Greeting a customer by their first name in an email is not personalisation. Showing them the same product they just purchased is not personalisation. Real relevance requires understanding behavioural signals in real time and quietly reshaping the catalogue to remove friction before the customer notices it is there.
The reality of modern infrastructure, unpacked.
As an e-commerce catalogue grows, it eventually exceeds a customer's willingness to browse it manually. Finding the right product begins to require either high intent and active searching, or a platform that surfaces relevance automatically. When a store treats every visitor as the average customer, the experience breaks in two directions: high-intent buyers leave because they cannot quickly find their specific requirement, and low-intent browsers bounce because nothing captures their distinct interest.
Most commerce platforms come with built-in recommendation blocks that rely on basic correlation—'Frequently Bought Together' or 'Recently Viewed'. They do not account for where the customer is in their lifecycle, their specific compatibility constraints, or the sequence of their current session. Building a system that tracks session behaviour, maps it to complex product attributes, and serves a uniquely ranked catalogue in milliseconds requires a data architecture that sits outside the core commerce engine.
A merchandising team spending hours manually curating homepage grids that remain irrelevant to the majority of traffic. A marketing director paying rising acquisition costs to drive targeted ad traffic to a site that converts poorly because the post-click experience immediately reverts to a generic storefront. A customer who abandons their cart because the suggested accessories are incompatible with the main product they intend to buy. These are the costs of treating personalisation as a visual widget rather than a core data strategy.
We target specific workflows where manual effort meets its ceiling, delivering measurable, high-leverage outcomes.
The majority of e-commerce traffic consists of anonymous or first-time visitors with no purchase history. Waiting for them to create an account or complete a purchase before adapting the experience means losing them during the first visit.
A static homepage forces every user to navigate through the same category hierarchy, regardless of whether they are a loyal customer looking for a specific consumable or a new visitor browsing high-ticket items.
Segments based entirely on static demographics—age, location, or gender—assume that people who look the same buy the same things. This leads to broad, ineffective marketing campaigns that train customers to ignore communications.
A customer interacts with a brand on a mobile app, clicks an email, and browses on desktop, but each channel acts independently. A product dismissed on the website is still heavily promoted in the next day's email.
The line between helpful relevance and intrusive tracking is thin. Personalisation that relies on opaque third-party data brokering or cross-site tracking damages customer trust and runs counter to modern data regulations.
Outcomes in the reader's language, focused on actual usage.
Custom algorithmic models (collaborative filtering, content-based, and sequential) that serve highly relevant product suggestions across the homepage, product pages, and cart step.
Implementation and tuning of unified customer profiles, tying anonymous browsing sessions to known profiles once authentication occurs to create a single behavioural record.
Real-time content adaptation for websites and mobile apps, allowing hero banners, navigation, and product grids to shift based on the individual user's affinity and current session intent.
Statistical testing frameworks built directly into the serving layer, allowing the business to measure the exact revenue contribution of a personalisation model against a neutral baseline.
Logic layers that identify churn risk, discount affinity, and replenishment timing, feeding these signals directly into your existing email and marketing automation platforms.
Measurement tooling that tracks the effectiveness of recommendations—attribution modelling, click-through rates, and incrementality—so teams understand exactly how personalisation is driving results.
Machine learning models require data density to be useful. For a boutique retailer with a small catalogue and a few hundred orders a month, algorithmic recommendations will not outperform a human merchandiser's logic, and the infrastructure cost is not justified. We are direct about this threshold. We do not deploy complex AI personalisation unless the catalogue size and traffic volume provide enough signal for the models to actually learn. The concern we hear most often is about privacy—specifically whether deep personalisation requires invasive tracking. We believe relevance should not require surveillance. We design engines that rely strictly on first-party data and transparent consent. If a customer declines tracking, the system does not break; it simply falls back to context-aware logic (e.g., 'people who view this item also view...') rather than individual profiling.
By analysing the exact order in which products are viewed and purchased across millions of sessions, the model learns complex relationships—understanding that a customer viewing a specific technical component needs a precisely compatible accessory, not just the store's overall best-seller.
For new products with no purchase history (the 'cold start' problem), the engine uses natural language processing and computer vision to understand the item's attributes—style, material, technical specs—and recommends it to users who have shown an affinity for those specific traits.
An algorithm observes a user's interaction with discounted versus full-price items, segmenting them by price sensitivity. This allows the business to offer promotional codes only to users who actually require an incentive to convert, preserving margin on users who do not.
We audit the data layer before we tune the models. An algorithm trained on broken analytics will confidently recommend the wrong things at scale.
Most personalisation projects fail because the underlying event tracking is inconsistent. Before building any models, we audit the data collection layer—ensuring that add-to-carts, variants selected, and search refinements are captured cleanly, accurately, and without duplication.
Algorithmic recommendations often suggest out-of-stock items, broken sizes, or low-margin products if left unchecked. We build business logic into the serving layer, allowing merchandising teams to pin specific products, boost high-margin categories, and apply inventory-aware filters over the AI outputs.
A personalised page that takes three seconds to load will lose more revenue to page speed than it gains from relevance. We design for the edge—using server-side rendering and in-memory data stores to ensure personalised recommendations are served in under 50 milliseconds.
Personalisation is only valuable if it drives incremental revenue. We do not accept clicks as proof of success. Every model is deployed with a global holdout group (e.g., 5% of traffic seeing the generic experience) to calculate the actual commercial uplift generated by the system.
Measured by operational outcomes, not just technical uptime.
0.0% → 3.8%
Conversion rate improvement
fashion retailer following behavioural segmentation deployment
~0%
Reduction in cart abandonment
electronics marketplace via compatibility-aware recommendations
< 0ms
Recommendation latency
maintaining site speed while calculating real-time relevance
Quiet, honest, and specific results.
A mid-sized fashion retailer had static product recommendations showing the same items to all customers. Email campaigns were returning open rates of around 2%, and website conversion was plateaued at roughly 1.2%. They had traffic, but lacked customer behaviour insights to convert it efficiently.
Overall conversion rate improved from roughly 1.2% to approximately 3.8%. Email open rates increased to around 18% as content became contextually relevant to the recipient. Average order value grew by roughly 22% as the discovery process became aligned with individual preferences.
An electronics marketplace with over 50,000 SKUs struggled with product discovery. Customers could not find relevant accessories, leading to poor search-to-purchase conversion and high cart abandonment because the default recommendations were largely incompatible with the primary items.
Cart abandonment decreased by approximately 35%. Search-to-purchase conversion increased by roughly 180%. The average time-to-purchase reduced significantly as customers no longer had to leave the site to verify compatibility independently.
A beauty brand wanted to personalise skincare recommendations but lacked technical capability. Customers were struggling to navigate the catalogue to find products suitable for their specific skin types, resulting in high return rates and low repeat purchase volume.
Recommendation accuracy—measured by the rate of users adding suggested items to their cart—reached approximately 92%. Repeat purchase rates increased by around 58%, and average order value rose by roughly 31% through relevant product bundling.
Style recommendations based on browsing behaviour, size prediction that hides unavailable dimensions, and seasonal trend adaptation that adjusts to the user's local climate.
A two-week assessment of your current event tracking, data quality, and CDP configuration. We identify missing signals, data leakage, and the specific gaps preventing effective personalisation. The output is a clear remediation plan.
A 6–8 week sprint focused on implementing core, high-leverage features—typically product recommendations and basic behavioural segmentation—to establish a baseline and prove commercial viability through A/B testing.
An 8–12 week engagement to build out dynamic content, cross-channel decisioning, and full customer data platform integration, connecting the web experience with your email and mobile infrastructure.
Continuous involvement post-launch to tune algorithms, adjust to inventory shifts, build new predictive segments, and manage the statistical testing queue to ensure the system continues to drive incremental revenue.
Enterprise-grade security embedded at the core.
Enterprise-grade controls, rigorous compliance baselines, and delivery discipline woven into the architecture from day zero.
Personalisation architecture is integrated directly with your consent management platform. Granular consent controls ensure that if a user declines analytical cookies, the system automatically falls back to context-based serving rather than user profiling.
Machine learning models are trained on anonymised session IDs and behavioural vectors, not Personally Identifiable Information (PII). Enterprise-grade encryption applies to data at rest and in transit, with strict role-based access controls.
The models rely entirely on the behavioural data generated within your own properties. We do not use third-party data brokers, cross-site tracking pixels, or external audience graphs, ensuring compliance with GDPR and CCPA requirements.
Adhering to the highest standards of security and regulatory compliance.
Our foundational technology stack is designed around principles of immutability, deterministic performance, and zero-trust security. We deploy modern, enterprise-grade tooling to ensure every architecture we deliver is robust and extensible.
Infrastructure for building, training, and deploying recommendation and prediction models
Everything you need to know about partnering with us and our engineering standards.
Every retailer has a different volume of data, different catalogue complexity, and different constraints around their existing commerce platform. Whether you are trying to move beyond basic recommendation widgets, looking to unify your web and email behaviour, or just want to understand if your traffic volume justifies algorithmic personalisation, we are glad to hear where you are. No presentation. Just a conversation.