Relewise and Centra
Integration Agency & Consultants
Personalisation decays the moment product data and customer behaviour fall out of sync. When Centra serves as your commerce core, any delay in pushing catalogue updates or stock levels to Relewise results in irrelevant recommendations and lost margin. We build the connection between these systems so your engine operates on a defined schedule, transforming static merchandising into a journey that reflects actual availability.
Auditing system health and data mapping plans
Cogent will efficiently connect your Relewise and Centra systems, enhancing your personalisation and ecommerce capabilities. Our consulting services are invaluable, offering system audits that empower both our consultants and your team to address issues, ensuring your tech ecosystems operate smoothly. This enables you to deliver an exceptional customer experience. By integrating Relewise and Centra, we optimise personalisation and ecommerce processes, allowing your business to function effectively and meet customer expectations. Our audits identify inefficiencies, providing actionable insights for improvement.
Solution Design
The integration for Relewise and Centra establishes Centra as the authoritative source for product catalogues and market-specific pricing, while Relewise owns the behavioural analysis. A primary design decision involves the trade-off between catalogue fan-out and synchronisation latency. We commonly push full catalogue updates on a defined schedule but trigger updates for price and stock changes to maintain recommendation accuracy. This avoids the fragility of constant full-index syncs under high load. We explicitly map Centra market filters to Relewise to prevent showroom or internal items from appearing in public feeds. This design means the ecommerce team manages commercial rules in Centra, while Relewise handles the individual placements. The digital team monitors the engine for conversion lift, while operations relies on Centra for fulfilment and order truth.
Managing product attributes and customer event loops
The integration functions as a continuous feedback loop between Centra transactional data and Relewise behavioural analysis. Centra acts as the system of record for the product catalogue, including attributes like variants and stock levels. These are updated in Relewise to ensure the recommendation engine surfaces purchasable items. Simultaneously, Relewise captures real-time customer events, which are processed alongside Centra order data to refine profiles. We ensure that data maps consistently across both systems, preventing duplicate records and ensuring that historical purchases correctly influence future suggestions. Monitoring is built-in to detect interruptions in the product feed before they impact the customer journey.
Orchestrating secure flows via compliant infrastructure
Cogent2 leverages IPaaS to integrate Relewise and Centra, enhancing Ecommerce and Personalisation securely. IPaaS platforms, with ISO 27001 and SOC 2 compliance and above, ensure secure data handling. Relewise and Centra benefit from streamlined Personalisation and Ecommerce processes, improving efficiency and security.
Surfacing synchronisation gaps and pricing errors early
Standard dashboards rarely surface the operational gaps that drain performance, such as products that are in stock in Centra but missing from recommendations due to mapping errors. We provide visibility into the data health between both systems, identifying where synchronisation has stalled or where attribute mismatches are causing irrelevant results. Hidden issues, like out-of-date pricing or incorrect regional availability, can damage brand trust. We focus on surfacing these failures early, allowing the ecommerce team to intervene before conversion rates are impacted. This shift to proactive monitoring ensures that personalisation remains an asset rather than a source of operational friction.
Technical handover for daily merchandising ownership
Training ensures the ecommerce and digital teams confidently own the logic behind automated merchandising. We provide operational documentation that identifies where data lives and how to manage the rules governing recommendations. Handover focuses on practical ownership: what to check daily to ensure Centra product data is surfacing correctly in Relewise. Teams learn to interpret alerts from the integration layer and understand who owns specific exception types, such as data mismatches or sync delays. This documentation serves as a manual for running the business day to day, ensuring that the team understands the operational connection between their Centra catalogue and the personalised customer experience.
Maintaining regional feed integrity after launch
Post-launch support focuses on the integrity of the data loop between Centra and Relewise. We monitor for synchronisation errors, attribute mismatches, and feed delays that create a sync illusion where the storefront looks active but recommendations are stale. Our model includes specific monitoring for Centra market mapping failures, preventing Relewise from displaying products that are restricted or out of stock in specific regions. We prioritise alerts based on their impact on conversion, ensuring that data gaps are closed before they affect site performance. This oversight allows merchandising teams to trust the automation even during high-volume peak trading.
Common failures
Stale product data polluting recommendations
Operational impact: When a product's price, stock status, or existence changes in Centra but not in Relewise, the engine recommends incorrect or unavailable items. This erodes customer trust and directly impacts conversion rates. The customer experience team must handle complaints about unavailable SKUs, while merchandising teams investigate why outdated product data is still live.
Prevention / Action: The integration must use Centra webhooks for real-time SKU updates, but also include a scheduled full-catalogue sync to correct any missed events. Integration logic needs to explicitly handle product status changes. For instance, a product disabled in Centra must be marked as inactive in the Relewise index to prevent it from appearing in any recommendations.
Incomplete user behaviour tracking
Operational impact: If user actions like 'add to cart', 'view product', or a completed order are not sent reliably from Centra, the Relewise personalisation engine operates on a partial data set. This results in weak or irrelevant product recommendations, which makes it very difficult to measure any revenue growth attributable to the personalisation programme.
Prevention / Action: Map all key Centra user journey events to their corresponding Relewise tracking events before developing the integration. A combination of client-side tracking for user interactions and server-side tracking for critical events like order confirmation creates a more resilient data capture process. The integration's exception handling must be able to queue and retry failed tracking events to avoid data loss.
High latency during catalogue updates
Operational impact: During a flash sale or new collection launch, delays syncing product data from Centra to Relewise means the recommendation engine is always a step behind. It may fail to surface a new hero product or continue recommending items that sold out minutes ago. This leads to a frustrating customer journey and impacts sales velocity at critical trading moments.
Prevention / Action: Design the integration to handle webhook bursts from Centra, especially during bulk product updates to a collection. Use a queuing system to manage incoming messages and prevent the integration from being rate-limited by Relewise APIs. Critical events like inventory updates can be given a higher priority in the queue than less time-sensitive changes like a product description update.
Mismatched product categorisation
Operational impact: Relewise's ability to generate relevant 'shop the look' or alternative product recommendations is dependent on a clean category structure. If Centra's 'Collections' are mapped inconsistently to Relewise 'Categories', the engine will produce illogical suggestions. This forces the merchandising team into a cycle of manual data correction instead of focusing on commercial strategy.
Prevention / Action: Before implementation, define a clear and maintainable mapping schema between Centra's Collections and Relewise's product categories. The integration logic must enforce this data model, including how to handle products in multiple collections or nested collections. Establish Centra as the source of truth for all product and category data to ensure consistency.
Frequently asked questions
What happens in Relewise if we make a SKU unavailable in Centra?
When a SKU is disabled or removed in Centra, the integration must command Relewise to remove that item from its active index. Without this, you risk showing products that cannot be purchased, eroding customer trust. A common failure occurs when internal or showroom markets are not filtered correctly, leading to incorrect availability on the storefront.
We frequently run promotions by updating product collections in Centra. Can the integration handle this?
Bulk updates to Centra collections can trigger high event volumes that overwhelm standard integrations, causing recommendations to become stale. We design the flow to manage these triggers reliably, ensuring merchandising rules update without the lag that often affects generic connectors.
How quickly does customer activity in Centra affect Relewise recommendations?
Behavioural data is typically sent from Centra to Relewise on a defined trigger. This enables the engine to adjust models so the next page load reflects recent intent. We focus on minimising operational latency to ensure the journey remains relevant to the current session.
Our store uses multiple price lists and markets in Centra. Will Relewise show the correct pricing?
Respecting Centra's Market attribute is critical. The integration must map these to Relewise global filters to ensure recommendations show the correct currency and restricted stock for the shopper's specific region. Failing to map these correctly often leads to users seeing products they cannot actually order.