Dynamic pricing and personalized promotions, built for your store.
Flat pricing and blanket sitewide sales leave money and relevance on the table. SEMPITE builds the engines that fix that: behavior-triggered offers, transparent rules-based pricing, and the experimentation discipline to prove each change made money. The Steam-and-Sony playbook, without a team of fifty.
We start with the low-risk, high-return layer, personalized promotion, where every shopper can get the same price but each sees the offer most likely to convert them. Where it fits your market, we add rules-based pricing. Every step is measured against a control group, and every step has trust guardrails.
One price for everyone is a tax on your best customers.
A sitewide “20% off this weekend” discounts everyone, including the shoppers who would have paid full price, and it trains the rest to wait for the next sale. A single flat price treats a loyal repeat buyer, a first-time bargain hunter, and someone ready to check out in thirty seconds as identical. Two of the biggest storefronts in gaming just walked away from that model at the same time, because the data said it was costing them. Most stores are still running the billboard.
A pricing and promotion engine, in four layers.
Signal audit
We map the behavioral data you already have before building anything new.
- Wishlist, cart, and browse-depth signals
- Repeat-purchase cadence and churn risk
- What your platform’s native APIs can already do
Personalized promotion layer
The Steam model: same price for all, the right offer surfaced per shopper.
- Behavior-triggered offers, not blanket sales
- Segment-matched bundles, shipping, and coupons
- Nudge the shopper who hesitates, not the one already buying
Rules-based dynamic pricing
Transparent, defensible price logic, where it fits your market and margins.
- Regional pricing and loyalty / first-purchase offers
- Demand- and inventory-aware markdowns on aging stock
- Competitor-aware guardrails, never opaque willingness-to-pay
Experimentation & trust guardrails
Every change is a measured test, and every rule stays on the right side of the line.
- Control groups and clean incremental-lift measurement
- Revenue, margin, and conversion impact per rule
- Fairness and disclosure policy baked in
Scoped to your catalog and traffic · highest-return layer first
Steam and Sony just validated this at scale.
In early 2027 Steam is replacing its calendar of sitewide sales with personalized, algorithmic discounts; in testing it surfaced roughly ten times more games per day with higher visits, wishlist adds, and cart adds, without changing anyone’s price. Sony, meanwhile, has reportedly been A/B testing different prices for different shoppers on the PlayStation Store, and earned a wave of backlash for it. The lesson is not to copy Sony’s risk. It is to capture Steam’s upside, deliberately and transparently. We broke down the full playbook in what ecommerce can learn from Steam and Sony.
It also matters because buying itself is changing. When shoppers ask an assistant for the best price, or an AI shopping agent buys on their behalf, flat pricing and blanket sales get even blunter. Pricing that responds to context, cleanly and transparently, is what wins the transaction.
Audit the signals, build the layer, prove the lift.
We start with the signal audit, then build the personalized-promotion layer first because it carries the least risk and usually the fastest return. We add rules-based pricing only where it fits, and we wrap every change in an A/B framework with a control group so you see the incremental impact, not just the activity. If a rule does not beat its holdout, we cut it. This pairs naturally with our eCommerce and marketplace operations and Shopify SEO work.
For product brands and multi-channel sellers.
If you sell physical or digital products online and you are still running one flat price and the occasional sitewide sale, this is built for you, from a store doing five figures a month to a six-figure-SKU catalog. The mechanics scale down; the data you need is almost always already there. Start with an audit of your catalog, traffic, and signal data.
Straight answers on dynamic pricing.
What is dynamic pricing for ecommerce?
Changing a product’s price or promotion based on data rather than leaving it fixed. It runs from rule-based pricing driven by demand, inventory, and timing, through personalized promotion where everyone can get the same price but different shoppers see different offers, to personalized pricing where the price itself varies by shopper. We start most clients with personalized promotion: most of the upside, far less risk.
Do you charge different customers different prices for the same product?
Only when it is transparent and rule-based, such as regional pricing, loyalty tiers, or first-purchase offers, and only when it fits your market. We do not build opaque willingness-to-pay pricing that quietly charges one shopper more than another for the identical item, because that is the approach that burns trust and draws regulators. Trust guardrails are part of every engagement.
What platforms do you build this on?
Shopify and Shopify Plus, headless and custom storefronts, and the major marketplaces, using each platform’s native discount, segmentation, and pricing APIs plus our own logic layer where the platform falls short. The signal audit maps what your specific stack can already do first.
How do you measure whether it actually works?
Every change ships with a control group and a measured lift, the same A/B discipline Sony used on the PlayStation Store. You get the incremental revenue, margin, and conversion impact of each rule, not just activity. If a rule does not beat its holdout, we cut it.
We’re a small store. Is this only for large catalogs?
No. The mechanics scale down. A store doing five figures a month usually already collects the wishlist, abandoned-cart, and repeat-purchase data a personalized-promotion engine needs. We scope the build to your catalog and traffic and start with the highest-return layer.
Stop taxing your best customers with one flat price.
Start with the $199 Brand Audit, or run a free visibility check first. No lock-in, ever.