Read time: 3 minutes.

Welcome to the 269th edition of The Growth Elements Newsletter. Every Monday and sometimes on Thursday, I write an essay on growth metrics & experiments and business case studies.

Today’s piece is for 9,000+ founders, operators, and leaders from businesses such as Shopify, Hubspot, Zoho, Freshworks, Chargebee, Servcorp, Zomato, Postman, and Razorpay.

Today’s The Growth Elements (TGE) is brought to you by:

Home furniture brand Blu Dot blew up on CTV with help from Roku Ads Manager. Here’s how:

After a test campaign reached 211,000 households and achieved 1,010% ROAS, the brand went all in to promote its annual sales event. It removed age and income constraints to expand reach and shifted budget to custom audiences and retargeting, where intent was strongest.

The results speak for themselves. As Blu Dot increased their investment by 10x, ROAS jumped to 2,308% and more page-view conversions surpassed 50,000.

“For CTV campaigns, Roku has been a top performer,” said Claire Folkestad, Paid Media Strategist, Blu Dot. “Comping to our other platforms, we have seen really strong ROAS… and highly efficient CPMs, lower than any other CTV partner we've worked with.”

Using Roku Ads Manager, the campaign moved from a pilot to a permanent performance engine for the brand.

Learn More

^{Thank you for supporting our sponsors, who keep this newsletter free.}

A single negative brand mention in ChatGPT, Google AI Overviews (AIO), Perplexity, Gemini, or Claude can silently kill 10 to 30% of category-query pipeline before any buyer visits your website.

Unlike a bad Google review, a negative AI answer plays before the buyer even lands on your G2 page, and most AI models will repeat the same framing across thousands of buyer sessions.

This is Part 15 of the AEO Playbook, a systematic method for detecting, diagnosing, and repairing negative AI mentions.

Negative AI mention management matters in 2026 because AI models are increasingly the first source buyers consult during category research.

Per Quolity AI's 2026 tracking data, 43% of B2B buyers surveyed said they revised or removed a vendor from consideration based on how an AI answered a question about that vendor.

When the AI framing is negative or misleading, the compounding revenue impact runs into millions of dollars for mid-market to enterprise B2B SaaS.

The problem is structural. AI models often surface stale information from Reddit threads, outdated review posts, or single negative articles that scored high in the training corpus.

The vendor cannot appeal or edit the AI's answer directly. Repair requires changing what the AI sources from, not fighting the AI output itself.

Detecting negative AI mentions in 2026 requires a structured monitoring cadence across all five major Large Language Model (LLM) surfaces plus AIO.

The six-metric AIO dashboard from AEO Playbook Part 9 should include a sentiment layer per platform.

Detection checklist:

Tools that support this at scale come from the top 5 AEO tracking tools for 2026, specifically Otterly.AI, Quolity AI, and Semrush Enterprise AEO for sentiment alerting.

Diagnosing the source of a negative AI mention requires tracing the AI answer back to the underlying source content the model likely learned from.

AI models rarely name their training sources for a given claim, so diagnosis is a triangulation exercise.

Diagnosis process:

Common sources of persistent negative AI framing: outdated review platform reviews from 2 to 4 years ago that never got responded to, a single viral Reddit thread, a competitor's comparison page that ranks in Google's top 10, or a news article from a moment of company crisis that never got balanced with positive coverage.

Repairing a negative AI mention requires displacing the source content and flooding the surfaces the AI re-learns from with accurate, positive-framed information. The repair timeline is 90 to 180 days minimum because AI models re-train on cycles.

Repair workstreams:

Measure repair effectiveness by re-running the same 30 to 50 queries every 30 days and tracking sentiment shift. Expect the first sentiment lift 60 to 90 days into the repair, with meaningful improvement by day 180.

[1] Set up sentiment alerting this month. Missing a negative framing for 90 days can cost 10 to 30% of category pipeline before you know it exists.

[2] Never argue with the AI. Fight the sources, not the output. Public arguments with AI answers on social media invite more negative coverage, not less.

[3] Assume repair takes 6 months. AI reputation compounds slowly in both directions. The organizations that started this work in 2025 are the ones dominating positive AI framing in 2026.

Next in the series: AEO Playbook Part 16 covers the AEO content production workflow.

Wishing you a productive week ahead!

I always appreciate you reading.

Thanks,
Chintankumar Maisuria