Pricing & Profitability
DNet Price Movement & Period-over-Period Analytics Engine
Period-over-period pricing analytics engine that compares Dealer Net and List price movement across monthly snapshots, classifies part-level changes, and produces executive-ready summaries.
Pricing movement command view
DNet Price Movement Engine
Snapshot cache, period comparison, price deltas, movement flags, and executive summaries
Snapshot
Monthly cache
Compare
PoP delta
Classify
Movement
Summarize
Executive
Deliver
Excel / data
Movement mix
Price movement preview
DNet / List| Part | Previous | Current | Delta | Status |
|---|---|---|---|---|
| 100-4412 | $84.20 | $91.40 | +8.6% | Appreciated |
| 225-0198 | $142.50 | $136.10 | -4.5% | Depreciated |
| 300-7710 | $39.80 | $39.80 | 0.0% | Stable |
| 410-2250 | — | $72.30 | New | New |
Analytics outputs
Business problem
Dealer Net and List price changes needed structured comparison across periods and part-level detail. Without a repeatable period-over-period engine, price movement review depended on manual file comparison and scattered summary logic.
The process needed a way to preserve monthly snapshots, compare current and prior pricing, identify movement direction, and package the results into review-ready outputs.
System built
Built a monthly DNet and List price movement engine with snapshot caching, part-level comparison, appreciation and depreciation tracking, Parquet-ready datasets, detail outputs, and executive Excel summaries.
The system turns raw price files into a controlled analytics workflow that highlights what changed, how much it changed, and where business review should focus.
Pricing signals
Signals reviewed
The engine evaluates current and prior price records, monthly snapshot state, and movement classifications before producing business-ready outputs.
Price movement flow
How it works
Snapshot
Capture monthly Dealer Net and List price data into reusable period snapshots.
The engine creates a stable historical reference point so price movement can be compared consistently across reporting periods.
Compare
Compare current and prior period price records at the part level.
The comparison layer identifies what changed, what stayed stable, what was newly observed, and what no longer appears.
Classify
Classify movement as appreciation, depreciation, no change, new, missing, or review-needed.
Movement classification turns raw price deltas into categories that are easier to review and explain.
Summarize
Create period-over-period summaries, detail outputs, and exception views for business review.
The summary layer helps teams understand the size, direction, and business impact of price changes.
Deliver
Publish detail files, Parquet-ready datasets, Excel summaries, and analytics outputs.
The final outputs support executive reporting, operational review, and downstream pricing workflows.
Analytics layers
What the engine coordinates
Snapshot cache
Stores monthly period snapshots so repeat comparisons can run faster and more consistently.
Price comparison
Compares DNet and List values across periods to identify movement and part-level deltas.
Movement classification
Labels appreciation, depreciation, stable pricing, new parts, missing parts, and review flags.
Executive outputs
Produces detail files and summary workbooks that make price movement easier to communicate.
Impact signals
What the engine improved
Dealer Net and List price movement tracking across reporting periods
Snapshot cache acceleration for repeatable comparison
Part-level appreciation and depreciation classification
Parquet-ready datasets and detail outputs
Executive Excel summaries for pricing review
Operational value
Price movement turned into executive-ready analytics
Clearer price-change review
Turns raw pricing files into structured movement views that show what changed and by how much.
Faster repeat comparisons
Snapshot caching reduces rework and makes period-over-period reporting more repeatable.
Better exception focus
Movement classifications help prioritize which parts need review instead of scanning every record manually.
Executive-ready communication
Summary outputs make pricing movement easier to explain to non-technical stakeholders.
Why this project matters
Pricing changes converted into a repeatable analytics process.
This project shows how pricing review can move from manual comparison to a structured period-over-period engine. Snapshot caching, part-level deltas, movement classification, and executive outputs create a cleaner process for understanding price movement.
The value is not just identifying a price change. The value is making price movement explainable, repeatable, and ready for operational or leadership review.
Confidentiality note
Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, customer records, credentials, raw exports, internal pricing, operational screenshots, or proprietary source files.