Founder of Blueprint. I help companies stop sending emails nobody wants to read.
The problem with outbound isn't the message. It's the list. When you know WHO to target and WHY they need you right now, the message writes itself.
I built this system using government databases, public records, and 25 million job posts to find pain signals most companies miss. Predictable Revenue is dead. Data-driven intelligence is what works now.
Your GTM team is buying lists from ZoomInfo, adding "personalization" like mentioning a LinkedIn post, then blasting generic messages about features. Here's what it actually looks like:
The Typical Voyantis SDR Email:
Why this fails: The prospect is a VP of Growth who gets 30 of these per day. There's no indication you understand their specific channel mix problem, their actual unit economics, or why they need this right now. The "I noticed you're hiring" line is what every sales email says. Delete.
Blueprint flips the approach. Instead of interrupting prospects with pitches, you deliver insights so valuable they'd pay consulting fees to receive them.
Stop: "I see you're hiring for growth roles" (job postings - everyone sees this)
Start: "Your Q3 guidance miss of 18% happened right after you cut TikTok spend from 31% to 12%" (public earnings + ad spend intelligence)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use public earnings data, ad spend patterns, and timeline correlation.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - benchmark analysis already done, channel optimization scenarios already built, competitive intelligence already synthesized - whether they buy or not.
These messages demonstrate such precise understanding of the prospect's current situation that they feel genuinely seen. Every claim traces to specific public data sources with verifiable numbers and timelines.
Target fintech and SaaS companies that missed quarterly revenue guidance and have publicly disclosed or observable ad spend patterns showing channel reallocation. Cross-reference SEC filings with marketing intelligence platforms to identify companies that increased Meta spend while cutting TikTok, then still missed guidance.
Growth leaders are under intense pressure after missing guidance. The specific numbers (18% miss, 34% Meta increase) prove you've done research beyond reading their earnings release. Asking "Is someone reviewing your channel allocation strategy?" is diagnostic not accusatory - it helps them identify the internal gap.
Access to ad spend data by channel (Meta, TikTok, Google) which could come from marketing intelligence platforms, direct integrations, or partnerships with ad networks
If you have this data, this insight becomes genuinely differentiated - most competitors can't connect channel reallocation to guidance misses.Target growth-stage companies (fintech, DTC, SaaS) that reduced TikTok ad spend significantly in the quarter before missing revenue guidance, while simultaneously increasing Meta spend. The timeline correlation makes the causal connection clearer and more actionable.
The specific timeline (TikTok fell from 31% to 12% in Q2, then Q3 guidance miss) creates a causal story that's harder to dismiss than correlation alone. The question "Who owns your cross-channel budget allocation?" is organizationally diagnostic - it helps them identify who should be in the conversation.
Quarterly ad spend tracking by channel (TikTok, Meta) cross-referenced with public earnings announcements to identify correlation patterns
This requires consistent tracking of ad spend changes over time - the timeline specificity is what makes this message powerful.Target fintech companies where you can calculate or estimate their LTV:CAC ratio from public disclosures, then compare it to your aggregated benchmark data. Focus on companies at 1.8-2.2x ratio (below the 3:1 profitability threshold) who are likely facing CFO pressure to limit growth spend.
You're doing the unit economics math they should be doing internally, using their own numbers. The 3:1 threshold is industry-standard, making your diagnosis credible. The hypothesis "Is your CFO limiting growth spend due to unit economics?" is probably accurate and helps them articulate why they're having internal budget battles.
Aggregated customer lifetime value data across your fintech customer base, enabling you to calculate median LTV by segment and compare to industry benchmarks
This requires deep data on your existing customers' LTV - combined with public CAC data, this creates unique competitive intelligence.Target fintech companies with CAC significantly above your aggregated benchmarks (top 75th percentile). Use your internal data on top-performing customers to show what the best companies do differently tactically - optimizing for 90-day LTV prediction instead of CPA.
You're not just saying "you're underperforming" - you're explaining exactly WHAT top performers do differently (optimize for 90-day LTV vs CPA, feed signals to ad platforms within 7 days). The diagnostic question "Is your team optimizing for CPA or predicted LTV?" helps them identify their current approach and see the gap.
Deep data on your top-performing customers' strategies and tactics, including what optimization signals they use (90-day LTV predictions) and timing of platform feedback (7 days)
This is highly differentiated intelligence - you're sharing playbook insights from your best customers that competitors can't access.Target companies that increased Meta spend significantly while cutting TikTok, then missed revenue guidance. Calculate the implied customer quality difference (2.7x in this example) to show that TikTok was delivering better customers per dollar, not just cheaper volume.
The 2.7x customer quality calculation reframes the entire conversation from "volume" to "quality" - which is exactly what Voyantis helps solve. The diagnostic question "Are you tracking LTV by acquisition channel or just CPA?" hits the root cause - they're probably optimizing for the wrong metric and didn't realize it.
Customer LTV data by acquisition channel across your customer base, enabling you to calculate relative channel efficiency and quality differences
The 2.7x calculation is what makes this message powerful - it quantifies customer quality, not just acquisition volume.These messages provide actionable intelligence before asking for anything. The prospect can use this value today whether they respond or not.
Provide fintech companies with their exact CAC compared to your aggregated dataset of 847 fintech companies, segmented by ACV and business model. Give them the benchmark range that similar companies achieve within 6 months of optimization.
The 847-company dataset is massive and credible - they can't access this benchmark data anywhere else. The specific target range ($140-180 CAC) is immediately actionable for their internal planning. The low-commitment ask for the report makes it easy to say yes and continue the conversation.
Aggregated CAC and ACV data across 800+ fintech customers, with the ability to segment by business model (subscription, transaction-based, etc.) and company stage
This is a defensible competitive moat - the 847-company benchmark dataset is unique and can't be replicated by competitors.Provide fintech companies with their exact percentile ranking for CAC performance compared to your aggregated dataset. Position them in the 89th percentile (bottom performers) with median benchmark for comparison, then offer the top 10 drivers you see in bottom quartile performers.
The percentile framing is brutal but specific - knowing they're in the 89th percentile (worst performers) is embarrassing but extremely valuable information they can't get elsewhere. The "top 10 drivers" offer is concrete and useful for their internal improvement efforts. This helps them argue for budget and organizational changes internally.
Comprehensive benchmark database maintaining CAC, ACV, and performance metrics across 800+ fintech customers, with percentile ranking capabilities and analysis of bottom-quartile performance drivers
The percentile ranking + top 10 drivers analysis is uniquely valuable - this is intelligence they absolutely cannot access anywhere else.Build custom optimization scenarios specifically for the target company showing projected efficiency gains from channel rebalancing (shifting 15% of Meta budget back to TikTok). Base scenarios on their current monthly ad spend and Q3 channel performance data.
You built something SPECIFICALLY for them - not a template or generic analysis. The 23% efficiency gain is a concrete, quantified benefit. Knowing their exact monthly ad spend ($2.1M) proves you've done deep research. Three scenarios gives them options to evaluate internally. This is genuinely useful even if they don't buy - it helps them optimize their own customer acquisition.
Q3 channel performance data (ROAS, conversion rates by channel) combined with public revenue/guidance information to build optimization scenarios with projected efficiency gains
Building custom scenarios specifically for the prospect is what makes this incredibly valuable - it's not a template, it's personalized analysis they can use immediately.Analyze the correlation between TikTok budget cuts and Meta ROAS decline over 90 days, revealing that TikTok was driving higher-intent traffic that Meta retargeting campaigns depended on. Offer day-by-day correlation analysis showing the causal mechanism.
The specific ROAS numbers and 90-day timeline are concrete and verifiable. The cross-channel dependency insight (TikTok driving high-intent traffic for Meta retargeting) is NOT obvious and genuinely valuable - they almost certainly didn't see this correlation themselves. The causal mechanism makes total sense once explained. This is genuinely valuable intelligence that improves their acquisition strategy.
Daily/weekly ROAS tracking by channel cross-referenced with budget allocation changes to identify correlation patterns and cross-channel dependencies
This is genuinely differentiated analysis - identifying that TikTok drove high-intent traffic for Meta retargeting is non-obvious and immediately actionable.Provide anonymized case studies from 213 companies that reduced CAC by 35-60% within 4 months using predicted LTV optimization. Show the tactical shift (14-day LTV predictions vs 90-day actuals) that enabled value-based bidding optimization.
213 companies is a massive sample size that creates credibility. The 35-60% CAC reduction range is concrete and compelling. The tactical shift (14-day predictions vs 90-day actuals) is specific enough that they could potentially try this approach themselves - you're giving them value whether they buy or not. Case studies from similar companies are immediately actionable for their internal planning.
Customer success metrics tracking CAC reduction timelines, specific optimization tactics used by successful customers, and results achieved within defined time periods
This is a concrete playbook the recipient can use to improve their own acquisition efficiency - the tactical specificity (14-day predictions vs 90-day actuals) makes it actionable immediately.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use channel performance data and benchmark intelligence to find companies with specific profitability problems. Then mirror that situation back to them with evidence.
Why this works: When you lead with "Your $340 CAC is 3.2x the fintech median" instead of "I see you're hiring for growth roles," you're not another sales email. You're the person who has access to benchmark data they need but can't get anywhere else.
The messages above aren't templates. They're examples of what happens when you combine public earnings data, ad spend intelligence, and proprietary benchmark datasets. Your team can replicate this by building similar data assets from your customer base.
Every play traces back to verifiable data sources. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| SEC EDGAR | customer_acquisition_cost, lifetime_value_metrics, churn_rate_disclosed, marketing_spend, guidance_revisions | Public Guidance Miss, Down Round Multi-Signal |
| Crunchbase | funding_stage, total_funding, funding_timeline, deal_type, valuation_trends | Post-Funding Acquisition Quality, Down Round Multi-Signal |
| Sensor Tower | daily_active_users, monthly_active_users, retention_rates_30d_90d_180d, app_install_trends | Post-Funding Acquisition Quality, Complaint Surge with Retention Collapse |
| CFPB Consumer Complaints | company_name, complaint_category, submitted_date, complaint_status | Complaint Surge with Retention Collapse, Down Round Multi-Signal |
| LinkedIn Economic Graph | job_posting_volume_trends, new_hire_counts, hiring_growth_rate, role_types_hiring | Post-Funding Acquisition Quality, Complaint Surge with Retention Collapse |
| Marketing Intelligence Platforms | ad_spend_by_channel, ROAS_trends, conversion_rates, budget_allocation_changes | Channel Mix Problem, Channel Quality Efficiency, Cross-Channel Dependency |
| Voyantis Internal Customer Data | cohort_profitability, CAC_benchmarks, LTV_by_channel, payback_periods, optimization_strategies | Vertical Profitability Benchmarks, Top Quartile Strategy, CAC Reduction Playbook |