NewStore POS and InRiver
Integration Agency & Consultants
At scale, inconsistencies between the InRiver catalogue and NewStore POS create a specific type of operational latency that manifests at the till. When enriched SKU data, pricing, or variant attributes drift, store associates are forced into manual workarounds while customers wait. This integration establishes a clear ownership boundary, ensuring that InRiver serves as the definitive source of truth for enriched product information. By synchronising validated data directly to the POS, we remove the data gaps that cause transaction errors and pricing mismatches in the retail environment.
Auditing tech stacks and data dependencies
We connect your NewStore POS and InRiver PIM quickly, ensuring your POS and PIM work together efficiently. Our consulting services are invaluable, with our system audit services providing a thorough review of your tech stack. This enables our consultants and your team to identify and resolve issues, helping your NewStore POS and InRiver PIM integrations run smoothly. As a result, your technology ecosystem operates efficiently, allowing you to deliver an excellent customer experience.
Solution Design
In this NewStore POS and InRiver architecture, we typically establish InRiver as the absolute source of truth for enriched product data. A core design decision involves the sequencing of synchronisation: core attributes and pricing flow on a prioritised schedule, while heavy media assets are handled separately to protect POS responsiveness. A primary trade-off is the handling of the product hierarchy. InRiver often holds complex relationships that must be flattened for NewStore to ensure store associates see clean, searchable variants without lag. While frequent updates for every attribute change provide high accuracy, they can put pressure on system performance during bulk enrichment; we often implement delta-based syncs to maintain stability. This design ensures retail teams operate off reliable data while finance reconciles sales against accurate PIM-governed pricing.
Enforcing PIM data integrity at checkout
This integration enforces InRiver as the system of record for all enriched product data. Updates to SKUs, pricing, and key attributes flow to NewStore POS through a sequenced validation layer. We implement rules to ensure only complete products with required attributes are synchronised, preventing missing variants at the checkout. Monitoring is embedded to detect when an update is rejected due to formatting or hierarchy issues. By ensuring data integrity during the sync, the store remains protected from incomplete product information that slows down transactions or leads to incorrect sales reporting.
Orchestrating workflows via secure IPaaS middleware
Leveraging IPaaS with ISO 27001 and SOC 2 and above accreditations enables secure, efficient integration between NewStore POS and InRiver PIM. This approach simplifies connecting NewStore POS with InRiver PIM, ensuring data flows reliably between POS and PIM systems. IPaaS platforms offer centralised management, robust security, and scalability, reducing manual effort and risk. Security accreditations guarantee data protection, making integrations both straightforward and compliant with the highest standards.
Surfacing data gaps before they reach stores
Standard dashboards rarely surface why a SKU is missing or why a price update failed to post. This is rarely a total system crash; it is usually a series of small gaps where the store team sees different data from the head office. We provide visibility into these failures upstream. Whether it is a variant that fails a validation check or an incomplete attribute set that prevents a product from publishing, we identify the issue before it reaching the shop floor. This ensures that data errors are caught long before they cause friction at the till.
Handing over operational ownership to teams
Handover ensures the ecommerce, retail ops, and finance teams own the NewStore POS and InRiver relationship. We train teams on the operating model, specifically where product enrichment ends in InRiver and where it must be surfaced in NewStore. Teams learn to check synchronisation status and respond to common alerts, such as missing variants or attribute mismatches. Ownership of exceptions is clearly defined: ecommerce owns enrichment gaps, while ops handles store-level visibility. Documentation is provided as a practical operational reference for the people running the business, not a technical archive, ensuring teams can resolve data gaps during daily operations.
Monitoring synchronisation and attribute mapping health
Post-launch support is focused on the integrity of the product data link between InRiver and NewStore POS. We monitor the flow from InRiver to catch attribute synchronisation failures or mapping errors before they reach the shop floor. If price updates or new SKU attributes fail to synchronise, we identify the root cause to prevent recurrence. This operational oversight means your store associates are not forced to perform manual price overrides or deal with 'item not found' errors at checkout. By maintaining this flow, we ensure the PIM remains the authoritative driver of your physical retail transactions.
Common failures
Incomplete product data synchronisation
Operational impact: New SKUs created in InRiver fail to appear in NewStore POS, making them un-scannable at the till and invisible to store staff. This forces manual workarounds, delays sales, and prevents the application of associated promotions. The disconnect between the enriched master data and the point of sale creates confusion for merchandising and customer experience teams, especially during new product launches.
Prevention / Action: Define InRiver as the explicit source of truth for all product master data destined for the POS. The integration should use incremental updates on a frequent schedule, rather than relying solely on large nightly batches. Implement robust error handling and a retry queue to manage transient API failures, and configure monitoring to alert the relevant teams when a specific SKU record fails to synchronise.
Incorrect pricing at the point of sale
Operational impact: Price updates managed in InRiver do not propagate correctly to NewStore POS, forcing staff to void transactions or apply manual discounts. This leads to inaccurate sales orders and creates significant reconciliation challenges for the finance team comparing POS data against payout reports. It directly erodes gross margin and customer trust, while increasing the manual workload on store and finance personnel.
Prevention / Action: Isolate price updates into a high-priority synchronisation job that runs independently of other product attribute changes. The integration logic must guarantee the sequencing of price updates to prevent older prices from overwriting newer ones. Introduce a regular, automated audit process that compares the full price book between InRiver and NewStore to identify and correct any discrepancies.
Catalogue update failures during peak loads
Operational impact: Large-scale catalogue updates from InRiver, such as for a seasonal collection launch, can trigger API rate-limiting in NewStore. This causes partial updates, leaving the product catalogue in an inconsistent state across the store network. Merchandising and store operations teams are left with an unreliable data set, making accurate sales, inventory management, and reporting impossible.
Prevention / Action: Design the integration to be aware of NewStore's API rate limits, incorporating a queueing mechanism and an exponential backoff strategy for retries. Deconstruct large updates into smaller, logical chunks to avoid sending single, oversized payloads. Proactive monitoring of API response headers and error codes can help dynamically adjust the synchronisation speed to prevent hitting the limits.
Missing product attributes or digital assets
Operational impact: Enriched content from InRiver such as detailed descriptions, specifications, or 'how-to-care-for' instructions are missing in NewStore POS. Store associates are unable to answer customer questions effectively, leading to lost sales opportunities. For complex products, missing data can result in incorrect items being sold, leading to higher return rates and a degraded customer experience.
Prevention / Action: Ensure the integration maps all required InRiver entity fields to the correct attributes in the NewStore product record. The data model must be agreed by both merchandising and store operations teams before build. Implement validation checks within the integration layer to flag any product sync where mandatory attributes are null or improperly formatted before they reach NewStore.
Frequently asked questions
If we unpublish a product in InRiver, is it removed from NewStore POS?
Removal is rarely automatic. The integration layer must be configured to recognise an unpublish or delete event in InRiver and trigger a corresponding status update in NewStore. Without this logic, discontinued SKUs remain searchable in-store, leading to confusion at the till for items that have been decommissioned.
Should our store team manage pricing directly in NewStore POS?
Managing local price overrides in NewStore often leads to data inconsistencies. If changes are made at the POS level, they risk being overwritten by the next bulk synchronisation from InRiver. To maintain operational clarity, pricing should typically originate in InRiver and flow downstream.
How are attributes like colour or material synchronised?
InRiver serves as the system of record for rich attributes. These are typically mapped from the PIM's internal lists to specific NewStore attribute fields. A common failure mode is the incorrect mapping of these attributes, which leads to missing variants or unsearchable products at the till.
How does the integration prevent incomplete data from reaching the till?
The integration establishes InRiver as the master for product data through strict validation rules. We implement checks to ensure only approved and fully enriched product records are synced to NewStore POS, protecting the store team and the customer experience from data gaps.