[{"data":1,"prerenderedAt":312},["ShallowReactive",2],{"article-list":3,"article:ad-attribution":239},[4,18,30,44,57,70,82,94,106,119,131,144,156,168,180,193,205,217,228],{"slug":5,"path":6,"title":7,"excerpt":8,"topic":9,"tags":10,"published":14,"updated":14,"readingMinutes":15,"cover":16,"coverAlt":17},"advertising-reporting-mistakes","\u002Fadvertising-reporting-mistakes\u002F","Advertising Reporting Mistakes: 10 Common Errors and How to Fix Them","Most misleading ad reports come from a handful of predictable errors. Learn why clicks and sessions differ, how Google Ads dates conversions and how to stop double counting.","measurement",[11,12,13],"advertising reporting mistakes","google ads reporting errors","misreading ad data","2026-11-13",9,"\u002Fcovers\u002Fadvertising-reporting-mistakes.svg","Diagram of a reliable reporting process shown as four connected steps: define, align, compare and annotate",{"slug":19,"path":20,"title":21,"excerpt":22,"topic":9,"tags":23,"published":27,"updated":27,"readingMinutes":15,"cover":28,"coverAlt":29},"ad-attribution","\u002Fad-attribution\u002F","Ad Attribution: How Conversion Credit Is Assigned and Where It Falls Short","Attribution divides credit for a conversion among ad interactions, but it does not prove the ads caused the sale. This guide covers what current models do and how to read them.",[24,25,26],"ad attribution","attribution models google ads","data-driven attribution","2026-11-10","\u002Fcovers\u002Fad-attribution.svg","Diagram of ad interactions passing through a conversion window and an attribution model before credit is assigned",{"slug":31,"path":32,"title":33,"excerpt":34,"topic":35,"tags":36,"published":40,"updated":40,"readingMinutes":41,"cover":42,"coverAlt":43},"campaign-performance-investigation","\u002Fcampaign-performance-investigation\u002F","Google Ads Performance Investigation: A Step-by-Step Diagnostic Order","When results change suddenly, the order of your checks matters. This diagnostic sequence rules out tracking, account changes and market shifts before blaming fraud.","click-fraud",[37,38,39],"google ads performance investigation","campaign diagnostics","performance drop","2026-11-08",8,"\u002Fcovers\u002Fcampaign-performance-investigation.svg","Diagram of a diagnostic sequence shown as four connected steps: tracking, changes, market and traffic",{"slug":45,"path":46,"title":47,"excerpt":48,"topic":49,"tags":50,"published":54,"updated":54,"readingMinutes":41,"cover":55,"coverAlt":56},"mobile-ad-traffic","\u002Fmobile-ad-traffic\u002F","Mobile Ad Traffic: Accidental Clicks, In-App Placements and Fair Comparisons","Phones produce more stray taps, more app inventory and shorter visits. Learn to judge mobile traffic on its own terms, review in-app placements and spot patterns worth investigating.","ads-clickers",[51,52,53],"mobile ad traffic","accidental mobile ad clicks","in-app ad traffic quality","2026-11-06","\u002Fcovers\u002Fmobile-ad-traffic.svg","Diagram of four connected steps for reviewing mobile ad traffic: segment, placements, quality and adjust",{"slug":58,"path":59,"title":60,"excerpt":61,"topic":62,"tags":63,"published":67,"updated":67,"readingMinutes":15,"cover":68,"coverAlt":69},"google-ads-location-targeting","\u002Fgoogle-ads-location-targeting\u002F","Google Ads Location Targeting: Options, Exclusions and Reports Explained","Location settings decide who can see and click your ads. Learn how Google estimates location, why out-of-area clicks happen and how to set targeting on purpose.","google-ads",[64,65,66],"google ads location targeting","presence or interest","location exclusions","2026-11-03","\u002Fcovers\u002Fgoogle-ads-location-targeting.svg","Diagram of location setup shown as four connected steps: target, option, exclude and report",{"slug":71,"path":72,"title":73,"excerpt":74,"topic":35,"tags":75,"published":79,"updated":79,"readingMinutes":41,"cover":80,"coverAlt":81},"suspicious-ad-sessions","\u002Fsuspicious-ad-sessions\u002F","Suspicious Ad Sessions: How to Read Them Without Jumping to Conclusions","A single odd visit proves very little. Learn to examine individual paid sessions, separate real warning signs from harmless quirks and decide when to escalate.",[76,77,78],"suspicious ad sessions","investigate suspicious traffic","unusual ad sessions","2026-11-01","\u002Fcovers\u002Fsuspicious-ad-sessions.svg","Diagram of four connected steps for reviewing a suspicious ad session: session, signals, explanations and escalate",{"slug":83,"path":84,"title":85,"excerpt":86,"topic":9,"tags":87,"published":91,"updated":91,"readingMinutes":15,"cover":92,"coverAlt":93},"ad-click-tracking","\u002Fad-click-tracking\u002F","Ad Click Tracking: Following a Click from the Ad to Your Analytics","Follow one ad click from the auction to your analytics report, see where identifiers are added or lost along the way, and use a checklist to keep tracking healthy.",[88,89,90],"ad click tracking","gclid","auto-tagging","2026-10-29","\u002Fcovers\u002Fad-click-tracking.svg","Diagram of an ad click carrying a click identifier to the landing page and becoming an analytics session",{"slug":95,"path":96,"title":97,"excerpt":98,"topic":49,"tags":99,"published":103,"updated":103,"readingMinutes":15,"cover":104,"coverAlt":105},"bot-traffic-detection","\u002Fbot-traffic-detection\u002F","Bot Traffic Detection: Finding Non-Human Visits in Analytics and Server Logs","A practical method for spotting non-human sessions: built-in filtering, analytics anomalies, server-log evidence, crawler verification and the limits of each signal.",[100,101,102],"bot traffic detection","bot traffic in analytics","server logs","2026-10-27","\u002Fcovers\u002Fbot-traffic-detection.svg","Diagram of a bot detection workflow moving from built-in filters to analytics review to server logs to crawler verification",{"slug":107,"path":108,"title":109,"excerpt":110,"topic":62,"tags":111,"published":115,"updated":115,"readingMinutes":116,"cover":117,"coverAlt":118},"google-ads-budget-waste","\u002Fgoogle-ads-budget-waste\u002F","Google Ads Budget Waste: How to Find and Fix Avoidable Spend","Most wasted spend in Google Ads comes from settings, search terms and tracking rather than fraud. Here is how to find it, check it and decide what to fix first.",[112,113,114],"google ads budget waste","wasted ad spend","campaign budget analysis","2026-10-25",11,"\u002Fcovers\u002Fgoogle-ads-budget-waste.svg","Diagram of a budget review shown as four connected steps: spend, segment, diagnose and fix",{"slug":120,"path":121,"title":122,"excerpt":123,"topic":9,"tags":124,"published":128,"updated":128,"readingMinutes":116,"cover":129,"coverAlt":130},"cost-per-click-measurement","\u002Fcost-per-click-measurement\u002F","Cost per Click Measurement: Actual CPC, Average CPC and Fair Comparisons","Average CPC blends many separate auction prices. See what sets each price, why the blend shifts even when prices don't, and how to judge CPC against results.",[125,126,127],"cost per click measurement","average cpc","actual cpc","2026-10-22","\u002Fcovers\u002Fcost-per-click-measurement.svg","Diagram showing max CPC, actual CPC, average CPC and cost per result as connected steps",{"slug":132,"path":133,"title":134,"excerpt":135,"topic":9,"tags":136,"published":140,"updated":140,"readingMinutes":141,"cover":142,"coverAlt":143},"ad-traffic-analysis","\u002Fad-traffic-analysis\u002F","Ad Traffic Analysis: How to Judge the Quality of Paid Traffic by Segment","Traffic quality is not the same as conversion rate. Here is a segment-by-segment routine for reviewing paid traffic every week, with a simple scorecard and a worked example.",[137,138,139],"ad traffic analysis","advertising traffic quality","traffic quality metrics","2026-10-20",10,"\u002Fcovers\u002Fad-traffic-analysis.svg","Diagram of a recurring traffic review with segment, measure, compare and act shown as connected steps",{"slug":145,"path":146,"title":147,"excerpt":148,"topic":49,"tags":149,"published":153,"updated":153,"readingMinutes":141,"cover":154,"coverAlt":155},"automated-ad-traffic","\u002Fautomated-ad-traffic\u002F","Automated Ad Traffic: A Map of the Bots and Scripts That Touch Your Ads","Not every non-human visit is fraud. This taxonomy covers crawlers, prefetching, verification tools and sophisticated invalid traffic, and what each means for advertisers.",[150,151,152],"automated ad traffic","sophisticated invalid traffic","non-human traffic","2026-10-18","\u002Fcovers\u002Fautomated-ad-traffic.svg","Diagram mapping automated ad traffic from benign bots to general invalid traffic to sophisticated invalid traffic to the advertiser's response",{"slug":157,"path":158,"title":159,"excerpt":160,"topic":35,"tags":161,"published":165,"updated":165,"readingMinutes":141,"cover":166,"coverAlt":167},"invalid-click-protection","\u002Finvalid-click-protection\u002F","Invalid Click Protection: What Advertisers Can Actually Control","No setting makes a campaign immune to bad clicks. See how Google's filtering, your own campaign controls and outside tools fit together, and where each one stops working.",[162,163,164],"invalid click protection","click fraud protection","ip exclusions google ads","2026-10-15","\u002Fcovers\u002Finvalid-click-protection.svg","Diagram of four connected layers of invalid click protection: google filtering, your controls, lead checks and review",{"slug":169,"path":170,"title":171,"excerpt":172,"topic":62,"tags":173,"published":177,"updated":177,"readingMinutes":141,"cover":178,"coverAlt":179},"google-ads-click-fraud","\u002Fgoogle-ads-click-fraud\u002F","Google Ads Click Fraud: Who Commits It, Where It Happens and How to Report It","Click fraud is the deliberate part of invalid traffic. Learn who is behind it, how exposure differs by network and how to report a case to Google without overreacting.",[174,175,176],"google ads click fraud","report click fraud","competitor clicking ads","2026-10-13","\u002Fcovers\u002Fgoogle-ads-click-fraud.svg","Diagram of four connected steps for handling click fraud: motive, exposure, signals and report",{"slug":181,"path":182,"title":183,"excerpt":184,"topic":62,"tags":185,"published":189,"updated":189,"readingMinutes":190,"cover":191,"coverAlt":192},"google-ads-invalid-clicks","\u002Fgoogle-ads-invalid-clicks\u002F","Google Ads Invalid Clicks: How Google Filters, Reports and Credits Them","Google filters most invalid activity before you pay. Here are the definitions, the reporting columns, the credit process and what Google will and will not tell you.",[186,187,188],"google ads invalid clicks","invalid click rate","invalid click credits","2026-10-10",12,"\u002Fcovers\u002Fgoogle-ads-invalid-clicks.svg","Diagram of four connected steps for invalid clicks: detect, filter, report and credit",{"slug":194,"path":195,"title":196,"excerpt":197,"topic":35,"tags":198,"published":202,"updated":202,"readingMinutes":190,"cover":203,"coverAlt":204},"click-fraud-detection","\u002Fclick-fraud-detection\u002F","Click Fraud Detection: How to Recognize and Evidence Suspicious Clicks","Suspicious clicks are easy to suspect and hard to prove. Learn to compare traffic with baselines, rule out ordinary causes and document a case Google can review.",[199,200,201],"click fraud detection","suspicious click indicators","invalid clicks","2026-10-08","\u002Fcovers\u002Fclick-fraud-detection.svg","Diagram of four connected steps in click fraud detection: baseline, anomaly, evidence and request",{"slug":206,"path":207,"title":208,"excerpt":209,"topic":49,"tags":210,"published":214,"updated":214,"readingMinutes":190,"cover":215,"coverAlt":216},"ads-clicker-bot","\u002Fads-clicker-bot\u002F","Ads Clicker Bots: What They Are, Why They Fail and How They Are Caught","A conceptual guide to click bots: what they try to imitate, the motives behind them, the policy and legal risks, and the signals that expose automated clicking.",[211,212,213],"ads clicker bot","click bot","invalid traffic","2026-10-06","\u002Fcovers\u002Fads-clicker-bot.svg","Diagram of a click bot's path from motive to imitation to detection signals to filtering",{"slug":218,"path":219,"title":220,"excerpt":221,"topic":62,"tags":222,"published":225,"updated":225,"readingMinutes":190,"cover":226,"coverAlt":227},"google-ads-clicker","\u002Fgoogle-ads-clicker\u002F","Google Ads Clicker: How Google Ads Counts, Filters and Bills Clicks","The phrase covers everything from click bots to worried advertisers. Here is how a click is really recorded, filtered and charged, and how to audit your own traffic.",[223,224,201],"google ads clicker","google ads clicks","2026-10-03","\u002Fcovers\u002Fgoogle-ads-clicker.svg","Diagram of a Google Ads click passing through four connected steps: click, filter, report and bill",{"slug":229,"path":230,"title":231,"excerpt":232,"topic":49,"tags":233,"published":236,"updated":236,"readingMinutes":190,"cover":237,"coverAlt":238},"what-is-an-ads-clicker","\u002Fwhat-is-an-ads-clicker\u002F","What Is an Ads Clicker? Meaning, Contexts and Common Misconceptions","The phrase can mean a click bot, a paid clicker, a protest extension or a simple click counter. Here is what each sense means and how ad platforms treat those clicks.",[234,201,235],"what is an ads clicker","pay-per-click","2026-10-01","\u002Fcovers\u002Fwhat-is-an-ads-clicker.svg","Diagram of an ad click moving from impression to click to invalid-click filtering to billing",{"slug":19,"path":20,"title":21,"excerpt":22,"topic":9,"tags":240,"published":27,"updated":27,"readingMinutes":15,"cover":28,"coverAlt":29,"seoTitle":241,"description":242,"html":243,"toc":244,"wordCount":276,"sources":277,"related":304,"ogImage":311},[24,25,26],"Ad Attribution: Models, Windows and Their Limits","Ad attribution explained: which models Google Ads and GA4 still offer, how conversion windows and modeled conversions work, and why the two reports disagree.","\u003Cp>Ad attribution is the method that decides which ad interactions receive credit for a conversion, and how much each one gets. In Google Ads today that means choosing between data-driven attribution and last click. Either way, the result tells you how credit was divided among the interactions that were observed, not whether the ads caused the conversion.\u003C\u002Fp>\n\u003Cp>This guide covers which attribution models remain in Google Ads and Google Analytics 4, how conversion windows, interaction types and modeled conversions shape the numbers, why the two products disagree, and how attribution differs from incrementality testing.\u003C\u002Fp>\n\u003Ch2 id=\"what-ad-attribution-is-and-what-it-is-not\">What ad attribution is and what it is not\u003C\u002Fh2>\n\u003Cp>Attribution is an accounting rule. A conversion happens, the system looks back over the ad interactions it can connect to that conversion within a set window, and it distributes one conversion’s worth of credit among them. Switching models within one system does not change how many conversions happened. It changes how they are split between campaigns, ad groups and keywords.\u003C\u002Fp>\n\u003Cp>It helps to be clear about what attribution is not:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Not proof of cause.\u003C\u002Fstrong> A brand search click before a purchase gets credit even if the customer would have bought anyway.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Not the whole journey.\u003C\u002Fstrong> Each system sees only the interactions it can observe. Google Ads assigns credit among interactions with your Google ads, while an analytics tool sees the channels its tags record.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Not a measure of traffic quality.\u003C\u002Fstrong> A model can credit a campaign with conversions that later turn out to be junk leads. Quality needs its own review.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Not a click count.\u003C\u002Fstrong> Attribution starts after a click or view has been recorded and linked to a visitor, which depends on \u003Ca href=\"\u002Fad-click-tracking\u002F\">how ad clicks are tracked\u003C\u002Fa>.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"attribution-models-in-google-ads-today\">Attribution models in Google Ads today\u003C\u002Fh2>\n\u003Cp>Google’s page \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F6259715?hl=en\" rel=\"noopener\" class=\"ext\">about attribution models\u003C\u002Fa> lists two models:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Last click\u003C\u002Fstrong> gives all credit for the conversion to the last-clicked ad and its keyword.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Data-driven\u003C\u002Fstrong> distributes credit based on your past data for that conversion action. It estimates how much each ad interaction on the path actually contributed, instead of applying a fixed rule.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Google says data-driven is the default attribution model for most conversion actions. The same page confirms that first click, linear, time decay and position-based models are no longer supported, and that conversion actions which used them were moved to data-driven attribution.\u003C\u002Fp>\n\u003Cp>The timing was set out in Google’s April 2023 notice that these \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F13427716\" rel=\"noopener\" class=\"ext\">rule-based attribution models were going away\u003C\u002Fa>. From June 2023, advertisers could no longer select them for conversion actions not already using them. From September 2023, conversion actions still using them were switched to data-driven attribution, with last click available for anyone who preferred it. The notice states that data-driven attribution, last click and external attribution were not affected.\u003C\u002Fp>\n\u003Cp>Google Ads also offers a Model comparison report that sets two models side by side, which is the safest way to preview a model change.\u003C\u002Fp>\n\u003Ch2 id=\"attribution-models-in-google-analytics-4\">Attribution models in Google Analytics 4\u003C\u002Fh2>\n\u003Cp>Google Analytics has its own attribution settings, which apply to its reports. Google’s \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fanalytics\u002Fanswer\u002F10596866?hl=en\" rel=\"noopener\" class=\"ext\">attribution overview for Analytics\u003C\u002Fa> describes three models:\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\" role=\"region\" aria-label=\"Scrollable table\" tabindex=\"0\">\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>GA4 model\u003C\u002Fth>\n\u003Cth>How credit is assigned\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>Data-driven attribution\u003C\u002Ftd>\n\u003Ctd>Machine learning evaluates both converting and non-converting paths and assigns credit based on how likely each touchpoint was to drive the key event\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Paid and organic last click\u003C\u002Ftd>\n\u003Ctd>Ignores direct traffic and gives 100% of the credit to the last channel the customer clicked through\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Google paid channels last click\u003C\u002Ftd>\n\u003Ctd>Gives 100% of the credit to the last Google Ads channel clicked before converting\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\u003Cp>According to the same page, the first click, linear, time decay and position-based models have not been available in Analytics since November 2023. It also notes that conversions can be reattributed for up to 7 days after the conversion, so recent credit can still shift.\u003C\u002Fp>\n\u003Ch2 id=\"conversion-windows-and-types-of-ad-interaction\">Conversion windows and types of ad interaction\u003C\u002Fh2>\n\u003Cp>A \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F3123169?hl=en\" rel=\"noopener\" class=\"ext\">conversion window\u003C\u002Fa> is the period after an ad interaction during which a conversion is recorded in Google Ads. Google gives these defaults:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Click-through:\u003C\u002Fstrong> 30 days by default, adjustable from 1 day up to 30, 60 or 90 days depending on the conversion source.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Engaged-view:\u003C\u002Fstrong> adjustable from 1 to 30 days.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>View-through:\u003C\u002Fstrong> 1 day by default, adjustable from 1 to 30 days.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Google says changes to a window apply to conversions going forward, so changing a window does not rewrite history.\u003C\u002Fp>\n\u003Cp>The interaction types matter as much as the windows:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Click-through conversions\u003C\u002Fstrong> follow a click on the ad.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>\u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F10048752?hl=en\" rel=\"noopener\" class=\"ext\">Engaged-view conversions\u003C\u002Fa>\u003C\u002Fstrong> apply to video ads. For a skippable in-stream ad, the viewer must watch at least 10 seconds, or the whole ad if it is shorter, and then convert within the engaged-view window. In-feed and Shorts ads use a 5-second threshold. Google says default engaged-view windows vary by conversion type, and that these conversions appear in both the Conversions and All conversions columns.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>View-through conversions\u003C\u002Fstrong> occur, in Google’s words, when customers view but don’t interact with your ad and later complete a conversion on your site. Google’s guide to \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F6270625?hl=en\" rel=\"noopener\" class=\"ext\">conversion tracking data\u003C\u002Fa> says that on the Display Network the last viewable impression gets the credit. View-through conversions are not included in the Conversions column. They appear in the View-through conversions and All conversions columns.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>When comparing campaigns, check which column you are reading. A video campaign judged on All conversions and a search campaign judged on Conversions are not measured alike.\u003C\u002Fp>\n\u003Ch2 id=\"cross-device-and-modeled-conversions\">Cross-device and modeled conversions\u003C\u002Fh2>\n\u003Cp>Some conversions cannot be observed directly. The same Google conversion data guide describes cross-device conversions as starting with a click on an ad from one device and ending with a conversion on another.\u003C\u002Fp>\n\u003Cp>Google uses \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F10081327?hl=en\" rel=\"noopener\" class=\"ext\">modeled conversions\u003C\u002Fa> to estimate conversions it cannot observe directly. The model predicts whether an ad interaction led to a conversion that occurred, not whether a conversion occurred at all. Google lists these situations:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Some browsers, such as Safari and Firefox, don’t allow conversion measurement using third-party cookies.\u003C\u002Fli>\n\u003Cli>Advertisers using consent mode will see modeling for users who did not consent.\u003C\u002Fli>\n\u003Cli>Conversions from ads on traffic affected by Apple’s App Tracking Transparency are modeled.\u003C\u002Fli>\n\u003Cli>Cross-device journeys may not be attributable to the ad interaction without modeling.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Google reports both modeled and observed conversions in the Conversions column. It says the modeling uses data that doesn’t identify the user, that it is checked with holdback validation, and that modeled conversions are included only when Google is highly confident they occurred. For analysis, this means the Conversions column is partly an estimate. Treat small week-to-week differences with caution, especially in low-volume campaigns.\u003C\u002Fp>\n\u003Ch2 id=\"why-google-ads-and-ga4-report-different-conversion-numbers\">Why Google Ads and GA4 report different conversion numbers\u003C\u002Fh2>\n\u003Cp>Different numbers for “the same” conversions are expected. Google’s conversion data guide and the Analytics attribution overview point to these main differences:\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\" role=\"region\" aria-label=\"Scrollable table\" tabindex=\"0\">\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Factor\u003C\u002Fth>\n\u003Cth>Google Ads\u003C\u002Fth>\n\u003Cth>Google Analytics\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>Date assigned\u003C\u002Ftd>\n\u003Ctd>Primary conversion columns use the time of the click, not the time of the conversion\u003C\u002Ftd>\n\u003Ctd>Google suggests comparing it with Google Ads conversions reported by conversion time\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Default model\u003C\u002Ftd>\n\u003Ctd>Data-driven for most conversion actions, with last click as the alternative\u003C\u002Ftd>\n\u003Ctd>Defaults to data-driven attribution\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Channels that can receive credit\u003C\u002Ftd>\n\u003Ctd>Only interactions with your Google ads\u003C\u002Ftd>\n\u003Ctd>Paid and organic last click can credit organic and non-Google channels\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Counting\u003C\u002Ftd>\n\u003Ctd>“Every” or “One” conversion per interaction, set per conversion action\u003C\u002Ftd>\n\u003Ctd>Key events, counted according to the property’s own settings\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Estimation\u003C\u002Ftd>\n\u003Ctd>The Conversions column may include modeled conversions\u003C\u002Ftd>\n\u003Ctd>Depends on the property’s own consent and modeling setup\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\u003Cp>Within Google Ads itself, the Conversions column covers primary conversion actions. All conversions adds secondary actions and view-through conversions, so the two columns will rarely match each other, let alone Analytics.\u003C\u002Fp>\n\u003Cp>Google says discrepancies of often up to 20% are expected because of different attribution models. It also notes that reporting can lag by 24 to 48 hours. Google Ads can report conversions by the time the conversion occurred, which makes comparisons with other tools fairer.\u003C\u002Fp>\n\u003Cp>To reconcile the two, compare one conversion action at a time, over a range that excludes the last few days. Set Google Ads conversions by conversion time against the Advertising section of Google Analytics, which Google points to for fairer comparisons, and note which model each side uses. Mixing models or columns in one report is one of the classic \u003Ca href=\"\u002Fadvertising-reporting-mistakes\u002F\">advertising reporting mistakes\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2 id=\"attribution-versus-incrementality\">Attribution versus incrementality\u003C\u002Fh2>\n\u003Cp>Attribution answers “how should credit be shared among the ad interactions we saw?” Incrementality asks “how many conversions would not have happened without the ads?” The two can give very different answers, for example for brand search or retargeting of people already close to buying.\u003C\u002Fp>\n\u003Cp>Google describes incrementality experiments as the way to measure the causal impact of ads. In \u003Ca href=\"https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F12003020?hl=en\" rel=\"noopener\" class=\"ext\">Conversion Lift\u003C\u002Fa>, the audience is split into a treatment group that sees your ads and a control group that does not. The difference in conversions between the two groups is the lift. Similar logic underpins simpler holdout designs, such as pausing a campaign in a set of comparable regions while it keeps running elsewhere.\u003C\u002Fp>\n\u003Cp>Experiments have limits too. They need enough volume and time for a clear result, they describe one period and one setup, and the control group forgoes conversions. A common pattern is to optimize day to day on attribution and run occasional lift tests to check that the attributed numbers are plausible.\u003C\u002Fp>\n\u003Ch2 id=\"an-illustrative-example-same-conversions-different-credit\">An illustrative example: same conversions, different credit\u003C\u002Fh2>\n\u003Cp>The figures here are hypothetical and the split is invented for illustration. A real data-driven model derives its split from your account’s data. Imagine 100 conversions, all from people who clicked a generic search ad first and a brand search ad last.\u003C\u002Fp>\n\u003Cdiv class=\"table-wrap\" role=\"region\" aria-label=\"Scrollable table\" tabindex=\"0\">\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Campaign\u003C\u002Fth>\n\u003Cth>Cost\u003C\u002Fth>\n\u003Cth>Last click: conversions\u003C\u002Fth>\n\u003Cth>Last click: cost per conversion\u003C\u002Fth>\n\u003Cth>Data-driven (hypothetical): conversions\u003C\u002Fth>\n\u003Cth>Data-driven: cost per conversion\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>Generic search\u003C\u002Ftd>\n\u003Ctd>$2,000\u003C\u002Ftd>\n\u003Ctd>0\u003C\u002Ftd>\n\u003Ctd>none credited\u003C\u002Ftd>\n\u003Ctd>40\u003C\u002Ftd>\n\u003Ctd>$50\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Brand search\u003C\u002Ftd>\n\u003Ctd>$1,200\u003C\u002Ftd>\n\u003Ctd>100\u003C\u002Ftd>\n\u003Ctd>$12\u003C\u002Ftd>\n\u003Ctd>60\u003C\u002Ftd>\n\u003Ctd>$20\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>\u003Cstrong>Total\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003Ctd>\u003Cstrong>$3,200\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003Ctd>\u003Cstrong>100\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003Ctd>\u003Cstrong>$32\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003Ctd>\u003Cstrong>100\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003Ctd>\u003Cstrong>$32\u003C\u002Fstrong>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\u003Cp>The account total is identical under both models: $3,200 for 100 conversions, or $32 each. Only the split changes. Under last click, the generic campaign looks worthless and could be cut. Under the data-driven split, it looks like a useful contributor. Neither view proves that cutting the generic campaign would cost conversions, which is a question for an experiment. The same caution applies to any per-campaign figure that depends on the model, including the cost-per-result comparisons described in \u003Ca href=\"\u002Fcost-per-click-measurement\u002F\">cost per click measurement\u003C\u002Fa>.\u003C\u002Fp>\n\u003Ch2 id=\"practical-guidance\">Practical guidance\u003C\u002Fh2>\n\u003Col>\n\u003Cli>\u003Cstrong>Choose one model as your reporting standard.\u003C\u002Fstrong> Document it, and do not compare figures produced under different models.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Know which column you are reading.\u003C\u002Fstrong> Conversions and All conversions answer different questions. Check whether view-through and engaged-view conversions are included.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Respect windows and lag.\u003C\u002Fstrong> Leave the most recent days out of judgments, and remember that window changes apply only from the change onward.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Preview before you switch.\u003C\u002Fstrong> Use the Model comparison report before changing a model, and annotate the date of any change.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Check attribution when results shift.\u003C\u002Fstrong> A model or window change can move reported conversions overnight. It belongs early in any \u003Ca href=\"\u002Fcampaign-performance-investigation\u002F\">structured performance investigation\u003C\u002Fa>.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Pair credit with quality.\u003C\u002Fstrong> Attributed conversions still need to be real. Segment-level review of engagement and lead outcomes, as in \u003Ca href=\"\u002Fad-traffic-analysis\u002F\">ad traffic analysis\u003C\u002Fa>, catches credit flowing to junk.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Test the big assumptions.\u003C\u002Fstrong> Before large budget moves based on attributed results, consider a lift or holdout test.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Start from the click.\u003C\u002Fstrong> Attribution can only allocate what tracking captured, so the basics of \u003Ca href=\"\u002Fgoogle-ads-clicker\u002F\">how Google Ads counts clicks\u003C\u002Fa> underpin everything above.\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"key-takeaways\">Key takeaways\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>Ad attribution divides credit among observed ad interactions. It does not measure whether the ads caused the conversion.\u003C\u002Fli>\n\u003Cli>Google Ads now offers data-driven and last click attribution. First click, linear, time decay and position-based models were retired in 2023 in both Google Ads and Google Analytics.\u003C\u002Fli>\n\u003Cli>Conversion windows, interaction types (click, engaged-view, view-through) and modeled conversions all shape the numbers you see.\u003C\u002Fli>\n\u003Cli>Google Ads and GA4 differ in dates, models, scope, counting and columns, so some disagreement is expected. Reconcile like with like.\u003C\u002Fli>\n\u003Cli>Use incrementality tests to check whether attributed results reflect real causal impact before making large budget decisions.\u003C\u002Fli>\n\u003C\u002Ful>\n",[245,249,252,255,258,261,264,267,270,273],{"id":246,"text":247,"level":248},"what-ad-attribution-is-and-what-it-is-not","What ad attribution is and what it is not",2,{"id":250,"text":251,"level":248},"attribution-models-in-google-ads-today","Attribution models in Google Ads today",{"id":253,"text":254,"level":248},"attribution-models-in-google-analytics-4","Attribution models in Google Analytics 4",{"id":256,"text":257,"level":248},"conversion-windows-and-types-of-ad-interaction","Conversion windows and types of ad interaction",{"id":259,"text":260,"level":248},"cross-device-and-modeled-conversions","Cross-device and modeled conversions",{"id":262,"text":263,"level":248},"why-google-ads-and-ga4-report-different-conversion-numbers","Why Google Ads and GA4 report different conversion numbers",{"id":265,"text":266,"level":248},"attribution-versus-incrementality","Attribution versus incrementality",{"id":268,"text":269,"level":248},"an-illustrative-example-same-conversions-different-credit","An illustrative example: same conversions, different credit",{"id":271,"text":272,"level":248},"practical-guidance","Practical guidance",{"id":274,"text":275,"level":248},"key-takeaways","Key takeaways",2017,[278,282,285,289,292,295,298,301],{"title":279,"publisher":280,"url":281},"About attribution models","Google Ads Help","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F6259715?hl=en",{"title":283,"publisher":280,"url":284},"First click, linear, time decay, and position-based attribution models are going away","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F13427716",{"title":286,"publisher":287,"url":288},"Get started with attribution","Google Analytics Help","https:\u002F\u002Fsupport.google.com\u002Fanalytics\u002Fanswer\u002F10596866?hl=en",{"title":290,"publisher":280,"url":291},"About conversion windows","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F3123169?hl=en",{"title":293,"publisher":280,"url":294},"About Engaged-view conversions","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F10048752?hl=en",{"title":296,"publisher":280,"url":297},"Understand your conversion tracking data","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F6270625?hl=en",{"title":299,"publisher":280,"url":300},"About modeled online conversions","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F10081327?hl=en",{"title":302,"publisher":280,"url":303},"About Conversion Lift","https:\u002F\u002Fsupport.google.com\u002Fgoogle-ads\u002Fanswer\u002F12003020?hl=en",[305,307,309],{"slug":83,"path":84,"title":85,"excerpt":86,"topic":9,"tags":306,"published":91,"updated":91,"readingMinutes":15,"cover":92,"coverAlt":93},[88,89,90],{"slug":5,"path":6,"title":7,"excerpt":8,"topic":9,"tags":308,"published":14,"updated":14,"readingMinutes":15,"cover":16,"coverAlt":17},[11,12,13],{"slug":120,"path":121,"title":122,"excerpt":123,"topic":9,"tags":310,"published":128,"updated":128,"readingMinutes":116,"cover":129,"coverAlt":130},[125,126,127],"\u002Fog\u002Fad-attribution.png",1794612999656]