An AI model that automatically adjusts product or service prices in real-time based on demand, competitor pricing, inventory levels, time of day, and other market signals to maximize revenue or profit.
Online retailers set prices in a spreadsheet and revisit them occasionally, yet the market around them moves constantly. Competitors run sales, stock levels shift, demand rises before holidays and falls on quiet weekdays. Manual pricing either reacts too late, leaving margin on the table when demand is strong, or holds prices too high while inventory ages. Tracking competitors across hundreds of products by hand is simply not feasible.
An AI dynamic pricing engine monitors those signals and recommends or applies price changes within rules the business sets. It learns how price changes have affected sales for each product, factors in stock on hand, supplier costs and competitor prices, and aims for a chosen goal such as margin or sell-through. Done well, it frees the merchandising team from constant manual updates. Done poorly, it can confuse customers or breach consumer law, so guardrails matter as much as the model.
The engine pulls historical orders, product costs, inventory levels and traffic from the e-commerce platform, such as Shopify or Magento, plus competitor prices from a monitoring feed.
A custom machine learning model estimates how demand for each product or category responds to price, time, season and competitor moves.
An optimiser proposes new prices that pursue the chosen objective while respecting minimum margins, maximum change limits and price-matching rules.
Proposed changes appear in a review screen; low-risk changes within tight bands can be auto-applied, while larger moves wait for a merchandiser to approve.
Prices update through the platform API, and results are tracked so the model can be retrained on fresh outcomes.
An e-commerce store uses a dynamic pricing engine for its top-selling products. The AI slightly increases the price during peak shopping hours and lowers it to match a competitor's sale, capturing maximum margin without manual intervention.
Custom ML models, Real-time data feeds, E-commerce platform integration (Shopify, Magento)
Under the Australian Consumer Law, prices shown to customers must not be misleading. Be careful with 'was/now' comparisons, strikethrough prices and sale claims when prices move often, since the reference price should be genuine. The ACCC has taken action over misleading pricing representations, and drip pricing of unavoidable fees is a known concern. Pricing that varies by individual customer profile raises privacy and fairness questions under the Privacy Act, and new transparency rules for automated decisions that significantly affect individuals apply to privacy policies from December 2026. Collecting competitor prices by scraping can breach website terms of service, so prefer licensed data feeds. Businesses should also avoid any coordination of prices with competitors, which would raise competition law issues.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Changing prices over time based on demand and costs is generally lawful. The risks lie in how prices are presented: misleading reference prices, false sale claims or hidden compulsory fees can breach the Australian Consumer Law. Personalised pricing based on an individual's data raises further privacy questions. Get advice on your pricing displays before launching an automated system.
A custom engine like this suits retailers with a large catalogue, meaningful sales volume and several seasons of data. Smaller stores often get better value from simpler rule-based repricing apps or competitor alerts first. The high complexity and three-month-plus timeframe reflect the data work and testing needed for a model you can trust.
Only if you configure it that way. Most businesses start with recommendations that a merchandiser approves, then allow automatic changes within narrow bands for selected products once trust is established. Every change is logged with the reason, so you can see exactly what moved and why.
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