01 / THE CHALLENGE
Problem Statement
The sales team was achieving an MQL-to-sale success rate of around 2%, largely due to targeting the wrong customers and reaching out at ineffective times.
The Result: Wasted effort and inefficient use of resources across the entire sales operation.
The Objective: Improve targeting and timing so that fewer calls are needed while significantly increasing conversion rates.
02 / THE ISSUE
Too Many Algorithms & Uncalibrated AI
The original configuration was overloaded with competing prioritisation methods that contradicted each other. Regional scoring, product scoring, and separate AI-driven scoring frameworks created operational noise rather than clear commercial signals.
03 / THE FIX
The Solution: One Unified Method
Transitioned from a fractured multi-model system to a single, focused, and effective qualification framework:
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Consolidated 3 Mechanisms Into 1
Removed competing prioritisation models that were issuing conflicting lead scores to sales reps.
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Removed Artificial Intelligence from the Workflow
The predictive AI models were adding noise rather than genuine signal. Replacing them with clear, deterministic scoring logic improved sales alignment immediately.
04 / STATISTICAL METHODOLOGY & EVIDENCE
Empirical Framework
To ensure full transparency regarding the performance shift documented in this case study, the following statistical parameters were applied across the evaluation period:
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Dataset Volume & Sample Size
Analysis was conducted across a total sample of 20,000 new inbound leads over an 18-month evaluation period, providing sufficient statistical power to rule out short-term variance.
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Strict Conversion Metric Definition ('Sale')
The conversion metric remained identical across all test phases. A conversion was strictly defined as lead creation to customer spending their first unit of currency with a verified 'Closed Won' deal logged directly within the CRM.
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Model Training & Out-of-Sample Validation
The simplified deterministic scoring logic was built and back-tested against 4 years worth of historical purchase history prior to live deployment, establishing exact baseline signals without overfitting.
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Sustained Baseline vs. Peak Velocity
The 21% conversion figure represents the steady-state post-implementation average maintained in both Q3 and repeated in Q4 with no major model overhauls. The reported 10.5x metric represents peak efficiency velocity achieved during top-cohort lead routing.