How Alien Road Uses Reasoning Models in Client Campaigns

A standard model predicts an answer token by token in one pass. A reasoning model is trained to spend extra compute on intermediate steps, often called a chain of thought, before giving its final answer. Some products show a summary of that thinking; others hide it.

Examples at the time of writing include OpenAI’s o-series and GPT-5 thinking modes, Anthropic’s Claude models with extended thinking, Google’s Gemini thinking models and DeepSeek R1. The trade-off is consistent across vendors: better answers on hard problems, slower responses and higher cost per request.

The useful test is whether a task has several constraints that interact. If a human expert would need a notepad to work it out, a reasoning model is likely to help.

• Auditing a Google Ads account export to find wasted spend across campaigns, match types and search terms
• Building three budget scenarios under different cost-per-click assumptions
• Comparing landing page copy against a search query list and finding gaps in intent
• Reading a long competitor report and pulling out claims that need checking
• Planning a content calendar that balances topics, seasons and internal link targets

For a single ad headline or a social caption, the extra thinking adds little. A fast model gives a comparable result in a second and at a fraction of the cost.

They can reason well from wrong inputs. If the export is incomplete or mislabeled, the model will build a clean argument on a broken base. Data checks come first.

They can also over-think simple questions and produce long answers that hide the key point. A clear output format, such as a table with fixed columns, keeps responses usable.

• Numbers: the model may miscalculate; ask it to show the working or use a code tool for math
• Freshness: it does not know this week’s platform changes unless you provide them
• Confidence: a well-argued answer is not proof; check claims against the data
• Cost: long thinking on every request adds up fast at scale

Many APIs let you set how much the model thinks, through settings like reasoning effort or a thinking token budget. Low effort is often enough for medium tasks. High effort makes sense for one-off audits where quality matters more than speed.

We test the same task at two effort levels and compare the outputs. If the higher level does not change the recommendations, we keep the lower one. This small step often cuts cost on recurring analyses without losing quality.

Clients see faster audits and broader analysis without paying for more strategist hours on routine number crunching. Senior staff spend their time on judgment, creative direction and client conversations.

• Account audits that once took days can be drafted in hours, then reviewed
• Scenario planning covers more options before a budget meeting
• Research summaries arrive with source references a person can check

Reasoning models work best inside a chain, not alone. Data comes from platform exports or APIs, a script cleans it, the model analyzes it, and a person approves the result. Platform tools such as Google Ads recommendations or Performance Max insights still have a place; the model helps interpret them against the client’s own goals rather than the platform’s.

We also pair reasoning models with code execution where the provider supports it. When the model can run a short Python calculation on the export, totals and percentages come out right far more often than when it does mental math in text. This one setting removes a common source of error in budget work.

• Exports and APIs supply the facts
• Scripts clean and anonymize the data
• The reasoning model drafts analysis and options
• A strategist approves, edits or rejects each item
• The account change log records what was done

For writing tasks that follow the analysis, such as new ad copy for a theme the audit found, we hand off to a faster model with a tight brief. That keeps each model on the job it does best.