Why Signal-Based Propensity Analysis is the Only Way to Scale

The 1% Problem: Why Your Outbound is Stalling

The “spray and pray” era is officially over. The average email outreach reply rate has plummeted to 1%.

Here’s why, most teams are outbound blindly, trapped in what I call the “Snapshot Era” of Lead Scoring. 

They define an ICP, buy a static list of titles, and hit “send.” But a lead being “qualified” on paper doesn’t mean they have a propensity to buy right now.

To scale in 2026, modern revenue teams are moving toward Propensity Analysis, a research-backed methodology that shifts the focus from who the lead is to why they will convert today.

The Gro Propensity Model: How We Turn Data Into Deals

To scale revenue without increasing headcount, you need a system that thinks like a top-tier SDR but evolves at the speed of an LLM. 

We move from a database of 650M contacts to high-converting conversations via the 3-Layer Propensity Model:

Layer 1: The Static Fit (The AI ICP Canvas)

Before you send a message, you must ensure a “Fundamental Fit.” If a lead doesn’t pass this gate, they’re discarded to protect your domain reputation.

The Tech: You describe your business in natural language in the AI ICP Canvas. Gro uses Binary Filtering to cross-reference our 650M+ contact database against your specific product DNA.

The Result: You discard the 95% of the market that will never buy, focusing only on Tier-1 targets.

Layer 2: The Signal Stack (The Propensity Multiplier)

A “Fit” is a lead. A “Signal” is an opportunity.

This layer merges the trinity model analysis, Company, Personal, and Technology. To calculate the Propensity Score, the statistical likelihood that now is the right time to buy.

Company Layer: Is this the right company? Gro analyzes firmographics, industry, growth signals, recent funding, and market expansion to determine if they’re in your ICP and solving problems your product addresses.

Personal Layer: Who has the authority? Gro evaluates seniority fit. A VP of Sales has an 8.6/10 Seniority Score. A Sales Coordinator has a 2.1/10. You need to talk to the person who can actually sign the contract.

Technology Layer: What’s their incumbent solution? Gro scans the company’s technographics to determine if they have the tech infrastructure required to integrate with your solution. Are they using a competitor? Is their contract expiring?

The Logic: Based on our Calculation Logs, we don’t just score,we weigh. A persona match is the baseline, but if Gro detects high Tech Relevance, the total score is multiplied. This is why two identical VP of Sales contacts can have wildly different propensity scores. One is static. One is showing active buying signals.

Layer 3: Action (Contextual Intelligence  and Strategic Recommendations)

The final layer determines your angle and what to do next.

The Data: Gro analyzes the target’s personal and company LinkedIn profile and their latest posts from the last 90 days.

The Evidence Card: Our system summarizes these flags into a “Reasoning Block.” Instead of 20 minutes of manual research, your rep sees exactly why to reach out based on the prospect’s actual social activity and industry influence.

Stakeholder Recommendations:

Persuade or Give Up: Should we pursue this company and contact, or move on?

Campaign Recommendations: Which sequence and message angle will resonate best?

Advanced Stakeholder Mapping:

If the person isn’t the right fit, Gro automatically searches for the correct decision-makers within the same organization and lists them out

If the person is the right fit, Gro recommends relevant stakeholders within the organization for multi-threading

How to Implement Propensity Analysis Today

For a BDM or Sales Leader, implementing this requires a shift from “Manual Research” to “Automated Intelligence.”

The Manual Way: The “Research Tax”

If you aren’t using an AI engine, your reps are likely doing this manually. 

To find a single high-propensity lead, an SDR must open 5 separate tabs: 

LinkedIn (profile), Apollo (contact info), BuiltWith (Tech), Social Media (Hooks), and their CRM.

The Cost: This “Tab Tax” takes roughly 20 minutes per lead.

The Math: For a list of 50 leads, your rep spends 16.5 hours on research before sending a single email.

The Gro Way: Automated Proprietary Intelligence. Gro eliminates the research tax by consolidating the world’s largest data lakes into one interface.

Stop Scoring, Start Weighting: Use Calculation Logs to set multipliers. If Gro detects a Tech Relevance match of 8.4/10, the lead is automatically prioritized.

Kill the Tab Tax: The Evidence Card extracts the “Business Essence” and “Industry Fit” automatically. Your reps see the reasoning instantly without switching tools.

Focus on Depth over Breadth: Gro identifies the 50 leads with the highest propensity so you can ignore the noise of the 500 “average” leads.

The ROI of the Gro Propensity Engine

1. Reclaiming Your Week (Time)

By automating the research layer, you reclaim 16+ hours per week for every SDR on your team. That’s two full workdays given back to your reps to do what they do best, closing deals.

2. Consolidating the Stack (Money)

Stop paying separate bills for data, tech signals, and research tools. Gro provides native access to a 650M+ contact database, including our proprietary 60M APAC-focused first-party DB.

You slash your software overhead while gaining deeper data on markets that Western tools miss.

3. The Evidence Card Advantage (Results)

Our UI doesn’t just give you a number. It provides a breakdown of Tech Relevance, Persona Fit, and Industry Alignment. 

Your Evidence Card tells your rep exactly why the lead is hot: “This person is a match because they use [Specific Tech] and are influential in [Specific Industry].”

The goal isn’t to send more DMs or target more people. It’s to be the most relevant person in your prospect’s inbox at the moment they need you or are open to a solution.

Why Outbound Still Works (If You Do It Right)

Email isn’t dead. Cold outbound isn’t dead. But you have to solve one of the top three problems your buyer is facing right now.

Senior executives have discretionary budgets, $500K to $1M in “use it or lose it” money for their top-three needs. If you solve one of those problems, they have the money without CFO approval. But they don’t care about their eighth problem. They don’t care about nice-to-haves.

If you’re in the top three, you’re going to see high open rates and strong conversion, if you have a differentiated product and crystal clear value proposition. 

What’s changed is that your prospects’ priorities have shifted. Many now have dedicated AI budgets and pressure to bring innovation into the enterprise.

The Reality About Training AI Agents

If you tried an AI SDR tool before March 2025 and had a bad experience, that’s irrelevant now. Better LLMs changed everything. But even with better models, most companies are screwing it up by buying a tool and saying “figure it out” without training it properly.

Here’s what actually works: First, you need proven processes that work with humans. If you can’t close customers yourself, the AI can’t close them for you. 

Second, you tell the AI what worked, literally: “Here’s the script I used with a customer that I closed.” Third, you iterate that prompt and connect it to your data sources. Fourth, you have to ingest the data to keep improving.

As long as you’ve closed customers and know what messaging works, you can train an AI to be almost as good as you, and it’ll work 24/7 at scale.

Stop Paying the Research Tax

The teams that figure out propensity-led selling in the next 12-18 months are going to see 10X performance improvements. The teams that don’t will keep wondering why their reply rates are stuck at 1%.

The math is simple: 50 high-propensity leads will always outperform 500 spray-and-pray emails. Stop wasting your reps’ time on manual research. Stop wasting your prospects’ time with irrelevant outreach.

Build your first Propensity Model with Gro.

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