Using Claude for Google Ads optimisation without believing in algorithm magic

AI as a shortcut sounds appealing, but Google Ads does not work that way

Anyone running an e-commerce brand and depending on Google Ads knows the temptation: there must be a smarter way to make the system do what you want. Especially with Performance Max, broad match campaigns, and automated bidding strategies, it can feel as though you are talking to a black box. You see costs, clicks, conversions, and ROAS, but you do not always see why certain products, search intents, or audiences suddenly win or lose.

That is why a promise like 'hacking the algorithm with Claude' sounds attractive. It implies that a well-crafted prompt is enough to scale faster, find better signals, or surface hidden patterns. In practice, that is an oversimplification. Google Ads is not a simple system you can crack with a single AI response. The auction, bidding strategy, competition, conversion data, product feed, landing page, and margin context continuously influence one another.

The useful approach is more grounded: Claude cannot rescue an ad account that receives poor input, but it can help you think more clearly. Do not treat Claude as a button for instant scale, but as a thinking partner that turns messy information into better questions, sharper hypotheses, and more structured experiments. That distinction is crucial. Using AI as a substitute for expertise increases the risk of arbitrary changes. Using AI as an additional analytical layer can instead bring calm and discipline.

Where Claude genuinely adds value in a Google Ads account

The first value Claude offers is in formulating better diagnostic questions. Many Google Ads problems are translated too quickly into tactical actions: raise the budget, adjust the bidding strategy, split an asset group, pause keywords, or test new creatives. Sometimes that is justified, but often the underlying question has not yet been clarified. Is the problem volume, margin, tracking, product-market fit, inventory, pricing position, landing page, search intent, or campaign structure?

Claude can help by organising available information. Think of an export of campaigns, product categories, conversion goals, average order value, margin classes, search terms, or feed fields. You do not need to share everything that is sensitive; you can often use anonymised or summarised data. The question to Claude is then not: 'What should I do?' The better question is: 'What possible explanations fit these differences, what assumptions are you making, and what data is missing to assess this more confidently?'

A second application is clustering. E-commerce accounts often contain tens to thousands of products. Not every product deserves the same advertising treatment. Claude can help organise product groups according to commercial logic: margin, price level, repeat purchase rate, seasonality, inventory sensitivity, return risk, or positioning. That does not mean Claude determines the perfect campaign structure, but it does mean you surface discussion points more quickly.

A third application is prioritisation. Google Ads specialists usually have more test ideas than time, budget, and data volume. Claude can rank test ideas by potential impact, risk, data requirements, and complexity. This prevents the team from jumping to the most interesting test when a mundane tracking or feed check is actually more urgent.

Where Claude becomes dangerous

Claude becomes dangerous when it is treated as an authority without account context. An AI model can produce convincingly worded advice that is roughly logical for many accounts: improve your landing page, refine your feed, test new assets, segment campaigns, check conversions. That can be useful as a checklist, but it is not evidence that this is the primary lever in your account.

The greatest risk is false certainty. If you give Claude an incomplete export, you receive an answer based on incomplete input. If margin data, conversion value settings, return information, inventory status, or differences between new and returning customers are missing, the advice can turn out to be commercially wrong. A product with high revenue but low margin can look attractive in ad data while the business earns little from it.

Another risk is change drift. AI makes it easy to generate twenty optimisation ideas in a single session. If you implement them all at once, you will not know later which change had an effect. Especially in accounts with automated bidding strategies, this can introduce noise. You change the feed, budgets, assets, bidding strategy, and landing page simultaneously, after which results shift. But you learn almost nothing, because the cause can no longer be isolated.

Claude also cannot predict black-box auction dynamics as though it has direct access to all signals. It can identify patterns, articulate assumptions, and sharpen test questions. It cannot guarantee which competitor bids at which moment, how the system assigns value to an impression, or what effect a change will have tomorrow. Respecting that boundary leads to better use of AI.

A practical framework for e-commerce Google Ads

Start with data minimalism: collect only data you can interpret and that is relevant to the decision at hand. For an e-commerce account, that typically means campaign structure, product categories, conversion goals, revenue value, margin indication, search terms where available, feed fields, inventory status, and landing pages. The goal is not to give Claude everything, but to provide enough context for a better analysis.

Then ask for hypotheses, not solutions. A strong prompt forces Claude to make uncertainty visible: 'Give three possible explanations for these performance differences. For each explanation, state what assumptions you are making, what data is missing, and what check I should run first.' This prevents the response from being treated as a final decision. You use it as analytical groundwork that a specialist then validates.

Translate the best hypotheses into small experiments. An experiment needs at minimum a hypothesis, a measurement point, an expected direction, a duration, a stopping criterion, and a risk assessment. For example: 'If we clarify product titles in this category around material and application, traffic relevance will increase and conversion value per click will improve.' That is different from: 'Optimise the feed.' The first is testable; the second is too vague.

Use Claude again for post-test analysis, but let the conclusion depend on your own data. You can ask Claude to summarise results, name alternative explanations, and formulate follow-up questions. Always check whether the measurement period, conversion delay, budget changes, and external factors could distort the outcome. AI accelerates the analysis but does not take over the responsibility.

Prompt categories that are genuinely useful

Work with prompt categories rather than magic prompts. A diagnostic prompt might be: 'What possible causes explain these differences between product categories, and what data is missing to confirm or rule out those causes?' This forces the model to produce not a single answer but an analytical map.

A prioritisation prompt might be: 'Rank these test ideas by potential impact, risk, complexity, and data requirements. For each idea, explain why it scores high or low.' This is especially helpful when a team is stuck in a loop of individual opinions. The output is not absolute truth, but it is a structured starting point for decision-making.

A feed and positioning prompt might be: 'What product information might be missing to better align the advertising promise with the landing page?' This is valuable because many Google Ads results do not hinge solely on bids. Product title, description, image, price, inventory, shipping information, and promise all influence traffic quality and the likelihood of conversion.

An experiment prompt might be: 'Turn this hypothesis into a test plan with a measurement point, minimum duration, risks, stopping criterion, and possible follow-up action.' This transforms an AI response from non-committal inspiration into a workable plan. For Google Ads teams, that translation is precisely what matters: from idea to controlled execution.

The foundation matters more than the tool

The biggest mistake is thinking that Claude makes the foundation less important. The opposite is true. The better your tracking, feed quality, margin information, and campaign structure, the more useful AI becomes. Poor input produces mostly well-worded uncertainty. Good input makes it possible to spot patterns faster and ask better questions.

For e-commerce, this means conversion goals must be clear. Are you measuring only purchases, or also micro-conversions? Are you optimising for revenue value, profit contribution, or volume? Are returns relevant? Do certain categories have lower margins or longer decision cycles? Without that context, an optimisation that looks good in Google Ads can still be commercially wrong.

The landing page must not be disconnected from ad analysis either. When traffic becomes more expensive or conversion lags, it is tempting to tinker only within Google Ads. Claude can help by placing the advertising promise, product information, and landing page side by side. Does the promise hold up? Is the pricing context clear? Does the category page match the search intent? Are objections such as delivery time, warranty, or return policy sufficiently visible?

For anyone working systematically on funnels and measurable growth, this is the core: Google Ads is not a standalone lever, but part of a commercial chain. Ad, feed, website, conversion tracking, follow-up, and margin must be assessed together. More background on that broader approach can be found in the knowledge base for funnel and advertising insights.

Conclusion: Claude accelerates thinking, not responsibility

Claude can be valuable for Google Ads specialists and e-commerce teams who already know which inputs matter. It helps you form hypotheses faster, organise test ideas, identify blind spots, and make analyses more readable. But it does not hack the algorithm. It does not replace conversion tracking, product knowledge, margin analysis, or a careful experimentation process.

The best way to use Claude is therefore modest and practical. Provide relevant context, ask for assumptions, have missing data named explicitly, and translate the output into small tests with clear criteria. That way AI becomes not a source of impulsive changes, but a tool for working more professionally.

For Pascal Bouman and Funnel Adviseur, AI in Google Ads fits primarily within a broader principle: make decisions more traceable. If you know why you are running a test, what you expect, when you will stop, and how you will evaluate the result, scaling becomes less dependent on gut feeling. Claude can accelerate that process. The responsibility remains with the specialist and with the commercial reality behind the account.