Anastasia Braitsik has spent years building high-impact programs at the intersection of SEO, content, and performance data. Her approach to AI-driven advertising is pragmatic: start with what works, measure fast, and keep creative and bidding anchored to intent signals. With tools like Adthena’s AdBridge and Arlo bringing search-style rigor to ChatGPT ads, she’s focused on helping teams go straight in without wasting cycles on reinvention.
What concrete steps should a performance team take to repurpose high-performing Google Ads keywords and negatives into ChatGPT ad formats, and where do you see the biggest mismatches in intent or prompt context? Please share an example with metrics showing what translated well and what didn’t.
Start with a clean export of top Google Ads keywords and negatives, then let AdBridge cluster them by prompt intent rather than query syntax. I map exact- and phrase-driven themes into prompt families, attach corresponding negative clusters, and create variant copy that mirrors the user’s “do/learn/compare” state. The biggest mismatches show up when transactional keywords are dropped into exploratory prompts—ChatGPT often privileges helpfulness over hard sell, so I lead with guidance and bring offers in the second or third sentence. In one four-week exercise, transactional phrases mapped well to “compare” prompts with assistant-style answers, while brand-protective negatives prevented drift into generic “how to” prompts; what didn’t translate were long-tail modifiers that relied on ad rank mechanics—those needed re-writing as intent statements to regain relevance.
When analyzing auctions where specific brands frequently appear, how do you translate share-of-voice and prompt-trigger insights into bidding, creative, and targeting decisions? Walk through a real scenario and the levers you pulled over a four-week test.
I segment by prompts where rivals “frequently appear,” then prioritize families with overlapping brand mentions and high assistant visibility. Week 1 focuses on baseline: capture prompt-trigger frequency, creative match rate, and placement mix. Weeks 2–3 I adjust bids only on prompts where our creative scores as a tighter intent match, and I deploy counter-messaging that acknowledges the category and differentiates value in the first sentence. By week 4 I expand targeting to adjacent prompts surfaced by AdBridge while locking negatives around competitor-branded intent that won’t convert; the net effect over four weeks is cleaner placement in head prompts and reduced waste in tail prompts where rivals dominate informational queries.
Low inventory and limited scale have constrained many early ChatGPT ad pilots. How do you stage budgets, pacing, and audience expansion to prove viability without overspending? Include thresholds, timelines, and kill-switch criteria you’ve actually used.
I stage budgets in three tranches across the first 90 days: a learning slice to validate prompt fit, a stability slice to confirm repeatability, and an expansion slice to test reach. In the opening weeks, I cap spend to match only the prompts with proven Google Ads analogs and let pacing float just enough to fill limited inventory without spiking frequency. My kill switch triggers when assistant placement skews heavily to informational prompts we’ve already negated, or when prompt-trigger diversity stalls over a two-week span. If inventory remains tight, I pause expansion and reroute back to high-intent prompt families while keeping the 90-day window intact to capture platform changes like lower minimum spend thresholds.
CSV-based workflows are familiar to search marketers. What file structures, naming conventions, and field mappings help teams go “straight in” with minimal rework? Provide a template-like breakdown and the QA checklist you rely on.
I mirror a search-style CSV so teams can upload “straight in.” Template: Campaign_Name, Ad_Group_Name, Prompt_Family, Keyword_Theme, Negative_Cluster, Creative_Variant, Landing_URL, Audience_Signal, Bid_Intent, Geo, Device, Start_Date, End_Date. Naming follows Campaign = Region-Objective-Channel (e.g., NA-Acquire-ChatGPT), Ad Group = PromptFamily-Theme, Creative = Intent-Variant. QA includes: alignment of Prompt_Family to Creative_Variant, negative collisions with brand terms, landing page intent match, date formatting, and one-to-one mapping of Keyword_Theme to Prompt_Family so AdBridge and Arlo references stay consistent.
Competitive insights can surface which prompts trigger rival placements. How do you prioritize counter-messaging, bid adjustments, or negative prompt strategies when competitors dominate certain journeys? Share a case where you shifted share with concrete before-and-after metrics.
I classify rival prompts into protect, contest, and concede. Protect means tightening negatives around competitor-brand-plus-generic prompts and doubling down on brand prompts we already own; contest gets counter-messaging that addresses category needs first; concede is where we suppress and reinvest. Over a four-week period, this triage moved us out of low-intent rival journeys and into head prompts where our assistant-style creative resonated, improving our presence without chasing every mention. The meaningful shift came from removing brand-plus-competitor overlap and focusing copy on the comparison state, which the assistant rewards with clearer, helpful language.
When generating negative keywords for ChatGPT ads, what patterns typically reduce waste without suppressing valuable exploratory traffic? Explain your cluster logic, thresholds, and an example where tightening negatives improved CPA and maintained discovery.
I cluster negatives around three patterns: competitor-brand plus generic, purely informational “how/why/what is” without commercial intent, and support queries that indicate post-purchase needs. Threshold-wise, I apply negatives only when prompts recur across multiple sessions and fail to align with our landing experience. In practice, that meant excluding repetitive “what is” prompts while leaving “compare” prompts open so discovery stayed intact. Over a four-week tightening cycle, the assistant served fewer low-value information prompts while keeping comparison prompts active, which stabilized efficiency without choking off new demand.
For enterprises testing across regions or product lines, how do you sequence experiments to compare ChatGPT ads versus paid search fairly? Detail your holdout design, success metrics, and the timeline needed to reach statistical confidence.
I run region-level A/B where one market activates ChatGPT ads and a matched market remains on search-only during the same four-week window. Success metrics go beyond CPA/ROAS to include prompt coverage, auction visibility, and time-to-first-insight captured by Arlo. After four weeks, I rotate conditions to control for seasonality and repeat the measurement. The full read usually takes the first 90 days, giving enough cycles to absorb inventory shifts and the platform’s evolving pricing models.
With flexible pricing models and lower minimum spends, how do you model ROI and risk in the first 90 days? Describe your forecast inputs, sensitivity ranges, and the decision gates that move a test to scale.
I build a 90-day model with three inputs: prompt-family reach from AdBridge, assistant placement mix, and landing-page conversion readiness. Sensitivities stress-test low inventory and fluctuating minimum spends so we can see upside and downside bands without guessing. Decision gates sit at day 30 and day 60 to evaluate prompt coverage growth, creative match rate, and learning velocity before scaling. By day 90, if coverage has expanded and assistant placements have diversified, we move into broader prompt families and unlock additional audience signals.
When prompts, not queries, drive placements, how do you adapt creative to align with user intent states? Provide a step-by-step framework for crafting variants and the diagnostic signals you monitor to iterate weekly.
I write creative in three intent states: learn, compare, and do. Step 1: extract prompt language patterns via AdBridge; Step 2: map each to a Creative_Variant with an opening line that mirrors the user’s stated need; Step 3: pair with a landing path that resolves the intent; Step 4: add a helpful, assistant-like bridge sentence before any offer. Weekly, I monitor which variants win placements in prompts where we “frequently appear,” then prune or expand. This brings the tone closer to what ChatGPT rewards—useful, human, and directly responsive to the prompt.
How do you operationalize an AI assistant that answers performance questions and compares channels? Share the governance rules, prompt libraries, and escalation paths that kept insights accurate and actionable across teams.
I standardize Arlo usage with a shared prompt library that maps to our CSV fields and naming conventions, which keeps answers consistent. Governance rules require linking every insight to a Campaign_Name and Prompt_Family, plus timestamping so teams know whether they’re looking at week 1 or week 4 data. If Arlo flags anomalies, analysts escalate to channel owners with the exact prompt and campaign context for verification. This cadence keeps the assistant’s insights grounded in our agreed taxonomy and reduces the risk of misinterpretation.
Integrations with partners like retail media or creative automation are expanding. Which data flows (audiences, product feeds, conversion signals) matter most for early wins, and how do you validate that each link in the chain is working?
For early wins, I prioritize audiences synced to intent states, product feeds aligned to Prompt_Family themes, and conversion signals that reflect the assistant’s helpful journey. Validation starts with a dry run: confirm audience availability, feed freshness, and signal receipt inside the ads manager that’s been rolling out. I then spot-check a handful of prompts where we and partners “frequently appear” to ensure the right creative and product data render. Any break in the chain triggers a rollback to core prompts until the partner link is fixed.
What KPIs best capture momentum during the transition—beyond CPA and ROAS? Explain how you track prompt coverage, auction visibility, learning velocity, and time-to-first-insight, with real numbers from a recent pilot.
I chart prompt coverage by counting unique Prompt_Family exposures over each of the four weeks, then track auction visibility where our brand appears. Learning velocity is measured by the number of creative iterations shipped per week, and time-to-first-insight is the span from upload to Arlo surfacing a reliable comparison. In a recent four-week pilot, we saw coverage expand steadily as we added families derived from existing search campaigns. Those operational KPIs told us we were compounding learnings even before scale caught up.
When shifting budget from search to ChatGPT ads, how do you prevent cannibalization while still capturing net-new demand? Walk through your attribution approach, incrementality testing, and the remediation steps you take when overlap appears.
I start with a search-baseline period, then introduce ChatGPT ads in matched markets with a holdout, keeping attribution rules consistent across both. Incrementality is judged by net lift in prompt families that have no direct search analog and by assistant placements in journeys where we didn’t “frequently appear” before. When overlap appears, I lean on negatives to separate brand-protective routes from discovery, and I retune creative so the assistant addresses adjacent needs rather than echoing search copy. This keeps net-new demand flowing while search retains its core role.
What is your forecast for ChatGPT advertising?
Over the next 90 days, expect steady improvement in inventory, more flexible pricing, and tighter integrations that make the experience feel like managing search with an assistant in the loop. As tools like AdBridge and Arlo align with CSV-first workflows, the friction to test and scale will keep dropping. The real unlock will be creative that sounds like a helpful guide rather than an ad, especially in the learn and compare states. Brands that build around prompt families, govern with clean taxonomies, and iterate on a four-week rhythm will be first in line to capture the shift.
