Stop Manual Bid Tuning: Use Claude to Scale Smarter Ad Spend

Audit Your Current Ad Accounts with Claude

Start by using Claude as a performance analyst for your existing campaigns. Export key metrics (spend, clicks, CPC, conversions, revenue, ROAS, CPA) by campaign, ad group, keyword, and audience for a recent period. Clean any sensitive identifiers and share the data with Claude in CSV or table form.

Prompt Claude to identify where bids and budgets are misaligned with your goals. Ask it to find campaigns with strong ROAS but limited budgets, segments with high spend but weak performance, and areas where you’re paying too much for low-intent clicks. This gives you a concrete baseline of “quick-win” reallocations before you even touch automation settings.

Example prompt for a Claude account audit:
You are a senior performance marketing analyst.

Goal:
- Improve overall ROAS while keeping total spend stable.
- Identify where bids and budgets are misaligned with performance.

Inputs:
- Here is a CSV extract with campaign, ad_group, audience, spend, clicks,
  cpc, conversions, revenue, roas, cpa for the last 30 days.

Tasks:
1) Summarise the top 10 biggest budget misallocations.
2) Flag campaigns/ad groups that deserve more budget based on ROAS and volume.
3) Flag areas where we should reduce bids or budgets.
4) Propose 3–5 concrete reallocation actions with estimated impact.

Expected outcome: a prioritised list of reallocation opportunities, usually revealing 5–15% of spend that can be quickly redirected without increasing risk.

Use Claude to Design Clear Bid Strategies Per Channel

Next, move from observations to codified bid strategies. For each major channel (e.g. Google Ads, Meta, LinkedIn), feed Claude your KPIs, business constraints, and historical patterns. Ask it to recommend which bidding strategies (tROAS, tCPA, Maximise Conversions, etc.) and target levels best fit each campaign type (brand, generic, competitor, remarketing).

Include examples of past changes you’ve made and their impact so Claude can learn from your context. Have it return a structured playbook describing when to switch strategies, how to set initial targets, and how to adjust them over time. This document becomes the backbone of your paid media governance.

Example prompt for bid strategy design:
You are helping us redesign our bidding strategy.

Context:
- Business goal: maximise profitable revenue at a blended ROAS of 4.0.
- Constraints: do not exceed a blended CPA of 60 EUR.
- Here is a summary of performance by campaign type and channel.

Tasks:
1) Propose recommended bidding strategies for each campaign type.
2) Suggest initial target ROAS/CPA ranges.
3) Describe when we should switch a campaign to a different strategy.
4) Provide a short, easy-to-follow playbook structure our team can adopt.

Expected outcome: a channel- and campaign-specific bidding playbook that your marketers can reference when configuring or scaling campaigns.

Generate Budget Reallocation Scenarios with Claude

Once you have a baseline and bid strategy, use Claude to build budget reallocation scenarios. Provide aggregated data by campaign or audience and specify the total budget you can move. Ask Claude to simulate different distributions, such as concentrating spend on top performers vs. spreading it more evenly, and to estimate the impact on volume and efficiency.

Make Claude explain its assumptions explicitly (e.g. linear vs. non-linear returns, expected saturation) and suggest KPIs to monitor during live tests. You can then implement one of the proposed scenarios in your ad platforms as a controlled experiment.

Example prompt for budget scenarios:
You are a marketing finance copilot.

Goal:
- Reallocate 20% of our current paid search budget without increasing risk.

Inputs:
- Table with campaign, current_budget, conversions, revenue, roas, cpa.

Tasks:
1) Create three budget reallocation scenarios:
   a) Aggressive growth
   b) Balanced
   c) Risk-averse
2) For each scenario, show new budgets by campaign and expected impact
   on total conversions, revenue, ROAS, and CPA.
3) Recommend which scenario we should test first and why.

Expected outcome: 2–3 realistic alternative budget plans with clear trade-offs that can be A/B tested or sequentially trialed in your ad accounts.

Codify Guardrails and Alert Rules with Claude

Use Claude to convert fuzzy risk concerns into explicit guardrails and alert rules. Share examples of days or weeks where performance deteriorated and ask Claude to identify early signals that could have triggered intervention: sudden CPC jumps, conversion rate drops, changes in device mix, or anomalies in specific audiences.

Then have Claude draft monitoring rules you can implement using platform scripts, BI dashboards, or simple spreadsheet checks. It should specify exact thresholds, lookback windows, and recommended actions (pause, cap budget, or tighten targets).

Example prompt for guardrail design:
You are helping us design risk controls for our ad accounts.

Inputs:
- Examples of dates and campaigns with very poor performance.
- Our current risk appetite: we accept up to 10% daily variance in ROAS.

Tasks:
1) Identify early warning signals that typically precede bad days.
2) Propose concrete monitoring rules with numeric thresholds.
3) For each rule, specify the automatic or manual action to take.
4) Output the rules in a table we can easily hand over to our ops team.

Expected outcome: a practical set of risk controls that can be implemented as scripts or checks, reducing the downside risk of increased automation.

Turn Claude’s Insights into Repeatable Weekly Workflows

To move beyond one-off audits, embed Claude into your weekly optimization rituals. Standardise prompts and inputs so every week your team runs the same analyses: top budget misallocations, campaigns eligible for more investment, underperformers to fix or pause, and potential test ideas.

Store your best prompts in a shared library and refine them based on experience. Integrate Claude outputs directly into your existing planning formats (e.g. weekly performance docs, sprint boards), so the AI’s recommendations translate into concrete tasks with owners and deadlines.

Example weekly workflow prompt:
You are our weekly paid media optimization assistant.

Goal this week:
- Maintain ROAS >= 3.8 while testing growth opportunities.

Inputs:
- Updated performance extract (last 7 and last 30 days).
- Last week's action log (changes we made).

Tasks:
1) Summarise key trends and anomalies.
2) List the top 10 actions we should take this week, labelled as
   "Scale", "Fix", or "Test".
3) For each action, estimate potential impact and urgency.
4) Flag any risks or data quality issues you see.

Expected outcome: a consistent, AI-supported optimisation routine that can reduce manual analysis time by 30–50% while making budget and bid decisions more data-driven and less ad hoc.

Use a PoC to Validate the Approach Before Full Rollout

Before rebuilding your entire marketing operating model, run a contained AI proof of concept. Select one or two key channels and a limited number of campaigns. With Reruption’s 9.900€ AI PoC approach, you can define a narrow scope (e.g. “use Claude to optimise budget allocation and bid strategy for our core search and remarketing campaigns”), prototype the workflows above, and measure impact on ROAS and team effort.

Within a few weeks, you’ll know if the Claude-based governance and decision-support layer works in your environment, what integration steps are needed, and how to scale it responsibly. Many organisations see 5–15% efficiency improvements and a noticeable reduction in “spreadsheet time” even at this early stage.

Expected outcomes: clearer bid and budget strategies, 5–15% better efficiency on targeted campaigns, and 30–50% less manual analysis time for the scoped area, with a concrete roadmap for scaling the approach across channels.