Case Study
Built a Repeatable Playbook for a Supply Chain AI Startup
Background & Problem Statement
Company details were redacted in order to respect the privacy of our client.
Client Profile: Early-stage supply chain analytics startup targeting scaling consumer brands in the $5M–$50M revenue range.
Ideal Customer Profile:
Beauty, Apparel, and CPG brands with 20–100+ SKUs
Selling through multiple channels (DTC, retail, Amazon, wholesale)
Pre-ERP or ERP-skeptical but feeling operational strain
Lean ops/finance teams managing complex procurement, cost tracking, and inventory workflows manually
Primary Challenge: The client's growth team was working with a large, stale lead list and lacked a systematic process to identify the highest-fit accounts. Static firmographic signals alone were failing to surface the right targets, especially those struggling with SKU-level data fragmentation — their primary pain point.
The Process: Creating a Systematic Outbound Engine
Blossomer implemented a structured, experiment-driven outbound process, focusing on advanced targeting, signal enrichment, and personalized engagement to unlock meaningful pipeline.
Phase 1: Advanced Targeting & Signal-Based Prospecting
The Problem: The client's static, unrefined lead list did not effectively identify companies experiencing SKU complexity pain — missing high-fit accounts stuck in spreadsheet chaos.
Our Approach:
Enriched a list of 20,000 stale leads to uncover signal-based indicators of operational pain.
Qualified 200 high-priority accounts based on SKU complexity, funding stage, hiring signals, tech stack fit, and leadership changes.
Discovered that 75% of qualified accounts (150 of 200) would have been missed if relying on static signals alone.
Advanced Research Differentiator: By layering behavioral and intent signals (hiring patterns, product launches, engagement with ERP-adjacent topics) on top of static firmographics, we dramatically improved target accuracy over baseline methods.
Phase 2: Historical Success Pattern Analysis
The Problem: Past outbound efforts lacked clarity on what defined a "good" target, leading to wasted effort on low-fit accounts.
Our Approach:
Analyzed previous campaign engagement data to identify success patterns.
Benchmarked signal-weighted scoring vs. traditional list-building methods.
Prioritized signals tied to actual engagement (ERP mentions, hiring activity, SKU complexity).
Key Findings:
Signal-enriched accounts were 3x more likely to engage than accounts selected via static signals alone.
Engagement rates for signal-qualified accounts were 2x higher than previous outbound efforts.
Pattern Recognition Impact: This backtesting validated our scoring approach and focused outbound resources on the right targets — increasing efficiency and engagement quality.
Phase 3: Messaging Framework & Conversion Optimization
The Problem: Prior outbound messaging lacked resonance with the true pain points of high-priority personas, reducing response rates.
Our Approach:
Developed persona-specific messaging mapped to operational pain (SKU visibility, ops-finance misalignment).
Anchored outreach in observed signals (tech stack fit, hiring activity, LinkedIn engagement).
Ran structured A/B tests on messaging variants.
Test Results:
High-touch, signal-personalized messaging achieved open rates over 50%.
Reply rates exceeded 5%, more than double previous outbound efforts.
Phase 4: Systematize, Automate, and Scale
The Problem: Without structured infrastructure, high-performing campaigns couldn’t scale reliably.
Our Approach:
Built lead enrichment and scoring workflows using tools like Clay, Builtwith, and Trigify.
Integrated CRM tracking and engagement metrics.
Automated signal monitoring and prioritization for future outreach cycles.
The Results: Measurable, Data-Validated Outcomes
3x increase in engagement likelihood compared to static lead lists
2x+ improvement in outbound reply rates through signal-driven qualification and messaging
75% of high-priority accounts unlocked exclusively through enriched signal data — accounts that would have been missed by standard approaches
Resources
Background & Problem Statement
Company details were redacted in order to respect the privacy of our client.
Client Profile: Early-stage supply chain analytics startup targeting scaling consumer brands in the $5M–$50M revenue range.
Ideal Customer Profile:
Beauty, Apparel, and CPG brands with 20–100+ SKUs
Selling through multiple channels (DTC, retail, Amazon, wholesale)
Pre-ERP or ERP-skeptical but feeling operational strain
Lean ops/finance teams managing complex procurement, cost tracking, and inventory workflows manually
Primary Challenge: The client's growth team was working with a large, stale lead list and lacked a systematic process to identify the highest-fit accounts. Static firmographic signals alone were failing to surface the right targets, especially those struggling with SKU-level data fragmentation — their primary pain point.
The Process: Creating a Systematic Outbound Engine
Blossomer implemented a structured, experiment-driven outbound process, focusing on advanced targeting, signal enrichment, and personalized engagement to unlock meaningful pipeline.
Phase 1: Advanced Targeting & Signal-Based Prospecting
The Problem: The client's static, unrefined lead list did not effectively identify companies experiencing SKU complexity pain — missing high-fit accounts stuck in spreadsheet chaos.
Our Approach:
Enriched a list of 20,000 stale leads to uncover signal-based indicators of operational pain.
Qualified 200 high-priority accounts based on SKU complexity, funding stage, hiring signals, tech stack fit, and leadership changes.
Discovered that 75% of qualified accounts (150 of 200) would have been missed if relying on static signals alone.
Advanced Research Differentiator: By layering behavioral and intent signals (hiring patterns, product launches, engagement with ERP-adjacent topics) on top of static firmographics, we dramatically improved target accuracy over baseline methods.
Phase 2: Historical Success Pattern Analysis
The Problem: Past outbound efforts lacked clarity on what defined a "good" target, leading to wasted effort on low-fit accounts.
Our Approach:
Analyzed previous campaign engagement data to identify success patterns.
Benchmarked signal-weighted scoring vs. traditional list-building methods.
Prioritized signals tied to actual engagement (ERP mentions, hiring activity, SKU complexity).
Key Findings:
Signal-enriched accounts were 3x more likely to engage than accounts selected via static signals alone.
Engagement rates for signal-qualified accounts were 2x higher than previous outbound efforts.
Pattern Recognition Impact: This backtesting validated our scoring approach and focused outbound resources on the right targets — increasing efficiency and engagement quality.
Phase 3: Messaging Framework & Conversion Optimization
The Problem: Prior outbound messaging lacked resonance with the true pain points of high-priority personas, reducing response rates.
Our Approach:
Developed persona-specific messaging mapped to operational pain (SKU visibility, ops-finance misalignment).
Anchored outreach in observed signals (tech stack fit, hiring activity, LinkedIn engagement).
Ran structured A/B tests on messaging variants.
Test Results:
High-touch, signal-personalized messaging achieved open rates over 50%.
Reply rates exceeded 5%, more than double previous outbound efforts.
Phase 4: Systematize, Automate, and Scale
The Problem: Without structured infrastructure, high-performing campaigns couldn’t scale reliably.
Our Approach:
Built lead enrichment and scoring workflows using tools like Clay, Builtwith, and Trigify.
Integrated CRM tracking and engagement metrics.
Automated signal monitoring and prioritization for future outreach cycles.
The Results: Measurable, Data-Validated Outcomes
3x increase in engagement likelihood compared to static lead lists
2x+ improvement in outbound reply rates through signal-driven qualification and messaging
75% of high-priority accounts unlocked exclusively through enriched signal data — accounts that would have been missed by standard approaches
Resources
Background & Problem Statement
Company details were redacted in order to respect the privacy of our client.
Client Profile: Early-stage supply chain analytics startup targeting scaling consumer brands in the $5M–$50M revenue range.
Ideal Customer Profile:
Beauty, Apparel, and CPG brands with 20–100+ SKUs
Selling through multiple channels (DTC, retail, Amazon, wholesale)
Pre-ERP or ERP-skeptical but feeling operational strain
Lean ops/finance teams managing complex procurement, cost tracking, and inventory workflows manually
Primary Challenge: The client's growth team was working with a large, stale lead list and lacked a systematic process to identify the highest-fit accounts. Static firmographic signals alone were failing to surface the right targets, especially those struggling with SKU-level data fragmentation — their primary pain point.
The Process: Creating a Systematic Outbound Engine
Blossomer implemented a structured, experiment-driven outbound process, focusing on advanced targeting, signal enrichment, and personalized engagement to unlock meaningful pipeline.
Phase 1: Advanced Targeting & Signal-Based Prospecting
The Problem: The client's static, unrefined lead list did not effectively identify companies experiencing SKU complexity pain — missing high-fit accounts stuck in spreadsheet chaos.
Our Approach:
Enriched a list of 20,000 stale leads to uncover signal-based indicators of operational pain.
Qualified 200 high-priority accounts based on SKU complexity, funding stage, hiring signals, tech stack fit, and leadership changes.
Discovered that 75% of qualified accounts (150 of 200) would have been missed if relying on static signals alone.
Advanced Research Differentiator: By layering behavioral and intent signals (hiring patterns, product launches, engagement with ERP-adjacent topics) on top of static firmographics, we dramatically improved target accuracy over baseline methods.
Phase 2: Historical Success Pattern Analysis
The Problem: Past outbound efforts lacked clarity on what defined a "good" target, leading to wasted effort on low-fit accounts.
Our Approach:
Analyzed previous campaign engagement data to identify success patterns.
Benchmarked signal-weighted scoring vs. traditional list-building methods.
Prioritized signals tied to actual engagement (ERP mentions, hiring activity, SKU complexity).
Key Findings:
Signal-enriched accounts were 3x more likely to engage than accounts selected via static signals alone.
Engagement rates for signal-qualified accounts were 2x higher than previous outbound efforts.
Pattern Recognition Impact: This backtesting validated our scoring approach and focused outbound resources on the right targets — increasing efficiency and engagement quality.
Phase 3: Messaging Framework & Conversion Optimization
The Problem: Prior outbound messaging lacked resonance with the true pain points of high-priority personas, reducing response rates.
Our Approach:
Developed persona-specific messaging mapped to operational pain (SKU visibility, ops-finance misalignment).
Anchored outreach in observed signals (tech stack fit, hiring activity, LinkedIn engagement).
Ran structured A/B tests on messaging variants.
Test Results:
High-touch, signal-personalized messaging achieved open rates over 50%.
Reply rates exceeded 5%, more than double previous outbound efforts.
Phase 4: Systematize, Automate, and Scale
The Problem: Without structured infrastructure, high-performing campaigns couldn’t scale reliably.
Our Approach:
Built lead enrichment and scoring workflows using tools like Clay, Builtwith, and Trigify.
Integrated CRM tracking and engagement metrics.
Automated signal monitoring and prioritization for future outreach cycles.
The Results: Measurable, Data-Validated Outcomes
3x increase in engagement likelihood compared to static lead lists
2x+ improvement in outbound reply rates through signal-driven qualification and messaging
75% of high-priority accounts unlocked exclusively through enriched signal data — accounts that would have been missed by standard approaches
Resources
Background & Problem Statement
Company details were redacted in order to respect the privacy of our client.
Client Profile: Early-stage supply chain analytics startup targeting scaling consumer brands in the $5M–$50M revenue range.
Ideal Customer Profile:
Beauty, Apparel, and CPG brands with 20–100+ SKUs
Selling through multiple channels (DTC, retail, Amazon, wholesale)
Pre-ERP or ERP-skeptical but feeling operational strain
Lean ops/finance teams managing complex procurement, cost tracking, and inventory workflows manually
Primary Challenge: The client's growth team was working with a large, stale lead list and lacked a systematic process to identify the highest-fit accounts. Static firmographic signals alone were failing to surface the right targets, especially those struggling with SKU-level data fragmentation — their primary pain point.
The Process: Creating a Systematic Outbound Engine
Blossomer implemented a structured, experiment-driven outbound process, focusing on advanced targeting, signal enrichment, and personalized engagement to unlock meaningful pipeline.
Phase 1: Advanced Targeting & Signal-Based Prospecting
The Problem: The client's static, unrefined lead list did not effectively identify companies experiencing SKU complexity pain — missing high-fit accounts stuck in spreadsheet chaos.
Our Approach:
Enriched a list of 20,000 stale leads to uncover signal-based indicators of operational pain.
Qualified 200 high-priority accounts based on SKU complexity, funding stage, hiring signals, tech stack fit, and leadership changes.
Discovered that 75% of qualified accounts (150 of 200) would have been missed if relying on static signals alone.
Advanced Research Differentiator: By layering behavioral and intent signals (hiring patterns, product launches, engagement with ERP-adjacent topics) on top of static firmographics, we dramatically improved target accuracy over baseline methods.
Phase 2: Historical Success Pattern Analysis
The Problem: Past outbound efforts lacked clarity on what defined a "good" target, leading to wasted effort on low-fit accounts.
Our Approach:
Analyzed previous campaign engagement data to identify success patterns.
Benchmarked signal-weighted scoring vs. traditional list-building methods.
Prioritized signals tied to actual engagement (ERP mentions, hiring activity, SKU complexity).
Key Findings:
Signal-enriched accounts were 3x more likely to engage than accounts selected via static signals alone.
Engagement rates for signal-qualified accounts were 2x higher than previous outbound efforts.
Pattern Recognition Impact: This backtesting validated our scoring approach and focused outbound resources on the right targets — increasing efficiency and engagement quality.
Phase 3: Messaging Framework & Conversion Optimization
The Problem: Prior outbound messaging lacked resonance with the true pain points of high-priority personas, reducing response rates.
Our Approach:
Developed persona-specific messaging mapped to operational pain (SKU visibility, ops-finance misalignment).
Anchored outreach in observed signals (tech stack fit, hiring activity, LinkedIn engagement).
Ran structured A/B tests on messaging variants.
Test Results:
High-touch, signal-personalized messaging achieved open rates over 50%.
Reply rates exceeded 5%, more than double previous outbound efforts.
Phase 4: Systematize, Automate, and Scale
The Problem: Without structured infrastructure, high-performing campaigns couldn’t scale reliably.
Our Approach:
Built lead enrichment and scoring workflows using tools like Clay, Builtwith, and Trigify.
Integrated CRM tracking and engagement metrics.
Automated signal monitoring and prioritization for future outreach cycles.
The Results: Measurable, Data-Validated Outcomes
3x increase in engagement likelihood compared to static lead lists
2x+ improvement in outbound reply rates through signal-driven qualification and messaging
75% of high-priority accounts unlocked exclusively through enriched signal data — accounts that would have been missed by standard approaches
Resources
Start Building Your Outbound System
Book a free discovery call to understand how we work. You'll receive actionable strategies upfront, then decide if we're the right fit. We move fast, with no lengthy onboarding delays.
Start Building Your Outbound System
Book a free discovery call to understand how we work. You'll receive actionable strategies upfront, then decide if we're the right fit. We move fast, with no lengthy onboarding delays.
Start Building Your Outbound System
Book a free discovery call to understand how we work. You'll receive actionable strategies upfront, then decide if we're the right fit. We move fast, with no lengthy onboarding delays.
Start Building Your Outbound System
Book a free discovery call to understand how we work. You'll receive actionable strategies upfront, then decide if we're the right fit. We move fast, with no lengthy onboarding delays.
We help early-stage B2B SaaS founders land their first 100 customers through expertly built, fully managed outbound systems. Get qualified opportunities without hiring SDRs, and build predictable revenue with systems that scale.
Quick Links
Blossomer, LLC © 2024, All Rights Reserved
We help early-stage B2B SaaS founders land their first 100 customers through expertly built, fully managed outbound systems. Get qualified opportunities without hiring SDRs, and build predictable revenue with systems that scale.
Quick Links
Blossomer, LLC © 2024, All Rights Reserved
We help early-stage B2B SaaS founders land their first 100 customers through expertly built, fully managed outbound systems. Get qualified opportunities without hiring SDRs, and build predictable revenue with systems that scale.
Quick Links
Blossomer, LLC © 2024, All Rights Reserved
We help early-stage B2B SaaS founders land their first 100 customers through expertly built, fully managed outbound systems. Get qualified opportunities without hiring SDRs, and build predictable revenue with systems that scale.
Quick Links
Blossomer, LLC © 2024, All Rights Reserved