We audited the marketing at Listen Labs
AI market research platform delivering insights in hours, not weeks
This page was built using the same AI infrastructure we deploy for clients.
Month-to-month. Cancel anytime.
Enterprise customer base (Google, Microsoft, SKIMS, Nestlé) suggests strong product-market fit but limited mid-market visibility and demand generation
Recent Series B funding and 400% YoY headcount growth indicate scaling phase, but marketing infrastructure likely lags behind sales velocity
Autonomous research positioning is differentiated but underexploited in competitive messaging across paid, organic, and AI discovery channels
AI-Forward Companies Trust MarketerHire
Plaid
MasterClass
Constant Contact
Netflix
Noom
TinuitiListen Labs's Leadership
We mapped your current team to understand where MH-1 fits in.
MH-1 doesn't replace your team. It becomes your marketing team: dedicated humans + AI agents running execution at scale while you focus on product.
Here's Where You Stand
Mid-stage SaaS with strong product but underdeveloped demand gen and thought leadership relative to funding stage
Likely capturing some researcher and insights manager searches, but content depth on market research methodologies and AI-powered speed advantages appears limited
MH-1: SEO agent targets high-intent keywords around 'faster market research', 'autonomous research platforms', 'AI research design' to compete for researcher buyer intent
As an AI-native product, Listen has minimal presence in LLM context windows and AI agent recommendations, missing visibility where buyers research at scale
MH-1: AEO agent optimizes for inclusion in Claude, ChatGPT, Perplexity queries about market research, competitive analysis, and rapid consumer insights
No visible brand advertising or retargeting presence; competitive landscape (NWO, Resonance AI) likely running ads while Listen appears to rely solely on inbound and sales
MH-1: Ads agent builds campaigns targeting research operations managers, product teams, and insights leaders on LinkedIn and search, highlighting speed and autonomy advantages
Strong customer logos and credibility but limited published research, case studies, or founder content positioning Listen as research methodology innovators
MH-1: Content agent publishes autonomous research playbooks, industry benchmarks from Listen studies, and Marshall's perspective on AI-powered research transformation
Early-stage company likely focused on new customer acquisition over expansion revenue; no visible customer success or upsell content addressing research at scale
MH-1: Lifecycle agent drives expansion into new research use cases (competitive intelligence, product validation, market expansion) and builds cross-functional buying alignment
Top Growth Opportunities
Competitors don't quantify speed and cost savings versus traditional research firms. Listen can own the 'hours vs weeks' positioning with data-backed case studies
Content and ads agents create buyer-stage content around research velocity metrics and TCO comparisons to drive mid-market deal velocity
Research leaders increasingly ask Claude and ChatGPT about research tools; Listen has minimal presence in these high-intent research moments
AEO agent targets LLM context windows with methodology guides, research automation white papers, and Listen case studies to capture AI-native buyer behavior
Marshall's CMU background and AI research expertise are underlevered; competing founders are visible in industry conversations about AI-powered insights
LinkedIn and content agents amplify Marshall's perspective on autonomous research, recruiting narrative around founding team, and research methodology innovations
3 Humans + 7 AI Agents
A dedicated marketing team built specifically for Listen Labs. The humans handle strategy and judgment. The AI agents handle execution at scale.
Human Experts
Owns Listen Labs's growth roadmap. Pipeline strategy, account expansion playbooks, board-ready reporting. Translates AI insights into revenue.
Runs paid acquisition across LinkedIn and Google. Manages creative testing, budget allocation, and pipeline attribution.
Builds thought leadership on LinkedIn. Creates long-form content targeting your ICP. Manages the content-to-pipeline engine.
AI Agents
Monitors AI citation visibility across 6 LLMs weekly. Builds content targeting category queries to increase Listen Labs's presence in AI-generated answers.
Produces LinkedIn ad variants targeting your ICP. Tests headlines, visuals, and offers at 10x the speed of manual production.
Builds lifecycle sequences: onboarding, expansion triggers, champion nurture, and re-engagement for dormant accounts.
Founder thought leadership. Builds the narrative that drives enterprise inbound from senior decision-makers.
Tracks competitors. Monitors positioning changes, ad spend, content strategy. Informs your counter-positioning.
Attribution by channel, pipeline velocity, budget waste detection. Weekly synthesis reports with AI-generated recommendations.
Weekly market intelligence digest curated from Listen Labs's industry signals. Positions you as the intelligence layer. Drives inbound pipeline from subscribers.
Active Workflows
Here's what the MH-1 system would be doing for Listen Labs from week 1.
AEO agent monitors Claude, ChatGPT, and Perplexity queries about market research tools and AI research automation, ensuring Listen methodology guides and case studies surface in LLM recommendations
Marshall's LinkedIn agent publishes weekly insights on autonomous research trends, Listen customer wins, and hiring for research operations roles to build founder credibility with buyer audience
Paid agent runs LinkedIn and search campaigns targeting research operations managers, product insights leaders, and chief customer officers with Listen speed/cost advantages and autonomous research demos
Lifecycle agent identifies expansion opportunities in existing accounts by analyzing research frequency, proposing new use cases (competitive intelligence, pricing research, market expansion studies) and building cross-functional stakeholder alignment
Competitive watch agent monitors NWO, Resonance AI, Luminoso messaging and positioning, alerting Listen team to differentiation gaps and emerging buyer objections
Pipeline intelligence agent enriches research operations teams and insights functions at target accounts, mapping buying committees and orchestrating multi-touch sequences through LinkedIn, email, and content
Traditional Marketing vs. MH-1
Traditional Approach
MH-1 System
Audit. Sprint. Optimize.
3 phases. Real output every 2 weeks. You see results, not decks.
AI Audit + Growth Roadmap
Full diagnostic of Listen Labs's marketing infrastructure: SEO, AEO visibility, paid, content, lifecycle. Prioritized roadmap tied to pipeline metrics. Delivered in 7 days.
Sprint-Based Execution
2-week sprint cycles. Real campaigns, not presentations. Each sprint ships measurable output across your priority channels.
Compounding Intelligence
AI agents monitor your channels 24/7. They catch budget waste, detect creative fatigue, track AI citation changes, and run A/B experiments autonomously. Week 12 is measurably better than week 1.
AI Marketing Operating System
3 elite humans + AI agents operating your growth system
Output multiplier: ~10x output at a fraction of the cost. The system gets smarter every week.
Month-to-month. Cancel anytime.
Common Questions
How does MH-1 differ from a marketing agency?
MH-1 pairs 3 elite human marketers with 7 AI agents. The humans handle strategy, creative direction, and judgment calls. The AI agents handle execution at scale: generating ad variants, monitoring competitors, building email sequences, tracking citations across LLMs, running A/B experiments autonomously. You get the quality of a senior marketing team with the output volume of a 15-person department.
What kind of results can we expect in the first 90 days?
MH-1 immediately activates AEO and content agents to establish Listen thought leadership around autonomous research, builds paid campaigns targeting research operations roles on LinkedIn, and orchestrates outbound sequences to research-heavy teams at enterprise accounts. Concurrently, competitive watch and lifecycle agents identify expansion opportunities in existing customers and monitor competitor positioning. By day 90, you'll have consistent visibility across LLM discovery, new pipeline from paid and outbound, and expansion revenue opportunities from existing customer base.
How does AEO help Listen reach research buyers in AI agents?
Research leaders and product teams increasingly ask ChatGPT and Claude about market research tools and methodologies. AEO ensures Listen's autonomous research methodology, speed advantages, and customer case studies surface in these high-intent LLM queries, capturing buyers at the moment they're evaluating research platforms and competing against traditional firms.
Can we cancel anytime?
Yes. MH-1 is month-to-month with no long-term contracts. We earn your business every sprint. That said, compounding effects kick in around month 3 as the AI agents accumulate data and the system learns what works for Listen Labs specifically.
How is this page personalized for Listen Labs?
This page was researched, audited, and generated using the same AI infrastructure we deploy for clients. The channel scores, team mapping, growth opportunities, and recommended agents are all based on real analysis of Listen Labs's current marketing. This is a live demo of MH-1's capabilities.
Turn research velocity into a competitive advantage with autonomous demand generation
The system gets smarter every cycle. Let's talk about building it for Listen Labs.
Book a Strategy CallMonth-to-month. Cancel anytime.