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HubSpot AEO is one option for continuous tracking and prioritized recommendations, while HubSpot’s free AI Search Grader provides a one-time baseline. HubSpot’s guide to AI-powered brand tracking offers additional context for teams building a broader measurement program.

This guide compares six Ahrefs Brand Radar alternatives for marketing teams that need a different mix of AI model coverage, evidence, integrations, pricing, and rollout fit. It also explains how to connect AI visibility with content performance, pipeline, and broader AI marketing analytics.

Why Marketers are Considering Ahrefs Brand Radar Alternatives Now

Ahrefs is an SEO platform for backlink analysis, keyword research, rank tracking, site audits, competitive research, and AI visibility. Ahrefs Brand Radar now tracks brand mentions and citations across major AI answer engines. It supports custom prompt monitoring, so teams comparing alternatives should evaluate its fit with their current workflow rather than assume Ahrefs lacks conversational AI tracking.

Marketers may still choose another AI visibility tool when they need a different mix of model coverage, prompt-level detail, integrations, evidence, pricing, or reporting workflows. HubSpot AEO is one alternative for teams that want ongoing visibility tracking alongside prioritized recommendations.

1. They needed more granular data.

Large marketplaces can need more prompt-level context and reporting flexibility than a broad visibility score provides. Alexandra Novikava, a marketer at Truck1, describes her team’s experience:

“At first, we used Ahrefs Brand Radar to observe what sorts of references were being made to us by AI engines but have now transitioned toward custom API tracking and alternative SEO intelligence tools,” Novikava explains.

Novikava says Truck1 needed more detail about the queries where competitors gained visibility and greater flexibility in its data-collection workflow. Ahrefs has since added custom prompt tracking with configurable models, locations, and tracking frequencies, so teams should compare the level of detail they need with Brand Radar’s current capabilities.

2. They wanted prompt-specific tracking.

In specialized B2B markets, one high-intent prompt can matter more than a broad brand-mention count. Colleen Barry, head of marketing at Ketch, puts it plainly:

“In B2B, one mention in the right context matters more than ten generic mentions,” Barry explains.

Ketch adopted Ahrefs Brand Radar in early 2025 to establish an initial baseline for AI-search visibility. Barry’s team later wanted more control over industry-specific prompt libraries so it could evaluate how conversational engines handled nuanced privacy and compliance queries and determine whether thought-leadership content influenced those responses.

Ahrefs now supports custom prompts, so Ketch’s experience should be read as a historical workflow-fit decision rather than evidence that Brand Radar currently cannot track specific prompts.

3. They looked for a lower-cost fit.

Pricing can become the deciding factor when teams compare AI visibility tools. This was Ashot Nanayan’s experience as founder and CEO of B2BSEO. During Brand Radar’s open beta, Nanayan estimated that the Ahrefs subscription and AI-monitoring add-ons his team needed would have cost about $800 per month. He also wanted localized prompt customization and broader model coverage at the time.

Ahrefs’ packaging has changed since that experience. Standalone Brand Radar currently starts at $199 per month for a single platform, while separate custom-prompt packages start at $50 per month. Nanayan ultimately preferred a dedicated AI visibility platform that offered broader coverage and a lower overall cost for his team’s use case.

4. They needed stronger links between visibility and customer actions.

For Matthew Kinneman, founder of Bully Max, Ahrefs Brand Radar provided a useful starting point for understanding how AI-search platforms referenced the brand. His team moved toward a broader measurement approach because visibility alone did not show whether AI mentions aligned with traffic, engagement, or conversions.

As Kinneman explains, “the biggest lesson for me was that AI visibility is only valuable if you can tie it back to actions customers take afterward. Otherwise, it’s easy to end up optimizing for a metric that looks good but doesn’t drive business growth.”

Today, Bully Max combines multiple data sources to evaluate AI-driven discovery alongside traditional search performance and customer behavior. Rather than treating AI visibility as a standalone KPI, Kinneman argues that teams should evaluate it within the context of the entire customer journey.

His experience reflects one of the most important criteria for evaluating Ahrefs Brand Radar alternatives: marketing teams need to connect AI visibility with CRM, attribution, pipeline, and revenue reporting. HubSpot AEO is one example of a product built around that broader measurement workflow.

Ahrefs Alternatives at a Glance

These six Ahrefs Brand Radar alternatives offer different combinations of AI model coverage, prompt tracking, citation evidence, integrations, pricing, and reporting depth.

Use this table to compare the core differences before reviewing each tool in detail.

Bonus diagnostic: HubSpot’s free AI Search Grader provides a one-time baseline across ChatGPT, Perplexity, and Gemini before a team commits to ongoing tracking.

What Features Matter Most for AI Visibility Tools

AI visibility tools track brand mentions, citations, and recommendation patterns across AI assistants and AI-search surfaces. Unlike a traditional rank tracker, an AI visibility platform also needs to account for generated answers, preserve evidence, and show how results change over time.

The strongest Ahrefs Brand Radar alternatives should be evaluated on AI model coverage, data freshness, evidence quality, segmentation, integrations, pricing, and team fit.

AI Model Coverage and Data Freshness

Model coverage matters because buyers do not rely on a single answer engine. A tool that monitors several AI platforms gives teams a broader view of where a brand appears, while a single-engine tracker can make sense when one platform dominates a company’s audience or workflow.

Data freshness matters for a different reason: AI-generated answers change. Compare how frequently each tool reruns tracked prompts, whether cadence varies by plan or model, and how much historical data the product preserves. Daily data can be useful for active optimization, while weekly or monthly tracking may be enough for broader trend reporting.

LLM Visibility Tools That Support Segmenting by Persona or Journey

Aggregated visibility scores can hide where a brand is actually gaining or losing ground. Segmenting prompts by buyer persona or journey stage helps marketing teams distinguish broad awareness from high-intent consideration and purchase questions.

G2’s 2026 Answer Economy report found that 71% of B2B software buyers rely on AI chatbots at some point in their research process. That makes it useful to measure visibility at more than one stage of the buying journey.

• Awareness stage: Track broad category and problem-oriented prompts, such as “How to automate B2B lead routing,” to see whether answer engines associate the brand with the problem it solves.
• Consideration stage: Track comparison and shortlist prompts, such as “Top CRM systems for mid-market software companies,” to see which competitors answer engines group together and which sources influence those recommendations.
• Decision stage: Track bottom-of-the-funnel prompts, such as “HubSpot vs. Livespace pricing and integrations,” to check whether answer engines return accurate, conversion-ready product information. In its own 2025 analysis, Ahrefs found that AI-search visitors converted at a 23x higher rate than traditional organic-search visitors for Ahrefs. Treat that as an Ahrefs-specific result, not a universal conversion benchmark.

Pro tip: Segmenting by persona prevents a strong aggregate score from hiding an important audience gap. High visibility among developers evaluating APIs, for example, does not mean the brand is equally visible to the marketing leaders who control budget.

AI Visibility Tool Integrations and Reporting

Integrations determine whether AI-search insights stay trapped in another dashboard or become part of a team’s existing reporting workflow. Useful platforms can connect AI visibility with CRM data, business intelligence, web analytics, marketing automation, or exportable data that teams can combine with broader AI marketing analytics.

Those connections matter because marketing teams eventually need to compare visibility with traffic, leads, pipeline, and revenue. HubSpot’s 2026 State of Marketing report found that 12.4% of marketers cite difficulty sharing data across their organization as a top challenge. Adding another disconnected measurement tool can make that problem worse.

Reporting quality matters just as much as the integration itself. Historical trends, exportable evidence, custom dashboards, and stakeholder-friendly summaries help teams understand whether changes in AI search engines coincide with changes in content performance and business outcomes.

Pro tip: Look for a tool that preserves historical AI visibility data rather than showing only the latest score. Products such as HubSpot AEO show visibility trends over time, which gives teams a more useful baseline for evaluating content, PR, and brand work.

Evidence Quality, Pricing, and Team Fit

A visibility score is much more useful when a team can inspect the evidence behind it. Look for products that preserve the underlying AI response, cited sources, timestamps, model information, or other records that make results auditable. That evidence can also strengthen competitive intelligence by showing which sources help competitors appear in recommendations.

Three practical questions can narrow the shortlist.

• Evidence quality: Can the team inspect the response and citations behind a visibility score, or does the platform show only an aggregate metric?
• Pricing and scale: How does cost change as the team adds prompts, models, locations, projects, users, or reporting frequency?
• Team fit: Does the product support the workflows, permissions, exports, integrations, and reporting cadence that marketing, SEO, content, and revenue teams actually need?

A lower entry price does not automatically make one AI visibility tool a better fit. Compare the cost of the monitoring program the team will realistically run, not only the cheapest advertised plan.

1. HubSpot AEO

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HubSpot AEO is an AI visibility tool that tracks how a brand appears across ChatGPT, Perplexity, and Gemini. It measures visibility and sentiment, compares competitor share of voice and citation sources, tracks buyer-relevant prompts, and turns visibility gaps into prioritized recommendations.

Standalone HubSpot AEO does not require a HubSpot subscription. Marketing Hub Professional and Enterprise include AEO, where CRM data can inform prompt suggestions and recommendations and help teams connect AI visibility with pipeline and revenue.

Key Features

• Brand visibility and sentiment: Tracks how frequently a brand appears for monitored prompts across ChatGPT, Perplexity, and Gemini and shows the sentiment around those mentions.
• Competitor and citation analysis: Compares competitor visibility and shows which domains and content AI engines cite, giving teams evidence they can use to prioritize content and outreach.
• Prompt tracking and prioritized recommendations: Suggests relevant prompts, monitors them over time, and recommends specific actions based on visibility gaps.

Best for: Marketing teams that want ongoing AI visibility measurement plus a clear workflow for turning findings into content and campaign actions.

Pricing: $50 per month for 25 prompts across ChatGPT, Perplexity, and Gemini, or $45 per month with annual billing. HubSpot offers a 28-day free trial, and teams can purchase additional prompts.

What we like: HubSpot AEO combines measurement with an action layer. Instead of stopping at a visibility score, it shows where competitors are winning, what sources influence those answers, and what the team can do next.

What customers say: One customer success director writes in a G2 review, “I like the potential of HubSpot AEO to bring structure to how we understand and improve our discoverability. The ability to surface gaps in where we’re not being found — and recommend content to fill those gaps — is especially valuable, particularly when it can be filtered by audience or segment.”

Another reviewer writes in a G2 review, “I love the citation analysis in HubSpot AEO. Knowing exactly which domains and content AI engines cite when recommending software in our category has been a genuine game-changer for us. It moved AEO from something abstract we monitored to something we could actively build a strategy around.”

2. Profound

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Profound is an AEO platform built for teams that need detailed visibility analysis plus workflows for acting on that data. Its Answer Engine Insights product tracks how brands and competitors appear across AI platforms, while its Agents, Agent Analytics, and Prompt Volumes products extend the platform into content workflows, AI-sourced traffic analysis, and prompt research.

Its public plans range from ChatGPT-only monitoring to broader enterprise coverage, making Profound a stronger fit for organizations that expect AI visibility to become a dedicated program rather than an occasional reporting task.

Key Features

• Answer Engine Insights: Tracks how a brand and its competitors appear in AI answers and preserves response-level visibility data.
• Agent Analytics: Measures AI-sourced traffic and attribution across monitored domains and connects with analytics tools.
• Agents and Prompt Volumes: Supports AEO content workflows and helps teams understand which topics and queries people are prompting in answer engines.

Best for: Larger companies and agencies building a dedicated AEO program that needs detailed reporting, broader model coverage, and enterprise controls.

Pricing: Profound pricing starts at $99 per month, billed yearly, for ChatGPT tracking and 50 prompts. Growth costs $399 per month, billed yearly, and includes three answer engines and 100 prompts. Enterprise uses custom pricing and can add up to nine answer engines.

What we like: Profound goes beyond a single visibility score. Its combination of response monitoring, AI-sourced traffic analysis, prompt research, and enterprise functionality gives teams several ways to build a broader AEO program.

3. Peec.ai

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Peec AI is an AI search analytics platform for tracking brand visibility, citations, competitors, and prompts across multiple AI models. Teams can organize prompts by project, compare performance over time, and examine the domains and URLs that appear in AI-generated responses.

Peec complements traditional AI SEO rather than replacing it. Its focus is the answer-engine layer: which brands appear, which sources get cited, and how those patterns change across tracked prompts.

Key Features

• Multi-model visibility tracking: Public brand plans let teams choose three models and run tracked prompts daily.
• Citation and competitor analysis: Shows cited domains and URLs alongside competitor visibility so teams can investigate why other brands appear.
• Reporting and access: All public brand plans include unlimited users; Advanced adds multi-country reporting and Looker Studio, while Enterprise adds API access and SSO.

Best for: SEO and content teams that need broad AI model coverage, daily tracking, and collaborative access without pricing seats separately.

Pricing: Peec AI pricing starts at $95 per month for Starter with 50 prompts and one project. Pro costs $245 per month for 150 prompts and two projects. Advanced costs $495 per month for 350 prompts and five projects. Enterprise uses custom pricing.

What we like: Peec keeps public-plan user access unlimited while scaling primarily through prompts, projects, countries, and model requirements. That structure can make collaboration easier when SEO, content, and other stakeholders all need access to the same visibility data.

4. Xofu

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Xofu focuses on bottom-of-the-funnel and purchase-intent prompts — the questions buyers use to shortlist vendors, compare products, and evaluate solutions. It tracks recommendations over time and helps teams see which competitors appear, which sources support those recommendations, and where citation gaps exist.

That narrow focus distinguishes Xofu from broader brand-monitoring tools. It is designed around prompts that sit closer to a buying decision rather than every possible mention of a brand.

Key Features

• Bottom-of-the-funnel prompt tracking: Monitors prompts such as “best X for Y,” vendor comparisons, and solution evaluations.
• Competitive benchmarking: Shows which competitors appear for each tracked buyer prompt and how visibility changes over time.
• Citation-gap analysis: Identifies the sources that answer engines cite when competitors appear so teams can prioritize content and off-site opportunities.

Best for: SaaS companies, consultants, and agencies that care most about AI visibility in commercial and purchase-intent prompts.

Pricing: Xofu pricing includes a free plan with 50 prompts per model and weekly reporting. Consultant costs $99 per month for 200 prompts per model and weekly reporting. Agency Weekly costs $499 per month for 500 prompts per model. Agency Daily costs $999 per month for 200 prompts per model and daily reporting.

What we like: Xofu keeps the analysis centered on the prompts that influence vendor selection. That makes its reports easier to connect with competitive positioning and pipeline questions than a broad visibility score alone.

5. Mangools AI Search Grader

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Mangools AI Search Grader is a free diagnostic tool that measures how a brand performs across AI-search models. It combines visibility and ranking into an AI Search Score and shows how the brand compares with competitors for automatically generated prompts.

Users can run the tool without an account using three supported models. A free Mangools account unlocks all seven supported models and additional searches.

Key Features

• AI Search Score: Combines visibility, average ranking position, and model weighting into a score from 0 to 100.
• Model-level results: Shows where a brand appears for analyzed prompts across multiple AI models.
• Competitor benchmarking: Compares the brand with other companies that appear in the same generated results.

Best for: Marketers and SEO teams that want a free baseline before investing in continuous AI visibility monitoring.

Pricing: AI Search Grader is free. For continuous monitoring, Mangools offers the separate paid AI Search Watcher product.

What we like: The AI Search Score reduces several visibility and ranking signals to one easy-to-read benchmark while still letting users inspect the underlying model-level results.

6. Morningscore ChatGPT Rank Tracker

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Morningscore ChatGPT Rank Tracker monitors whether ChatGPT mentions or recommends a brand for selected prompts and shows the answer and source evidence behind each result. It sits inside Morningscore’s broader SEO platform, giving teams one place to work with traditional search metrics and ChatGPT visibility.

Its narrower engine focus is the main distinction from multi-model AI visibility platforms: the tracker centers on ChatGPT, and prompt lookups run weekly.

Key Features

• ChatGPT prompt tracking: Monitors selected prompts on a weekly schedule.
• Mention and citation evidence: Shows the actual ChatGPT answer and sources instead of reporting only an aggregate visibility score.
• Competitor comparison: Helps teams compare which brands appear for the prompts they monitor.

Best for: Teams that primarily care about ChatGPT visibility and want that monitoring inside the same product they use for SEO.

Pricing: Morningscore pricing starts at $69 per month for Lite with 10 ChatGPT prompts. Business costs $99 per month for 100 prompts, Pro costs $159 per month for 500 prompts, and Premium costs $299 per month for 2,000 prompts. A 14-day free trial is available without a credit card.

What we like: Morningscore shows the underlying ChatGPT response and source evidence, which makes a reported mention easier to validate than a score alone.

Bonus: HubSpot AI Search Grader

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HubSpot AI Search Grader is a free, one-time diagnostic that checks how ChatGPT, Perplexity, and Gemini represent a brand. It scores five dimensions — sentiment, presence quality, brand recognition, share of voice, and market competition — and returns a composite score with written interpretation.

The Grader serves a different purpose from paid HubSpot AEO. AI Search Grader answers “How do answer engines represent my brand right now?” while HubSpot AEO continuously tracks specific prompts, competitors, and changes over time.

Key Features

• Five-dimension scoring: Measures sentiment, presence quality, brand recognition, share of voice, and market competition.
• Cross-platform analysis: Runs the assessment across ChatGPT, Perplexity, and Gemini rather than relying on one answer engine.
• Written interpretation: Explains the score and category results so marketers can understand what the numbers mean.

Best for: Marketing leaders, brand managers, SEO professionals, and content teams that want a free baseline before choosing a continuous AI visibility tool.

Pricing: Free. HubSpot describes AI Search Grader as a one-time check and says no account is required.

What we like: AI Search Grader makes a new measurement category easier to understand. The five-part score gives teams a simple starting point without requiring them to design a prompt-tracking program first.

The Answer Engine Report similarly calls its five-dimension scoring “the most pedagogically useful framework in the directory for explaining what AI visibility actually means to non-specialists.” The review also notes that the report is easier for marketing leaders to understand than many exhaustive practitioner dashboards.

Choosing an Ahrefs Brand Radar Alternative That Connects to Pipeline

AI visibility becomes more useful when teams can compare it with the metrics that already matter to the business: traffic, leads, opportunities, pipeline, and revenue. An AI visibility product does not need to function as a CRM. Still, its reporting workflow should make it possible to connect visibility trends with the systems marketing and sales teams already use.

The same principle applies to content ROI. Visibility data can show where a brand appears, but teams also need evidence that helps them identify which pages, topics, and sources may be contributing to those results and what they should work on next.

HubSpot’s integrated AEO experience in Marketing Hub Professional and Enterprise uses CRM data to surface prompts that are more relevant to actual buyers. HubSpot also positions that CRM-connected experience as a way to connect AI visibility with pipeline and revenue.

For teams using HubSpot, several products contribute to that workflow.

• HubSpot Smart CRM: HubSpot’s AI-powered system of record gives marketing and sales teams shared customer context across contacts, deals, and attribution data.
• Marketing Hub: AEO in Marketing Hub Professional and Enterprise can use CRM context to inform prompt tracking while marketing teams manage campaigns and reporting in the same platform.
• Content Hub: Content teams can use visibility and citation findings to prioritize updates, strengthen topic coverage, and create content that addresses gaps surfaced by AEO analysis.

HubSpot AEO also provides prioritized recommendations rather than stopping at a visibility score. Those recommendations can point teams toward content updates, new pages, and citation opportunities based on where the brand is losing visibility.

That turns AI visibility into actionable competitive intelligence: teams can see what brands answer engines recommend, which sources shape those recommendations, and where the brand has an opportunity to improve.

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Ultimately, the best Ahrefs Brand Radar alternative depends on the outcome a team needs. Basic mention monitoring may be enough for an early-stage program. Teams that want to connect AI visibility with content performance, attribution, pipeline, and revenue should give more weight to reporting, integrations, evidence, and actionable recommendations.

How to Implement an Ahrefs Brand Radar Alternative

Implement an Ahrefs Brand Radar alternative as a controlled rollout: establish reporting, standardize prompts, validate results, preserve evidence, and test the workflow before expanding it. The goal is to add a repeatable AI visibility measurement layer alongside traditional search reporting, not to replace SEO measurement entirely.

Step 1: Set up integrations and reporting foundations.

Start by deciding where AI visibility data will live and how the team will compare it with existing marketing and revenue metrics.

• Establish a shared taxonomy: Align prompt groups with the product lines, audience segments, journey stages, or reporting categories the team already uses.
• Separate informational and commercial prompts: Keep broad awareness questions distinct from high-intent comparison and purchase prompts so one aggregate score does not blur different objectives.
• Define the reporting destination: Decide whether the team will use the vendor dashboard, exports, an analytics tool, or a CRM-connected reporting workflow.

Step 2: Establish prompt governance.

Consistent prompts make trend data easier to interpret.

• Use real buyer language: Build the tracking set around questions prospects actually ask rather than internal marketing terminology.
• Document prompt changes: When comparability matters, keep prompts stable during a reporting period and record intentional edits so the team understands when a time series changes.
• Assign ownership: Decide who can add, remove, or revise tracked prompts and how often the team reviews the library.

Step 3: Implement multi-run quality assurance.

Generated answers vary, so teams should not treat one response as definitive.

• Repeat high-priority tests: Run important prompts more than once when the product and budget allow it.
• Compare supported models and locations: Check whether a result persists across the answer engines or geographic settings that matter to the business.
• Investigate outliers: Review the underlying answer and citation evidence before changing strategy because of a sudden visibility spike or drop.

Step 4: Build an evidence repository.

Keep enough evidence to explain what happened behind a visibility score.

• Capture the underlying response: Save response text, screenshots, exports, or other evidence the platform provides.
• Preserve citation information: Record the source URLs an answer engine cited, especially when a competitor gains or loses visibility.
• Add context: Store the date, model, prompt, location, and other relevant settings so the team can reproduce or interpret the result later.

Step 5: Pilot, compare, and scale.

Test the workflow with a limited prompt set before rolling it out across every product, region, or audience.

• Evaluate data quality: Determine whether the platform produces evidence the team trusts and can explain.
• Measure operational fit: Compare reporting friction, integration needs, prompt limits, model coverage, and cost with the current workflow.
• Scale what works: Expand the prompt set, teams, models, or locations only after the pilot establishes a useful baseline and a repeatable reporting process.

Frequently Asked Questions About Ahrefs Brand Radar Alternatives

How do I validate AI visibility data across different models?

Validate AI visibility data by running the same priority prompts more than once and comparing outputs across the models, locations, and tracking frequencies the tool supports. Keep the underlying response and citation evidence so the team can confirm that a reported visibility score matches what the answer engine actually returned.

How long does it take to see results after switching tools?

A new tool can begin collecting baseline data as soon as tracking starts, but there is no universal four-to-six-week window for meaningful results. The time needed depends on the product’s tracking cadence, the stability of the prompt set, changes in the underlying answer engines, and how quickly content changes appear in generated responses.

Can I track brand visibility by region or persona?

Some AI visibility tools support geographic segmentation, persona-oriented prompt groups, or both, but capabilities vary by vendor and plan. Use location settings where the product supports them, and organize separate prompt groups around specific buyer roles or journey stages so the comparisons remain consistent.

What’s the best way to connect AI visibility to revenue?

Connect AI visibility to revenue by bringing visibility, referral traffic, CRM, and attribution data into the same reporting workflow. Compare changes in high-intent prompt visibility with qualified leads, influenced opportunities, and closed revenue, but avoid claiming causal impact when the data only shows a correlation.

Do I still need traditional SEO tools with an AI visibility tracker?

Yes. AI visibility trackers and traditional SEO tools answer different questions and work best together. AI visibility platforms show how brands appear in generated answers, while SEO tools measure rankings, backlinks, technical health, keyword opportunities, and organic traffic. For most marketing teams, AI visibility complements traditional SEO rather than replacing it.

Which Ahrefs Brand Radar alternative is right for your team?

Ahrefs Brand Radar is one of several ways to measure AI visibility. The six alternatives in this guide range from free diagnostics and ChatGPT-specific tracking to multi-model platforms built for ongoing reporting, evidence collection, and enterprise workflows. The right choice depends on the models a team needs to monitor, the evidence it needs to preserve, the integrations it relies on, and the cost of running the program at the required scale.

For teams that want continuous monitoring plus prioritized next steps, HubSpot AEO combines visibility, sentiment, competitor citation analysis, prompt tracking, and recommendations. Marketing Hub Professional and Enterprise add CRM context for teams that want AI visibility to sit closer to their existing marketing and revenue reporting.

After reviewing these options, I think the biggest differentiator is whether the data gives a marketing team a clear next action and a credible way to connect visibility with business outcomes. A tool that only produces a score can answer “Are we visible?” A stronger workflow can also help answer “Why?” and “What should we do next?” That is the standard I would use when choosing an Ahrefs Brand Radar alternative.

This phrase reflects the belief that customers who appear satisfied should be left alone. The assumption is that proactively reaching out about pricing, renewals or service improvements could create dissatisfaction, remind customers they have alternatives or even accelerate churn. If customers are not complaining, contacting support or asking to cancel, why initiate a conversation that could put a healthy customer relationship at risk?

As a result, many businesses conclude that leaving customers alone is the safer strategy because proactive engagement appears more costly.

The reality is that customers rarely leave without warning. Dissatisfaction often begins long before a cancellation request, and by the time customers raise their hand, the conversation has already shifted from prevention to recovery.

The real question is not whether proactive engagement leads to dissatisfaction and churn or requires more investment. It is whether subscription businesses can afford to wait until customers announce their decision to leave.

To answer that question, it is worth understanding why this belief became conventional wisdom in many subscription businesses and what the cost of avoiding proactive engagement is for these businesses.

Why 'Don't Poke The Bear' Became Conventional Wisdom ​

The “don’t poke the bear” mindset did not emerge because businesses ignored retention. It emerged because they lacked the visibility to identify customer risk and determine the right intervention.

Without that visibility, proactive retention felt like guesswork. Engaging every customer without context was costly and inefficient, making waiting seem like the safer option.

At the time, identifying the right customer, determining the right intervention and acting at the right moment was difficult to do consistently at scale.

The problem was that customers were not standing still.

They were questioning value, hearing competitor offers, using services less frequently and gradually disengaging. These signals often appeared weeks or months before cancellation but remained invisible until it was too late.

By the time customers called to cancel, businesses were no longer preventing churn. They were trying to reverse a decision that had been in the making for months.

That fundamentally changes the economics of retention. ​

The Hidden Cost Of Waiting ​

One of the most persistent myths in subscription businesses is that proactive retention increases costs.

That belief begins to break down the moment a customer says, “I want to cancel.”

At that point, businesses have little leverage left. Yet this is often when they make their biggest retention investments.

They throw the kitchen sink at the problem: deeper discounts, aggressive save offers and last-minute incentives to recover relationships that have been weakening for months. Even then, some customers cannot be saved because the decision has already been made.

Consider a subscriber who pays about $50 a month and whose product usage has steadily declined. Under a traditional "don't poke the bear" strategy, nothing happens until the customer calls to cancel. At that point, the retention team may reduce the monthly price to $20 simply to keep the account.

A profitable customer has now become a marginal one—not because the customer changed, but because the business waited too long to engage.

Now imagine engaging that same customer earlier. In many situations, a simple rate-lock offer could preserve both the relationship and the margin.

That is why I do not see proactive retention as an added cost. I see reactive retention as the far more expensive strategy.

From 'Don't Poke The Bear' To Strategic Customer Engagement ​

If waiting is no longer the answer, the next question becomes: How can businesses engage customers without creating unnecessary cost?

The answer is not to engage everyone.

Proactive customer engagement begins with one simple truth: Not every customer is equal. Some remain for months, while others stay for years and generate significantly greater lifetime value.

Treating every customer the same leads to inefficient retention strategies.

Instead of asking whether to engage customers, businesses need to combine customer risk with customer lifetime value to determine who to engage, when to engage and which action can create the greatest impact.

The next best action may be a rate-lock offer, a renewal conversation, a service intervention or simply reinforcing the value a customer already receives.

Turning Intelligence Into Action

Strategy creates value only when it becomes part of everyday customer interactions.

One question I often ask is, "Who are the heroes of our story?" They are the marketers, contact center agents and field service technicians who shape the customer experience every day. Every interaction is an opportunity to strengthen relationships, retain customers and create additional value.

For years, these teams relied largely on experience and instinct. Today, we can identify high-risk customers, highlight customer lifetime value and recommend the next best action while there is still time to influence the outcome.

When a customer mentions a competitor’s offer, asks about pricing or talks about a feature they no longer use, that interaction becomes an opportunity to reinforce value before dissatisfaction becomes cancellation.

Every interaction can be both a retention opportunity and a revenue opportunity.

Proactive customer engagement creates value when intelligence becomes action while the business still has the leverage to influence the relationship.

Ask yourself: Can you afford to remain silent while a valuable customer relationship quietly deteriorates?

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Well, according to the film's screenwriters, Jean Grey did not actually kill anyone while terrorizing New York. Not the guy she made walk in front of a bus, or the guy left stuck on a crane, and definitely not anyone in the building that she blew up.

"I think technically, she's not responsible for any deaths," Erik Sommers told The Hollywood Reporter. "But you come away with the impression, certainly, that she's caused a lot of destruction [and] mayhem."

While you don't actually see anyone Jean Grey controls definitively die on screen, fans have certainly noted the level of violence on display from a character who ultimately is meant to be sympathetic.

"I audibly gasped when that poor man was made to step into oncoming traffic," one fan wrote in a lengthy reddit thread discussing Jean Grey's violent tendencies. "I looked around the cinema to see if anyone saw what I just saw, but to my surprise the scene just went on and it was not addressed at any other point. Did he die? Did he survive? That felt really dark and unnecessary especially without any resolution or closure."

But Sommers has argued that Jean Grey needed to have that level of threat attached to her character in order to "feel formidable," and be a believable enough villain that could carry the movie and make the Department of Damage Control — at least initially — seem justified in their actions.

"Even if you're going to have a villain who really is redeemed at the end or goes through a transformation, you have to start them off in a place where they feel formidable," Sommers continued. "If it doesn't feel like this is going to be someone who is a challenge to our hero, physically, emotionally, mentally, then you don't really buy it."

"What we were really excited about was this villain got to be a mystery for so long in the movie," Sommers' fellow co-writer Chris McKenna added. "We were in Peter's shoes. We were not putting the audience ahead of him, so he was reacting to everything, and we were with him reacting."

Other fans have dismissed the hand-wringing over Jean Grey's (definitely not) murderous tendencies as simply by-the-by, considering the antics of other Marvel heroes in the movie such as Yelena and Frank.

"Peter's best friend in this movie is an insane psychopath who casually kills people," countered another fan. "Peter's contact in the New Avengers is a trained assassin. Peter spent the entire last movie trying to redeem murderous criminals from themselves just because he thought that if they were going to die, at least they could die as good guys." Point taken.

Of course, Sadie Sink has a big future ahead of her in the MCU, with an X-Men movie reboot now in pre-production and an expectation among Marvel fans that we'll see her again even sooner, likely in 2027's Avengers: Secret Wars. Maybe Jean Grey will have calmed down a bit by then?

Image credit: Axelle/Bauer-Griffin/FilmMagic.

Tom Phillips is IGN's News Editor. You can reach Tom at [email protected] or find him on Bluesky @tomphillipseg.bsky.social %!s()

A leadership team I worked with recently did everything right on paper: licenses bought, kickoff held, people walking out excited. Months later, most had drifted back, and not one blamed the software. What I heard was "I didn't trust it enough to put my name on what it gave me."

I have spent more than two decades at Future Point of View helping organizations through big technology shifts. AI is different: It does not just ask people to learn a tool; it asks them to rethink how they work, and most companies still treat it like a software rollout.

The Mistake Most Companies Make

Almost everyone starts with training. Here is the platform, here are some prompts. What they skip is what makes it stick: helping people grasp what the technology does well, where it falls down, and where their own judgment still carries the day. Skip that and people either trust the tool too much or avoid it altogether.

There is a version of this that starts in the corner office. Leaders hand AI off to IT as a technical problem when it is really a workforce-design decision. Move faster than people can absorb, and the gap fills with anxiety, foot-dragging and the nobody-is-watching experimentation that should worry you most.

A Framework For Building That Understanding

The employees who adapt best are rarely the most technically proficient; they are the ones who have honestly worked out what AI can and cannot do in their own jobs. We start every engagement with three questions everyone should be able to answer about their own role.

First, which parts of my work are really just pattern recognition? AI is good at volume and repetition: first drafts, summaries, sorting feedback. Naming those tasks drains the fear: People stop seeing a replacement and start seeing room to breathe.

Second, where does my work depend on things AI cannot know? The history of a relationship, the politics in the room, the thing a client said that was never in an email. The people who use these tools well carry that context in rather than taking the output at face value.

Third, what does good actually look like in my world? A seasoned person can glance at an AI draft and feel what is thin or overstated, what would collapse the moment a client pushed on it. That instinct is the muscle organizations forget to train.

I watched this land on a client-services manager who came in with her arms crossed. By the first question, she admitted that the weekly summaries she dreaded were pure pattern; by the third, she was naming the judgment that made the job hers rather than asking whether the tool could replace it. That is what I mean by the new Humalogical Balance: human capability, tools and automation lined up on purpose so judgment leads and technology serves.

What This Looked Like In Practice

One engagement stays with me. A regional organization of a few hundred people had hit exactly that wall. Rather than repeat the training, we got honest about where each team stood, built capability at a manageable pace, reworked broken workflows and let a few roles change openly. The hardest resistance came from a respected veteran who took it as an insult to his experience. The turn came when we asked him not to adopt the tool but to tear it apart, holding his judgment against its output. He went from the biggest obstacle to the loudest advocate, and flat adoption began to move.

The Comfort Question Is Actually A Trust Question

Leaders love to file resistance under comfort, assuming time will settle the nerves. In my experience, that is half of it. The rest is trust, running both ways: People need to believe you are not quietly using AI to thin the payroll, and to feel safe challenging a tool that can be confidently, articulately wrong.

I think of a junior analyst who caught a polished AI summary that had flipped a key number backward. In many companies, she would have stayed quiet. She spoke up, and her manager thanked her in front of everyone and made clear that catching it was the job now. You could feel the room relax. People take their cue from what happens to the first person brave enough to speak up.

None of this is soft. When flagging an error feels like admitting you are behind, people nod along with bad output or use the tool where no one can see. The organizations handling this well give explicit permission: to experiment, to say when the output misses, to build habits that fit the real work rather than a tidy version on a slide.

What Leaders Need To Do Differently

The executives moving the needle use the tools themselves, visibly, which says more than any memo. They make space for teams to talk openly about what AI gets right and wrong, and treat it as ongoing rather than a box checked at a Tuesday training. What lasts is the thinking underneath: assessing, adapting and applying judgment to whatever comes next. That is how you move people from fear-based friction to real momentum, long after the launch buzz fades.

Getting your workforce comfortable with AI is not a technology problem, or even a training problem. It is change management, and it requires the same honesty and patience that every real transformation demands. The strongest AI-empowered workforces will be built by organizations that take the time to understand AI deeply instead of simply rushing to adopt it. Success depends on more than whether your people are using AI. It depends on whether they understand it well enough to use it effectively, and whether your organization has created an environment where that understanding can grow.

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Series director Rodrigo Blaas appeared alongside Zhou Wang, head of Randong Studio Group and vice president of content production at Tencent Video, offering fans a behind-the-scenes look at how the adaptation has come together creatively.

That source material, books written under the pen name Erin Hunter, follows four Clans of wild cats bound by a code handed down from their warrior ancestors, as one Clan comes under threat from a rival and its warriors begin dying under suspicious circumstances. At the center of the story is Rusty, an ordinary housecat who joins the Clan and eventually rises to lead it.

A centerpiece of the event was a 3D character model of Rusty, personally built by Blaas and unveiled to the crowd for the first time. It gave attendees a hands-on sense of how the cats from the book series are being reimagined for the screen.

Tencent Video also debuted an exclusive work-in-progress screening at the event, giving fans their first public glimpse of the animated characters and environments and an early sense of the show’s visual direction.

For Blaas, the physical model offered a way to work through the characters outside the screen as the animation develops. At the same event, Blaas walked fans through his approach to adapting the “Warrior Cats” universe into animated form and discussed how the series has taken shape creatively.

Original Force is animating the series on behalf of Tencent Video. A.C. Bradley is showrunner, with Blaas directing. The creative team is drawing directly on the books’ characters and plotlines, using the first book arc, “Warrior Cats: The Prophecies Begin,” as its foundation.

The series is set to launch in 2028 across Tencent Video’s platforms in China and Southeast Asia.

Separately, “Warrior Cats: Clans of the Forest,” a turn-based RPG inspired by the “Warrior Cats” novel series, is in the works at Trailmark Games and the Coolabi Group.

Tencent Video counts itself among China’s leading streaming platforms, carrying a broad slate of content that spans drama series, animation, film, variety programming, sports and documentaries, alongside an expanding international footprint. The company has leaned into original productions in recent years, distributing its shows across multiple devices domestically while pushing Asian content out to audiences abroad through its overseas streaming arm. %!s()