Pricing & Margin Optimizer
An ML layer on the portco's product, customer, and channel data — estimates price-elasticity per segment, identifies the margin levers worth pulling, simulates the impact of pricing and packaging changes before they ship.
- Engagement
- 8–14 week build per portco · ongoing iteration
- Built for
- Operating partners · Portco commercial leads · CROs
Pricing decisions at portcos are made on instinct and unit economics that haven't been updated since acquisition. The price-elasticity is unknown, the segment-level margin profile is fuzzy, and the levers that would move the next print are hidden in customer data nobody has time to analyze.
What this is
A pricing intelligence engagement for hold-period portcos where price and margin are the next-quarter-print lever. Three deliverables:
- Elasticity model. Per-segment price-elasticity (or discount-elasticity, for negotiated B2B), trained on the portco's transaction history. Output: "raise price on segment X by Y%, expect Z% volume impact," with uncertainty bands.
- Margin-lever map. The set of pricing and packaging changes that would move EBITDA, ranked by expected impact and risk. Surfaces the obvious-in-hindsight ones (segment-level under-pricing, sub-scale discounts) that don't show up in the existing reports.
- Simulation environment. Before-and-after scenario modeling for each proposed change. The operating partner sees the modeled impact before the change ships, with documented assumptions.
How it's built
Bayesian elasticity modeling (PyMC) for the small-sample / strong-prior cases; LightGBM with monotonic constraints where the data is rich enough to learn non-linear patterns. Integration with the portco's billing or CRM system for execution; or, more commonly, a recommendation document the portco's commercial team executes.
What you get
- The elasticity model and the margin-lever map.
- The simulation environment, configured to the portco's pricing structure.
- A working session with the portco commercial team to prioritize and sequence changes.
- Ongoing iteration as pricing changes ship — the model re-calibrates on the new data.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 8–14 week build per portco · ongoing iteration
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Private Equity Suite
Portfolio Intelligence Platform
A unified portfolio platform: ETL pipelines from each portco system (Excel, QuickBooks, NetSuite, Sage, Stripe) plus third-party data (Preqin, Bloomberg), normalized to a single schema, exposed through cross-portco dashboards with covenant tracking and liquidity alerts.
- Same suite · Private Equity Suite
Portfolio Forecasting Engine
Predictive forecasting models per portco — quarterly cash-flow and EBITDA projections trained on the portco's internal financials and sector-comparable data, with structured scenario planning (cost-cut sensitivity, price-elasticity scenarios, demand-shock testing) that feeds straight into the fund's valuation model.
What buyers ask about this one.
Most price-elasticity work is academic and doesn't deploy. How is this different?
Two ways. First, the model output is an action-oriented recommendation — 'raise price on segment X by Y%, expect Z% volume impact' — not a regression coefficient. Second, we wire the recommendation through to a deployable change, so the operating partner can see the impact instead of asking the portco team to interpret an academic output.
What data does the model need?
Transaction-level history (customer, product, date, price, quantity, channel), customer-segment metadata, competitive pricing data where the portco has access, and ideally one or two prior pricing changes to anchor the elasticity estimate. Where the portco has thin data, we use industry-comparable elasticity priors with documented uncertainty.
How do you handle B2B portcos where each customer is negotiated?
Negotiated B2B is the harder case — list price doesn't matter, what matters is the discount distribution. The model adapts to estimate discount-elasticity by segment and surface the customers where the discount is leaving margin on the table. Same structure, different lever.
What's the typical EBITDA impact?
Honestly varies wildly — 50bps to 400bps depending on how much the portco was leaving on the table. The early engagements always find more upside than expected; the later ones find optimization. We scope to a concrete pricing-experiment-and-measurement plan rather than promise a multiplier.
Pricing?
Per portco, scoped to data complexity and the breadth of the pricing program. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026