The Framework

Every system I build is derived from a formal commercial architecture. This page is the canonical reference. Service pages link here for depth. If you're the kind of person who wants to understand the model before you hire, start here.

The Three Confusions

Most CRMs collapse three fundamentally different measurements into a single number — usually called a "lead score." That conflation is the root cause of most pipeline problems I see.

1. Lead Scoring — Projection

Question: How structurally close is this contact to your ideal buyer?

Lead Scoring is static. Demographic and firmographic fit. A snapshot of alignment between the contact's attributes and your ideal customer profile. Industry, company size, job title, geography — the variables that define whether this contact could be a buyer.

What goes wrong: Companies treat a high score as intent. It's not. A perfect-fit contact who hasn't engaged in six months is not a hot lead. They're a cold match. Scoring tells you about fit. It tells you nothing about timing, interest, or readiness.

2. Lead Health — Derivative

Question: Is this contact accelerating, stalling, or decaying?

Lead Health is dynamic. Temporal. The rate of change of engagement over a defined window — typically 70 days. It's the forgetting curve applied to your pipeline. A contact who opened three emails and attended a webinar last week has rising health. A contact who did the same thing four months ago and nothing since has decaying health.

What goes wrong: Without health as a separate measurement, your team chases contacts who looked promising three months ago. The CRM doesn't know they've gone cold because nothing measures decay. The pipeline is full of ghosts — contacts with high scores and zero recent activity — and every Monday morning's pipeline review is a fiction.

3. Lead Qualification — Predicate

Question: Have the necessary Boolean conditions been met to advance this contact to the next stage?

Lead Qualification is binary. Categorical. A state machine with discrete transitions. Each stage has hard criteria. You either meet them or you don't.

What goes wrong: Companies use fuzzy stages — "Interested," "Engaged," "Nurturing" — that mean different things to every rep. Qualification isn't a feeling. It's a set of conditions. Without predicate-based qualification, stage transitions are subjective and the pipeline report is a work of fiction.

Why All Three Matter Together

A contact can have:

High Score + Low Health + Not Qualified = Perfect-fit company, went dark months ago, never met MQL criteria. Don't chase.
Medium Score + High Health + Qualified = Decent fit, highly engaged right now, met all MQL conditions. Act immediately.
High Score + High Health + Not Qualified = Great fit, engaging heavily, but hasn't crossed the qualification threshold. Nurture with intent.

When your CRM conflates these into one number, every scenario looks the same. That's why routing breaks, forecasting fails, and sales wastes time on the wrong contacts.

The Commercial Ontology

Your company sells things. The question is whether the structure of what you sell is explicit or implicit.

The commercial ontology is a formal hierarchy:

Brand → Product → Feature → Solution → Use Case → Persona

Brand — the commercial entity (e.g., hiretimothysolomon.com)
Product — a named engagement with defined scope (e.g., CRM Architecture)
Feature — a specific deliverable within a product (e.g., Object Model)
Solution — an outcome achieved by combining features (e.g., Model CRM)
Use Case — a buyer segment or persona with specific problems (e.g., For RevOps)
Persona — the individual decision-maker the messaging addresses

Why This Matters

When this hierarchy is explicit in your CRM, every campaign, every asset, every piece of attribution data maps cleanly to what you actually sell. Every deal is associated with a specific product. Every campaign targets a specific use case for a specific persona. Attribution becomes structural rather than probabilistic.

Without it, you're running campaigns into a void and hoping the revenue shows up somewhere. The website says one thing, the sales deck another, the CRM a third. Nobody can draw a clean line from marketing spend to fee-paying clients because the product definitions aren't formally connected to the data model.

Composition Rules

The ontology has compositional algebra:

Pipeline = Product Type × Opportunity Type
Campaign = Pipeline × Persona Type × Use Case
Asset Group = Campaign × Content Theme
Asset = the individual piece of content, ad, or landing page

These compositions ensure that every piece of marketing activity has a precise coordinate in the commercial taxonomy. When a campaign is defined as Pipeline A × Persona B × Use Case C, the attribution data knows exactly which product generated the inquiry, which buyer it targeted, and which problem statement was used.

The Qualification Chain

The qualification chain replaces fuzzy pipeline stages with a state machine. Four discrete states. Boolean transitions. No ambiguity.

MQL — Marketing Qualified Lead

Criteria: Identified, communication confirmed, minimum data thresholds met.

The contact exists in the CRM with enough data to be useful. They've confirmed they're reachable (opted in, filled a form, responded to outreach). Their firmographic and demographic data meets minimum thresholds for potential fit.

An MQL is not "interested." An MQL is "identifiable and contactable." The bar is low and clearly defined.

SQL — Sales Qualified Lead

Criteria: Human-confirmed interest. Product(s) identified. Cart confirmed.

A human — typically an SDR or sales rep — has confirmed that this contact has genuine interest. The conversation has advanced far enough to identify which product(s) they're interested in. The "cart" is confirmed: they know what they might buy, and you know what you might sell them.

An SQL is not "they seemed interested on the call." An SQL is "they confirmed interest in Product X and the next step is a proposal."

FTP — First-Time Purchase

Criteria: Transaction conditions met. One action from revenue.

All conditions for a first transaction are met. Contract terms agreed. Pricing confirmed. The deal is one signature or one payment away from being revenue. This is not a forecast probability. This is a Boolean: are the conditions met, or are they not?

RTP — Returning Purchase

Criteria: Repeat buyer. All prior conditions inherited plus retention criteria.

The contact has purchased before and is purchasing again. RTP inherits all prior qualification criteria plus retention-specific conditions: satisfaction confirmed, renewal terms agreed, upsell/cross-sell product identified.

Transition Rules

Each transition is won or lost. No "60% likely." No "warm." The CRM either knows the answer or it doesn't. If the criteria for the next stage aren't met, the lead stays where it is. If the criteria for the current stage are no longer met (e.g., the contact's health has decayed below threshold), the lead regresses.

The Campaign Algebra

The campaign algebra connects marketing activity to the pipeline architecture.

Pipeline = Product Type × Opportunity Type

A pipeline is defined by what you sell and what kind of opportunity it is (new business, renewal, upsell, partner). If you sell three products and have two opportunity types, you have six potential pipelines.

Campaign = Pipeline × Persona Type × Use Case

A campaign targets a specific pipeline, speaks to a specific persona, and addresses a specific use case. This ensures that every campaign has a precise purpose and every conversion is attributable to a specific product-persona-problem combination.

Multi-Pipeline Environments

When a company runs multiple pipelines — acquiring tutors and students for the same EdTech platform, or selling to enterprises and consumers through different qualification paths — the algebra handles it. Each pipeline has its own qualification criteria. Each campaign targets one pipeline. The reporting infrastructure unifies them.

I spent four years building and running parallel pipelines for EdTech platforms. Tutor acquisition (supply-side B2C) and student acquisition (demand-side B2C) had different qualification logic, different channels, different messaging, and different conversion metrics. The campaign algebra unified both into a single attribution model. The framework isn't theoretical — it's tested on hard multi-pipeline problems.

Oblio

Oblio is the formal commercial ontology system I've been building for 15 years. It treats pipeline management as discrete mathematics — formalising the relationships between entities, the transition logic between states, and the compositional rules that govern campaigns and attribution.

The frameworks on this page are derived from Oblio. The service pages on this site describe how I implement them for clients. For the full mathematical treatment, I've written about the ontology in depth on timothysolomon.com.

How This Connects to What I Build

Every engagement I offer is an implementation of some part of this framework:

CRM Architecture implements the object model and lifecycle rules
Revenue Systems implements the attribution architecture
CRM Architecture includes pipeline audit — diagnosing where the qualification chain breaks
CRM Architecture includes commercial ontology — formalising the product taxonomy
Fractional CMO is the engagement where I redesign the whole system

The framework is the architecture. The products are the implementation.

Ready to see how this applies to your business? → Book a Diagnostic Call or email tim@hiretimothysolomon.com