KPI tree decomposition from Goal to Drivers, formula-based factorization, impact_weight assignment and dynamic update, Crux identification via importance x tractability, constraint Driver identification, input vs output metric classification. Use when decomposing Goals, prioritizing Drivers, or re-evaluating impact weights.

KPI Tree Decomposition

When to Apply

Decomposing a new Goal into Drivers
Re-evaluating Driver priorities after metric updates
Identifying the constraint (Crux) Driver
When the founder asks "where should we focus?"
Periodic impact_weight recalculation

Core Framework

Goal -> Driver Decomposition

Decompose the Goal metric into its constituent factors using a formula:

Goal: 100 paying customers
Formula: customers = visitors x CVR x trial_to_paid_rate

KPI Tree:
  visitors (1,200 -> 10,000)
    organic_search (400 -> 5,000)   -> Driver: SEO content
    social (300 -> 2,000)           -> Driver: Social growth
    referral (200 -> 1,500)         -> Driver: Referral program
    direct (300 -> 1,500)           -> Driver: PR & media

  CVR (1.5% -> 3.0%)
    lp_optimization                 -> Driver: Landing page A/B
    cta_improvement                 -> Driver: CTA copy

  trial_to_paid (20% -> 40%)
    onboarding                      -> Driver: Tutorial improvement
    follow_up                       -> Driver: Nurture email design

Each Driver inherits a sub-metric from the KPI tree.

Impact_weight Assignment

Initial assignment based on estimated effort-to-impact ratio. Updated dynamically as measured results come in:

Initial (agent estimate):        After 2 weeks (measured):
  SEO content:     0.30            SEO content:     0.15 (down - no traffic change)
  Social growth:   0.25            Social growth:   0.35 (up - viral post)
  Referral:        0.20            Referral:        0.25 (up - high CVR)
  LP A/B:          0.15            LP A/B:          0.15 (pending)
  Nurture:         0.10            Nurture:         0.10 (not started)

Update rules:

Actions completed but no metric movement -> weight reduced
Metric improvement detected -> weight increased
Highest weight = current bottleneck = constraint Driver

Crux Identification (Importance x Tractability)

Every Driver evaluated on two axes:

                    Tractability ->
               Low              High
Importance  +---------------+---------------+
    |       |               |               |
  High      | AVOID         | CRUX          | <- All effort here
            | (important    | (important    |
            |  but can't    |  AND solvable)|
            |  solve now)   |               |
            +---------------+---------------+
  Low       | IGNORE        | NICE TO HAVE  |
            |               | (do later)    |
            +---------------+---------------+

Tractability evaluation:

Does the founder have relevant skills or assets?
Are there existing channels/relationships to leverage?
Is budget sufficient for meaningful action?
Is there a proven playbook (Action Template)?
Can measurable progress be achieved within weeks?

The Crux Driver gets highest impact_weight AND is designated as the TOC constraint.

Constraint Driver Identification

The constraint is the Driver that most limits Goal achievement. It receives:

Highest impact_weight
Relay runner priority (Actions execute first, serially)
TOC 5 Focusing Steps applied
All other Drivers subordinated to it

Input vs Output Metric Classification

Every metric is classified. 98% of weekly attention goes to Input metrics (Amazon principle: 490 input vs 10 output out of 500 total).

Three-part test for Input metrics:

Controllable — you can directly allocate resources to move it
Customer-experience-linked — maps to a step in the customer journey
Causally connected — improving it demonstrably improves revenue/growth

INPUT (controllable, 98% attention):    OUTPUT (result, 2%):
  Threads posted/week                     Revenue, MRR
  Outreach messages sent                  Paying customers
  Demos scheduled                         NDR
  Referral invites sent

When founder fixates on output: "Revenue is an output metric. Let's focus on the inputs that drive it: demo count, response rate, close rate."

Decision Rules

Formula-based decomposition — every Goal must have an explicit formula connecting Drivers to the Goal metric
MECE decomposition — Drivers must be mutually exclusive, collectively exhaustive
Dynamic weights — recalculate after every measurement cycle, not static
One constraint — exactly one Crux Driver at a time receives priority focus
Avoid is explicit — important-but-intractable Drivers are explicitly deprioritized with explanation
Input focus — redirect attention from output metrics to input metrics

Anti-Patterns to Detect

Anti-Pattern	Signal	Response
Composite fitness function	User combines 4+ metrics into single score	"Composite scores obscure which actions drive which results. Track each input metric independently."
Output fixation	Founder asks "how do I increase revenue?"	Redirect to input metrics that drive revenue.
Static weights	impact_weight unchanged after 4+ weeks	"Weights should reflect measured data, not estimates. Time to recalculate."
Missing formula	Goal with no KPI decomposition	"Without a formula, we can't identify which factors to improve. Let's decompose."
Non-MECE Drivers	Overlapping or missing factors	"These Drivers overlap/have gaps. Restructure for clean decomposition."
Tractability blindness	Pouring effort into intractable Driver	"This Driver is important but not currently solvable. Redirect to [tractable Driver]."

kpi-tree