Dynamic Pricing Software
for Ecommerce.
Every product in your catalogue has a price that is leaving margin on the table right now. This is the straight-to-production guide for online sellers: demand-led pricing, margin guardrails, and what a 4 to 9% gross margin improvement looks like in your P&L.
Every pricing problem this page describes has a fix — through automated price optimisation built for your catalogue.
Founder, TwoDots · May 2026 · Updated May 2026
15+ years building retail AI at Kohl's and Sears. Founder of HappySellers, which runs pricing and inventory automation for 250+ live ecommerce sellers across India. Sunil has designed and shipped AI pricing engines for catalogues ranging from 200 to 50,000 SKUs.
TL;DR
- → Using AI for dynamic pricing means your store adjusts prices continuously based on demand velocity, competitor moves, inventory depth, and margin floors — not gut feel or weekly spreadsheet updates. Brands doing $1M to $20M on Shopify or Amazon typically see 4 to 9% gross margin improvement within 90 days of a properly trained pricing engine going live.
- → Dynamic pricing software that actually moves margin is not a plugin or an off-the-shelf dashboard. They are model-driven engines trained on your specific catalogue, cost structure, and competitive set — built into your Shopify or Amazon stack at the platform level, not sitting on top of it waiting for manual approval.
- → TwoDots builds and ships a custom pricing engine for online sellers doing $1M to $20M in 4 to 8 weeks — with hardcoded margin floors, channel parity rules, and a shadow-mode validation period before a single live price changes.
Definition
What is dynamic pricing software for ecommerce?
Dynamic pricing software is a machine learning system that continuously adjusts product prices based on demand, competitor moves, and inventory — within margin floors you set. It replaces manual spreadsheet updates with a model trained on your own sales data, pushing prices to Shopify or Amazon automatically on a cadence you control. The goal is not the lowest price. It is the right price for your margin.
The problem
Why static pricing costs you margin daily.
Quick answer
Static pricing loses margin every day your catalogue goes unchanged. Demand spikes, competitor drops, and overstock all create pricing gaps — and without automation, none of them close until someone manually updates a spreadsheet.
Amazon sellers using automated repricing update prices 3–5 times per day. A Shopify catalogue updated monthly loses margin on demand spikes, misses competitor moves, and clears overstock too late. The leakage shows up four ways: demand spikes sell out at the wrong price, overstocked SKUs sit at full price, competitor drops go unnoticed until the sales report, and blanket discounts destroy margin on products that were selling fine. A trained pricing model closes all four — automatically.
Across 250+ live sellers on HappySellers, demand-led pricing consistently outperforms rule-based repricing on gross margin — with sellers seeing an average 4 to 7% margin improvement in the first 90 days after switching from manual price updates. For a brand doing $5M, a 5% gross margin improvement is $250,000 in additional margin per year — from data that already exists in your order history and inventory system.
The engine
How ecommerce price optimisation works.
Quick answer
The model reads demand signals, competitor prices, inventory levels, and your costs — and calculates the best price per product within your margin floor. Prices update automatically on the schedule you set.
The engine looks at four things — demand, competitors, inventory, and your costs — and works out the best price for each product at that moment. It learns from your own sales history, so the weights it uses are based on what has actually driven margin for your catalogue, not a formula someone guessed.
Prices update on a schedule you set: every hour for high-velocity marketplace listings, once a day for a slower Shopify catalogue, or instantly when a competitor drops price or stock runs low. Before any price goes live, your margin floor is checked. The model cannot write a price below it — ever.
What the pricing model reads.
How fast is this product selling? Is interest growing or fading? The model reads your sales patterns by day and hour, spots seasonal trends, and learns from 18 months of your own order history — not generic benchmarks.
What are competitors charging right now on Amazon, Google Shopping, and other marketplaces? Competitor prices are tracked continuously and used as a guardrail — not blindly matched. If a competitor drops below cost, your price stays put.
How much stock do you have, and how long will it last at the current sell rate? Products running low hold a higher price. Products sitting in the warehouse too long get a gentle nudge down — before they become a clearance problem.
What does this product actually cost you to sell — after platform fees, shipping, and fulfilment? Your real cost per unit sets the floor the model works within. It never recommends a price that eats into the margin you need.
How the TwoDots AI dynamic pricing model produces prices
AI Pricing
Model
your data
Margin
floor check
Optimal price
Shopify / Amazon
AI dynamic pricing pipeline
- 1
Collect the signals
Sales velocity, competitor prices, stock levels, and your cost data are pulled into one feed the model can read.
- 2
Model scores every SKU
The model calculates the price that maximises your margin for each product right now, given current demand and competition.
- 3
Hard rules are checked
Before any price is written, your margin floor, brand constraints, and channel parity rules are verified. The model cannot override these.
- 4
Price is approved
The price either applies automatically or sits in a review queue for your team to approve — you choose per category.
- 5
Storefront or marketplace updated
The new price is pushed to Shopify, Amazon, or WooCommerce via API. No manual export, no copy-paste.
- 6
Outcome feeds the next cycle
Did the price convert? Did margin hold? Every result is logged and used to improve the model at the next retraining cycle.
The short answer
Automated price optimisation replaces a static number on a product page with a model-driven price that adjusts based on demand, competition, and inventory — within margin floors you define. It is not the lowest price strategy. It is the right price strategy: charging what the market will bear at each moment, without breaching the floor that protects your business.
The numbers
Before and after: what changes when the TwoDots pricing engine goes live.
Quick answer
At 90 days post-implementation, sellers typically see gross margin improve by 3 to 10 percentage points and repricing frequency shift from weekly manual to hourly or event-triggered.
Measured at 90 days post-implementation, across ecommerce catalogues of 200 to 5,000 active SKUs. The "before" figures reflect typical baselines for sellers doing $1M–$20M on static or rule-based pricing.
Before — static or rule-based pricing
After — TwoDots AI dynamic pricing engine
| Seller type | Gross margin | Gross margin | Repricing frequency | Repricing frequency | Annual gain at $5M* |
|---|---|---|---|---|---|
| Shopify D2C apparel | ~35% | 39–43% | Weekly / manual | Daily / event-triggered | $40K–$160K |
| Amazon marketplace (1P or 3P) | ~28% | 33–37% | Manual or rule-based | Hourly / event-triggered | $50K–$270K |
| WooCommerce multi-category | ~32% | 35–39% | Weekly / manual | Daily / event-triggered | $30K–$140K |
| Multi-channel (Shopify + Amazon) | ~30% | 36–40% | Manual, no parity rules | Hourly, parity enforced | $60K–$350K |
| B2B ecommerce (wholesale) | ~38% | 42–45% | Monthly / contract-driven | Per-segment, rule-bound | $40K–$210K |
*Annual gain estimated for brands doing $5M GMV. "Before" margins are typical baselines for the seller type on static pricing — your starting point may differ. Source: industry benchmarks and TwoDots implementation data. Results are not guaranteed.
TwoDots has validated pricing signal behaviour on 250+ live sellers through HappySellers before applying it to client implementations. The ranges above reflect what demand-led pricing achieves when the engine is trained correctly, the margin floor is hardcoded, and shadow mode is used to validate before go-live.
The process
How TwoDots ships AI dynamic pricing.
Quick answer
Five phases over 4 to 8 weeks: catalogue audit, model build, rules layer, shadow mode validation, then go-live. No live price changes until you sign off at the end of shadow mode.
Five phases, 4 to 8 weeks from first data handoff to a live pricing engine — covering signal integration, margin guardrails, shadow mode validation, and closed-loop retraining. Each phase has a clear deliverable before the next begins.
Catalogue and margin audit
Days 1–7We map your full SKU catalogue, landed costs, channel mix, and current pricing rules. We identify which product segments have the most pricing leverage and where the margin floor risks are highest. Most Shopify and WooCommerce brands have the cost data they need in their existing exports. Amazon sellers get a COGS reconciliation against Seller Central fee structures. We tell you what pricing upside is realistically available before any model work begins.
Signal integration and baseline model
Days 8–18We connect demand data (your sales velocity and search trend signals), competitor price feeds (via scraper or API depending on your category), and inventory depth. We build the first pricing model on your last 12 to 18 months of order data and backtest it against actual price performance. You see what the model would have charged versus what you actually charged, and the margin delta.
Rules layer and margin guardrails
Days 19–28Think of this as guardrails the engine can never cross. We take three things — your minimum margin per SKU, your channel parity rules (so your Shopify and Amazon prices stay aligned), and any brand constraints like 'this product never goes on sale' — and lock them into a separate layer the model cannot touch. The model then finds the best price it can within those limits. It cannot go below your margin floor. It cannot break parity. It cannot discount a SKU you have marked as protected. Not during a flash sale. Not at 2am on Black Friday. The rules win, every time.
Shadow mode validation
Days 29–42The engine runs in parallel with your current pricing for two weeks. You see the prices it would have set versus the prices you actually set, and the margin outcome of each. No live prices change during this phase. You review the shadow log, flag any decisions that look wrong, and we tune the model and rules before any live traffic touches it. You approve go-live. We do not flip the switch without your sign-off.
Go-live, monitoring, and retraining
Days 43–56+We push live via Shopify Admin API, Amazon SP-API, or direct catalogue integration. Weekly performance reports cover margin delta, price change volume, and buy box win rate for Amazon sellers. Retraining runs on a four-week cadence. Drift detection alerts you when the model's price recommendations diverge from expected ranges, which typically signals a new competitor entrant or a seasonal demand shift the current model has not seen.
The short answer
Implementing an automated pricing engine on Shopify, Amazon, or WooCommerce takes 4 to 8 weeks from first data handoff to go-live. Shadow mode runs for two weeks before any live price changes, with your sign-off required. The engine adjusts prices within your hardcoded margin floors, retrains every four weeks, and alerts you when market conditions shift beyond the model's current training range.
In practice
What it looked like for one Amazon seller.
Quick answer
A $6.8M multi-channel seller improved buy box win rate from 61% to 79% and gross margin from 32% to 38.5% in 90 days — after a 6-week implementation with shadow mode validation.
Nestora Living — Amazon and Shopify, home & lifestyle
620 active ASINs, $6.8M annual GMV, India & UAE channels
"We were repricing manually twice a week and still losing buy box on our top 40 ASINs. Within 10 weeks of going live, our buy box win rate went from 61% to 79% and gross margin recovered above where it was the prior year. The shadow mode period was what gave us confidence — we could see every decision the engine would make before it made a single live change."
The situation
Manual repricing twice per week on Amazon. Buy box win rate at 61% on competitive ASINs. Shopify prices unchanged for four months. 140 SKUs in overstock with no markdown trigger. Gross margin at 32%, down from 37% the prior year.
The approach
Six-week implementation. Demand-led pricing model trained on 16 months of order data. Competitor feed via Amazon SP-API pricing endpoint. Shopify integrated via Admin API. Margin floor encoded per category. Shadow mode ran for 12 days. Buy box parity rules hardcoded per ASIN.
The result
Buy box win rate: 61% to 79% in 10 weeks. Gross margin: 32% to 38.5% in 90 days — above the prior-year baseline. Overstock SKUs cleared 23% faster via demand-nudge pricing. No manual repricing runs required after week 8.
Results at 90 days
Buy box win rate
Before
61%
→
After
79%
Gross margin
Before
32%
→
After
38.5%
6 wks
to implement
Results depend on data quality, catalogue size, and implementation scope and are not guaranteed.
Want to see what this looks like for your catalogue and current margin?
Who this is built for
Built for online sellers at $1M–$20M, not enterprise SaaS.
Quick answer
Best suited for online sellers doing $1M–$20M with 200 or more active SKUs on Shopify, Amazon, or WooCommerce. If you have fewer than 200 SKUs in one channel, rule-based repricing tools are usually sufficient and cheaper.
Automated pricing earns its cost at complexity and scale — large catalogues, multiple channels, and fast-moving categories where manual price optimization is simply not possible. Here is where it applies — and where it does not.
Shopify D2C brands
You have 300 or more active SKUs and prices that have not changed in months. A standard Shopify pricing app applies rules — match a competitor, run a sale, set a fixed discount. AI price optimization for Shopify goes further: it reads your demand signals, learns from your sales history, and adjusts prices daily within the margin floor you set.
Amazon marketplace sellers
Buy box position drives 80 to 90% of sales on most ASINs (Amazon Seller Central) — and you lose it the moment a faster seller undercuts you while you are asleep. Rule-based tools follow the price down but never raise it when demand is high. A trained pricing engine optimises for margin and buy box position simultaneously.
Multi-channel retailers
You sell on Shopify and Amazon and spend time manually keeping prices in parity. Channel parity violations suppress your buy box and can trigger Amazon account warnings. A properly built AI pricing engine enforces parity as a hard rule across every update cycle — no manual check required.
When automated pricing is NOT the right tool
If you have fewer than 200 active SKUs in a single channel, rule-based dynamic pricing software like RepricerExpress or Prisync is probably sufficient and significantly cheaper. A model-driven engine earns its cost when you have catalogue complexity, multi-channel parity requirements, or a large overstock problem that needs demand-weighted pricing to resolve. The AI Fit Score will tell you which camp you are in within three minutes.
How it compares
TwoDots vs Wiser vs Prisync vs RepricerExpress.
Quick answer
Wiser, Prisync, and RepricerExpress track competitor prices and fire your rules. A custom-built model engine learns the relationship between price, demand, and margin for your catalogue — and optimises price as a lever, not just a reaction.
| Feature | Wiser | Prisync | RepricerExpress | TwoDots |
|---|---|---|---|---|
| Competitor price tracking | ✓ | ✓ | ✓ | ✓ |
| Rule-based repricing | ✓ | ✓ | ✓ | ✓ |
| ML model trained on your data | ✗ | ✗ | ✗ | ✓ |
| Demand-signal pricing | ✗ | ✗ | ✗ | ✓ |
| Hardcoded margin floor (non-overridable) | Partial | Partial | Partial | ✓ |
| Shadow mode validation before go-live | ✗ | ✗ | ✗ | ✓ |
| Shopify Admin API integration | ✓ | ✓ | ✗ | ✓ |
| Amazon SP-API integration | ✓ | ✗ | ✓ | ✓ |
| Multi-channel parity enforcement | Partial | ✗ | ✗ | ✓ |
| Closed-loop model retraining | ✗ | ✗ | ✗ | ✓ |
| Best for | Price monitoring | Price monitoring | Amazon only | $1M–$20M multi-channel |
Wiser and Prisync are excellent tools for price monitoring at $1K–$2K/month. RepricerExpress works well for single-channel Amazon sellers with fewer than 200 ASINs. A custom model engine earns its cost when you have multi-channel complexity, 200+ SKUs, and margin optimisation (not just price matching) as the goal.
Beyond ecommerce
Where else automated pricing creates margin.
Quick answer
Automated price optimisation applies wherever demand fluctuates, competitors move, and inventory or capacity is finite — from ecommerce catalogues to hotel rooms, event seats, freight lanes, and SaaS subscriptions.
Demand fluctuates, competitors move, and inventory depletes in every industry that sells time, capacity, or product. The same model logic applies wherever those three forces exist.
Hotels & vacation rentals
Room rates that adjust to occupancy levels, local events, booking window, and seasonal demand — the same model logic that powers airline revenue management, now accessible without enterprise contracts.
Events & ticketing
Concert, sports, and experience pricing that responds to sell-through rate and demand velocity. Hold price when seats are moving fast. Nudge price when they are not — before the day of the event.
Restaurants & food delivery
Menu and delivery pricing by time of day, kitchen capacity, and real-time demand. Higher prices during peak hours when every table is full. Nudge pricing during slow periods to fill capacity without a blanket discount.
Car & equipment rental
Fleet utilisation-based pricing — higher rates when inventory is running low, gentle markdown when assets sit idle beyond their target utilisation window. Turns a static rate card into a margin lever.
SaaS & subscription pricing
Usage-based and seat-based pricing tiers that adapt to customer segment, conversion signals, and competitive positioning. AI price optimization surfaces the plan price that maximises annual contract value without increasing churn risk.
B2B distribution & manufacturing
Quote optimisation and contract pricing based on order size, customer lifetime value, material cost signals, and competitive bids. Automated pricing replaces the gut-feel discount that sales teams apply inconsistently across accounts.
Travel & airlines
Seat inventory and ancillary pricing by booking window, route demand, and competitive fares. The core logic of AI dynamic pricing was born in airline revenue management — and the same demand-elasticity modelling now runs on a fraction of the infrastructure cost.
Energy & utilities
Time-of-use tariffs and demand response pricing that shift consumption and optimise grid revenue. AI dynamic pricing models peak demand windows and customer price sensitivity to set rates that balance load and margin.
Logistics & freight
Spot rate and contract pricing that responds to lane demand, capacity availability, and fuel cost signals. Automated AI pricing replaces the weekly rate sheet with a live model that captures margin when lanes are tight and wins volume when they are not.
Why TwoDots
Why TwoDots.
Quick answer
TwoDots builds custom pricing engines validated on 250+ live sellers. Every implementation includes a hardcoded margin floor, two weeks of shadow mode before any live price changes, and milestone-based delivery — you only pay when each phase ships.
Validated on 250+ live sellers before we sold it
Every pricing signal choice, model architecture decision, and rules layer design in this playbook was tested on real transactions through HappySellers before we brought it to a client engagement. When we say demand-led pricing outperforms pure competitor-matching on margin, we have the annotated order data from 250+ active sellers to back it up. Not a vendor claim. Observed behaviour.
Shadow mode is non-negotiable
Most vendors push live and fix problems in production. We run shadow mode for two weeks before any price on your Shopify store or Amazon listing changes. You see every recommended price, every margin impact, and every decision the engine would have made — before it makes them. You approve go-live. If something looks wrong in shadow mode, we fix it before it costs you money.
Margin floor is hardcoded, not a setting
In most AI pricing tools, the margin floor is a configuration field someone can accidentally clear. In our implementation, the margin floor is encoded in a rules layer that the model cannot override, cannot negotiate with, and cannot be talked out of by a demand spike. If your cost floor is $14.00 per unit, the engine never writes a price below it. Not at 2am on Black Friday. Not during a competitor pricing war.
We build for your catalogue, not the average catalogue
Off-the-shelf AI tools for dynamic pricing are optimised for the median seller. Your product mix, your competitive set, and your margin structure are not median. The model we build is trained on your order history, calibrated on your product segments, and constrained by your specific cost structure. Generic tools give you generic results.
Retail DNA means we know what Q4 looks like
November and December reprice dynamics on Amazon are unlike any other period of the year. Competitors temporarily undercut on high-velocity gift SKUs, then recover. Demand spikes are real but short. A model that has not seen Q4 repricing patterns will make wrong calls during the highest-margin window of the year. Sunil built inventory and pricing AI at Kohl's and has run HappySellers across 250+ Indian ecommerce businesses for seven years. Those seasonal patterns are in the implementation from day one.
Ship-or-don't-bill delivery model
We deliver on milestones, not hours. Each phase of the implementation has a defined deliverable. If we do not hit the deliverable, you do not pay for that phase. This is how we have always worked. It is the only engagement model that aligns our incentives with yours.
Common questions
Everything operators ask before starting.
What is dynamic pricing software and how is it different from rule-based repricing?
Dynamic pricing software uses a machine learning model trained on your own sales data to find the price that produces the best margin outcome given current demand, competition, and inventory. Rule-based repricing follows fixed instructions — match the lowest price, never go below cost. The difference: rules react to one variable at a time and never learn. A trained model weighs all signals simultaneously and improves with every retraining cycle.
Will the engine ever price below my cost?
No. A properly built pricing engine includes a hardcoded margin floor the model cannot override. Your landed cost plus required margin percentage defines the floor. It is set in a rules layer during implementation and validated in shadow mode before any live price changes.
How long does the implementation take from start to live?
Four to eight weeks for most Shopify and WooCommerce brands with clean order history. Amazon sellers typically add one to two weeks for parity rule configuration. One week audit — ten days model build — ten days rules layer — two weeks shadow mode — go-live.
Does it work with Shopify, Amazon, and WooCommerce?
Yes. Shopify connects via the Admin API at variant price level. WooCommerce via REST API or direct database update. Amazon via the SP-API Pricing and Listings endpoints. Multi-channel brands get channel parity rules encoded as hard constraints so the engine stays compliant across all platforms simultaneously.
What data do I need to get started?
At minimum: 12 months of order history with SKU-level price and quantity data, your landed cost per SKU, and your current channel mix. Shopify and WooCommerce export this natively. Amazon sellers use Seller Central business reports. Eighteen months of clean history meaningfully improves model accuracy.
How does this compare to tools like Wiser, Prisync, or RepricerExpress?
Wiser, Prisync, and RepricerExpress track competitor prices and apply your rules to respond. A model-driven engine goes further — it learns the relationship between price, demand, and margin, and optimises price as a lever, not just a reaction to competitors. It earns its cost at scale, across multiple channels, with a large catalogue.
What happens when a competitor drops their price below your cost floor?
The engine holds your floor and does not follow the competitor below your minimum margin. You may temporarily lose buy box on that ASIN. The model monitors whether the competitor sustains the lower price or recovers. If they sustain it, the SKU is flagged for manual review rather than chasing the loss.
How often does the AI pricing model retrain?
Every four weeks as standard. This captures seasonal shifts and new competitor entrants without retraining so frequently that the model becomes unstable. Between cycles, drift detection monitors whether recommended prices diverge from market-clearing prices — if they do, you get an alert before the next retraining.
How do I set up dynamic pricing on Shopify?
A trained model reads demand signals, competitor data, and stock levels for each SKU, then pushes a recommended price per variant to Shopify via the Admin API on your chosen cadence. A hardcoded rules layer checks your margin floor before any price is written. Shadow mode runs first — nothing goes live until you approve it.
What is the best pricing approach for online sellers doing $1M to $20M?
For sellers doing $1M to $20M, custom model stacks trained on your specific catalogue and cost structure outperform any off-the-shelf SaaS. Core components: a gradient boosted model trained on your order history, a competitor price feed, a margin floor rules layer, and an API integration to your storefront.
Is dynamic pricing legal for ecommerce sellers?
Yes. Dynamic pricing is legal in most markets for standard ecommerce products. It becomes a legal issue only in specific regulated categories — medicines, essential goods during declared emergencies — or when used to discriminate by protected characteristic. For Shopify and Amazon sellers in standard product categories, automated price adjustments are both legal and standard practice. Amazon's own Buy Box algorithm rewards competitive pricing and expects sellers to update prices regularly.
What are examples of dynamic pricing in ecommerce?
Common ecommerce examples: an Amazon seller raising prices on a high-velocity ASIN when buy box competition drops; a Shopify brand discounting overstock SKUs that have sat in the warehouse beyond their target sell-through window; a multi-channel retailer adjusting prices automatically when a competitor runs a flash sale. In each case, the pricing engine reacts to a real signal — demand, inventory, or competition — rather than waiting for a manual spreadsheet update.
Ready to ship this?
Your competitors are repricing while you read this.
Start with a free AI Fit Score to see whether your catalogue and data are ready for AI dynamic pricing. Or book a 30-minute call with Sunil to put a number on what 4 to 9% gross margin improvement looks like in your P&L.
Ship-or-don't-bill. Milestone-based. Shadow mode before a single price changes.