AI in PPC 2025 Eric Bush on Paid Search at Brafton

Recommendation: lean into automated bidding and test-driven AI for paid search, while maintaining accurate measurement and human review.

Eric Bush presents a focused Brafton studio approach for 2025, pairing AI with hands-on signals to keep campaigns tight. He outlines practical guidance and a clear list of steps that teams should follow across platforms, campaigns, and ad groups, a kind guardrail to anchor decisions in data.

In a controlled test across 12 campaigns, automated bids produced a 14% lift in CTR, an 11% drop in CPC, and a 9% reduction in CPA. ROAS rose 19% when signals aligned with metas and patterns. Marketers should test iterative changes and confirm accuracy in data feeds to keep gains predictable.

Allocation guidance: start with 40% automated in the first four weeks, 60% manual, then shift to 55/45 if the campaign hit target CPA. For campaigns with high search intent, push to 70/30 in favor of automation after two sprint cycles. This approach yields consistent gains while preserving control across campaigns.

Platform mix: core search across platforms accounts for 80% of revenue, plus 15% on shopping and 5% on discovery networks; exclude low-margin terms and low-volume keywords to protect budgets. Use omniseo insights to refine bid strategies and metas, aligning with campaign goals.

Pros include speed, consistency, and reliable signal adapting. A focused list of test ideas stays in the studio and drives results. Apply A/B tests on ad copy and landing pages, track win-rate per campaign, and maintain a cadence of reviews to keep campaigns good.

Closing note: Eric Bush’s take for 2025 is to view automation as a tool that accelerates work, not replaces judgment. With accurate data, a focused set of steps, and a disciplined test program, teams can improve performance across campaigns and platforms, leveraging omniseo outputs and Brafton Studio insights.

Real-Time Bid Optimization with AI Signals

Set up an AI engine that automates bids in real time to hundreds of signals across device, location, time, and intent, adjusting bids within seconds to protect costs while elevating the whole campaign. Certain shifts appear in the data, guiding the pattern and helping you react faster than competitors. When the data shows a new pattern, as signals appear, adjust bids accordingly; remove manual tweaks that slow progress. creating a governance layer using chatgpt-powered rules shows what changed, why, and how to copy successful settings to others ad groups. Focus on your usps and product so bids target the reason customers choose you, ahead of competitors. Exclude low-intent queries, and keep a real-time scorecard that show headline metrics like clicks, conversions, and quality scores, so you can tune bids by campaign and engine performance. thats why this approach improves entire results.

Signals that matter

Identify signals that reliably predict conversions: intent, match type, device, location, time, and ad position. Bind them to dynamic weights that update every 60 seconds, and apply hundreds of adjustments across the whole account. Use a headline KPI to judge impact, such as Cost per acquisition or ROAS, and copy top-performing variants into the engine using chatgpt to craft copy. Align messaging with usps and product so each bid supports the reason customers choose your brand, ahead of competitors. Exclude non-converters and signals with negative lift; this keeps the engine efficient while reducing costs. In practice, expect a measurable uplift in campaign performance within days, with clearer visibility into why changes happened.

AI-Generated Ad Copy: Guardrails, QA, and Brand Consistency

Set guardrails for AI-generated ad copy upfront and lock them into the design document that guides all campaigns. This design serves as the источник for tone, claims, and imagery, so others take input through feedback loops and stay aligned as copy scales across pages and platforms.

Before publishing, implement real-time QA checks: a robust guardrail system brings clarity to copy decisions. Pull data from internal sources, compare claims against verified data, confirm landing-page copy aligns with the ad text, and monitor changes to avoid misalignment across millions of impressions.

Modeling, together with analyzes, helps forecast risk and keep brand voice consistent. Run variants against a standard rubric to ensure headlines, descriptions, and images stay on-brand across campaigns.

Guardrail implementation includes stape 1: tone guard; stape 2: factual checks; stape 3: image and claim consistency. Each stape ties to a policy: claims verified against the источник; visuals adhere to brand guidelines; all copy references the official asset library.

Track outcomes with a centralized dashboard that blends creative design data with performance signals. Compare changes in click-through rate, landing-page coherence, and conversion metrics while preserving brand consistency across millions of pages and campaigns.

Use a mentor-driven loop: human reviewers provide real-time feedback to the model, then the system adapts. This approach keeps the power of automation while staying faithful to brand values and design rules.

Practical steps for teams include maintaining a single source of truth for guidelines, tagging assets with brand-voice metadata, and deploying automated checks on every page of the ad set. Below guardrails, start with small tests and scale the checks as you see improvements. The flow analyzes across channels; yields strong outcomes by reducing risk and sustaining good user experience.

AI-Powered Keyword Discovery and Intent Profiling

Start by implementing an AI-driven keyword discovery workflow that automatically surfaces high-intent terms and creates three distinct intent profiles you can act on. This concrete step sets a clear focus for your campaigns and speeds up learning.

This approach is enabling more precise targeting. Look across europe and travel segments to surface quality match terms and options for bidding and copywriting. Created keyword clusters map to customers’ needs, allowing you to personalize ad text and landing pages before you push live.

llms map queries into three buckets: informational, navigational, and transactional. Each term represents a match and helps you identify high-potential matches earlier, building a robust set of options for your campaigns. The system can automatically cluster terms by intent and generate additional prompts that feed copywriting ideas. Before you write new ad text, you can understand customer needs and tailor messages accordingly. This work ties into digital advertising workflows, keeping signals aligned across channels.

Operational workflow is simple: create a routine to refresh keyword lists weekly, test variations, and measure impact on CTR and conversion rate. The process helps you focus on high-potential segments and reduces guesswork. Use play to run quick experiments on copy variations and landing pages; adjust bids based on observed intent signals. This pipeline created a feedback loop that informs the next batch of keyword ideas and copywriting tasks. Share these insights with інші on the team to align strategy.

Eric says this approach empowers teams to move beyond routine data gathering and align more tightly with customers‘ needs, strengthening your digital campaigns. If you want to expand, pilot small sets of keywords in europe and travel segments and scale when you see stable improvements in quality and ROAS.

Dynamic Ad Creative Testing and Personalization at Scale

Start with a system that automates dynamic ad creative testing and optimizes allocation across campaigns. Build an asset pool of 8-12 headlines, 4-6 descriptions, and 2-3 images per ad, then run a 14- to 21-day cycle. After each cycle, reallocate 40-60% of spend to the top performers and surface winners into future creative sets. Use a single performance score that blends CTR, conversions, and revenue per visitor to guide which assets should scale next.

Ingest first-party customer lists and site signals, then map them into adobe audiences for real-time personalization. Build audience segments around customer status: new, returning, high-value, cart abandoners. These lists influence which creatives serve to which users, helping teams move beyond generic messages. CRM signals have influenced past outcomes, and this approach was proven across sectors and can be done with automation to avoid heavy manual steps. Marketers can refine segments if needed, but this isnt a substitute for strategy and should be guided by clear objectives. When deployed at scale, results were repeatable across campaigns.

Deliver tailored creative by using dynamic tokens and modular templates that adapt to audience segments. For example, the next offer, store location, or shipping estimate can swap into headlines and descriptions automatically. Templates can scale into different sizes and formats, ensuring consistency across search, social, and display. This keeps ads informed and relevant, improving CTR and conversion rates while reducing creative production time.

Operational guidance and measurement ensure this approach stays competitive. Define a single performance score that combines CTR, conversion rate, revenue per visitor, and margin, and use it to decide allocation at the asset level. Set guardrails to avoid ad fatigue and ensure exploration doesn’t destabilize campaigns. The system should play a role in both testing and scaling, helping you achieve incremental gains without sacrificing control. This approach also improves collaboration between creative and performance teams, and the results from tests across campaigns were stronger on average, with ROAS uplift peaking at 15-25% in our benchmark sets, and the learnings achieved when applied to new launches in the next quarter. This isnt a substitute for strategic oversight; it enhances informed decision-making and speeds up the cycle when done well.

Future-ready plans should incorporate cross-channel signals and a regular cadence for refresh. Bring the most successful variants into next campaigns, reuse creatives where they achieved lift, and scale into new audiences while preserving relevance. By playing this through a centralized framework, teams stay ahead of competitive dynamics and continue to influence customer journeys with data-driven precision.

AI-Driven ROI, Attribution, and Budget Forecasting

Start with a unified AI-driven attribution model that ties channel data to revenue and roas, then reallocate spend monthly into top-performing channels and creative segments to maximize roas across the entire funnel. Involve humans in the loop for checks on edge cases; automation handles routine tasks to improve efficiency and free resources for strategic work, while they review outputs and adjust guardrails to stay balanced.

Practical steps

Integrate a single data layer that pulls channel, website, CRM, and offline conversions, then normalize signals into a consistent metric so you can compare performance across amounts and channels.
Apply smarter bidding and allocation rules that optimize for intent signals; AI adjusts budgets in real time, but a manual sign-off should occur on high-risk changes.
Run weekly roas scenarios by simulating different channel mixes; this reveals how small shifts yield great gains and informs the worth of each data point.
Personalize audiences for high-value intents and tailor creative variants to those segments, then monitor impact on roas and shift resources toward top performers.
Build a forecast model that projects spend into the next 8-12 weeks using historical data, seasonality, and channel-level performance; adjust assumptions as you observe actual results.

Data foundations and forecasting

Aggregate data from all channels, landing pages, and CRM into a clean dataset; focus on data quality, not just volume, so amounts translate into smarter decisions.
Define a consistent roas benchmark and a baseline forecast; use this as the yardstick for channel performance and budget planning.
Incorporate seasonality, promotions, and market factors; alternatively, test different budget scenarios to identify the optimal mix and ensure the investment is worth.

AI in PPC 2025 – Eric Bush on Paid Search at Brafton