AI Price Optimisation: Prices That Maximise Revenue
Static pricing ignores market dynamics. While your annual list sits still, competitors with dynamic pricing AI reprice weekly and leave your commercial team defending yesterday's numbers. AI models that analyse demand elasticity, competition, and costs recommend prices that protect margin and grow revenue.
We build the price optimisation engine that pushes approved changes into the systems you already sell from.
Sound Familiar?
These are the exact issues commercial leads faced before AI price optimisation:
- Annual or quarterly price lists sit unchanged while input costs, FX, and competitor moves shift weekly
- Pricing and commercial teams burn 10+ hours a week on spreadsheet reviews that still lag the market
- Hero SKUs get undercut by rivals running dynamic pricing AI before your next list update lands
- Sales quotes from a stale CRM price book, so margin leaks deal by deal without anyone seeing the pattern
- You cannot tell which SKUs are overpriced, which are leaving money on the table, or what elasticity would support
Competitors already reprice 8 to 15% of their catalogue every week, and high-velocity categories move 25 to 40% of SKUs daily. In South Africa, cost-of-living pressure and sticky retail pricing mean static annual lists fall behind both input costs and the market. Ecommerce platforms are shipping AI pricing add-ons: waiting for the next spreadsheet cycle is now a competitive risk.
What AI Price Optimisation Actually Does
Signals in → model recommends → commercial lead approves → channels update. No overnight blind repricing.
Signals Arrive
Sales history, costs, FX, inventory, and competitor prices feed the model from your systems
AI Recommends Prices
Elasticity and competitive position produce recommended prices inside your margin floors
Commercial Approves
Pricing or sales leadership accepts, amends, or rejects before anything reaches a customer
Channels Update
Approved prices sync to ecommerce, ERP catalogues, and CRM quote tools the same day
Everything You Need for Dynamic Pricing AI That Commercial Trusts
Elasticity-Aware Recommendations
AI pricing models analyse demand elasticity, seasonality, and sell-through so price optimisation targets revenue or margin, not a flat across-the-board increase.
Competitor & Cost Signals
Competitor price feeds, landed cost, and FX inputs refresh the model so recommended prices stay aligned with the market you actually sell into.
Approval Before Push
Commercial leads review and approve recommendations. Nothing hits ecommerce, ERP, or CRM quotes until a human signs off.
Channel Price Sync
Approved prices write back to Shopify, WooCommerce, ERP catalogues, and CRM quote tools so every channel sells from the same source of truth.
Guardrails & Floors
Margin floors, MAP rules, brand exclusions, and customer-segment caps keep dynamic pricing AI inside the commercial policy your board already agreed.
Margin Attribution
Dashboards show revenue lift, margin change, and Rand recovered by category so the CEO can prove payback from price optimisation, not gut feel.
Platforms We've Wired for AI Pricing
From a Static Annual List to R3.1M Recovered
How a Gauteng wholesaler cut pricing review from 14 hours a week to 4 and lifted margin 6% on pilot categories with AI price optimisation.
The Manual Process
- Commercial team maintained an annual price list updated in Excel every quarter
- 14 hours a week spent checking competitors, costs, and CRM quote exceptions by hand
- Hero SKUs sat overpriced for weeks while rivals with dynamic pricing undercut same-day
- Discount waterfall quietly gave away an estimated 8 to 12% of list on many deals
- Leadership had no clear view of which price moves would actually grow margin
The Optimised Process
- AI pricing models score elasticity, competitor moves, and landed cost daily
- Commercial lead approves a short recommendation queue instead of rebuilding the list
- Approved prices push into Shopify and the ERP catalogue the same afternoon
- Pilot categories delivered about 3.8% revenue lift and 6% margin improvement
- Dashboards attribute Rand recovered so the CEO can defend the investment
Before vs After AI Price Optimisation
How It Works
From first conversation to live AI pricing recommendations in 4–8 weeks.
Tell Us Your Pricing Reality
Catalogue size, how often lists change, which channels quote from which systems, and where margin is leaking today.
Free Scoping Call
30-minute call to map elasticity signals, competitor sources, approval workflow, and the revenue or margin target that matters.
Build & Pilot
We train on your sales and cost history, wire recommendations with guardrails, and pilot on selected categories before full rollout.
Go Live & Monitor
Approved prices push into ecommerce, ERP, and CRM. Monitoring tracks lift, margin, and exception rates continuously.
Frequently Asked Questions
How is AI price optimisation different from an annual price increase?
An annual list is a snapshot. AI price optimisation continuously weighs demand elasticity, competition, and cost so recommended prices move when the market moves, then push only after your commercial team approves. The goal is sustained revenue and margin, not a once-a-year percentage hike.
Which systems can receive the approved prices?
We have pushed approved prices into Shopify, WooCommerce, Sage and other ERPs, HubSpot and Salesforce quote tools, and custom catalogues. If your channel can accept a price update via API or feed, we can wire it into the same approval flow.
Will this undercut customers or look unfair?
No. You set the rules: margin floors, customer-segment pricing, MAP where required, and how often a SKU may change. Dynamic pricing AI recommends inside those guardrails. Commercial leads still decide what goes live.
Do we need perfect competitor data to start?
Useful competitor coverage helps, but most pilots start with your own sales history, costs, and a focused set of hero SKUs. We expand competitor feeds category by category so you are not blocked waiting for full-catalogue scraping.
Is a custom engine better than an off-the-shelf pricing SaaS?
Volume-priced SaaS often charges per SKU or per channel and still needs local cost, VAT, and ERP mapping. A custom AI pricing build (typically R45,000 to R95,000) is tuned to your channels, Rand cost base, and approval workflow, without a fee that scales every time the catalogue grows.
How much does AI price optimisation cost?
Focused category pilots with recommendation and approval start from around R45,000. Full catalogue coverage with ecommerce, ERP, and CRM quote push typically ranges from R60,000 to R95,000. Businesses recovering even a mid-single-digit margin lift on a multi-million Rand catalogue usually see payback within 2 to 4 months.
Stop Leaving Revenue on a Static Price List
If competitors already reprice weekly and your list only moves quarterly, you are funding their growth with your margin.
Tell us your catalogue size, how quotes leave the CRM, and which channels must stay in sync. We will show you how AI price optimisation would work for your business, with commercial approval built in and payback measured in Rand, not theory. Related capability: our AI development practice.