Pricing Decision Framework

Scope

This framework defines how product prices are adjusted under two distinct pricing contexts:

• Platform based pricing (structural, long-term discounts)
• Promotional pricing (short-term, event-based discounts)

Both modes share common guardrails (margin protection, discount caps, rounding), but differ in how the target discount is derived.

The objective is consistent, explainable pricing decisions under operational constraints.

Why This Matters

Pricing decisions are not about finding a single “optimal” price. They are about navigating trade-offs between margin protection, inventory risk, and commercial governance.

This project demonstrates how pricing decisions can be:

• policy-driven, but adaptive
• explainable to non-technical stakeholders
• constrained by real business guardrails

Shared Concepts & Definitions

Term	Definition
List Price	Reference price before any discount
Cost	Unit cost of the product
Margin	(price − cost) / price
Discount	1 − (price / list_price)
Margin Floor	Minimum acceptable margin
Max Discount	Absolute upper bound on discount
Rounding Step	Price granularity (e.g. 1, 5, 10)

All prices are evaluated relative to list price and cost, without VAT, FX, or country-specific adjustments.

Mode A — Platform Based Pricing (Target-Margin Strategy)

Business intent

Maintain a stable, policy-driven discount while gradually increasing aggressiveness if sell-through lags behind expectations.
This mode reflects strategic pricing, not urgency.

Step A1 — Current state assessment

For each product:

• Compute current margin from list price and cost
• Observe progress against an expected sell-through trajectory
• Estimate time pressure (e.g. weeks behind target or distance to end-of-life)

Step A2 — Target margin adjustment

A revised target margin is computed as:

revised_margin =
    current_margin
  − (time_pressure × margin_decay_rate)

margin_decay_rate represents how much margin can be sacrificed per unit of time pressure.
Time pressure is capped to avoid extreme reactions.

Step A3 — Guardrails

The revised margin is constrained by:

revised_margin = max(
    margin_floor,
    min(revised_margin, current_margin)
)

This ensures:

• Margins never drop below a strategic floor
• Prices never increase as a result of the adjustment

Step A4 — Price derivation & rounding

The revised margin is converted back into a price, then rounded to the nearest allowed step.

The output is a recommended platform based price.

Mode B — Promotional Pricing (Constraint-Based Discounting)

Business intent

Maximize sell-through during short promotional windows while strictly respecting margin and governance constraints.
This mode reflects tactical pricing under urgency.

Step B1 — Feasible discount space

For each product, compute:

• Margin-based discount ceiling (derived from margin floor)
• Governance-based discount ceiling (category / lifecycle cap)

The maximum feasible discount is the minimum of these constraints.

Step B2 — Stock pressure signal

Inventory pressure is mapped into discrete tiers:

Stock Pressure	Effect
Low	No uplift
Medium	Moderate discount uplift
High	Aggressive discount uplift

This signal influences how close the discount moves toward the feasible maximum.

Step B3 — Discount selection

A target discount is proposed based on stock pressure and promo intensity, then clamped:

final_discount = min(
    proposed_discount,
    margin_based_max,
    governance_max
)

This makes the binding constraint explicit.

Step B4 — Price derivation & rounding

The discounted price is computed from list price and rounded according to pricing rules.

The output is a promo-specific price, valid only for the event window.

What This Framework Explicitly Excludes

To keep the framework focused on decision logic, the following are intentionally omitted:

• VAT and tax handling
• FX conversion
• Country-specific pricebooks
• Brand-level contractual exceptions
• Platform-specific operational rules

These elements increase operational complexity but do not change the core decision structure.

Why Two Modes

Although both modes share guardrails, they answer different questions:

Mode	Core Question
Platform Based	“How should prices evolve over time if sell-through deviates from plan?”
Promo	“How aggressive can we be right now without breaking constraints?”

Keeping these strategies distinct is essential for explainability and governance.

Output

For each product, the framework produces:

• A recommended platform based price
• A recommended promotional price
• An explicit explanation of which constraint is binding

Dataset

Column	Type	Example	Notes
product_id	string	P000123	synthetic ID
category	string	Shoes	used for discount caps / policy
platform	string	Platform_A	for platform based policy differentiation
lifecycle_stage	string	new, core, end_of_life	controls floors/caps
list_price	float	250.0	base price
unit_cost	float	110.0	must be < list_price
stock_on_hand	int	45	drives stock pressure
avg_weekly_sales	float	2.3	used to derive “time pressure” proxy
weeks_to_eol	int	18	used in platform based TM mode
target_sell_through	float	0.55	target by now (0–1)
actual_sell_through	float	0.42	current (0–1)
max_discount	float	0.60	governance cap (0–0.8)
margin_floor	float	0.10	floor (0–0.3)
rounding_step	int	5	1/5/10