View-through attribution windows (VTW):<\/strong> How long after an ad impression (where the user saw but did not click the ad) a conversion can be credited to the impression. These are shorter and often more controversial.<\/p>\n\n\n\n A click-through window is straightforward. A user clicks an ad on Monday. If you have a 7-day click-through window, any install that occurs between Monday and the following Sunday can be attributed to that click.<\/p>\n\n\n\n If the user installs on Tuesday, attribution is clear. If they install on the following Saturday after visiting your app store page multiple times, it is also still within the window. If they install two weeks later, the click falls outside the window and the install is organic.<\/p>\n\n\n\n The click-through window creates an opportunity for credit inflation. Consider a user who sees your ad, ignores it, discovers your app through a friend's recommendation, and installs two days later. Under last-click attribution with a 7-day window, the ad gets credit even though the friend's recommendation was the actual driver. This is the attribution window interacting with the attribution model, and it is a good reason to think about both together.<\/p>\n\n\n\n View-through attribution credits an ad impression (without a click) for a later conversion. The rationale is that seeing an ad plants a seed of awareness that later drives an install, even if the user never clicked.<\/p>\n\n\n\n View-through windows are typically much shorter than click-through windows because the connection between impression and install is weaker and harder to validate. Common defaults are 1 day for view-through versus 7-30 days for click-through.<\/p>\n\n\n\n The controversy around view-through attribution is legitimate. In programmatic advertising, users are shown hundreds of ads per day. The probability that any specific impression actually influenced an install is low. Ad networks love view-through attribution because it dramatically increases the number of installs they can claim credit for. Advertisers should be much more skeptical.<\/p>\n\n\n\n A reasonable approach is to enable view-through attribution only for placements where you can credibly argue the user actually saw and processed the ad: in-stream video ads, interstitials, or high-visibility placements rather than banner ads where viewability is poor.<\/p>\n\n\n\n Different app categories have different consideration cycles, which means different attribution window settings make sense.<\/p>\n\n\n\n Gaming:<\/strong><\/p>\n\n\n\n E-commerce and retail:<\/strong><\/p>\n\n\n\n Finance and fintech:<\/strong><\/p>\n\n\n\n Health and fitness:<\/strong><\/p>\n\n\n\n B2B and productivity:<\/strong><\/p>\n\n\n\n These are starting points, not rules. Your actual consideration cycle may differ based on your user acquisition channels, your app's category within a vertical, and your price point.<\/p>\n\n\n\n The relationship between window length and attributed conversions is not linear. The first few days after a click capture the vast majority of installs. Each additional day of window adds fewer incremental installs because install probability drops off sharply with time.<\/p>\n\n\n\n This means the difference between a 1-day and 7-day window is much larger than the difference between a 14-day and 30-day window. Most of the attribution sensitivity happens in the first week.<\/p>\n\n\n\n A useful exercise is to look at your actual click-to-install time distribution. Pull a cohort of users attributed to paid campaigns and look at how many days elapsed between their last click and their install. If 90% of your attributions happen within 3 days of a click, a 30-day window is mostly capturing organic installs that happen to have a click in the lookback period. If 40% of your attributions happen at day 7 or later, a short window is undercounting your paid impact.<\/p>\n\n\n\n Attribution windows also apply to in-app events, not just installs. This is sometimes called the "post-install attribution window" or "reattribution window."<\/p>\n\n\n\n When an app event occurs (a purchase, a subscription start, etc.), the platform looks back to find which ad interaction should get credit. This uses the same window logic: if there is a click within the window, the event is attributed to that campaign.<\/p>\n\n\n\n For reattribution (when a lapsed user reinstalls or re-engages), most platforms use a separate "re-engagement window." This is typically shorter: 1-7 days for re-engagement campaigns.<\/p>\n\n\n\n Attribution windows and attribution models are not independent decisions. A long window combined with last-click attribution creates a large surface area for credit inflation. Any ad that runs a long click-through window effectively claims every organic install within that window as long as it was the most recent click.<\/p>\n\n\n\n Multi-touch attribution with long windows creates a different problem: too many touchpoints get credit, diluting the signal and making it harder to identify which channels are actually driving installs.<\/p>\n\n\n\n The combination that tends to work best: use windows that match your actual consideration cycle (use your click-to-install distribution data), and choose your attribution model based on your channel mix complexity. For more on this, see the last-click vs multi-touch attribution guide<\/a>.<\/p>\n\n\n\n A source of frequent confusion is that ad platforms use their own attribution windows that often differ from yours. Facebook's default click-through window is 7 days. Google's is 30 days. Your MMP might be set to 14 days.<\/p>\n\n\n\n When you compare install counts in a platform's dashboard to your MMP data, discrepancies are normal. The platform uses its own window and its own last-click logic. Your MMP uses your configured settings. Neither is "right" in an absolute sense.<\/p>\n\n\n\n What matters is consistency over time within your MMP. As long as you are comparing campaigns against each other using the same window settings, you can make valid relative comparisons even if the absolute numbers differ from platform-reported figures.<\/p>\n\n\n\n In Tolinku, attribution window settings are configured at the Appspace level and can be customized per campaign or channel type. The default settings are 7-day click-through and 1-day view-through, which are appropriate for most apps.<\/p>\n\n\n\n To find your configuration options:<\/p>\n\n\n\n You can also inspect the click-to-install time distribution in your attribution reports, which helps you validate whether your current window settings match your actual user behavior. See the Tolinku analytics user guide<\/a> for step-by-step instructions.<\/p>\n\n\n\n For a broader look at attribution concepts, the Tolinku attribution documentation<\/a> covers how all the pieces fit together.<\/p>\n\n\n\n Start with category defaults.<\/strong> Use the benchmarks above as a starting point if you do not have data yet.<\/p>\n\n\n\n Pull your click-to-install distribution.<\/strong> Look at how many days pass between click and install for your current attributed users. This tells you where the actual signal is.<\/p>\n\n\n\n Set your window to capture 90-95% of legitimate attributions.<\/strong> If 95% of your installs happen within 5 days of a click, a 7-day window is a reasonable buffer. A 30-day window is probably capturing significant organic volume.<\/p>\n\n\n\n Run a holdout test if you are uncertain.<\/strong> Reduce your window for a subset of campaigns and compare the attributed install volume against campaigns with the original window. If the reduction has little effect on install count but improves apparent ROI, your current window was too long.<\/p>\n\n\n\n Revisit when you change channels.<\/strong> If you start running brand awareness campaigns (TV, podcast sponsorships, outdoor), longer view-through windows become more relevant because the path from exposure to install is genuinely longer.<\/p>\n\n\n\n Attribution windows determine how far back a click or ad impression can receive credit for a conversion. Setting them wrong costs you either accuracy or budget. This guide covers click-through and view-through windows, industry benchmarks by vertical, and how to configure them correctly.<\/p>\n","protected":false},"author":2,"featured_media":716,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"Attribution Windows: Setting the Right Lookback Period","rank_math_description":"Learn how attribution windows work, click-through vs view-through windows, industry benchmarks by app vertical, and how to configure the right lookback period in Tolinku.","rank_math_focus_keyword":"attribution windows","rank_math_canonical_url":"","rank_math_facebook_title":"","rank_math_facebook_description":"","rank_math_facebook_image":"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/og-attribution-windows-guide-1.png","rank_math_facebook_image_id":"","rank_math_twitter_title":"","rank_math_twitter_description":"","rank_math_twitter_image":"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/og-attribution-windows-guide-1.png","footnotes":""},"categories":[14],"tags":[37,28,110,134,26],"class_list":["post-717","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analytics","tag-attribution","tag-marketing","tag-measurement","tag-user-acquisition"],"_links":{"self":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/717","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/comments?post=717"}],"version-history":[{"count":1,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/717\/revisions"}],"predecessor-version":[{"id":718,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/717\/revisions\/718"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/media\/716"}],"wp:attachment":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/media?parent=717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/categories?post=717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/tags?post=717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}How Click-Through Windows Work<\/h2>\n\n\n\n
View-Through Attribution Windows<\/h2>\n\n\n\n
Industry Benchmarks by Vertical<\/h2>\n\n\n\n
\n
\n
\n
\n
\n
How Window Size Affects Attribution<\/h2>\n\n\n\n
Post-Install Event Windows<\/h2>\n\n\n\n
The Interaction with Last-Click vs Multi-Touch<\/h2>\n\n\n\n
Ad Platform Windows vs Your MMP Windows<\/h2>\n\n\n\n
Configuring Attribution Windows in Tolinku<\/h2>\n\n\n\n
![\"Tolinku](\"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/platform-platform-analytics.png\")
<\/p>\n\n\n\n\n
A Practical Approach to Window Optimization<\/h2>\n\n\n\n
\n
Further Reading<\/h2>\n\n\n\n
\n