Switching Costs Lock in Strength Assessment Grid

Caller independently measured monetary/time/data-portability switching-cost components against explicit bands, not causal customer lock-in or moat certification. Unknown indices distinguish zero placeholders from known zero margins. Caller independently verifies policy, evidence, dates and applicability; not legal/compliance advice or causal business-success prediction.

Description

Switching-cost assessments use defined observations and comparison intervals.

Switching-cost assessments use defined observations and comparison intervals.^[1]

Their interpretation depends on the metric and evidence rather than a universal lock-in score. Use this tool to calculate switching Costs Lock in Strength Assessment Grid from the supplied measurements and assumptions.

When to use Switching Costs Lock in Strength Assessment Grid

Use this tool to calculate switching Costs Lock in Strength Assessment Grid from the supplied measurements and assumptions. Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.

How to use Switching Costs Lock in Strength Assessment Grid and understand its results

Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.
Enter observedObservedSwitchingCostComponents, callerObservedSwitchingCostComponentsLower, callerObservedSwitchingCostComponentsUpper, knownObservedSwitchingCostComponentsFlags, using the field meanings and rules below, then run the tool.
Read signedKnownMargins, knownReviewIndices, unknownIndices. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
observedObservedSwitchingCostComponents	Observed observed switching cost components	list of number	Required
callerObservedSwitchingCostComponentsLower	Caller observed switching cost components lower	list of number	Required
callerObservedSwitchingCostComponentsUpper	Caller observed switching cost components upper	list of number	Required
knownObservedSwitchingCostComponentsFlags	Known observed switching cost components flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Enter nonempty aligned measurements, lower bounds, upper bounds and binary known flags. Each upper bound must be at least its lower bound, including equal bounds. For known observations calculate the smaller of measured−lower and upper−measured; an outside observation has a negative margin and its zero-based index enters knownReviewIndices. Equality is inside the window. Unknown observations receive placeholder margin 0 and their indices enter unknownIndices; that 0 does not mean an observed pass. Bounds are caller inputs, with no installed optical threshold or ray-tracing model.

margin_i=known_i ? min(observed_i−lower_i,upper_i−observed_i) : 0; review={i:known_i and (observed_i<lower_i or observed_i>upper_i)}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
signedKnownMargins	Per-item smaller distance to either bound; negative outside, zero placeholder for unknown items	list of number
knownReviewIndices	Zero-based indices of known observations strictly outside the supplied inclusive window	list of values
unknownIndices	Zero-based indices of observations marked unknown	list of number

json

{
  "input": {
    "observedObservedSwitchingCostComponents": [
      2,
      5
    ],
    "callerObservedSwitchingCostComponentsLower": [
      1,
      1
    ],
    "callerObservedSwitchingCostComponentsUpper": [
      3,
      4
    ],
    "knownObservedSwitchingCostComponentsFlags": [
      1,
      0
    ]
  },
  "output": {
    "signedKnownMargins": [
      1,
      0
    ],
    "knownReviewIndices": [],
    "unknownIndices": [
      1
    ]
  }
}

Limitations and assumptions

Their interpretation depends on the metric and evidence rather than a universal lock-in score. Interpretation depends on the units, sampling scope and exact model described above.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Seven Powers Hamilton Helmer Moat Taxonomy for this related task: A moat taxonomy groups independently annotated business characteristics.

Use North Star Metric Selection Tree when its separate inputs and assumptions fit the question: A north-star metric should represent an independently chosen business objective.

References

Switching Costs Lock in Strength Assessment Grid — tool.io

Similar or alternative tools

Seven Powers Hamilton Helmer Moat Taxonomy
A moat taxonomy groups independently annotated business characteristics.
North Star Metric Selection Tree
A north-star metric should represent an independently chosen business objective.