Relewise and BigCommerce
Integration Agency & Consultants
Search relevancy usually fails when the product catalogue drifts from the personalisation engine. At scale, the lag between a BigCommerce price update or stock change and its reflection in Relewise results becomes an operational risk. This integration ensures that product data flows from BigCommerce to Relewise as the master source, protecting the customer experience from displaying unavailable or incorrectly priced products. We focus on cataloguing truth so recommendations remain grounded in actual inventory.
Auditing the product and personalisation data flow
We connect your Relewise and BigCommerce integration quickly, supporting your ecommerce personalisation goals. Our consulting services are invaluable for businesses using Relewise and BigCommerce, as our system audit services uncover inefficiencies and integration issues. This enables our consultants and your team to take decisive action, ensuring your ecommerce technology ecosystem runs efficiently. With a focus on personalisation, our audits help you deliver a superior customer experience, keeping your systems aligned and optimised for growth.
Solution Design
In this integration, BigCommerce serves as the master and source of truth for the product catalogue, inventory levels, and customer order history. We typically design Relewise to ingest this data via a scheduled batch sync for full catalogue updates, while pushing clickstream and behavioural events to ensure personalisation remains responsive. A key design decision involves mapping BigCommerce custom fields and variant data into Relewise schemas to ensure search filters reflect product modifiers accurately. We acknowledge the trade-off that high-frequency catalogue syncing can increase API overhead, so we often prioritise stable batch updates for the full index while using triggers for immediate stock adjustments. This design ensures the ecommerce team works from a search index that aligns with BigCommerce inventory, reducing the risk of displaying out-of-stock items in recommendations.
Sequencing the catalogue sync and attribute mapping
The integration treats BigCommerce as the authoritative source for product data, including custom fields and variant-level pricing. Data flows are sequenced to ensure that attribute updates and stock levels are pushed to Relewise on a regular cadence, preventing the personalisation engine from recommending items that are no longer available. We map BigCommerce schemas to Relewise entities, ensuring that recommendation logic respects actual catalogue relationships. Monitoring is embedded at the point of data exchange, allowing for the detection of sync failures before they lead to broken site search results or incorrect pricing in recommendations.
Standardising connections on secure integration middleware
Leveraging IPaaS with ISO 27001 and SOC 2 and above security accreditations, Relewise and BigCommerce integrations for Ecommerce and Personalisation are delivered efficiently and securely. IPaaS enables rapid, reliable connections between Relewise, BigCommerce, and other Ecommerce platforms, supporting advanced Personalisation. The platform’s robust compliance ensures data protection, while its flexibility reduces manual effort and risk, making integration straightforward and secure.
Surfacing data drift and variant sync failures
Standard dashboards often fail to show when a product variant has stopped syncing or when a specific BigCommerce field is missing from Relewise. We move beyond basic monitoring to surface inconsistencies, such as price drift between the storefront and the recommendations. Our approach ensures that any failure in the data push from BigCommerce is surfaced based on its impact on the customer journey. If critical personalisation data stops flowing, the system flags it, allowing the team to intervene before the customer experience is impacted by irrelevant search results.
Defining data ownership for merchandising teams
Ecommerce and merchandising teams must take ownership of how BigCommerce attributes and variant segments map into Relewise personalisation rules. Handover focuses on the operating model, defining how to read alerts from the integration layer and which team member owns each exception type, such as unmapped custom fields or inventory sync gaps. We establish a routine for checking search relevancy and reviewing data health. Documentation is provided as an operational reference for the people running the shop, not a technical archive for IT, ensuring your team can manage routine adjustments and data drift without external support.
Managing search relevancy and inventory integrity post-launch
Ongoing support focuses on maintaining the integrity of the data stream from BigCommerce to Relewise. We monitor the exchange to identify catalogue changes that could skew personalisation rules or search result relevancy. If a product data shift or sync failure occurs, the integration layer triggers an alert for investigation. We manage the resolution process to ensure recommendations remain grounded in actual inventory and price data. This active monitoring prevents technical debt from accumulating and ensures your personalisation strategy adapts as your product catalogue evolves.
Common failures
Recommendation of unavailable products
Operational impact: Relewise widgets continue to display products with outdated pricing or zero stock because BigCommerce variant-level inventory changes often fail to trigger the top-level product webhook. This creates a sync illusion where customers click recommendations only to find the item is unavailable at checkout. In many implementations, high-volume catalogue updates can lead to operational latency where the storefront reflects stock that the personalisation engine cannot yet see.
Prevention / Action: Map specific variant-to-Relewise logic to capture inventory updates directly. Implement queue management to handle high-volume catalogue churn during sales, ensuring the Relewise index stays in step with BigCommerce.
B2B customer-specific pricing mismatches
Operational impact: When BigCommerce Customer Group IDs are not synchronised with the Relewise User context, logged-in B2B users see retail pricing in recommendations. This results in friction at checkout when the basket total shifts to match the BigCommerce Price List. Finance and sales teams may then inherit manual work to correct these discrepancies.
Prevention / Action: Use Relewise price groups to represent BigCommerce Price List overrides. The integration must pass the Customer Group ID to Relewise to ensure price parity between recommendations and the B2B storefront rules.
Performance-starved models
Operational impact: If clickstream events fail to reach Relewise, recommendation models revert to generic results. Merchandising teams lose the ability to verify which personalisation tactics are driving sales, creating a gap between reporting and actual BigCommerce transaction data.
Prevention / Action: Ensure the front-end tracking covers all BigCommerce interaction points and monitor for event-tracking failures.
Frequently asked questions
How does the integration handle our product data stored in BigCommerce custom fields or metafields?
The integration maps BigCommerce custom fields and variant-level attributes directly into the Relewise data schema. This ensures specialised product data, like 'material' or 'technical specifications', can be used to build effective search filters. Without this explicit mapping from the BigCommerce metafield to the Relewise index, the recommendation engine cannot see these critical attributes, leading to generic results.
What stops out-of-stock products from appearing in Relewise recommendations?
The integration pushes catalogue changes from BigCommerce to Relewise as they happen, using the former as the source of truth for inventory. When a SKU's inventory level is updated in BigCommerce, this event immediately updates the Relewise product index. This process prevents customers from seeing or clicking on an unavailable product in search results or recommendation widgets.
We use BigCommerce Price Lists for different customer groups. Can Relewise show the correct, personalised price?
Yes, the integration provides Relewise with the user's context, including their BigCommerce customer group. When a logged-in user searches or views a page, Relewise is able to query the correct BigCommerce Price List for that specific user. This ensures any product shown in a search result or recommendation carousel accurately reflects the price from the assigned price list.
If BigCommerce holds all the product data, how does this integration improve search relevance?
While BigCommerce is the master for the product catalogue, Relewise analyses real-time user behaviour, including clicks and previous purchase history from the customer record. The integration feeds this clickstream data from the storefront to Relewise. This allows the engine to combine rich product data with demonstrated user intent, delivering more relevant results than simple keyword matching against the BigCommerce item record.