Every customer on your site sees the same homepage, the same featured collection, and the same 20% off email -- even though your high-LTV customers and one-time browsers are completely different buyers?
You're running blanket promotions to your entire list when only a fraction of customers actually need an incentive to buy -- and you're training the rest to wait for discounts?
AI Personalisation for E-commerce
AI-driven personalisation across your entire e-commerce experience -- not just product recommendations, but pricing, email, search results, on-site content, and retention offers, all calibrated per customer.
We've shipped 100+ products. A single homepage, a single email blast, and a blanket discount code treat every customer the same. The stores growing on first-party data are doing the opposite -- showing each customer the product, price, and message most likely to convert them.
Dynamic pricing per customer segment with personalised discount offers based on predicted churn probability
Personalised email and SMS triggered by browse abandonment, price drops, and restock events
Personalised search results ranked per user based on brand affinity, price range, and category preferences
Churn prediction model scoring each customer's lapse probability with automated win-back offers calibrated to LTV
RaftLabs builds AI personalisation for e-commerce stores across six layers: dynamic pricing by customer segment, personalised email and SMS campaigns triggered by behavioural signals, personalised search results ranked per user, on-site content adaptation by visitor segment, churn prediction with automated win-back offers, and a customer data platform that unifies web, mobile, email, and POS data into a single customer profile. We have shipped 100+ products and work at a fixed cost. Most AI personalisation projects deliver in 10--14 weeks.
100+Products shipped
·RFM + MLCustomer scoring
·FixedCost delivery
·10-14Week delivery
Personalisation is the gap between what your customers want and what your store shows them.
Most e-commerce stores are running the same playbook: one homepage for everyone, one email to the full list, one discount percentage for every campaign. That approach made sense when first-party data was hard to collect. It doesn't make sense now. The data is there -- browse history, purchase history, wishlist activity, email engagement, session behaviour -- but it's sitting in separate tools with no connection between them.
We build the connection. AI personalisation for e-commerce means each customer's session, email, search result, and offer is shaped by their own data. High-LTV customers see new arrivals and loyalty benefits. First-time visitors see acquisition messaging and social proof. Customers who browsed a product three times and didn't buy see a targeted offer, not a blanket site-wide promotion. The result is higher conversion from the same traffic, lower discount costs, and customers who feel like the store actually knows them.
What we build
Dynamic pricing personalisation
Personalised pricing by customer segment, purchase history, and demand signals -- not uniform site-wide sales. Loyalty member pricing applied automatically at session start so members see their price without a code. Personalised discount offers generated per customer based on predicted churn probability: a customer likely to lapse gets a targeted 15% offer; an active repeat buyer who was going to purchase anyway gets full price. Offer amounts calibrated to customer LTV so your best customers get your best treatment, and your discount budget goes to the customers who actually need an incentive to buy.
Personalised email and SMS campaigns
Customer segmentation by RFM score, product affinity, and LTV -- so your email platform sends the right message to the right segment rather than blasting the full list. Behavioural triggers built into your email flow: browse abandonment email for a session that viewed a product three times, price drop alert for a wishlisted item, restock notification for a previously viewed out-of-stock product. Subject line and product selection personalised per recipient so the email your customer opens shows their category and their price range. Connects to Klaviyo, Mailchimp, or your existing email platform via API.
Personalised search results
The same search query returns different ranked results for different users. A customer who consistently buys premium brands sees premium results ranked first; a price-sensitive buyer sees value options at the top. Sort-by defaults adjusted to match each user's demonstrated preferences -- a customer who always filters by rating gets rating-sorted results by default. Personalised autocomplete suggestions shaped by past searches and purchases, so the search box is already anticipating what each customer is looking for. Search personalisation is one of the highest-leverage conversion interventions because it works on every session where a customer uses search.
On-site personalised content
Hero banner, featured collection, and promotional messaging adapted per visitor segment without changing your CMS setup. New visitors see acquisition messaging and bestseller social proof. Returning customers who haven't purchased in 60 days see a win-back offer. High-LTV members see new arrivals, loyalty point balance, and exclusive access messaging. Segment definitions are configurable without code changes -- your marketing team can define a new segment and assign content to it through a management interface rather than opening a pull request. Segments are built on first-party data, not third-party cookies.
Churn prediction and retention offers
A machine learning model scoring each customer's likelihood to lapse based on purchase recency, frequency, and category -- updated daily so the score reflects current behaviour. An automated win-back trigger fires at the predicted churn point, before the customer has already left. The offer sent to a lapsing customer is calibrated to their LTV: a customer who spent $2,000 over three years gets a more generous offer than a customer who placed one small order. Churn risk is surfaced in a dashboard so your CRM team can see which segments are currently at risk and which retention campaigns are performing. The model improves as it collects more purchase data.
Customer data platform and identity resolution
A unified customer profile that pulls together web sessions, mobile app events, email engagement, and point-of-sale transactions into a single record per customer. Anonymous sessions resolved to known customers at login or email capture -- so a customer who browsed on mobile and purchased on desktop is a single profile, not two separate records. Purchases attributed to the correct session and channel so your acquisition reporting reflects reality rather than last-click. The unified profile feeds your email platform, your ad platform custom audiences, and your on-site personalisation layer -- so every tool is working from the same customer data rather than its own isolated slice.
Frequently asked questions
We build AI personalisation for Shopify, Shopify Plus, WooCommerce, Magento, BigCommerce, and custom e-commerce stacks. The personalisation layer sits between your data sources -- storefront events, order data, email engagement -- and your customer-facing channels. For Shopify, we integrate with the Storefront API and Customer API to read session and order data and write personalised content blocks. For other platforms, we integrate via REST API or direct database connection. The personalisation engine is platform-agnostic -- what matters is that we can read customer behaviour data and write content decisions back to the storefront.
For Shopify, personalised pricing is delivered through Shopify's discount and pricing rule APIs rather than by modifying product prices directly. Loyalty member pricing applies a customer-tag-based discount at checkout. Targeted offers for churn-risk customers use unique discount codes generated per customer with single-use limits. For Shopify Plus, checkout extensibility allows us to apply personalised pricing logic directly in the checkout flow. This approach means personalised pricing sits within Shopify's supported model, survives platform updates, and doesn't require unsupported hacks that break during Shopify version changes.
The personalisation models need purchase and behavioural data to train on before they improve meaningfully. For a store with 12+ months of order history and a few thousand monthly active customers, the churn prediction and RFM segmentation models are useful from day one -- they run on existing data. Recommendation and search personalisation improve over the first 60--90 days as the models collect session-level signals. Most clients see measurable conversion rate improvement within 60 days of launch. The projects themselves deliver in 10--14 weeks from kick-off to production launch.
A personalisation project covering RFM segmentation, behavioural email triggers, and on-site content personalisation for one to two segments typically runs $30,000--$70,000. Adding personalised search, dynamic pricing, churn prediction, and a customer data platform typically runs $70,000--$150,000. Cost depends on your e-commerce platform, the number of data sources to unify, which personalisation layers you need, and how many marketing platform integrations are required. We scope every project before pricing -- contact us with your platform, your monthly order volume, and the customer behaviour you're trying to change.
Talk to us about your AI personalisation project.
Tell us your platform, your current email and conversion setup, and which customer segments you're trying to reach differently. We'll scope the right personalisation stack and give you a fixed cost.
