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 IXOPAY SDR Email:
Why this fails: The prospect is an expert. They've seen this template 1,000 times. There's zero indication you understand their specific situation. 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 compliance people" (job postings - everyone sees this)
Start: "Your Stripe declines EU Mastercard at 14% while Adyen approves 92% of identical profiles" (aggregated processor performance data)
PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use behavioral signals like card routing patterns or expansion geography.
PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, processor comparisons already benchmarked, geographic payment data already aggregated - whether they buy or not.
These messages are ordered by quality score. The best plays appear first, regardless of whether they use public or proprietary data.
Use aggregated authorization data across your payment network to show merchants which processors are over-declining specific transaction types in their geography, with quantified recovery opportunities.
You're revealing blind spots in their current payment stack. Most merchants accept processor decline decisions as final without realizing different processors approve/decline the same card profiles at dramatically different rates. The specificity of "Stripe declines 8% higher than Adyen for EU Mastercard" proves you're not guessing - you have real cross-processor visibility they lack.
This play requires aggregated authorization rates across 30+ merchants per processor per geography, reported as ranges and percentiles without exposing individual merchant volumes.
This is proprietary data only you have - competitors cannot replicate this play.Use aggregated payment method performance data from your customer base to show merchants entering new geographies exactly which local payment methods they need to prioritize for optimal conversion.
Geographic expansion is expensive and risky. Merchants assume their existing payment method mix (usually Visa/Mastercard dominance) will work everywhere. You're giving them regional intelligence they can't get from individual processors, who only see their own slice. The insight "iDEAL converts 31% better than cards in Netherlands" is immediately actionable and prevents costly expansion mistakes.
This play requires aggregated conversion rates by payment method and geography across 15+ merchants per region.
Combined with public expansion signals from funding announcements and state license filings. This synthesis is unique to your business.Reveal non-obvious timing patterns in processor decline rates that merchants wouldn't discover without cross-processor visibility.
This is a genuinely surprising insight. Most merchants analyze their own decline rates but never compare processor behavior across days/times. The fact that Stripe specifically spikes on Saturdays while Adyen stays flat is the kind of pattern you'd only see with aggregated cross-processor data. It's immediately actionable through time-based routing.
This play requires aggregated authorization rates by processor, day of week, and time patterns across your payment network.
This is proprietary data only you have - competitors cannot replicate this play.Show merchants which processors handle Strong Customer Authentication (3DS) most successfully, addressing a critical pain point for EU transactions.
3DS is a regulatory requirement in Europe that adds friction and failure points. Merchants know it's necessary but don't realize processors vary dramatically in 3DS success rates. The insight "Worldpay rejects 3DS attempts 12% more than Checkout.com" directly addresses a compliance pain point with a clear optimization path.
This play requires aggregated 3DS authentication success rates across processors for EU transactions.
This is proprietary data only you have - competitors cannot replicate this play.Provide dramatic conversion data for India showing UPI's dominance over traditional cards, preventing expansion mistakes.
The conversion difference is so dramatic (47% vs 22%) that it becomes impossible to ignore. Merchants expanding to India without this insight will lose half their potential revenue. The specificity of the data makes it credible and immediately actionable.
This play requires aggregated conversion rates by payment method across Asia-Pacific markets.
This is proprietary data only you have - competitors cannot replicate this play.Reveal non-obvious patterns where high-value transactions decline at significantly higher rates, with processor-specific routing recommendations.
This is a blind spot for most merchants. They focus on overall decline rates without segmenting by transaction size. The insight that $500+ orders decline 28% more is both surprising and valuable - these are the highest-margin transactions. Routing high-value orders to processors with better fraud score tolerance has immediate revenue impact.
This play requires aggregated authorization rates by transaction value ranges across processors.
This is proprietary data only you have - competitors cannot replicate this play.Provide non-obvious insights about which card issuers perform better with specific processors, enabling issuer-based routing optimization.
This is an extremely specific optimization that merchants would never discover on their own. The relationship between issuing bank and processor performance is hidden from single-processor merchants. If Chase cards are common in their customer base, this insight delivers immediate value through better routing.
This play requires aggregated authorization rates by issuing bank and processor combination.
This is proprietary data only you have - competitors cannot replicate this play.Show dramatic conversion differences between cash payment methods (like OXXO) and cards in Latin American markets.
The conversion difference is so stark (39% vs 18%) that it fundamentally changes expansion strategy. Merchants assuming card-first checkout will work globally need this regional intelligence. The insight prevents losing the majority of potential customers in cash-preferred markets.
This play requires aggregated cash payment method vs card conversion data across Latin American markets.
This is proprietary data only you have - competitors cannot replicate this play.Combine internal authorization data with public regional acquiring regulations to reveal geographic authorization rate differences for specific card networks.
This is a non-obvious cross-border optimization strategy. Merchants expanding to DACH region wouldn't realize AMEX approval rates vary so dramatically by geography. The routing recommendation (route AMEX through UK acquiring instead) is specific and actionable.
This play requires aggregated AMEX authorization data by geography combined with public regional acquiring regulations.
This synthesis is unique to your business.Identify merchants expanding to Southeast Asia who are missing critical regional e-wallet integration, using proprietary conversion data to quantify the impact.
The question assumes they're missing a critical capability and quantifies the consequence (63% conversion loss). This creates urgency around expansion planning. The routing question makes it conversational while surfacing a technical decision-maker.
This play requires aggregated e-wallet vs card conversion rates across Southeast Asian countries.
This is proprietary data only you have - competitors cannot replicate this play.Use proprietary regional payment data to identify merchants expanding to CEE markets without local payment method support.
The question surfaces a specific technical gap (Przelewy24 integration) with quantified impact (41% of transactions). This helps them avoid a costly expansion mistake. The routing question identifies who owns payment integration decisions.
This play requires aggregated payment method adoption and conversion data for Central/Eastern European markets.
This is proprietary data only you have - competitors cannot replicate this play.Identify merchants missing automated cross-processor retry capabilities using proprietary decline recovery data.
The question exposes a specific technical gap (retry routing) with quantified recovery potential (87% success on retry). This assumes they're missing a sophisticated capability and creates urgency to implement it.
This play requires aggregated decline recovery success data across processors by BIN and decline reason.
This is proprietary data only you have - competitors cannot replicate this play.Identify B2B merchants who aren't segmenting corporate card routing, using proprietary authorization data to quantify the opportunity.
This surfaces a blind spot for B2B payment operations. The insight that corporate cards authorize 23% better on specific processors is non-obvious and valuable. The question helps identify who owns payment routing decisions.
This play requires aggregated authorization rates by card type (consumer vs corporate) across processors.
This is proprietary data only you have - competitors cannot replicate this play.Identify merchants losing prepaid card customers due to processor-specific decline patterns, using proprietary authorization data.
This addresses a specific customer segment (gift card users, prepaid card holders) that merchants want to capture. The insight that prepaid cards decline 34% more on Stripe but work fine on Checkout.com is non-obvious and actionable.
This play requires aggregated authorization rates for prepaid vs standard cards across processors.
This is proprietary data only you have - competitors cannot replicate this play.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use proprietary payment data to find companies making specific payment infrastructure mistakes. Then show them exactly what they're losing.
Why this works: When you lead with "Your Stripe declines EU Mastercard 8% higher than Adyen - that's $2.1M recoverable" instead of "I see you're hiring payment engineers," you're not another sales email. You're the person with cross-processor visibility they lack.
The messages above aren't templates. They're examples of what happens when you combine proprietary transaction data with public expansion signals. Your team can replicate this using the data recipes in each play.
Every play traces back to verifiable data sources. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| IXOPAY Internal Transaction Data | authorization success/failure, decline reasons, retry success rates, card type, geography, processor, 3DS authentication, transaction value, issuing bank, day of week | All PVP plays - processor performance benchmarking, payment method optimization, decline recovery intelligence |
| Crunchbase Fintech Funding & Company Database | funding round, funding amount, expansion geography | Identifying geographic expansion signals for payment method optimization |
| NMLS Consumer Access Money Transmitter Database | license date, state jurisdiction, license status | Identifying money transmitters expanding to new states (compliance and processor integration triggers) |
| Public Regional Acquiring Regulations | cross-border acquiring rules, regional card network requirements | Cross-border authorization optimization (combined with internal data) |