{"id":4956,"date":"2026-08-09T01:25:26","date_gmt":"2026-08-08T22:25:26","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/ecomteam-2025\/"},"modified":"2026-08-09T01:25:26","modified_gmt":"2026-08-08T22:25:26","slug":"ecomteam-2025","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/ecomteam-2025\/","title":{"rendered":"Google Ads Secrets: A Scientist’s Approach to PPC Optimization at ecomTEAM 2025"},"content":{"rendered":"\n
In short:<\/strong> At ecomTEAM, Igor Ivitskiy argued that the most useful advertising evidence often sits inside rows teams dismiss as weak, unprofitable or irrelevant. He grouped this hidden evidence into three areas: search terms, the product feed and audience data. The point was not to add another optimization tactic, but to ask what an awkward metric may reveal about demand, merchandising or the customer journey. That shift turns an ad report into a source of business questions.<\/div>\n\n\n\n

ecomTEAM 2025<\/a> took place in Brasov, Romania, on September 18-19, 2025; this was an in-person conference talk on September 18. The speaker was Igor Ivitskiy<\/a>, a scientist and advertising practitioner who has applied mathematical modelling to PPC for 18 years.<\/p>\n\n\n\n

\u201cGoogle Ads Secrets: A Scientist’s Approach to PPC Optimization\u201d<\/strong> was about the overlooked side of familiar reports. Instead of treating every weak row as something to remove, Igor connected search and keyword evidence<\/a> with product range, page relevance, basket value and long-term customer behaviour.<\/p>\n\n\n\n

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1<\/span>Search terms<\/strong>
Missed demand, intent mismatches and low-value orders can sit behind apparently weak rows.<\/div>\n
2<\/span>Product feed<\/strong>
A product can introduce, retain or reconnect a customer even when its direct return looks poor.<\/div>\n
3<\/span>Audience data<\/strong>
Search behaviour and geography can reveal purchasing context that broad averages conceal.<\/div>\n<\/div>\n
Source: ecomTEAM 2025 talk, Igor Ivitskiy.<\/div>\n\n\n\n

The useful data is often the data that looks wrong<\/h2>\n\n\n\n
E-commerce funnel with a hidden signal under a magnifier<\/figcaption><\/figure>\n\n\n\n

Advertising dashboards encourage a narrow verdict: scale what converts and exclude what does not. The talk challenged that reflex. An impression without a click may point to demand for a product the store does not carry. A click without a sale may expose a mismatch in price, terms, colour, size or page content. A low-value order may say more about cross-selling than about the query that brought the visitor.<\/p>\n\n\n\n

The same logic changes how a retailer reads its catalogue. Google explains that Shopping and Performance Max campaigns use product data such as images, prices and descriptions<\/a> to match products with searches. That makes the feed more than an ad input. It is also a map of how the assortment attracts attention, creates repeat demand and keeps shoppers from leaving for a competitor.<\/p>\n\n\n\n

This is why the talk sits above debates about broad match<\/a> or any single campaign type. The underlying principle is to separate an observed metric from the business explanation behind it. One number can support several stories, so the next useful move is often a better question, not an automatic exclusion.<\/p>\n\n\n\n

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1<\/span>Visible signal<\/strong>
A weak click, conversion or product-level return appears in the report.<\/div>\n
2<\/span>Business question<\/strong>
Is the cause demand, offer fit, merchandising, retention or audience context?<\/div>\n
3<\/span>Decision layer<\/strong>
The answer may belong to advertising, the website, the product range or the commercial model.<\/div>\n<\/div>\n
Source: conceptual model from the ecomTEAM 2025 talk.<\/div>\n\n\n\n

My principle for reading weak traffic<\/strong><\/p>

I do not treat a poor row as a verdict. Even when negative keywords<\/a> are relevant, I first ask whether the data is describing an advertising problem or exposing a gap elsewhere in the business.<\/p><\/section>\n\n\n\n

This habit of connecting campaign data with the wider business is also part of how we teach Google Ads: the report is evidence, while the decision depends on context. The training goes deeper into the working process without turning one conference example into a universal recipe.<\/p>\n\n\n\n\n\n\n\n

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