We build behaviour-driven personalisation systems across your website, email and campaigns. Content, messaging and journeys adapt based on real user actions, not assumptions.
We build behaviour-driven personalisation systems across your website, email and campaigns. Content, messaging and journeys adapt based on real user actions, not assumptions.
Most personalisation attempts fail because they're too generic ("new vs returning"), not connected to real behavioural data, inconsistent across channels, difficult to maintain and disconnected from CRM or workflows. Recommendations feel irrelevant. The same message fires across every touchpoint. Email and website behave as if they've never met the same customer.
Personalisation Engine solves this by structuring personalisation as part of the operational layer, not a front-end feature. Segments are built from real behaviour. Rules are centralised. Content, email and campaigns all read from the same source of truth. So the experience is consistent, measurable and scales without breaking.
New vs returning. Country-based banners. Generic "recommended for you" widgets pulling popular products regardless of user intent. Rules live in one tool, data lives in another, CRM doesn't know either exist. Complexity grows, measurable uplift doesn't.
Behaviour-driven segmentation, rule-based dynamic content, channel-aligned delivery and measurable impact. Website, email and campaigns adapt from the same live behavioural data. Integrated with CRM, workflows and analytics rather than bolted on at the front end.
Personalisation only works when the system has
Behavioural signals
Segments built from browsing behaviour, product interactions, purchase history, engagement patterns and lifecycle stage. High-intent product viewers, repeat customers, first-time users, churn-risk cohorts, category-specific interest groups. Structured definitions that update continuously rather than static lists that go stale within weeks.
Personalised homepage banners, dynamic category messaging, product recommendations, conditional content blocks and user-specific CTAs. Returning users see recently viewed products. High-value users see different offers. New users see onboarding-focused messaging. All aligned with UX Templates and the underlying site structure rather than overlaid awkwardly on top.
Dynamic content blocks inside emails, behaviour-based messaging and segmented campaign variations. Abandoned cart emails show exact products. Post-purchase flows tailored to order type. Re-engagement emails driven by inactivity patterns. Integrates directly with Email Creative and CRM systems. One segmentation logic, consumed across every channel.
Personalisation aligned across paid search landing pages, paid social audiences, email campaigns and on-site experience. Ad messaging matches email and landing page. Audiences receive consistent offers across channels. Campaign timing aligns across systems. So the customer experiences one campaign, not five disconnected fragments of one.
Conditional content blocks, rule-based display logic, dynamic page elements and personalised messaging layers. Reusable, scalable, controlled through defined rules rather than bespoke hacks on individual pages. A new segment added at the engine level propagates to every page, email and campaign that uses the shared rule set.
Personalised vs non-personalised content, segment performance, messaging variations and conversion impact, tested using click-through data, conversion rates and behaviour tracking. Personalisation is measured and improved continuously, not deployed once and forgotten. Underperforming segments retire, strong ones scale.
Phase 01
We analyse existing user journeys, behavioural patterns, current segmentation and data quality. Identifying what behavioural signals are already being captured, where the gaps are, and which segments will deliver measurable uplift before any rules are built.
Phase 02
We define segment structures, rules and triggers, content variations and messaging hierarchy. Documented as a personalisation architecture rather than a pile of ad-hoc rules, so every team touching it understands what fires, when, and for whom.
Phase 03
We integrate with Shopify and CMS platforms, CRM systems, Klaviyo and email platforms, analytics tools and automation workflows. Personalisation becomes part of the operational stack. Not a siloed feature inside one tool that can't talk to the rest of the system.
Phase 04
We deploy dynamic content blocks, segmentation rules and personalisation logic across website, email and campaigns. Content variations are launched, rules go live and measurement is wired in. The system starts adapting from day one rather than after a quarter of setup.
Phase 05
Behaviour triggers, content delivery, edge cases and performance impact are all validated. Personalisation improves over time. New segments added, underperforming rules retired, winning patterns scaled. The engine compounds in precision with every cycle of data.
Personalisation Engine is the adaptive layer of Qwrki OS. The system that makes every other part of the stack behave differently based on who the customer actually is.
Personalisation Engine
AI & Automation
UX Templates
Dynamic layouts and content variation. UX templates define the structural zones; the Personalisation Engine decides what fills those zones for each user segment, so personalisation works within governed layouts rather than breaking them.
Email & Lifecycle
Segmentation and lifecycle messaging. The same segment logic that drives on-site personalisation drives Klaviyo flows and CRM messaging, so a user identified as churn-risk on the site is treated as churn-risk in email too.
Workflow Automation
Behaviour triggers and system updates. Personalisation signals feed into automation workflows, triggering CRM updates, tag assignments, fulfilment rules and cross-system events rather than staying trapped inside the front-end.
Analytics Dashboards
Measurement of segment and content performance. Every rule, variation and segment tracked inside analytics dashboards so personalisation is a measurable system, not a leap of faith. Underperforming logic surfaces quickly.
Paid Search
Audience targeting and messaging alignment. Paid search landing pages adapt based on the same segmentation logic, so paid traffic lands on pages that already know who they are rather than generic catch-all variants.
Paid Social
Audience mirroring across channels. Behavioural segments built on-site sync into paid social audiences, so Meta and TikTok campaigns target the same user groups with aligned messaging rather than parallel, disconnected audience lists.
More relevant user experiences. Messaging, offers and content that match the actual user, not a generic broadcast to all traffic.
Higher conversion rates. High-intent users see the most relevant content, reducing friction between consideration and purchase.
Improved retention and lifetime value. Post-purchase journeys tailored to order type, category and customer stage rather than one-size-fits-all.
Better use of existing customer data. Behavioural signals already being captured are actively used to drive experience, not stored and ignored.
Consistent messaging across channels. Ad, email, landing page and on-site experience all reference the same segmentation logic.
Scalable personalisation systems. New segments and rules added at the engine level propagate across every surface without rework.
Measurable performance improvements. Every rule, segment and variation tracked against CVR, RPV and lifetime value, not vanity metrics.
Personalisation as an operational layer. Integrated into CRM, workflows and analytics rather than existing as a disconnected front-end feature.
Replacing static homepages and category pages with dynamic content that adapts to returning users, high-value customers and first-time visitors. Each segment sees the message and offer most likely to move them forward rather than a shared average.
Turning generic welcome, post-purchase and re-engagement sequences into behaviourally-driven flows. Content blocks adapt based on order history, product interest and engagement patterns so every email reads as individually relevant.
Ending the disconnect between ads, emails, landing pages and on-site experience. One segmentation logic powers them all, so a customer moving across touchpoints experiences one coherent conversation rather than three unrelated ones.
Building structured, behavioural audiences for campaign use. High-intent viewers, category-specific interest cohorts and churn-risk users all defined once, reusable across paid, email and on-site campaigns rather than rebuilt each time.
Moving beyond "customers also bought" widgets to behavioural recommendations that combine browse history, purchase history and category affinity. Relevant suggestions that actually match intent rather than surfacing whatever's most popular.
In categories where every competitor shows the same generic experience, differentiating on relevance. Showing the right product, message and offer to the right segment so conversion improves without competing purely on price.
Get a personalisation audit and system proposal for your business.
Talk to a humanPerformance evidence
Source requiredLCP, INP and CLS are published only from current field evidence. No field source is connected for this page.
Most personalisation attempts fail because they're too generic ("new vs returning"), not connected to real behavioural data, inconsistent across channels, difficult to maintain and disconnected from CRM or workflows. Recommendations feel irrelevant. The same message fires across every touchpoint. Email and website behave as if they've never met the same customer.
Personalisation Engine solves this by structuring personalisation as part of the operational layer, not a front-end feature. Segments are built from real behaviour. Rules are centralised. Content, email and campaigns all read from the same source of truth. So the experience is consistent, measurable and scales without breaking.
New vs returning. Country-based banners. Generic "recommended for you" widgets pulling popular products regardless of user intent. Rules live in one tool, data lives in another, CRM doesn't know either exist. Complexity grows, measurable uplift doesn't.
Behaviour-driven segmentation, rule-based dynamic content, channel-aligned delivery and measurable impact. Website, email and campaigns adapt from the same live behavioural data. Integrated with CRM, workflows and analytics rather than bolted on at the front end.
Personalisation only works when the system has
Behavioural signals
Segments built from browsing behaviour, product interactions, purchase history, engagement patterns and lifecycle stage. High-intent product viewers, repeat customers, first-time users, churn-risk cohorts, category-specific interest groups. Structured definitions that update continuously rather than static lists that go stale within weeks.
Personalised homepage banners, dynamic category messaging, product recommendations, conditional content blocks and user-specific CTAs. Returning users see recently viewed products. High-value users see different offers. New users see onboarding-focused messaging. All aligned with UX Templates and the underlying site structure rather than overlaid awkwardly on top.
Dynamic content blocks inside emails, behaviour-based messaging and segmented campaign variations. Abandoned cart emails show exact products. Post-purchase flows tailored to order type. Re-engagement emails driven by inactivity patterns. Integrates directly with Email Creative and CRM systems. One segmentation logic, consumed across every channel.
Personalisation aligned across paid search landing pages, paid social audiences, email campaigns and on-site experience. Ad messaging matches email and landing page. Audiences receive consistent offers across channels. Campaign timing aligns across systems. So the customer experiences one campaign, not five disconnected fragments of one.
Conditional content blocks, rule-based display logic, dynamic page elements and personalised messaging layers. Reusable, scalable, controlled through defined rules rather than bespoke hacks on individual pages. A new segment added at the engine level propagates to every page, email and campaign that uses the shared rule set.
Personalised vs non-personalised content, segment performance, messaging variations and conversion impact, tested using click-through data, conversion rates and behaviour tracking. Personalisation is measured and improved continuously, not deployed once and forgotten. Underperforming segments retire, strong ones scale.
Phase 01
We analyse existing user journeys, behavioural patterns, current segmentation and data quality. Identifying what behavioural signals are already being captured, where the gaps are, and which segments will deliver measurable uplift before any rules are built.
Phase 02
We define segment structures, rules and triggers, content variations and messaging hierarchy. Documented as a personalisation architecture rather than a pile of ad-hoc rules, so every team touching it understands what fires, when, and for whom.
Phase 03
We integrate with Shopify and CMS platforms, CRM systems, Klaviyo and email platforms, analytics tools and automation workflows. Personalisation becomes part of the operational stack. Not a siloed feature inside one tool that can't talk to the rest of the system.
Phase 04
We deploy dynamic content blocks, segmentation rules and personalisation logic across website, email and campaigns. Content variations are launched, rules go live and measurement is wired in. The system starts adapting from day one rather than after a quarter of setup.
Phase 05
Behaviour triggers, content delivery, edge cases and performance impact are all validated. Personalisation improves over time. New segments added, underperforming rules retired, winning patterns scaled. The engine compounds in precision with every cycle of data.
Personalisation Engine is the adaptive layer of Qwrki OS. The system that makes every other part of the stack behave differently based on who the customer actually is.
Personalisation Engine
AI & Automation
UX Templates
Dynamic layouts and content variation. UX templates define the structural zones; the Personalisation Engine decides what fills those zones for each user segment, so personalisation works within governed layouts rather than breaking them.
Email & Lifecycle
Segmentation and lifecycle messaging. The same segment logic that drives on-site personalisation drives Klaviyo flows and CRM messaging, so a user identified as churn-risk on the site is treated as churn-risk in email too.
Workflow Automation
Behaviour triggers and system updates. Personalisation signals feed into automation workflows, triggering CRM updates, tag assignments, fulfilment rules and cross-system events rather than staying trapped inside the front-end.
Analytics Dashboards
Measurement of segment and content performance. Every rule, variation and segment tracked inside analytics dashboards so personalisation is a measurable system, not a leap of faith. Underperforming logic surfaces quickly.
Paid Search
Audience targeting and messaging alignment. Paid search landing pages adapt based on the same segmentation logic, so paid traffic lands on pages that already know who they are rather than generic catch-all variants.
Paid Social
Audience mirroring across channels. Behavioural segments built on-site sync into paid social audiences, so Meta and TikTok campaigns target the same user groups with aligned messaging rather than parallel, disconnected audience lists.
More relevant user experiences. Messaging, offers and content that match the actual user, not a generic broadcast to all traffic.
Higher conversion rates. High-intent users see the most relevant content, reducing friction between consideration and purchase.
Improved retention and lifetime value. Post-purchase journeys tailored to order type, category and customer stage rather than one-size-fits-all.
Better use of existing customer data. Behavioural signals already being captured are actively used to drive experience, not stored and ignored.
Consistent messaging across channels. Ad, email, landing page and on-site experience all reference the same segmentation logic.
Scalable personalisation systems. New segments and rules added at the engine level propagate across every surface without rework.
Measurable performance improvements. Every rule, segment and variation tracked against CVR, RPV and lifetime value, not vanity metrics.
Personalisation as an operational layer. Integrated into CRM, workflows and analytics rather than existing as a disconnected front-end feature.
Replacing static homepages and category pages with dynamic content that adapts to returning users, high-value customers and first-time visitors. Each segment sees the message and offer most likely to move them forward rather than a shared average.
Turning generic welcome, post-purchase and re-engagement sequences into behaviourally-driven flows. Content blocks adapt based on order history, product interest and engagement patterns so every email reads as individually relevant.
Ending the disconnect between ads, emails, landing pages and on-site experience. One segmentation logic powers them all, so a customer moving across touchpoints experiences one coherent conversation rather than three unrelated ones.
Building structured, behavioural audiences for campaign use. High-intent viewers, category-specific interest cohorts and churn-risk users all defined once, reusable across paid, email and on-site campaigns rather than rebuilt each time.
Moving beyond "customers also bought" widgets to behavioural recommendations that combine browse history, purchase history and category affinity. Relevant suggestions that actually match intent rather than surfacing whatever's most popular.
In categories where every competitor shows the same generic experience, differentiating on relevance. Showing the right product, message and offer to the right segment so conversion improves without competing purely on price.
Get a personalisation audit and system proposal for your business.
Talk to a humanPerformance evidence
Source requiredLCP, INP and CLS are published only from current field evidence. No field source is connected for this page.
Delivery
Real-time · adaptive
Dynamic delivery
Behavioural signals · Rules & segmentation · Dynamic delivery · Cross-channel relevance
eCommerce
PDP, PLP and merchandising personalisation. Product recommendations, category prioritisation and on-site offers adapt based on purchase history and browsing behaviour inside the Shopify or commerce layer.
Delivery
Real-time · adaptive
Dynamic delivery
Behavioural signals · Rules & segmentation · Dynamic delivery · Cross-channel relevance
eCommerce
PDP, PLP and merchandising personalisation. Product recommendations, category prioritisation and on-site offers adapt based on purchase history and browsing behaviour inside the Shopify or commerce layer.
