Stop pricing the way you did last year, let demand, competition, and inventory inform every price
ML-driven dynamic pricing: per-product, per-segment, per-time, with elasticity models and competitor signal integration. Built for operations where pricing is a real conversion lever.
Discuss a pilot →Quick facts
- Business size
- E-commerce, marketplaces, travel, hospitality, operations with more than 1k SKUs and meaningful margin
- Timeline
- 6-10 weeks test, 3-5 months pilot, 6-9 months production
- Budget range
- Pilot from €50k. Pricing is high-stakes, pilot scope is meaningful.
- Hardware
- Cloud-based data warehouse and pricing service.
- Data needed
- Historical transactions, current inventory, competitor pricing (where available), product catalogue.
- Evolution
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- Genesis
- Custom-built
- Product
- Commodity
No product gives you this. We assemble and train it around you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Static pricing leaves money on the table, and the leakage is hard to see. A product priced 5% too high loses conversions; priced 5% too low it gives away margin. Multiplied across thousands of SKUs and constantly shifting demand, the cumulative cost is substantial and invisible in monthly reports.
The conventional response is too slow and too coarse: quarterly pricing reviews, spreadsheet-driven adjustments, ad-hoc reactions to competitor moves. Pricing teams have hundreds of SKUs and limited bandwidth; they prioritize the top-100 and the rest drift.
ML-driven dynamic pricing changes the cadence. Prices update continuously based on demand signals, inventory positions, competitor pricing, and elasticity models. Decisions move from manual quarterly to algorithmic real-time.
What the Solution Does
A pricing engine that recommends or sets prices per SKU, per segment, per time.
- Ingest signals: transactions, inventory, competitor pricing (where available via APIs or scraping), seasonality, promotions.
- Model elasticity: per-SKU or per-segment elasticity from historical price-vs-volume data.
- Recommend prices: for each SKU, predict optimal price given the current signals and a target objective (margin, volume, inventory turn).
- Apply: push recommended prices to your e-commerce platform or PIM via API. Can run in recommend-only mode (humans approve) or auto-apply.
- Monitor and refine: track outcomes, refine elasticity models, A/B test rule changes.
Where It Fits
This makes sense if you…
- Operate more than 1,000 SKUs with meaningful margin variation
- Have historical transaction data (12+ months ideal)
- Can integrate price updates with your e-commerce / PIM via API
- Have a clear pricing objective (margin? volume? inventory turn?)
- Are ready to invest in the measurement / iteration loop
This is probably not the right time if you…
- Operate in regulated-price contexts (some food, some pharmacy) where price flexibility is constrained
- Have brand reasons for fixed pricing (premium positioning, contract pricing)
- Lack the data infrastructure to support continuous pricing decisions
- Don’t have authority to act on pricing recommendations, so the model produces insights nobody can apply
Business Value
Margin improvement. Typically 3-8% margin lift on dynamic-priced SKUs after pilot maturation. The gain depends heavily on the baseline: operations with disciplined manual pricing see smaller gains, operations with neglected long-tail pricing see larger.
Inventory efficiency. Inventory turn-rate typically improves 10-25% when pricing reflects current stock and demand. Stale inventory gets cleared via pricing; demand spikes get monetized.
Reaction speed. Competitor changes, demand shocks, seasonal patterns all get reflected in pricing within hours, where manual review takes weeks.
Long-tail coverage. Manual pricing teams cover top-100 SKUs. Algorithmic pricing covers the long tail too, which is where most operations leave value on the table.
How It Works
1. Data foundation
Transactions, inventory, competitor pricing, product catalogue, all in a unified data warehouse. This reuses the architecture from executive-bi-dashboards. Datapipe handles incremental data flow.
2. Elasticity models
For each SKU (or SKU group), historical price-vs-volume data trains an elasticity model. The model predicts how volume responds to price changes.
Cold-start: SKUs without enough history get pooled elasticity from similar SKUs (semantic similarity from the product-categorization embedder).
3. Objective function
What are you optimizing? Margin, revenue, volume, inventory turn, or a weighted mix. Different objectives produce different price recommendations. We co-design the objective with your pricing team.
4. Pricing engine
Given current signals (inventory, demand trend, competitor price, time-of-day / day-of-week, promotion calendar) and elasticity models, the engine computes optimal price per SKU.
5. Apply / recommend
Two modes:
- Recommend-only: pricing team reviews and approves before applying.
- Auto-apply: prices update automatically within configured guard rails (max move, min margin, contract constraints).
Most deployments start in recommend-only mode and graduate to auto-apply on stable SKU categories.
6. Monitoring
A/B testing infrastructure. Margin / volume / inventory metrics tracked per SKU group. Elasticity models retrain on new data.
Stack
Datapipe, ClickHouse / BigQuery / Snowflake for the warehouse, custom elasticity models, optimization layer (often gradient-boosted regression or specialized pricing libraries), Metabase / PowerBI for dashboards, REST API to your e-commerce / PIM.
What You Need to Make This Work
Data. Historical transactions (12+ months ideal). Inventory data. Competitor pricing if available (via API, scraping, or internal sources). Product catalogue.
Integrations. Read access to all of the above. Write access to e-commerce / PIM for price updates.
Hardware. Cloud-based.
Team. A pricing-strategy lead. A data engineer. An executive sponsor, pricing changes are high-stakes politically as well as technically.
Implementation Roadmap
1. Test (6-10 weeks)
Build data infrastructure. Train initial elasticity models on a subset of SKUs. Validate offline. Output: written report on expected pricing impact, recommendations for pilot scope.
2. Pilot (3-5 months)
Production deployment in recommend-only mode for a SKU group. A/B test against manual pricing. Measure margin / volume / inventory impact. Output: working production deployment with documented outcomes.
3. Production (6-9 months)
Expand SKU coverage. Graduate stable groups to auto-apply. Continuous monitoring and model refinement. Quarterly strategy review with pricing team.
Keep in Mind
- Pricing is high-stakes politically. Stakeholders disagree about whether algorithmic pricing is “right”. Plan for change-management.
- Bad data produces bad pricing. Stale inventory data, missing competitor signals, or transaction-data quality issues degrade recommendations. Data quality is the ceiling.
- Guard rails are critical. Auto-pricing without bounds risks weird outcomes (zero prices from bugs, max-margin runaway, contract violations). Configure carefully.
- Customer perception matters. Aggressive dynamic pricing can damage trust. Some operations deliberately constrain volatility for brand reasons.
- Regulator and contract constraints vary. Minimum advertised price (MAP) agreements, jurisdictional pricing laws, fairness regulations, all constrain what’s legal.
- Cold-start SKUs need care. New SKUs without history get conservative pricing until elasticity data accumulates.
FAQ
Can this work without competitor pricing data?
Yes, with less precision. Demand elasticity from your own historical data is the primary signal; competitor data is enhancement.
How does this compare to commercial pricing tools (Prisync, Competera, Omnia)?
Commercial pricing tools are excellent for organizations that want a ready-made product to configure. Our approach fits when you need custom elasticity modeling, integration with non-standard data sources, fully on-prem deployment, or substantial customization of the pricing objective.
What about cross-product cannibalization?
We model it explicitly. Pricing one product affects demand for related products; the optimizer accounts for this.
Can this work for B2B contract pricing?
This is a different problem: B2B pricing is more relationship-driven. We’ve engaged, though most deployments are consumer or mid-market.
Ready to Discuss?
If you operate e-commerce or marketplaces at meaningful scale and your pricing is currently a manual quarterly exercise, this is a worthwhile pilot. We’ll walk through your catalogue, your data, and your pricing strategy, and tell you what to expect.