Compute · Assess & Decide · Analysis & Finance
Compute economic value to the customer (EVE) and the viable price range
You get the total economic value to the customer (EVE) and the viable price range (ceiling and floor) for one product in one customer segment, built up from a next-best-alternative reference price and a set of signed monetary value-driver deltas.
You receive: A pure function that, given the next-best-competitive-alternative reference price, an array of signed value-driver deltas, and the product's variable cost, returns the total economic value (EVE), the differentiation value, whether the product is positively differentiated, and the viable price ceiling and floor.
Part of Choose Business Model
What's verified: STUD verifies the ARITHMETIC and the branch logic of the EVE model only: that the function sums the reference price and the signed value-driver deltas into the total economic value, selects the correct price floor for a positively vs negatively differentiated product, and that the declared algebraic invariants hold on held-out inputs. STUD does NOT verify that the supplied reference price is the true NBCA transaction price (the book is explicit that identifying the NBCA and its real net price requires market research and judgment); does NOT verify that the value-driver deltas are the right drivers, are correctly quantified, or avoid double counting (the book warns that driver selection and quantification are an in-depth-interview research exercise, that one must count only the difference from the NBCA, and must not double count cost-saved vs extra-benefit); does NOT assert that any customer will actually pay the EVE (EVE is the maximum a fully informed smart shopper would pay, not the price to charge, and perceived value can fall short); and does NOT pick a price within the range (sharing the differentiation value between buyer and seller is a separate strategic judgment). The source itself flags economic-value estimation as part research and part arithmetic; STUD owns only the arithmetic. Note on the range: when the product is positively differentiated the price_floor is the STRATEGIC, price-war-avoidance floor (the NBCA reference price) from Nagle and Holden, which sits ABOVE the theoretical breakeven floor (variable cost); it is not the minimum price at which the product is profitable. When price_ceiling is below price_floor there is NO viable price band (exit the segment), not a usable interval. Edge: if the NBCA reference price is itself below your variable cost, the strategic positive-branch floor can sit below your cost; STUD grades the arithmetic and branch selection, not whether the floor clears your own variable cost.
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Deliverable interface
The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.
{
"constants": {},
"entryName": "compute_eve_price",
"expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
"hiddenCaseCount": 12,
"hiddenCaseNames": [
"mixed_drivers_net_positive",
"negatively_differentiated",
"no_differentiation_empty",
"negative_eve_to_floor",
"empty_band_below_cost",
"single_large_positive",
"many_small_positives",
"net_zero_offsetting",
"negative_boundary_exactly_zero_diff",
"large_negative_below_cost",
"high_reference_modest_uplift",
"negative_diff_ceiling_above_cost"
],
"inputKeys": [
"reference_price",
"value_driver_deltas",
"variable_cost"
],
"normalizeSource": "def _normalize_compute_eve_price(r):\n if not isinstance(r, dict):\n return repr(r)\n try:\n # Round each field to the SAME precision the reference uses (6): the grader compares\n # normalize(submission) to the RAW reference output with exact equality.\n return {\n \"differentiation_value\": round(float(r[\"differentiation_value\"]), 6),\n \"eve\": round(float(r[\"eve\"]), 6),\n \"positively_differentiated\": bool(r[\"positively_differentiated\"]),\n \"price_ceiling\": round(float(r[\"price_ceiling\"]), 6),\n \"price_floor\": round(float(r[\"price_floor\"]), 6),\n }\n except Exception:\n return repr(r)\n",
"returnShapes": [
[
"differentiation_value",
"eve",
"positively_differentiated",
"price_ceiling",
"price_floor"
]
],
"signature": "def compute_eve_price(inp):",
"submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
"tier": "calculator",
"visibleCases": [
{
"expect": {
"differentiation_value": 2498,
"eve": 2528,
"positively_differentiated": true,
"price_ceiling": 2528,
"price_floor": 30
},
"input": {
"reference_price": 30,
"value_driver_deltas": [
1560,
384,
48,
468,
38
],
"variable_cost": 5
},
"name": "dyna_test_commercial"
},
{
"expect": {
"differentiation_value": 1655,
"eve": 1685,
"positively_differentiated": true,
"price_ceiling": 1685,
"price_floor": 30
},
"input": {
"reference_price": 30,
"value_driver_deltas": [
1055,
231,
29,
317,
23
],
"variable_cost": 5
},
"name": "dyna_test_academic"
}
],
"vocabulary": [
"differentiation_value",
"eve",
"positively_differentiated",
"price_ceiling",
"price_floor",
"reference_price",
"value_driver_deltas",
"variable_cost"
]
}Get early access to STUD the day it goes live.