Sales Pipeline Automation for Consultants
How I used branching logic to automate the decision-making process of a Sales Manager, reducing "Speed to Lead" from hours to milliseconds.
Technical Brief
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01 The Problem
The client, a high-ticket consulting firm, was generating 50+ leads a month. While the volume was good, the quality was mixed. The sales team was spending 20+ hours a week on "discovery calls" with prospects who had no budget or weren't ready to buy.
The Cost: While reps talked to unqualified leads, high-value "Whales" were waiting too long for a response and going to competitors.
02 The Architecture
We moved away from simple linear automation (like Zapier) and built a Conditional Logic Engine using n8n. The system acts as a "Traffic Cop," evaluating every lead in real-time against a 3-step protocol:
Step 1: The Intake (Diagnostic)
We replaced the standard contact form with a "Diagnostic Quiz" that captures key data points upfront: Budget, Timeline, and Authority. This ensures we have the raw data needed for scoring.
Step 2: The Algorithm (Scoring)
The system computes a "Fit Score" (0-100) based on weighted variables:
Step 3: The Traffic Cop (Routing)
A Switch Node evaluates the final score. High Scores (>80) get an instant Calendly link. Low Scores are routed to an educational email drip to nurture them for the future.
Figure 1: The "Traffic Cop" Logic Flow implemented in n8n
03 Under the Hood
The core of the system is the routing logic. Here is a snippet of the JSON configuration used in the Switch Node to determine the path:
{
"rules": {
"rules": [
{
"value2": 80,
"output": 0,
"label": "High Intent (Score > 80)"
},
{
"value2": 40,
"output": 1,
"label": "Medium Intent (Score 40-79)"
}
]
},
"fallbackOutput": 2,
"label": "Low Intent (Score < 40)"
}
04 The Impact
Test the Algorithm
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Fit Auditor
Live Scoring Engine
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