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EKM Insights integration for ecommerce reporting

Export EKM orders and events into your data estate reliably iWeb integrates EKM Insights into your data warehouse and BI platform with governed extraction schedules, schema alignment and exception handling that keeps dashboards and reconciliation trustworthy. Works with Adobe Commerce, Magento Open Source, Shopify Plus, BigCommerce and other storefronts.

Also searched as: reporting integration, analytics connector, API link, extension.

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Works with - EKM · Shopify Plus · BigCommerce · Adobe Commerce · Magento Open Source · Other storefronts
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01 · What you get

What a EKM Insights integration gives you.

Orders reconcile cleanly to ERP

Export EKM orders into your warehouse with consistent timestamps, line amounts and customer references. Finance teams can match web orders against invoices and dispatch confirmations with confidence.

Marketing has customer truth

Behavioural events and customer purchase history flow continuously into your CDP. Segments, triggered campaigns and suppression rules all rest on complete, trusted data.

Dashboards reflect storefront reality

Sales, conversion, traffic and inventory metrics populate BI tools on a consistent schedule. Leadership dashboards never lag or contradict the live store.

Data lineage is clear and auditable

You know where every reported figure came from, when it was extracted and what transformations were applied. Compliance and audit teams can trace figures back to EKM transactions.

Peak trading does not break exports

Extraction logic is sized and scheduled to handle traffic spikes without data loss or lag. High-volume days do not leave gaps in your analytics or reconciliation.

02 · When it's worth it

Where a EKM Insights integration earns its place.

If two or more of these are true, the integration usually pays for itself quickly.

Export daily order and transaction data into a data warehouse for finance and operational reporting
Pipe customer behaviour events (browse, cart, purchase, return) into a CDP or BI platform
Reconcile EKM sales records against ERP or accounting systems for month-end closure
Populate dashboards with real-time or near-real-time storefront metrics and KPIs
Archive historical catalogue and pricing snapshots for trend analysis and auditing
03 · The limits

Where off-the-shelf connectors fall short.

Vendor connectors are fine for simple cases. Here's where the real ones need more.

Limited real-time event capture

EKM Insights typically exports data on a batch schedule (daily or hourly). If your analytics or CRM requires sub-minute event latency, you may need middleware or event streaming to bridge the gap.

Catalogue export scope constraints

EKM Insights may not export all custom fields, multi-language variants or channel-specific SKU mappings. Your PIM or product governance rules might require enrichment or transformation before use downstream.

No built-in customer identity resolution

EKM customer IDs may not align with your ERP, CRM or CDP customer schemas. You must manage identity mapping and deduplication logic separately, especially across channels or offline transactions.

Limited exception visibility

Failed exports, incomplete record sets or schema mismatches may not surface with clear alerts. You need monitoring and data quality checks outside EKM Insights to catch stale or missing data.

No built-in transformation or enrichment

Raw EKM exports do not apply business logic, calculate derived fields or join third-party reference data. A transformation layer is required before data is trustworthy for reporting or finance reconciliation.

04 · The real work

Many retailers expect EKM Insights to automatically handle identity matching and data quality; in practice, they need warehouse design and reconciliation logic to ensure financial reporting is trustworthy.

05 · Where it sits

Where this integration sits in your estate.

EKM Insights holds the commercial record. The iWeb integration layer manages the rules, mappings, monitoring and exceptions. The commerce platform presents the customer-facing experience. The estate map helps agree ownership before anything is built.

Connect across your stack. EKM Insights plugs into the systems that run your trading operation, whichever ecommerce platform sits at the front.

System of record
Source / owner
EKM Insights
Data extraction and event streaming from EKM storefront to analytics warehouse
  • Order and transaction event export from EKM
  • Customer and product catalogue snapshots
  • Behavioural event capture (browse, cart, purchase, return)
  • API credential and extraction schedule management
  • Raw data landing zone in warehouse
iWeb integration layer
Customer-facing commerce
Commerce platform
EKMShopify PlusBigCommerceAdobe CommerceMagento Open SourceOther storefronts
  • Live storefront transaction processing
  • Customer account and profile management
  • Order fulfillment and dispatch coordination
  • Real-time catalogue and pricing
  • Email and transactional messaging
Connected neighbours
Integration layer
ERP (Sage, NetSuite, SAP)
Finance and operational records; EKM orders are reconciled against ERP invoices and dispatch confirmations for period close.
Integration layer
Data warehouse (Snowflake, Redshift, BigQuery)
Landing zone for EKM extracts; staging and curated reporting tables built on top of raw EKM data.
Integration layer
BI and analytics platform (Tableau, Looker, Power BI)
Consumes modelled warehouse tables to populate dashboards, KPI reports and executive scorecards.
Integration layer
CDP / CRM (Segment, mParticle, HubSpot)
Receives customer behaviour and purchase events from EKM for segmentation, campaign triggers and email personalisation.
Integration layer
Orchestration and transformation (dbt, Airflow, Stitch)
Schedules extraction jobs, transforms raw EKM data, applies business logic and manages data quality validations.
Two-way sync where relevant
06 · Surrounding systems

Systems this integration usually sits next to.

Examples, not a closed list. iWeb is platform-agnostic on both sides: we wire this integration into whatever ecommerce platform and surrounding systems your estate already runs.

Ecommerce platforms (examples)
  • EKM
  • Shopify Plus
  • BigCommerce
  • Adobe Commerce
  • Magento Open Source
  • Other storefronts
Surrounding systems (examples)
  • Data warehouse (Snowflake, BigQuery, Redshift)
  • ERP system (Sage 200, NetSuite, SAP)
  • BI platform (Tableau, Looker, Power BI)
  • CDP / CRM (Segment, mParticle, HubSpot)
  • dbt or SQL transformation engine
  • Email and marketing automation platform
  • Orchestration / scheduling tool (Airflow, Stitch, Fivetran)
Not sure?

Not sure if this works with your stack?

Tell us what you’re using and what needs to connect. We’ll give you a straight view on what’s possible, what might be awkward, and the safest way to approach it.

07 · Data flows

The data flows we wire.

Each flow has a direction and an owner. We agree both before a line of code is written.

→Into EKM
←From EKM
↔BOTH WAYS
Orders and transactions into analytics: Order records, line items, payment status and customer details flow from EKM into your data warehouse or BI platform daily or hourly
This ensures your financial dashboards and operational reports reflect live storefront activity.
Behavioural events to CRM or CDP: Browse, cart, purchase and return events stream from EKM into marketing or customer data platforms for segmentation, retargeting and campaign triggers
Event timing and customer identity drive accurate audience membership.
Product catalogue snapshots for audit: Periodic exports of active SKUs, descriptions, images, pricing and category assignments create historical snapshots in your warehouse
These support trend analysis, pricing audits and catalogue evolution tracking.
Suppression and segment lists from marketing: Curated suppression lists or audience segments generated in your CDP or email platform can be pushed back to EKM to control display rules or email eligibility, closing the activation loop.
Reconciliation data for ERP match: Orders exported from EKM are compared against invoice and dispatch records from your ERP, surfacing discrepancies in timing, amounts or line-item counts
Corrections flow back into your finance system.
08 · How we build it

How iWeb configures the integration around your business.

Same method on every integration. The decisions come before the code.

  1. 01
    Design your extraction and landing architecture

    We map EKM's native event and object schemas to your warehouse, define the batch schedule, transformation rules and data quality gates. We ensure the design aligns with your ERP's period close and your finance team's reconciliation workflow.

  2. 02
    Build and test the extraction pipeline

    We implement the connection from EKM into your warehouse using APIs, scheduled exports or event streaming. We validate that orders, customers, products and returns arrive completely and in the right sequence.

  3. 03
    Establish monitoring and alerting

    We set up dashboards and alerts that track export frequency, record counts, schema compliance and freshness. Data teams know immediately if an export fails or if a field goes unexpectedly empty.

  4. 04
    Build exception and retry logic

    We design rollback behaviour, retry windows and manual intervention queues for failed extracts. Operators know how to recover from network glitches, API limits or schema changes without manual re-extraction.

  5. 05
    Document ownership and runbooks

    We define who owns the extraction schedule, the warehouse schema, data quality rules and incident response. Runbooks and escalation paths ensure the pipeline stays reliable through personnel changes and platform updates.

09 · Ownership

Who owns what.

The single most important table in any integration. One system owns each field; everything else reads it.

Data
Source / owner
Maintained by
Notes
DataSource-system extracts from EKM
Source / ownerEKM Insights and EKM transaction ledger
Maintained byEKM (vendor) and data engineering team
NotesEKM owns the storefront data; data team owns the extraction schedule, API credentials and scheduling cadence.
DataWarehouse landing zone schema and staging tables
Source / ownerData warehouse
Maintained byData engineering and analytics teams
NotesData team owns the schema design, transformation rules and staging area; analytics owns curated reporting layer.
DataModelled and curated reporting tables
Source / ownerData warehouse and BI platform
Maintained byAnalytics and BI teams
NotesAnalytics team owns the dimensional models, metrics and dashboard definitions; BI tool owns visualization.
DataSemantic layer and metric definitions
Source / ownerBI platform semantic layer or dbt / SQL models
Maintained byAnalytics and data engineering teams
NotesAnalytics defines metric logic; data engineering owns the underlying table dependencies and refresh dependencies.
DataData freshness and extraction SLAs
Source / ownerData team runbooks and monitoring dashboards
Maintained byData engineering and platform operations
NotesData team owns the schedule and SLA window; ops owns monitoring, alerting and incident response.
DataCustomer identity mapping and deduplication
Source / ownerMaster data management or CDP system
Maintained byData or marketing operations teams
NotesCustomer ops or CDP team owns the identity resolution; data team integrates resolved IDs back into warehouse.
DataException handling and retry logic
Source / ownerIntegration orchestration and data pipelines
Maintained byData engineering and platform teams
NotesData team owns exception queue monitoring and manual recovery; ops owns escalation and incident triage.
10 · Experienced integrator

Built this before

iWeb has integrated EKM storefronts into data warehouse estates many times. We understand how EKM order and event data flows into analytics, how it reconciles against ERP, and how to keep extraction pipelines reliable through trading peaks.

We design EKM extracts that align with your ERP's transaction schema and finance close windows, ensuring clean reconciliation.
We build extraction monitoring and exception handling so data freshness and completeness are visible to both data and ops teams.
We understand how EKM customer IDs must map to your CRM and ERP, and how identity resolution breaks downstream segmentation if skipped.
We architect warehouse landing zones to support both historical audit trails and real-time BI dashboard refresh without colliding with peak-trading performance.
11 · Before launch

What we test before launch.

Every one of these is rehearsed before a customer ever sees the integration.

Verify that daily order exports include all order states (pending, confirmed, cancelled, refunded) and match the EKM admin order list.
Confirm that customer identity keys in exported data align with your CRM or ERP customer master and contain no nulls.
Test that export timestamps fall within the expected SLA window and that late exports trigger alerts.
Validate that order line totals and tax amounts match EKM receipts and reconcile against sample ERP invoices.
Confirm that schema changes (new or renamed fields) are caught by validation rules and logged in the extract manifest.
Run a rollback test: simulate an extract failure and verify that the retry mechanism re-runs the job without duplicate data.
Validate data freshness across the warehouse: ensure BI dashboards refresh within the SLA and reflect today's sales by 8 AM next business day.
12 · Failure points

Common risks and where they bite.

We name these on day one. A risk written down is a risk you can plan around.

Stale or partial order exports leave reconciliation gaps

If EKM Insights exports only summary totals or omits cancelled / refunded orders, your finance team cannot reconcile against the ERP order list. Partial exports often go unnoticed until month-end closure.

Customer identity mismatch breaks CRM segmentation

If EKM customer IDs do not align with your CRM or ERP, duplicate contacts or missing purchase history make audience segments unreliable. Triggered campaigns land in the wrong inbox or miss engaged customers entirely.

Event timing skew breaks funnel reporting

If event timestamps are rounded, cached or exported in batches, conversion funnels and journey analytics show artificial steps or missing touchpoints. Marketing spends money optimizing fiction.

Silent schema drift breaks dashboards mid-month

If EKM adds or renames a field, the extract silently drops that column and downstream BI tools show incomplete rows. Dashboards return wrong totals or stop updating altogether without clear alerts.

Export schedule collision with peak traffic

If heavy extracts run during peak trading hours, EKM performance degrades, export timeouts increase and data lag grows. Late-night or off-peak scheduling is essential but is not enforced by default.

No audit trail for what was exported when

Without an extract manifest or change log, you cannot verify that a missing order was actually exported or understand why a figure changed between two dashboard refreshes. Compliance audits fail.

13 · Where it applies

Relevant services and sectors.

14 · Questions

Common questions about EKM Insights integrations.

How often does EKM Insights export data to our warehouse?

Export frequency is configurable: daily, hourly or event-driven. Most retailers use daily extracts for overnight BI refresh or hourly for near-real-time dashboards. Peak-trading schedules may need adjustment to avoid EKM performance impact.

Can we export historical data from EKM into our warehouse for backfill?

Yes. EKM Insights can extract a date range of past orders, customers and events. Backfill timing depends on EKM's API rate limits and your warehouse capacity. Plan for 1-2 weeks for a multi-year historical load.

How do we handle the gap between web orders exported from EKM and invoices in our ERP?

Orders typically arrive in the warehouse within the export window (1-24 hours), while ERP invoices may lag 1-2 days. Build a reconciliation report that joins orders by date range and amount, flagging mismatches. A weekly or daily reconciliation dashboard surfaces discrepancies for finance to investigate.

What if EKM Insights export fails or times out during peak trading?

Set up monitoring that alerts data teams if an export is late or incomplete. Implement an automatic retry with exponential backoff. If retries exhaust, escalate to the ops team to decide whether to pause the export or wait for EKM to recover. Have a manual-export runbook ready.

How do we resolve EKM customer IDs against our CRM or ERP customer master?

Build an identity mapping table that matches EKM customer email or phone against CRM / ERP records. Handle new customers with a lookup and create workflow. Store the mapping in the warehouse so all downstream reports use the canonical customer key.

Can EKM Insights send suppression lists or marketing segments back to EKM for email or rules?

Yes. Export curated audience segments from your CDP or BI tool back to EKM via API. EKM can then apply suppression rules or targeted messaging based on the segment membership.

What product and catalogue data does EKM Insights export?

EKM exports active SKUs, descriptions, category assignments, pricing and image URLs. Custom fields or multi-language variants may require additional mapping. If your PIM stores richer product data, design the warehouse to blend EKM catalogue exports with your PIM source.

How do we audit which orders and customers were exported on a given day?

Maintain an extract manifest in the warehouse that logs the export start time, end time, row count by object type and any errors. Compare the manifest against your finance or CRM team's expectations. A missing manifest indicates a silent failure.

What happens if EKM Insights schema changes (new fields, renamed columns)?

EKM may add or rename fields over time. Use change-data-capture (CDC) metadata in the warehouse to track schema evolution. Set up alerts if new fields appear or if expected fields disappear. Test schema changes in a dev warehouse before promoting to production.

Can we integrate EKM data with data from other channels or POS systems in the warehouse?

Yes. Land all channel extracts in a common staging area with consistent date formats and customer identity keys. Build a unified fact table that unions orders across channels. Ensure each source has a channel code so reporting can filter or aggregate by source.

How do we handle refunds and returns in EKM data exports?

EKM exports refund transactions and return orders separately. Model them as negative-revenue line items in your fact table or as separate refund fact tables. Ensure your finance reconciliation logic treats them as offsets against sales, not separate transactions.

What data quality checks should we run on EKM exports before trusting them in dashboards?

Validate that order totals match line-item sums, that customer keys are not null, that dates are within expected ranges and that refunds do not exceed original order totals. Flag rows that fail validation and route them to a manual review queue. Do not mix dirty and clean data in reporting tables.

Who should own the extraction schedule and SLA if EKM data is late?

Data engineering owns the extraction configuration and monitoring. Platform ops owns alerting and incident escalation. Finance or BI leadership owns the SLA negotiation with EKM. Document this in a RACI matrix so everyone knows who to call when data is late.

How long should we keep historical EKM data in the warehouse?

Retain raw EKM extracts for at least 7 years to meet financial audit and compliance requirements. Archive older data to cheaper storage. Aggregate or summarize very old data to reduce query costs. Policy depends on your industry (retail, luxury goods, B2B all have different rules).

Next step

Have a EKM Insights integration brief?

Send the brief, or tell us what is breaking. You will get a written response from a senior expert: the integration boundary, the realistic shape, the risks worth naming, and what it takes to support after launch.
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