{"id":7911,"date":"2026-09-08T12:25:59","date_gmt":"2026-09-08T09:25:59","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/?p=7911"},"modified":"2026-09-08T12:27:03","modified_gmt":"2026-09-08T09:27:03","slug":"does-smart-bidding-hit-your-target","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/does-smart-bidding-hit-your-target\/","title":{"rendered":"Smart Bidding vs target: 303 campaigns"},"content":{"rendered":"<div class=\"tldr\"><strong>In short:<\/strong> I reviewed 303 campaigns across 23 and 15 advertisers for January-May 2026. A third of tCPA campaigns missed their target by 20% or more, with a median miss ratio of 1.029.<\/div>\n\n\n<p class=\"wp-block-paragraph\">33.5% of target CPA campaigns missed their goal by 20% or more. This is the result of analyzing 239 such campaigns across 23 advertisers for January-May 2026. In plain words: every third campaign in this sample delivered conversions notably more expensive than the algorithm was asked for. Combined with the target ROAS campaigns we get 303 campaigns, hence the number in the title.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Let me add an honest caveat right away, otherwise the number sounds scarier than it is. This describes a specific sample of accounts I have access to as a practitioner and educator, not an industry standard. This percentage is driven by a few large clients: remove them, and the picture changes. I explain this in detail below. Read this as a mirror for your own account, not a verdict on the whole industry.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is the miss ratio<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before looking at the tables, let us agree on terms. I constantly refer to the miss ratio, and it is calculated differently for the two strategies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to the dataset dictionary, for target CPA it is straightforward: we take the actual cost per conversion and divide it by the target. A 1.0 ratio means the campaign hit the target exactly. A 1.2 ratio means the actual cost is 20% higher than the target. If the ratio is less than one, the campaign did better than requested.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For target ROAS, according to the dataset dictionary, the formula is mirrored: we divide the target ROAS by the actual ROAS. There is a trap here that almost everyone falls into at first. A 1.2 ratio for ROAS does not mean 20% worse, but a return roughly 17% lower than the target. Why? Because we divide the target by the actual, not the other way around. The same ratio in the two strategies means a different percentage of a miss. Keep this in mind before you start blaming the algorithm.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">How to calculate it yourself. Take a window where the campaign has generated at least 5 conversions, preferably 30 days or more. Divide the actual by the target if you use target CPA, or target by actual for target ROAS. You will get your number and can compare it with the tables below. That is it, no magic involved.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Campaign level: who misses by how much<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">First view: every campaign individually. The median shows a typical campaign, the Q1 and Q3 quartiles give the boundaries where the middle half of the sample lives.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Strategy<\/th><th>Campaigns<\/th><th>Advertisers<\/th><th>Median<\/th><th>Q1<\/th><th>Q3<\/th><th>On target or better<\/th><th>Missed by 20% or more<\/th><\/tr><\/thead><tbody><tr><td>Target CPA<\/td><td>239<\/td><td>23<\/td><td>1.029<\/td><td>0.813<\/td><td>1.325<\/td><td>47.3%<\/td><td>33.5%<\/td><\/tr><tr><td>Target ROAS<\/td><td>64<\/td><td>15<\/td><td>0.959<\/td><td>0.765<\/td><td>1.463<\/td><td>56.3%<\/td><td>31.3%<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Source: Ivitskiy Ads Lab, smart_bidding_promise_gap_advertiser_level.csv, version 1.0. January-May 2026, campaigns with an active target and at least 5 conversions.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"\/blog\/wp-content\/uploads\/2026\/09\/vis-p16-gap-levels-en.png\" alt=\"tCPA: campaign level misses target by 20% or more in 33.5% of cases, advertiser level in 8.7%; on target 47.3% versus 60.9%\"\/><figcaption class=\"wp-element-caption\">Source: Ivitskiy Ads Lab, version 1.0.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">What matters here. In this sample (239 campaigns from 23 advertisers, January to May 2026) a typical target CPA campaign mostly keeps its word: the median is 1.029, meaning it overspends by less than three percent. But the variance is large. A quarter of the campaigns sit above 1.325, and 33.5% missed by 20% or more. On target ROAS the median is even below one at 0.959, meaning a typical campaign slightly overdelivers, yet the upper quartile of 1.463 shows the tail of misses is long there too.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Advertiser level: a softer picture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Now we collapse each advertiser&#8217;s campaigns into a single number: we take the median of their campaigns. It is important to understand this precisely. This is the median of the advertiser&#8217;s campaigns, not the total result of their portfolio. The dataset does not publish total spend and conversion figures, so you cannot calculate if the whole account is on target from this.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Strategy<\/th><th>Advertisers<\/th><th>Median<\/th><th>Q1<\/th><th>Q3<\/th><th>Median campaign on target<\/th><th>Missed by 20% or more<\/th><\/tr><\/thead><tbody><tr><td>Target CPA<\/td><td>23<\/td><td>0.977<\/td><td>0.757<\/td><td>1.113<\/td><td>60.9%<\/td><td>8.7%<\/td><\/tr><tr><td>Target ROAS<\/td><td>15<\/td><td>0.977<\/td><td>0.641<\/td><td>1.409<\/td><td>53.3%<\/td><td>33.3%<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">Source: Ivitskiy Ads Lab, smart_bidding_promise_gap.csv, version 1.0. January-May 2026, campaigns with an active target and at least 5 conversions.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">According to the same sample, on target CPA the median advertiser has a ratio of 0.977, meaning their typical campaign performs better than the goal. For 60.9% of advertisers the median campaign is on target or better, and only 8.7% have a median campaign missing by 20% or more. On target ROAS the picture is tougher: a third of advertisers have a median campaign with a major miss.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The main takeaway from the two tables<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Compare the two views. At the campaign level there are many misses: a third of target CPA campaigns are deep in the red. At the advertiser level the median is suddenly close to one. There is nothing strange here, and this is not a statistical trick. Within the same account one campaign overspends and overpays for conversions, while another underspends and gets cheaper results. The median smooths this out.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Hence the practical conclusion I repeat at every audit: you must look at the campaign, not the account average. The account average will keep you calm even when two out of five campaigns consistently overpay. The miss ratio is calculated for each campaign individually, and decisions are made on that exact level.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How stable are these numbers<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">An honest check I demand from any analytics, including my own. The medians of 1.029 and 0.959 are stable: remove any single advertiser from the sample, and they barely move. But the percentages are fragile. Both the miss by 20% or more and that 60.9% share rely on a few big clients. In 15 cells out of 20, the bulk of the spend comes from one major advertiser. So the sample structure affects the percentages more than I would prefer. This applies to this specific dataset, and I say this directly so you do not project the 33.5% onto the whole market.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Three reasons for misses from practice<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The data shows the fact of the miss but does not say why. Next comes my experience from audits, and I honestly separate it from the evidence: the numbers above prove the size of the issue, the explanations below are interpretation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Reason one: the target is unrealistic. A classic scenario: an owner looks at historical CPA, sets a much lower target to make the algorithm work harder, and gives no time for relearning. The algorithm is no wizard. It will not find conversions cheaper than the auction allows, it will just start choking impressions. The ratio will creep up, and Smart Bidding will not be the one to blame.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Reason two: the target changed mid-window. This sample does not contain target change history: the dataset holds a July 2026 snapshot of targets, so in the dataset I only see the current goal. If the advertiser moved the target three times over these five months, they created part of the miss themselves, and I only see the bottom line. This is not speculation, this is a direct data limitation, and it works both ways.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Reason three: the campaign was choked. A limited budget, overly narrow targeting, too few conversions in the window. The algorithm simply has nothing to learn from, it cannot calibrate itself and starts guessing. On the outside it looks like Smart Bidding missed the mark, but in reality Smart Bidding got no data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What to do: five steps<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Step one. Calculate the miss ratio for every campaign over a window of 30 days or more and at least 5 conversions. Fewer conversions mean the number will be random noise, not a signal. Write the ratios down in a spreadsheet, this will take half an hour for the whole account.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Step two. Check if the target was changed during the window. Go to the change history and look at what happened to this campaign. If the target was moved, the ratio reflects multiple goals instead of one, and it is too early to judge the algorithm. Recalculate on a clean window.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Step three. Assess whether the campaign has enough conversions to learn. If the campaign barely scrapes the minimum, a miss is almost guaranteed, and there is only one cure here: either expand the conversion event, consolidate campaigns, or honestly admit the strategy has no data to learn from.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Step four. If the target must be changed, move it by no more than 10-20% at a time and no more often than once every 2-3 weeks (a rule from our practice, not a number from the sample) at a time. Sharp jumps reset the learning phase, and you will be waiting for the algorithm to calibrate again. Three small steps are better than one big leap.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Step five. Do not judge a campaign in the first two weeks after any change. The algorithm is relearning during this period, and the ratio will bounce around. Looking at it daily during this time means making decisions on noise.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you have no time to dig into this or want a second opinion, this is exactly how I work with my students. We look at the account for 15-20 minutes and point out which campaigns miss the mark, where the goal is unrealistic, and where the algorithm simply lacks data. Just clear diagnostics based on numbers.<\/p>\n\n\n<!-- cta-anchor -->\n\n\n<h2 class=\"wp-block-heading\">Metric limitations, honestly<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Three things this analysis cannot do, and which you must know before changing anything in your account.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First. Target history is not present in this dataset. I only see the final goal at the moment of export. All misses caused by target changes mid-window are mixed with genuine algorithm misses, and it is impossible to separate them in the data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to the Ivitskiy Ads Lab panel, second. The metric does not separate an algorithm failure from an unrealistic target. A 1.325 ratio could mean the algorithm failed or the goal was pulled from thin air. The number is identical, the diagnosis is the opposite. That is exactly why the steps above start with checking history, not changing the strategy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Third. Campaigns with fewer than 5 conversions were excluded from the sample entirely. And these are often the most problematic campaigns that cannot gather enough data. This means failure cases are underrepresented in the sample, and the real picture on misses could be worse than the tables show.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">When this does not apply<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">There are situations where calculating the miss ratio makes no sense at all, and I do not want you to waste time on them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If conversions are tracked incorrectly: duplicates, test leads, broken imports. The ratio will be calculated honestly, but based on garbage input. Fix analytics first, then strategy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If the campaign is younger than 30 days. It is still learning, and any ratio during this period is a snapshot of the process, not a result. If the account has fewer than 5 conversions per campaign in the window, dividing actual by target is pointless: a single conversion skews the number completely.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">And if goals are adjusted for seasonal promos: you raised the target for a sale, lowered it after. Over a multi-month window you will get a mix of different targets, and no conclusion from it will be honest. In such accounts, calculate separately for each period with a stable goal.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Everything I showed here is one file from one sample. If you want to see the raw data, other slices, and calculation methods, here are the <a href=\"https:\/\/ivitskiy.com\/blog\/en\/google-ads-benchmarks-2026\/\">full benchmark tables and CSV<\/a>. Calculate your miss ratio and compare. It will be the most useful half hour you spend on your account this week.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I analyzed 303 campaigns from January-May 2026 to see if Smart Bidding hits tCPA and tROAS targets. 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