Sales Process Model · Authgnosis CRM

Sales Process-Customer Lifecycle CRM Visualization

The process flows left to right; each stage unfolds into layers. Use the filters to show only what a salesperson touches, the automations, the AI, the captured data, the critical milestones, the method detail, the process steps, or the management dashboard the data feeds.

AISDALS/L × MEDDPICC · Challenger · Sandler · SPIN CLTV-focused customer lifecycle AI-native RevOps

What this integrated process is built to deliver

  1. Self-healing forecast accuracy
  2. Pipeline leakage visibility and AI diagnostics for rapid repair
  3. Minimize salesperson touches through automation and AI analytics
  4. Capture data at every step that drives analytics to management dashboards
  5. Modular connectivity of Sales flows to Marketing, Services, and Customer Success flows
  6. AI monitoring of salesperson-collected data to surface garbage data
  7. AI analytics of key sales strategies such as the Mutual Action Plan (MAP)
  8. Alignment with MEDDPICC • Challenger • Sandler • SPIN selling methodologies
  9. Alignment with CLTV-growth and the AISDALS/L marketing customer lifecycle
85 Steps across 9 stages
0% Net Scalability Gain
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Closed deals to train on How much history the Track 3 model has to learn from. It sets the weight of the AI cross-check, not the reliability of the forecast.
06001,200
AI Model Reliability 0%  

AI Predictive Close Probability

Every sales leader asks two things of a deal: will it close, and when? The forecast is answered by the process, not by the model. Milestones are auto-checked from real evidence, qualification gates cannot be ticked by hand, and the Virtual Close commits the number. That integrity is structural, it comes from enforcing the practice rather than from predicting it, and it holds from the very first deal.

Track 3 is a second and independent read on the same question. It is an AI capability: a machine-learning model trained on your own history of won and lost deals, scoring each open opportunity so that what the process asserts can be triangulated against what past deals predict. The deals where the two disagree are the ones worth examining.

The chart measures the model, not the forecast. A trained model earns its accuracy from volume, so with little closed-deal history its second opinion is directional and should carry little weight, and as history accumulates it becomes a dependable cross-check. Read it as the strength of the corroboration available to you. It is not a floor on forecast reliability, and it does not mean a team needs a thousand closed deals before it can forecast: the process governs the forecast from day one, with or without the model. Zones: insufficient history below ~50 closed deals, directional in between, dependable at ~1,000 and above.

0255075100 ~50: min to train Strength of the AI cross-check, not the reliability of the forecast. ~1,000: dependable 02004006008001,0001,200 Closed deals used to train the AI model AI model reliability (%)

Reference: Integrating Sales Processes and a CLTV-Focused Customer Lifecycle · authgnosis.com.