AJTBD Methodology
Product DNA — AI CPO Methodology
Product DNA (Discovery — Navigation — Acceleration) is AI CPO's proprietary methodology, synthesizing best practices from 10+ product frameworks:
- Jobs Theory — understanding user tasks, job hierarchy, switch forces
- Unit Economics — business math: LTV/CAC, cohorts, payback
- RAT (Riskiest Assumption Test) — risk prioritization, quick hypothesis tests
- Behavioral Economics — loss aversion, anchoring, System 1/2
- Strategic Foresight — trend analysis, scenario planning
- ABCDX Segmentation — segmentation by jobs, not demographics
- GTM Strategy — channels, funnels, 5 awareness levels
- Conversion Framework — landing page structure, AHA moment
- Monetization Models — value-based pricing
- Respondent Recruitment — 9 channels for finding respondents
Just as DNA contains the complete blueprint of an organism, Product DNA contains the complete map of your product: who the customer is, what job they perform, why current solutions fail, how much they're willing to pay, and the route from idea to working business.
AI CPO automatically builds your product's Product DNA through dialogue, external data analysis, and generation of 21 strategic artifacts.
Three Pillars of Product DNA
| Framework | What It Studies | Unit of Analysis |
|---|---|---|
| Jobs Theory | Why people switch solutions | Job |
| Value-Based Pricing | How to package and sell the transformation | State transformation (Product DNA) |
| SaaS Unit Economics | How to measure whether the hypothesis works | Metric + signal |
Price = Value ÷ 10 [Value-based pricing (Nagle & Müller, 2018)]
Profit = (Price − CAC − Ops) × N customers [SaaS unit economics]
Jobs-to-be-Done (JTBD)
JTBD views products as tools that people "hire" to accomplish specific tasks. Instead of "What features do we need?" JTBD asks: "What job is the user trying to get done?"
Classic example: people buy a drill not because they need a drill — they need a hole in the wall. But the hole isn't the end goal either. The real Job: "Hang a shelf to organize space and feel order at home." This includes functional (hang), emotional (feel order), and social (show guests) components.
Job Hierarchy
Each job exists at a specific level in the hierarchy. This isn't an abstraction — it's a concrete model for navigating the job graph:
| Level | Definition | Example (time tracking) | Product Significance |
|---|---|---|---|
| Deep Need | Fundamental need | Financial independence | Inspires Big Job — not solved directly |
| Big Job | Major life/work outcome | Build a stable freelance income | Positioning at Big Job level = powerful strategy |
| Core Job | Task for which the product is hired | Accurately track time per project | This is your core value. Start here |
| Small Job | Sub-tasks within Core Job | Start timer, categorize | Product features solve Small Jobs |
| Micro Job | Atomic UI interactions | Tap the "Start" button in one tap | UX optimization at Micro Job level |
Job Types (Detailed)
| Type | Definition | Indicator | Product Action |
|---|---|---|---|
| Functional Job | Specific task to accomplish | Clear action + measurable outcome | Core product functionality |
| Emotional Job | How a person wants to feel | Verbs: feel, experience, be confident | UX, communication tone, design |
| Social Job | How a person wants to appear to others | Verbs: appear, show, impress | Sharing features, client reports, dashboards |
| Financial Job | Financial outcome | Money: save, earn, not lose | ROI calculator, savings in numbers |
| Ordinary Job | Direct action toward a result | Clear action + outcome | Solve better than competitors |
| Orientational Job | Understanding, choosing, evaluating options | Find / understand / choose / compare | SEO, content marketing, guides, comparisons |
| Tax Job | Arises from defects of the current solution | Caused by a bug or friction, not the user's goal | Kill through automation → powerful Push for switching |
| False Job | No behavioral evidence | Only stated intention, no time/money spent | Filter out of roadmap — no real demand |
Job Properties: Sequence, Frequency, Virality
Each job has three operational properties that determine product strategy:
| Property | What It Determines | Strategic Significance |
|---|---|---|
| Sequence | Position in the chain: what comes before and after, where the end-value is |
Early jobs = commodity (price competition). Late jobs = value capture (quality competition). Killable Jobs = jobs earlier in the chain that can be killed. Yandex.Rent killed inspection, deposit, contract — everything before "move in." |
| Frequency | How often the job occurs |
Daily = habit, retention, subscription. Monthly = needs reminders, engagement loops. Once = one-time purchase, no retention. |
| Virality | Is it performed with/for other people? |
Solo = paid traffic is the only path. Shared = referrals, word-of-mouth. Social = viral growth, K-factor > 1 possible. |
Switch Formula (4 Forces)
People switch products (or start using a new one) when:
PUSH = dissatisfaction with the current solution (intensity × frequency)
PULL = attractiveness of the new solution (desired outcomes)
INERTIA = cost/effort of switching (time, money, learning, habit)
ANXIETY = fear that the new solution won't work (perception of risk)
switchScore = Σ(push weights) + Σ(pull weights) − Σ(inertia weights) − Σ(anxiety weights)
switchScore > 0 → likely switches
switchScore ≤ 0 → likely stays
| Force | Description | Example | How to Amplify/Weaken |
|---|---|---|---|
| Push | What repels from the current solution | "Excel breaks, formulas get corrupted" | Content: "Sound familiar?", amplify pain in marketing |
| Pull | What attracts to the new solution | "Saw a colleague's tracker — convenient" | Demo, AHA moment, social proof, case studies |
| Inertia (Habit) | Habit with the current solution | "3 years on Excel — used to it" | Data migration, training, smooth transition |
| Anxiety | Fears about switching | "What if I lose my data?" | Guarantee, free trial, case studies, FAQ |
Important: the loss aversion coefficient is NOT fixed at 2.0x. Empirical range: 1.3x-2.5x depending on job category and segment.
Switch Formula Across Artifacts
- Interview Script — questions for each of the 4 forces
- Competitive Landscape — Push analysis (what's wrong with competitors)
- Offer Bank — offers amplify Push and Pull
- Landing Structure — "Fire Competitors" section = reducing Inertia, FAQ = reducing Anxiety
- Value Proposition — Pull ≥ 2x through Gain Creators
- Churn Diagnosis — Switch in reverse (why they "fire" the product)
19 Data Points
Each job in Product DNA is described through 19 data point types. AI CPO collects them from chat messages, files, interviews, and external sources. The more data points collected, the more accurate all artifacts become.
| # | Data Point | Job Level | Product DNA Category | What's Extracted |
|---|---|---|---|---|
| DP01 | First thought | Core/Big Job | Push trigger | First thought when the problem arose |
| DP02 | Triggering event | Core Job | Push | Specific triggering event |
| DP03 | Current solution | Core Job | Inertia | What they use before switching |
| DP04 | Passive looking | Big Job | Early push | Passive search phase |
| DP05 | Active looking | Core Job | Push escalation | Active search phase |
| DP06 | First solution | Core Job | Pull | First solution considered |
| DP07 | Decision criteria | Core Job | Pull/Anxiety | Criteria used to decide |
| DP08 | Social proof | Core Job | Anxiety reduction | Who they turned to for advice |
| DP09 | First use | Micro Job | Onboarding | First usage experience |
| DP10 | Moment of value | Core Job | Pull confirmation | AHA moment — when they realized the value |
| DP11 | What told others | Big Job | Language/framing | How they described it to others (→ marketing language) |
| DP12 | Recurring context | Small Job | Habit formation | Context of regular usage |
| DP13 | Workarounds | Small Job | Unmet micro job | Workarounds they invented |
| DP14 | Alternatives | Core Job | Competitive | What alternatives they considered |
| DP15 | Desired emotions | Deep Need | Emotional job | What emotions they want to experience |
| DP16 | Budget / WTP | Core Job | Economic | Budget and willingness to pay |
| DP17 | Others involved | Core Job | Social job | Who else is involved in the decision |
| DP18 | Time horizon | Core Job | Context | Urgency and time horizon |
| DP19 | Success definition | Big/Core Job | Outcome | Definition of success |
Data Quality Tiers
| Tier | DP Coverage | Interview Count | Artifact Reliability |
|---|---|---|---|
| Gold | DP01-DP19 all | ≥ 5 | High — can build the product |
| Silver | DP01-DP12 | ≥ 3 | Medium — can test hypotheses |
| Bronze | DP01-DP07 | ≥ 1 | Basic — only for initial drafts |
| Experimental | < 7 DPs | 1 | Low — all artifacts = hypotheses |
- DP11 (What told others) — user language → positioning, offers, landing page
- DP10 (Moment of value) — AHA moment → onboarding, demo, trial
- DP16 (Budget/WTP) — pricing, unit economics
- DP02 (Trigger) — when and how to reach users → GTM, advertising
- DP13 (Workarounds) — unmet tasks → new features
Consideration Set
The Consideration Set is a model of which alternatives exist in the buyer's mind before the purchase. Key Product DNA insight: the Consideration Set forms before the trigger, not after. By the time of active search, the buyer already knows 2-3 options.
Known alternatives: what they know, perceived pros/cons, how they learned about them
Beliefs: "all CRMs are expensive", "open source = no support"
Fears: "migration will break everything", "the team won't adopt it"
Activating Knowledge — 3-5 phrases that update the Consideration Set and trigger a Switch WITH YOUR product. Extracted from DP11 and orientational search queries. This is the bridge between content marketing and adoption.
ABCDX Segmentation
ABCDX is a segmentation model based on the economic value of the segment, not demographics. Each segment is defined through Jobs, Execution Criteria, and context, then evaluated on 4 parameters.
B/C Boundary Decision Tree
The hardest part of ABCDX is the boundary between B (good) and C (marginal). Operationalized rule:
1. Core Job confidence ≥ 0.60 (silver tier minimum)
2. Switch score > 0 (will switch)
3. WTP ≥ product price
4. Decision autonomy = individual or influencer
Customer = C (marginal) if ANY condition:
1. Core Job confidence 0.30-0.59 (bronze)
2. Switch score ≤ 0, but Push > 0.5 (may switch later)
3. WTP < price, but > 50% of price
4. Committee decision required (long sales cycle)
The 80/20 Rule
RAT Framework (Detailed)
RAT (Riskiest Assumption Tests) is a framework for systematically validating hypotheses. Detailed description: RAT Tests.
RAT Process
- Identify — List all product assumptions (10-20)
- Prioritize — Score P×I for each (1-25)
- Design Test — Choose the fastest, cheapest validation method
- Execute — Run the test with clear success criteria (CONFIRMED / REFUTED / UNCLEAR)
- Decide — Pivot, persevere, or kill based on results
6 risk categories, P×I scoring, Quick Test library, and Risk Card format — see Build Phase.
How It All Connects: Jobs → Segments → Artifacts
The entire AI CPO methodology works as a unified pipeline:
2. Job formulation: facts → Job Statements → Job Graph (hierarchy + connections)
3. Segmentation: Jobs + Execution Criteria + context → segments → ABCDX
4. Switch analysis: Push/Pull/Inertia/Anxiety → switchScore per segment
5. Strategy: segments + switch → positioning (for A), offers (by awareness levels), price (3 dimensions)
6. Validation: assumptions → RAT (P×I) → Quick Tests → CONFIRMED/REFUTED
7. Build: Jobs + RAT → feature prioritization → MVP
8. Unit Economics: LTV/CAC/Payback by segment → go/no-go
9. GTM: awareness levels + channels + offers → first customers
Artifact Dependencies
| Artifact | Depends On | Used In |
|---|---|---|
| Pain Map | Facts (pain) | Job Statements, Segments |
| Job Statements | Facts (pain, audience) | Job Graph, Positioning, VP |
| Job Graph | Job Statements | Segments, Competitors, Features |
| Segments (ABCDX) | Job Statements, Facts | Personas, Positioning, Pricing, Features |
| Positioning | Segments, Competitors | Offers, Landing, Pitch |
| RAT Tests | All facts (assumptions) | Features, ABCDX grades |
| Unit Economics | Segments, Pricing, Channels | Growth Strategy, Pitch |
Validation Sequence (Three-Framework Synthesis)
Stage 1: 7-15 Discovery interviews — job graph, Switch story, budgets. End with a sales attempt [Product DNA]
Stage 2: Manual service — 5 paying customers for a manual version. State transformation for each [Manual service validation (Ries, 2011)]
Stage 3: Prototype/landing — test visitor → lead conversion. First funnel data [Lean Startup (Ries, 2011)]
Stage 4: A/B test — only after sufficient traffic. Test concept, not button color [Controlled experimentation (Kohavi et al., 2020)]
— Lean Startup (Ries, 2011): "First 5 paying customers manually → then write code"
— Jobs Theory: "Jobs are studied based on past spending — NEVER about the future"
— Behavioral vs stated preference (Kahneman, 2011): "User opinion ≠ user behavior"
Platform Evolution
AI CPO is a self-improving system. Three evolution mechanisms run automatically:
- Feedback Digest (daily, 3:00 AM) — aggregates feedback per artifact. AI identifies patterns: "users frequently complain about segmentation accuracy."
- Evolution Analysis (weekly, Mon 4:00 AM) — Claude Sonnet analyzes patterns and generates improvements for the backlog: new prompts, features, fixes.
- Chat Audit (weekly, Mon 5:00 AM) — Groq analyzes chat sessions: where users get stuck, confused, or abandon the workflow.
Results are available in the backlog (click "Backlog" in the chat). Most requested improvements are promoted to actual platform updates.
Custom Artifacts and Voting
If you need an artifact that's not in the standard set — request it. Other users can vote for requests. With 3+ votes, the request becomes a candidate for promotion to the standard artifact set.