What is Click to Install Time? Why Advertisers Need to Map this to Detect Mobile Ad Fraud?
Bots are becoming sophisticated and more human-like every passing day. And with the emergence of AI, it is becoming a dominant force for shaping online traffic. According to Imperva BadBot Report 2025, 51% of the internet traffic is driven by bots, which is further amplifying with the introduction of AI and LLM. Unlike the basic bot traffic showing abnormal signs like high number of clicks/installs etc., the sophisticated bots can mimic human behaviour, therefore bypassing the validation checks. As a precautionary measure and to check if your campaigns are impacted by bots/invalid traffic, there are signs that you can look for in your campaign data. One of them being Click to Install Time to identify invalid installs in your mobile app campaigns. In this blog we will breakdown how CTIT can be seen as a signal to identify invalid traffic and how marketers can use it to take proactive action against mobile ad fraud. What is Click to Install Time? How to Identify Invalid Traffic Evaluating CTIT? Let’s simply breakdown what CTIT means before moving forward to understand the kind of patterns that reveal exploitation of mobile ad fraud and click to install time. Click Time: The moment a user clicks on your ad. Install Time: When the app actually finishes installing. Click-to-Install Time (CTIT): The time gap between these two. It is basically a metric used in mobile advertising to map the time it takes for a normal user to download an app after clicking on an ad. This gap varies naturally. Real users don’t install apps instantly every time; there can be delays, pauses, network differences, and human behaviour involved. What a genuine user’s install journey looks like This process takes time, usually a few seconds to a few minutes, depending on network speed and app size. However, fake installs show different timing patterns. Here’s are the two types of abnormal CTIT patterns we observed recently that clearly indicate towards install fraud: Examples of Abnormal CTIT Patterns Case 1: Extremely short click-to-install time (click injection) This snapshot compares the click time and install time for multiple installs coming from the same publisher. The gap between click and install is consistently just 1–3 seconds, and in several cases, the values are identical or nearly identical. Why it is a problem? A real user cannot click an ad, get redirected to the Play Store, download the app, complete the installation, all within a few seconds, repeatedly. This pattern strongly indicates click injection, where fraudsters: Detect that an app install is already in progress Inject a click at the last possible moment Steal attribution credit for a genuine install mFilterIt insight: Why this matters? Although these installs appear valid in attribution reports, the timing exposes manipulation. Extremely short and repeated click-to-install times are a strong indicator of high-risk fake attribution, not real user engagement. Learn more about common techniques of install fraud here. Case 2: Google Play install begins before the user clicked on an ad In this snapshot, the timestamps reveal something even more concerning. The Google Play install begin time occurs before the recorded ad click time. This results in a negative click-to-install time, meaning the install process started before the user supposedly clicked on the ad. Why is it a problem? This breaks the basic logic of attribution. A real user cannot start installing an app first and then click an ad for the same app afterward. When install activity precedes the click, it clearly indicates: Manipulated or falsified timestamps SDK tampering or fabricated attribution signals This is not caused by reporting delays or tracking errors; it points to deliberate attribution manipulation. mFilterIt insight: Why this matters? Any case where the install begins before the ad click should be treated as install fraud by default. These patterns strongly indicate fake attribution attempts, even if the installs are being credited by attribution platforms. Signs to Identify Abnormal CTIT Patterns CTIT mapping should be approached in two layers: what you can validate manually and what requires advanced detection at scale. As an advertiser, the following click to install time red flags should immediately raise concern, especially when they appear repeatedly. Installs within 1–3 seconds of a click Real users need time to reach the app store, download the app, and complete installation. Consistently instant installs are not normal human behaviour patterns. Identical CTIT values across multiple installs Human actions vary. When multiple installs show the same or near-identical timing, it often points to automated or scripted activity. Long delays followed by sudden attribution This pattern is commonly associated with click spamming, where random clicks are generated and later receive credit when an install happens. Negative CTIT values If an install begins before the recorded ad click, it breaks basic attribution logic and strongly indicates manipulated timestamps or fake signals. How Advanced Mobile Ad Fraud Detection Solutions Help Mobile ad fraud is often distributed across campaigns, publishers, and devices, making it difficult to detect without advanced analysis. Attribution platforms answer one primary question: Who gets credit for the install? They do not answer whether the install journey itself was genuine or a fake one. While they work on assigning credits rather than behavioural validation, brands need an advanced mobile ad fraud detection solution to ensure campaign efficiency. Here’s how it helps: Source-Level CTIT Pattern Analysis – Know who is installing your app Advanced solutions analyze click-to-install time across all campaigns and channels simultaneously. This makes it easier to spot publishers or sources that consistently show unnaturally fast or uniform CTIT patterns. It also helps identify install fraud patterns that may look normal in isolation but become obvious when viewed across the entire account. Analysis of CTIT with Click Behaviour – Don’t let sophisticated bots slip by Click-to-install time is evaluated alongside click signals such as click frequency, burst patterns, and timing alignment. This helps distinguish genuine user clicks from injected or spammed ones. Correlation with Device and Environment Signals – Differentiate between bot & human Advanced







