Stop Manual Bid Tuning: Use Gemini to Automate Ad Spend Optimization

Connect Gemini to Your Google Ads & SA360 Data with Clear Scopes

To use Gemini for bid and budget optimization, start by defining which Google Ads and SA360 views it should work with. In practice, this means preparing exportable reports or connecting via approved integrations so Gemini can see performance by campaign, ad group, keyword/audience, device, and geography.

When working in Gemini Advanced or within Google’s ecosystem, give it precise context in your prompts: time ranges, currency, main KPIs, and which campaigns are in-scope. This helps the model focus and avoids generic advice. A simple but powerful move is to maintain a standard “account briefing” prompt you reuse whenever you start a new optimization session.

Example prompt to brief Gemini on your account:
You are an AI performance marketing analyst.
You have access to Google Ads and SA360 export data for the last 30 days.
KPIs:
- Primary: ROAS (revenue/ad spend)
- Secondary: CPA and conversion volume
Scope:
- Include only campaigns with > 50 conversions last 30 days
- Focus on Search and Performance Max campaigns in Germany
Task:
Summarize key performance patterns and identify which campaigns or audiences
look overfunded (low ROAS) or underfunded (high ROAS, limited spend).

This briefing becomes the foundation for more advanced requests, such as bid strategy changes and budget reallocations.

Use Gemini to Diagnose Underperforming Segments Before Changing Bids

Instead of immediately editing bids when performance drops, use Gemini as a diagnostic engine. Feed it segmented data (by query category, audience list, device, time-of-day) and ask it to isolate where value is created or destroyed. The goal is to separate structural issues (wrong targeting, broken tracking, poor landing pages) from pure bid and budget questions.

Example diagnostic prompt:
You are analyzing a performance marketing account.
I will paste performance data from SA360 segmented by campaign, device,
audience, and hour of day for the last 14 days.
Task:
1) Identify segments with high spend and ROAS < 200%.
2) Identify segments with ROAS > 400% but limited impressions or impression share.
3) For each segment, recommend whether we should:
   - Reduce bids
   - Increase bids
   - Shift budget
   - Pause or restructure the segment
Return your answer as plain language recommendations grouped by priority.

By forcing Gemini to look for patterns before suggesting actions, you reduce knee-jerk bid cuts and ensure you tackle root causes, not just symptoms.

Generate Concrete Bid Strategy and Target Proposals

Once you understand where performance is strong or weak, ask Gemini to translate insights into specific bid strategy changes. This can include shifting from manual CPC to target ROAS, adjusting existing target ROAS values, or defining bid modifiers for devices, locations, or audiences. The key is to request actionable, parameter-level recommendations that can be implemented directly in Google Ads or SA360.

Example prompt to get concrete bid targets:
Based on the following campaign performance summary (pasted below),
propose specific bid strategy and target changes.
Constraints:
- Keep total daily budget constant at first.
- Preferred strategies: target ROAS for revenue-driving campaigns,
  target CPA for lead-gen campaigns.
- We accept a maximum 10% increase in CPA short-term if it unlocks
  at least 20% more conversion volume.
Output format:
- For each campaign: current strategy, recommended new strategy,
  recommended target (ROAS or CPA), and rationale in 2-3 sentences.

Review Gemini’s output, challenge it with follow-up questions (e.g., “What if we are more conservative on CPA?”), and then apply the chosen changes to your campaigns in a controlled test.

Run Controlled Budget Reallocation Experiments with What-If Scenarios

Use Gemini to run what-if simulations before you move significant budget. Provide recent performance metrics and ask Gemini to estimate the impact of reallocating spend under different assumptions (e.g., diminishing returns curves, seasonality). While these are not perfect forecasts, they are far more informative than guessing in a spreadsheet.

Example what-if scenario prompt:
You are modeling budget reallocation scenarios.
Data:
- Campaign A: 500€ daily, ROAS 180%
- Campaign B: 300€ daily, ROAS 420%
- Campaign C: 200€ daily, ROAS 350%
Assume:
- Campaigns with ROAS > 300% can scale up to 50% more budget with
  a 10-20% decline in ROAS.
Task:
1) Propose 3 budget allocation scenarios to maximize total revenue
   without increasing total spend.
2) Estimate expected ROAS and revenue for each scenario.
3) Recommend one scenario and explain the trade-offs.

Take the recommended scenario and implement it as an experiment or draft campaign, not as an immediate account-wide change. Track impact over 1–2 weeks before rolling out more broadly.

Codify Gemini’s Recommendations into Repeatable Playbooks

To avoid “AI theatre” — cool analyses that never become process — turn recurring Gemini workflows for bid tuning into playbooks. Define who runs them, how often, what input data is needed, which prompts to use, and how decisions are documented. This makes the process reliable and auditable.

For example, you might define a weekly routine: a performance marketer exports key SA360 views, runs a standard set of Gemini prompts to identify underperforming and underfunded segments, challenges the suggestions in a short review meeting, and then implements approved changes with clear change logs. Store the prompts and example outputs in an internal knowledge base so others can replicate and refine them.

Track the Right KPIs to Validate AI-Driven Optimization

To assess whether Gemini-powered bid and budget adjustments are working, track a focused set of KPIs over time. Beyond ROAS and CPA, watch volatility (day-to-day swings), impression share for high-performing segments, and the ratio of time spent on manual edits versus strategic work.

A realistic expectation after 6–8 weeks of disciplined use is not a miraculous doubling of performance, but tangible improvements such as 10–20% reduction in wasted spend on low-ROAS segments, 5–15% increased conversion volume at stable CPA/ROAS, and a significant drop in time spent on manual bid tinkering. Capture these metrics before and after implementation to build a concrete business case for scaling AI-driven optimization across more markets and channels.

Expected outcome: with a solid structure and repeatable workflows, marketing teams typically see smoother performance curves, faster reaction to market changes, and more time freed up for creative and strategic initiatives — all while maintaining or improving ROAS.