Market intelligence · Statistical modeling
Engineering a price-tier forecasting workflow
A Python workflow that allocates source units and revenue into price tiers, connects country and vendor views, and assembles model outputs with reconciliation and diagnostic reports.
- Client
- Market intelligence engagement
- Services
- Statistical modeling · Data engineering · Workflow automation · Model assurance
3 tiers
Low, mid, and high price bands
2 engines
Country and vendor views
2022–2030
Country output horizon in the reviewed build
Python → Excel
From source inputs to reviewable output
The challenge
Connected constraints
The allocation needs to account for source units and revenue, tier eligibility, historical records, quarterly totals, and geography roll-ups. A plausible distribution alone does not establish that all of these agree.
Source reconciliation
Country, vendor, and regional source totals can conflict. Those differences need to be identified separately from errors introduced by a model.
An evolving workflow
The work spans data preparation, distribution modeling, automated workbook assembly, and iterative validation of specific model changes.
The solution
A lognormal mixture
The allocator uses a blend of value and premium price distributions, anchored to source ASP and a dispersion estimate. It derives tier unit shares and conditional mean prices from that distribution.
Smoothing and constrained repair
Smoothing and optimization adjust tier allocations while preserving the applicable unit and revenue totals. Infeasible groups and historical exceptions require explicit review.
Repeatable execution
A command-line runner executes the country and vendor notebooks in order and produces Parquet datasets and an Excel workbook with flatfiles, formulas, and pivots.
A documented constraint contract
A written contract defines the intended rules and records remaining implementation gaps. Run-specific audits distinguish internal conservation from agreement with original source data.
The impact
Inspect the reasoning
Allocation logic, parameters, and diagnostic results are available in code and reports, making model changes easier to explain and review.
A scoped no-pinning result
In the September 21 validation of the 2026Q1 configuration, modeled-quarter boundary pins fell from 137 to zero in the vendor engine and from nine to zero in the country engine. This finding applies to that validation, not every subsequent build.
Visible source conflicts
The diagnostic work identifies differences between source authorities, historical records, and derived totals so that they can be resolved at the right stage.
Clear validation limits
The work demonstrates an implemented allocation workflow. It does not establish transaction-level calibration, out-of-sample forecast accuracy, or complete compliance with every contract rule.
Before & after
| Dimension | Before | After |
|---|---|---|
| Execution | Separate modeling and workbook steps | Ordered country-to-vendor runner |
| Constraint handling | Rules spread across workflow stages | Written specification and explicit integration gaps |
| Vendor boundary pins¹ | 137 | 0 |
| Country boundary pins¹ | 9 | 0 |
| Review outputs | Model figures viewed in isolation | Engines, flatfiles, and reconciliation evidence |
¹ Modeled-quarter counts in the September 21, 2026 validation of the 2026Q1 configuration. Historical exceptions were retained. These results do not certify later builds.
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