of your campaigns, it may make said ad platforms and publishers appear more impactful than they are. This keeps the advertisers in the dark and they make business decisions based on these skewed metrics which eventually impacts the performance of the digital ad campaigns. Moreover, the advertisers also keep spending on these platforms under the impression that it is providing them with performance.

What is Click Injection?

Click injection is an ad fraud technique similar to click spamming but more sophisticated. For advertisers, that means that detecting and avoiding instances of click injection is exponentially more difficult than detecting click spamming. Instead of frantically clicking on an ad, click injection uses a single click to conduct organic traffic poaching. This is done by ‘injecting’ a click right at the point of download.

Fraudsters make use of Android apps to listen to “download broadcasts”.

Simply put, these broadcasts are sent by Android apps whenever a user downloads a malicious app that has an Android broadcaster that notifies the fraudsters about a new install. When the fraudsters are notified of an app install, they ‘inject’ a click right before the installation is complete. When this happens, the fraudulent app gets access to the user’s unique device tracking code. Using this code, the fraudsters can make their click appear authentic.

By doing this, the fraudsters receive the credit (and the payout) for the app install, even though the app install is usually organic.

Impact of Click Injection

This sophisticated fraud technique not only leads to the wastage of ad spending but also hampers the organic traffic of the advertiser. Not just the advertisers, but the genuine publishers are also victims of click injection. Due to organic traffic stealing, they lose payout to a fraudulent install.

Furthermore, click injections attribute organic downloads to fraudulent websites and apps. This can mess with the ad reporting data advertisers use to make decisions about their future campaigns. Using this skewed data, advertisers may continue spending their budgets on ineffective platforms, resulting in more wasted ad spend.

This also costs advertisers in the form of lost opportunities by diverting their attention from other effective platforms that may deliver better results.

How can advertisers protect their ad spends?

As mentioned earlier, protection begins with awareness. Now that you know about click spamming and click injection, you can look at ways to detect these activities and take corrective actions. While click spamming can be detected manually, click injection is a sophisticated form of mobile ad fraud that is hard to detect by humans. These sophisticated fraud techniques are discreet, and human-like which makes it difficult to detect with general ad fraud detection practices. For these reasons, marketers valuing holistic protection must look beyond basic ad fraud tracking techniques to prevent mobile ad fraud.

You know you need an answer when your campaigns don’t perform as you expect them to. Or maybe they do perform, but just on dashboards? 

Click fraud is no longer limited to just basic bot traffic. In 2026, it has evolved into more sophisticated threats that many traditional detection tools are not equipped to identify. 

Fraudsters now deploy AI-powered bots that simulate real user behaviour to evade behavioural detection. They operate across performance marketing ecosystems, including Google Search, display, GDN, Pmax, Meta, affiliate networks, app install campaigns, lead generation, and re-engagement campaigns. 

According to mFilterIt, 18% of global digital ad traffic was invalid in 2025. Moreover, with global digital ad spend projected to reach $866.2 billion in 2026 and $916 billion by 2027, the scale of fraud opportunity is growing at exactly the same rate as advertiser investment. 

Therefore, the traditional method of identifying fraudulent clicks is not enough. Marketers need a tool that has advanced specifications that can help them stay ahead of such evolving threats. 

But the question remains, “What exactly should marketers look for in a click fraud prevention tool in 2026?” 

We have simplified this search for you in this blog. 

It breaks down:

What features should an advertiser prioritize in a click fraud protection tool? 

How is mFilterIt different from standard ad fraud detection solutions? 

Why isn’t click-level protection enough for web and app campaigns? 

So, if you’re comparing solutions or preparing to invest, this is the clarity you need to make the right decision. 

Key Features to Look for in a Click Fraud Protection Software

The right click fraud protection tool is supposed to give you actionable insights, measurable improvements, and cross-channel protection. Here’s what to expect from a tool that actually solves your business problems: 

Proactive Click Validation

Click fraud operates in milliseconds. Your click fraud protection tool should have the capability to detect fraud proactively before it reaches your deep funnel or MMPs. Click validation ensures invalid traffic is flagged and filtered before it drains your ad budget. Here are some checks that a robust solution must perform:  

Click Repetition Behavior: Spamming on the same Device ID, click and impression injections, IP address repetitions, clusters, and spikes  

Malicious IPs / VPNs: VPNs, proxies, and data center traffic should be identified in real time  

Invalid Device Make-Model: Invalid devices detected via User Agent analysis  

Invalid Geo: Non-applicable geographies flagged via IP address checks 

Multi-Channel Compatibility Across Performance Campaigns

Fraud is not confined to one platform.It spreads across Google Ads, affiliate programs, Meta, DV360, mobile app networks, and even OEM and influencer traffic. Your protection tool should have omnichannel compatibility to work seamlessly across all environments to give you consolidated protection. 

Integrated platform coverage must include Google Search, Google Search Partners, GDN, DV360, Facebook Audience Network, FB.com, YouTube, Bing, affiliate and direct publisher networks. 

Full-Funnel Traffic Scoring from Click to Conversion

Since ad fraud doesn’t stop at click, the right click fraud solution doesn’t just analyze a single click. It evaluates the entire customer journey from impression to post-click behaviour, and scores each interaction based on engagement, path anomalies, and conversion likelihood. This helps identify suspicious traffic that may initially look normal.

Here’s what full-funnel validation covers:

Impressions: Ad visibility and post-bid checks for invalid inventory. 

Clicks: Real customer clicks vs. bot-generated traffic. 

Visits: Actual customer visits vs. bot-simulated sessions, scored by intent. 

Leads/Events: Genuine leads vs. malicious leads; form submissions validated against bot detection and geo-IP matching. 

Purchase/Sale: Organic sales vs. falsely attributed conversions.

Behavioural & Session-Based Analysis for GIVT & SIVT Detection

Basic filters cannot catch sophisticated click fraud. It needs a deeper context, including behavioural and session-level analysis. The industry-standard classification splits invalid traffic into two categories:  

General Invalid Traffic (GIVT): Traffic from known crawlers and bots behaving in obviously non-human ways, easier to detect.  

Sophisticated Invalid Traffic (SIVT): Advanced ad fraud techniques like advanced bots, click spam, fake attribution, cookie stuffing, ad pixel stuffing, domain spoofing, fake clicks, click injection, punched leads, reseller fraud, duplicate users, and device farms, require ML and behavioural analysis for detection. 

Therefore, the advanced ad fraud prevention solution must analyze session depth, scroll behaviour, dwell time, bounce rate, and other engagement signals to understand true user intent. This helps distinguish a curious customer from a bot. 

Device Fingerprinting & IP Analysis

Fraudsters often disguise their identity using spoofed devices, anonymized browsers, and rotated IPs. Your tool should apply advanced device fingerprinting to track devices across campaigns, combined with real-time IP reputation scoring to catch proxies, VPNs, fraud networks, and repeated offenders. 

This helps detect same physical devices with multiple accounts running via app cloning, parallel spacing, or VPN cycling, creating multiple device environments on a single device. IP-only tools cannot detect this. Device environment analysis can. 

AI-Powered Detection Engine

Look for a solution that uses machine learning trained on large-scale, multi-industry datasets to flag both known and emerging fraud patterns. The ability to adapt to emerging patterns is what makes the difference between catching fraud that existed last quarter and catching fraud that is happening now.  

Sampled detection is a known gap. Fraud actors deliberately keep volumes below sample thresholds to avoid detection. Hence, a key evaluation question should be whether the click fraud prevention tool evaluates every data set, or does it work from a statistical sample. 

Custom Rules Engine

Every brand runs unique campaigns. A good tool should offer flexible custom rule configurations, letting you set thresholds for frequency, geo-targeting, source type, traffic origin, and campaign duration. This enables a fraud strategy that aligns with your media goals and market dynamics. 

Advertising platforms optimize for signals—not intent.  

In mobile marketing, the most important signal is the install. More installs usually mean a campaign is working. Platforms see this, assume success, and push more budget in the same direction.  

This is where install fraud begins.  

Fake installs are easier and cheaper to generate than real users. Fraudsters use bots, device farms, or incentivized tactics to create large volumes of installs that look genuine on the surface. Since the numbers look good, platforms assume the campaign is performing well. Budgets increase. The same sources get more spend.  

But the users aren’t real.  

At first, nothing feels wrong. Cost per install may even go down. Install numbers keep growing. The problem only becomes visible later, when users don’t open the app, don’t register, and don’treturn. What looked like growth turns into wasted spend.  

That’s why fake app installs are so hard to catch early. It doesn’t break campaigns overnight. It quietly trains platforms to invest in fake activity while genuine users get pushed out.  

In this blog, we’ll explain what install fraud is, the common ways it happens, and how marketers can spot and prevent it—before it starts impacting real growth. 

What is Install Fraud in Mobile Advertising? 

Install fraud occurs when fake app installs are generated or manipulated to claim attribution and payouts, without real user intent. 

In simple terms, a fake install appears as a genuine app download on your dashboard but doesn’t come from a genuine user who intends to engage with your app. These installs may be created by bots, emulators, manipulated devices, or deceptive techniques designed to game attribution systems. 

Install fraud falls under the broader category of mobile ad fraud, and it primarily targets CPI-driven campaigns. Since advertisers pay for installs, fraudsters focus on triggering that one event, regardless of what happens afterwards. 

What makes this problem more complex is that modern mobile ad fraud techniques don’t just stop at installs. When install traffic isn’t verified, the same fraudulent activity extends to post-install events as well, such as sign-ups, in-app actions, or other action-driven KPIs. These events may look legitimate in reports, but they’re often designed to reinforce false performance signals. 

The result? You end up paying for volume, but you don’t get real value in return, leading to weaker optimization signals and campaign inefficiency. 

Common Types of Install Fraud Techniques You Need to Know About 

Fraudsters use various techniques to generate fake installs and manipulate last-click attribution. These techniques closely mimic real user activity, making it impossible for basic tools to identifymobile ad fraud. Here are the most common install fraud techniques performance marketers should be familiar with: 

Click Injection

Click injection happens when a fraudulent source identifies that an install is about to take place. A click is fired right at that moment (by exploiting the narrow attribution window) to steal the last click attribution from the channel that actually drove the install. This is also known as organic poaching or install hijacking. 

Click Spamming

Click spamming is when a large volume of fake ad clicks are sent and injected into devices in advance. This increases the chance that one of those clicks gets credited whenever an organic install eventually takes place, stealing the attribution as a result. 

SDK Spoofing

SDK spoofing fakes app installs by imitating devices and app signals through emulators or scripts, making it appear as a real user installed on the app, without any actual download taking place.Fraudsters generate installs only to exhaust advertising budgets and spoof installs. 

Fake App Versions

Fraudsters use altered or cloned versions of the app that appear legitimate but generate fake installs and in-app events. These versions mimic normal activity and deceive attribution systems into counting non-genuine traffic.  

What makes all these techniques dangerous is not just how they work but also how normal they appear to human eyes in standard reports. 

How Does Install Fraud Impact Mobile Advertising Performance?

Install fraud operates silently. It passes basic attribution checks, mimics normal install behaviour, and avoids sudden spikes that might raise alarms. This happens because installs are counted before user quality is proven.  

The moment an install is attributed, it’s treated as success, long before anyone knows whether that user will engage, return, or convert. Therefore, the business impact begins to fall. It doesn’tjust affect one metric or one campaign. It spreads across attribution, the entire funnel, optimization, teams, and long-term strategy. Here’s how: 

Confusing performance metrics

Fake installs inflate metrics like CPI and install volume, masking real weaknesses in retention, engagement, and long-term value. 

Misleading attribution signals & optimization decisions

Mobile ad fraud techniques steal credit from genuine channels, making fraudulent partners look better than they truly are, leading teams to invest where value isn’t being created. 

Lower audience quality

Fake installs never engage meaningfully post-install. When these users enter retargeting lists or lookalike pools, overall audience quality drops. 

Budget misallocation

Because dashboards don’t always show red flags early, money keeps flowing toward channels that appear efficient but deliver little real return. 

Cross-team impact

Product, growth, and analytics teams end up working with skewed signals, affecting feature prioritization, engagement strategy, and user journey decisions. 

Unreliable forecasting and planning

Cohort trends, lifetime value projections, and performance forecasts become unreliable when they’re built on compromised data. 

Skewed event-level analysis

Low-intent users trigger surface-level actions, making it appear as though users are progressing through the funnel. This skews event-level analysis, weakens action-driven KPIs, and makes it harder to identify where genuine drop-offs are actually happening.