AI dynamic pricing software for ecommerce, retail and B2B distributors. It learns price elasticity per product, watches competitor prices, respects your margin and brand rules, and sends recommended prices to Shopify, your ERP or your quoting tool for approval.
Sample: gross profit per week, test cell vs forecast after elasticity-based repricing with a 22% margin floor.
AI dynamic pricing software is a system that uses machine learning to estimate how demand for each product responds to price, then combines that with competitor prices, stock and costs to recommend prices that meet a goal such as gross profit. It outputs price recommendations per SKU or customer segment, within the margin and brand guardrails you set.
Price lists get updated once a year with a flat uplift. Some products could carry more; others lose sales to a competitor who moved last Tuesday. Nobody has time to look at 20,000 SKUs one by one.
A 1% price improvement lifted operating profit by almost 8% on average across the S&P 1000 in McKinsey analysis.[1]
Retailers using dynamic pricing typically see 2% to 5% sales growth according to McKinsey.[2]
Margins improve by 5% to 10% in the same McKinsey work on retail dynamic pricing.[2]
A B2B medtech company saw a 4% to 8% margin uptick from a data-driven dynamic pricing approach (McKinsey).[3]
Transactions, costs, stock and competitor prices joined at SKU and channel level, cleaned of promotions and stock-outs.
Demand models estimate how volume moves with price, then an optimiser picks prices that hit your objective within rules.
Recommendations go to a review screen, then to your storefront, ERP price lists or B2B quote tool.
We load 18–24 months of transactions and costs, pick one category, and agree the metric, usually gross profit per week in a test cell against control.
Models are built and sanity-checked with your category manager. Margin floors, MAP and brand price ladders are written as hard rules.
Recommended prices go live on a test cell of SKUs or stores while a matched control keeps current pricing.
We compare gross profit, units and conversion between test and control and decide with you whether to scale.
| Criterion | Cost-plus spreadsheet | Repricing tool | Stratgik build + run |
|---|---|---|---|
| Logic | Fixed markup | Rules, often match or beat competitor | Elasticity plus competitors plus margin rules |
| Protects margin | Yes, ignores demand | Can race to the bottom | Hard floor and objective set by you |
| B2B customer pricing | Manual discount bands | Rarely | Segment-level guidance in CPQ or ERP |
| Best fit | Few SKUs, stable costs | Marketplace sellers matching Amazon | Thousands of SKUs, multichannel or distributor |
| Proof of lift | None | Dashboard claims | Test vs control cells |
| Pricing | Staff time | Per SKU or GMV share | Fixed pilot and monthly run fee |
Costs have moved faster than price lists. A small, well-targeted price change beats most cost programmes, if it is based on real demand rather than a flat uplift.
The margin uplift is an assumption; the pilot measures it in a test cell against a control.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
6-week pilot, one category or region
per month, production pricing
per month, all categories and channels
Competitor data subscriptions, model compute and cloud usage billed at cost with no markup; taxes excluded. GBP and AED prices are indicative conversions from USD.
AI dynamic pricing software learns from your sales history how demand for each product changes when price changes, after removing effects like promotions and stock-outs. It adds competitor prices, costs and stock, then an optimiser recommends prices that maximise your chosen objective, such as gross profit, without breaking margin floors, MAP or brand price ladders.
It does not have to. Most mid-market sellers change prices weekly, not minute by minute, and cap how far any price can move. We set hard rules for price ladders, MAP, key value items and maximum weekly change. Category managers approve changes, and B2B contract prices stay fixed unless you choose otherwise.
A Stratgik AI dynamic pricing software pilot costs $22,000 for six weeks on one category, including elasticity models, competitor data for one market and a test-versus-control readout. Production runs at $5,000 a month. Competitor data and compute are billed at cost. We charge fixed fees rather than a percentage of revenue.
Yes, but it looks different from ecommerce. For distributors the model usually produces target price and discount guidance by customer segment, product and order size, delivered in Salesforce CPQ or the ERP quote screen. Reps still negotiate, but with a data-backed floor and target instead of a flat discount band.
You need around 18–24 months of transaction history with prices, quantities and dates, plus product costs. Products with few sales are grouped with similar items so they still get sensible elasticity estimates. Competitor prices help but are not essential for the first category. We check data quality in week one before committing to the test.
For many sellers, yes. Repricing tools mostly react to competitors, often matching the lowest price. AI dynamic pricing software also considers how your own customers respond to price, your costs and stock, so it can raise prices where demand allows. For a small marketplace seller focused on the buy box, a repricer may be enough.
In a 30-minute call we review your data, channels and pricing rules and design a test-and-control pilot you can read in six weeks.