{"id":4948,"date":"2026-08-09T01:19:27","date_gmt":"2026-08-08T22:19:27","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/hero-conf-2025\/"},"modified":"2026-08-09T01:19:27","modified_gmt":"2026-08-08T22:19:27","slug":"hero-conf-2025","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/hero-conf-2025\/","title":{"rendered":"A Scientist’s Approach to PPC Optimisation: Igor Ivitskiy at Hero Conf 2025"},"content":{"rendered":"\n
In short:<\/strong> PPC benchmarks and platform scores can look authoritative while hiding the context that produced them. At Hero Conf 2025, Igor Ivitskiy proposed three durable principles: test decisions on your own data, choose values by profit, and segment averages before judging performance. In one case, segmentation turned an apparently unprofitable campaign with 85% ROAS into a profitable slice with 290% ROAS.<\/div>\n\n\n\n

Hero Conf<\/a> took place in Brighton, United Kingdom, in April 2025, with this session delivered as a conference talk. The speaker was Igor Ivitskiy<\/a>, a former mathematical-modelling scientist who moved into PPC and Google Ads.<\/p>\n\n\n\n

\u201cA Scientist\u2019s Approach to PPC Optimisation\u201d<\/strong> was about replacing borrowed best practices with account-level evidence. The talk connected scientific reasoning with practical PPC questions, from keyword research<\/a> to bidding targets and campaign segmentation, without treating one account\u2019s answer as a universal rule.<\/p>\n\n\n\n

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Overall campaign<\/strong><\/div>\n
<\/div><\/div>\n
85% ROAS<\/div>\n<\/div>\n
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Profitable segment<\/strong><\/div>\n
<\/div><\/div>\n
290% ROAS<\/div>\n<\/div>\n<\/div>\n
Source: Hero Conf 2025 talk, Igor Ivitskiy.<\/div>\n\n\n\n

A scientific answer is local, not universal<\/h2>\n\n\n\n
\"Averaged
Averaged campaign metric next to the same data split into segments<\/figcaption><\/figure>\n\n\n\n

The talk began by challenging a familiar kind of slide: a large cross-account data set distilled into one recommended number. An average can mix countries, niches, business models, devices and price bands. Correlation can then be mistaken for causation, while the variables below the surface remain invisible. A benchmark may describe the sample and still fail to answer the decision facing one advertiser.<\/p>\n\n\n\n

That distinction also applies to platform guidance. Google describes optimisation score<\/a> as an estimate of how well an account is set to perform. Igor\u2019s case showed why the score still needs a business test: after recommendations were applied, the account displayed a stronger score, but CPA was almost twice as high and ROAS almost twice as low. A platform signal and a commercial result are different measurements.<\/p>\n\n\n\n

The alternative is not to reject automation or broad match<\/a> by default. Google\u2019s own explanation of Ad Strength<\/a> presents it as feedback about an ad\u2019s relevance, quality and diversity. The scientific stance is to treat that feedback as a hypothesis, then judge it against the account\u2019s real profit. Igor had seen low-strength ads perform efficiently and found no dependable causal link between raising Ad Strength and raising business performance.<\/p>\n\n\n\n

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1<\/span>Decisions<\/strong>
Binary choices should be resolved with evidence from the specific account, business and location.<\/div>\n
2<\/span>Values<\/strong>
The useful target is the one associated with the strongest profit, not the most comfortable-looking metric.<\/div>\n
3<\/span>Segments<\/strong>
An unprofitable total may contain devices, locations or audiences with very different economics.<\/div>\n<\/div>\n
Source: the three principles presented at Hero Conf 2025 by Igor Ivitskiy.<\/div>\n\n\n\n

My standard for a useful PPC answer<\/strong><\/p>

I do not accept a setting because it is popular or because a platform rewards it with a stronger score. I look for the answer in the project\u2019s own data and judge it by business profit.<\/p><\/section>\n\n\n\n

This way of thinking also changes how practitioners approach negative keywords<\/a>, bidding and campaign structure: each is a decision to validate, not a ritual to copy. The same evidence-first discipline is part of how our school teaches Google Ads.<\/p>\n\n\n\n\n\n\n\n

Key insights<\/h2>\n\n\n\n