Offers & Recommendations
AI Offer Personalization
Each visitor sees the specific offer, discount or promotion most likely to convert them, inside the margin rules you set.
Showing every visitor the same discount gives away margin to people who would have bought anyway and misses the ones who needed a nudge. This build picks the right offer for each visitor from the options you approve, inside the margin floor you set. It never invents discounts, and visitors without a matching signal see your standard offer.
- Each visitor matched to an offer from your approved list
- Margin floors enforced automatically on every offer shown
- A/B testing that shows which offers actually convert
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Offer Personalization decides which specific offer, discount or promotion a visitor sees, choosing from the options you approve rather than inventing new ones, and staying inside the margin rules you define. Rules remain visible and editable, and anyone outside a matched segment sees your standard offer.
Why businesses buy it
Blanket promotions are expensive. They train loyal customers to wait for discounts and still fail to convert hesitant first-time visitors. This build matches each visitor to the offer most likely to help, from a catalog you control, without ever going below your margin rules. Your commercial team can read and adjust every rule. The Scope Card fixes the systems, workflows and test scenarios before you order, so the project's limits and safeguards are set in writing.
Problems this solves
Blanket discounts give money away to people who would buy anyway
The system chooses which offer each visitor sees from the options you approve. Discounts go where they are useful rather than to everyone by default.
Offer rules get bent and margins suffer
Every offer stays inside the margin rules you define. The system picks from your approved list and never invents new promotions or discounts of its own.
The wrong promotion shows to the wrong customer segment
Offers are matched to customer segments using data from up to two of your connected systems. Anyone outside a matched segment simply sees your standard offer instead.
Nobody can explain why a customer saw a particular offer
Rules remain visible and editable by your team. You can trace which rule led to an offer and adjust it through your own approval process.
A good fit if
You run more than one offer, discount tier or promotion and currently show the same one to every visitor regardless of fit.
You have margin limits that any offer shown must respect, and want those limits enforced automatically, not left to chance.
You already track purchase history or cart behavior through Shopify, WooCommerce or Klaviyo.
You want offer-matching rules visible to your commercial team, and an A/B test to confirm the right offers actually convert better.
Not the right fit if
You want the wording of a banner or popup personalized, not which offer gets shown. See AI Dynamic Messaging.
Your personalization should follow a recipient in their inbox, not on the site. See AI Email Personalization.
You are matching by target company for a B2B sale, not by individual visitor behavior. See AI B2B Account Personalization.
You need more than 2 connected systems or more than 6 workflows. Add a note at checkout for a quote, or start from our Custom AI enquiry form.
How it works
- Your offer catalog is loaded. The discounts, bundles or promotions you are willing to show are defined, along with the margin floor none of them can go below.
- Matching rules are set. Which offer suits a first-time visitor, a cart abandoner or a repeat customer is mapped out in rules your commercial team can read.
- Purchase history and cart behavior are read from Shopify, WooCommerce or Klaviyo as a visitor browses, without needing a full profile to work.
- The best-matching approved offer is shown, chosen from your catalog rather than generated freely, so nothing outside your margin rules ever appears.
- A visitor with no matching signal sees your standard offer, never a blank space or an unapproved discount.
- An A/B test compares the matched offer against your current default, so the lift in conversion is measured, not assumed.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your offer catalog: the discounts, bundles or promotions you are willing to show, and to whom.
- The margin floor or business rules any offer shown must stay inside.
- Access to purchase or cart data through Shopify, WooCommerce or Klaviyo.
- Your consent and privacy settings, so offer matching only runs on data you are allowed to use.
- The name of the approver for any change to which offers can be shown.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who can approve the offer rules before they go live.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified and your offer catalog confirmed.
Weeks 1–2
Offer-matching rules built inside your margin limits.
Week 3
Your store and CRM systems connected with read and write access.
Weeks 3–4
50 test scenarios run across your defined offers.
Week 4
Your review, with up to 2 rounds of changes.
Go-live
Live, recorded handoff session delivered, 30 days of support begin.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- Offer personalization that selects from your approved offers within your margin rules
- Up to six workflows with approval rules, visible and editable
- Read and write connections to up to two systems
- Your standard offer as the fallback for unmatched visitors
- A test report covering the documented scenarios
- An admin guide, a recorded handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Discount spend can go where it makes a difference.
- Margins can be protected by rules you control.
- Offers can feel more relevant to customers.
- Redemption rate by segment
- Track redemptions per offer view in your store or analytics.
- Average discount per order
- Compare your store's order reports before and after launch.
- Conversion for offer viewers
- Compare personalized-offer viewers with visitors shown your standard offer over the same period.
Before you start: Record current discount spend, redemption rates and conversion for the month before launch.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
Running this after launch usually costs $50 to $250 a month in AI model usage and hosting, alongside whatever you already pay for Shopify, WooCommerce or Klaviyo. Cost tracks how many visitors are shown a matched offer, not the number of offers in your catalog.
AI usage during the build and testing period is billed the same way, directly in your own accounts, so every provider relationship stays yours.
Ask about running costsCustom requirements
If your offer logic needs more than the Scope Card covers, note it at checkout. A written Change Quote follows, the extra work waits for your approval, and your original order keeps its planned date. Larger or bespoke projects use the Custom AI request form instead.
Request a custom versionKeep it running
Optional monthly maintenance covers monitoring, fixes, model and platform updates and enough monthly change hours to update the offer catalog as promotions change. It starts after the 30 days of post-launch support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A discount tool charges monthly per active campaign | An agency quotes promotion strategy work separately | Ours is one fixed price, shown on this page before you buy |
| Scope | A discount tool leaves margin protection to your team | An agency scope is agreed project by project | Ours is written on the Scope Card, margin rules built in |
| Delivery | A discount tool is quick to install but rules are manual | An agency promotion project usually runs a few weeks | Ours has a fixed delivery time shown before you buy, with faster options |
| Guardrails | A discount tool can show any offer you configure, mistakes included | An agency build depends on what was agreed | Ours only shows offers from your approved catalog |
| Measuring impact | A discount tool often sells reporting as an add-on | An agency build depends on what was agreed | Ours includes A/B test setup in the price |
Questions about this product
How much does AI Offer Personalization cost?
One fixed price covers offer matching across up to two connected systems and up to six workflows, plus 50 documented test scenarios run against your real offer catalog and margin rules. The panel on this page shows that price for each delivery speed, so the build cost is clear before you order and is never estimated after a briefing call.
How long until visitors start seeing matched offers?
Delivery time is fixed for each speed and shown in the panel on this page. It starts once intake is complete and access is verified. Most of the calendar time goes on finalizing your offer catalog and margin floors, so sharing the approved offers early is the quickest way to get matched offers live.
Can it invent new discounts on its own?
No, it only shows offers from the catalog you approve, chosen to match a visitor rather than generated freely. Every discount, bundle or promotion it can display is one your commercial team has already signed off, and none of them can go below the margin floor you set during setup.
What data does offer matching use to pick the right promotion?
It reads purchase history and cart behavior from tools such as Shopify, WooCommerce or Klaviyo, without needing a full customer profile to work. A cart abandoner and a first-time browser can see different offers from the same approved catalog, based on what their actual behavior suggests will convert.
What offer does a visitor with no matching signal see?
Your standard offer, exactly as it exists today, rather than a blank space or an unapproved discount. Fallback to your default offer is built into every matching rule, so a visitor outside your defined segments is never left without a clear, approved promotion to consider. That fallback behavior is tested during the build itself, so it is confirmed working before your standard offer ever goes live to real visitors.
What are the ongoing running costs for offer personalization?
Expect roughly $50 to $250 a month in AI model usage and hosting, on top of whatever you already pay for your store or ESP plan. That cost tracks how many visitors are actually shown a matched offer each month, not how many offers exist in your approved catalog overall.
Who owns the offer-matching rules this build creates?
You do. Every matching rule, margin constraint and workflow built around your offer catalog belongs to you once delivered, the same as work from an in-house commercial team would. We do not reuse your offer catalog, your margin rules or your matching logic for any other customer. That includes the right to export the rule set or move it to another provider at any point, with no ongoing license fee attached.
What if the wrong offer is showing after launch?
You have five working days after delivery to flag anything that does not match the Scope Card, and that gets fixed as part of the original build price, not a paid Change Quote. Beyond that window, further tuning of offer matching runs through monthly maintenance instead. Most fixes at that stage are narrow adjustments to a single matching rule, not a rebuild of the whole offer setup.
Stop giving your best discount to everyone, including those who did not need it.
Order AI Offer Personalization and visitors see the right offer from your approved catalog on a fixed delivery date, inside your margin rules.
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