Solutions · E-commerce
See demand, margin, and inventory in one place, and let merchandising teams answer their own questions.
See how Analytify ships ecommerce analytics dashboards your team and customers will actually use.
E-commerce is the most data-rich consumer category, every click, view, add-to-cart, abandon, purchase, return, and review is captured. Yet most retailers still operate on Shopify reports + GA4 + ad-platform-native dashboards stitched together with spreadsheets. The result: attribution is broken, inventory decisions lag demand, and customer cohorts are invisible.
Modern ecommerce analytics consolidates these silos into a single warehouse-native source of truth. Server-side event tracking captures what GA4 misses; multi-touch attribution models replace last-click; cohort retention curves expose the leaky bucket aggregate MAU hides; embedded merchant dashboards give your sellers (if you run a marketplace) the same data quality your internal team has.
The ROI: a 5% improvement in repeat purchase rate at $50M GMV is $2.5M; a 10% better attribution model can shift $100K+ of monthly ad spend into channels that actually convert; a personalised email triggered by behavioural signals lifts conversion 8-15%.
E-commerce analytics dashboards typically anchor on these KPIs across acquisition, conversion, retention, and operations:
| KPI | Category | Refresh | Typical Owner |
|---|---|---|---|
| Conversion Rate (Site / Channel) | Conversion | Daily | Growth |
| Average Order Value (AOV) | Conversion | Daily | Merchandising |
| Customer Acquisition Cost (CAC) | Acquisition | Weekly | Marketing |
| Customer Lifetime Value (LTV) | Retention | Monthly | CRM |
| LTV:CAC Ratio | Efficiency | Monthly | Finance |
| Repeat Purchase Rate | Retention | Weekly | CRM |
| Cart Abandonment Rate | Conversion | Daily | Growth |
| Return / Refund Rate | Operations | Weekly | Operations |
| Inventory Turnover | Operations | Weekly | Merchandising |
| ROAS by Channel | Acquisition | Daily | Marketing |
Move beyond last-click. Build a position-based or data-driven attribution model in your warehouse using server-side and client-side event data, then surface channel-level ROAS in a unified dashboard. Compare performance across paid social, search, affiliate, email, and organic with a consistent metric definition.
Group customers by acquisition month, channel, or first-product-bought, and track retention curves and revenue per customer over time. Surface which cohorts are getting better/worse and feed insights back into acquisition spend allocation.
Track elasticity by SKU, segment, and time-of-day. Run promo experiments with proper holdouts. Surface promo lift net of cannibalisation, not just gross sales bump.
Combine SKU-level demand history, seasonality, and lead-time variability to compute optimal reorder points. Flag overstock risk and stockout risk with confidence intervals. Feed predictions into the planning system via reverse ETL.
If you run a marketplace, give your sellers the analytics dashboard they expect: sales by SKU, conversion vs category benchmark, customer demographics (privacy-respecting), inventory health. White-labelled, mobile-responsive, with seller-level row-level security.
Tie returns and CX tickets back to product, supplier, and channel. Identify root-cause clusters (sizing issues, photo quality, delivery damage) and route the data to merchandising, fulfillment, and supplier teams.
An ecommerce analytics platform should connect to the platforms every modern retailer actually runs:
| Category | Examples |
|---|---|
| Storefronts | Shopify, BigCommerce, Magento, WooCommerce, Salesforce Commerce Cloud |
| Marketplaces | Amazon Seller Central, Walmart, eBay, Etsy |
| Ad platforms | Google Ads, Meta Ads, TikTok Ads, Pinterest, Reddit, Microsoft Ads |
| Analytics | GA4, Adobe Analytics, server-side via RudderStack/Segment |
| Email / SMS / CRM | Klaviyo, Braze, Iterable, HubSpot, Mailchimp |
| Fulfillment / 3PL | ShipBob, ShipStation, Flexport, internal WMS |
| Payments | Stripe, Adyen, PayPal, Klarna, Affirm |
| Cloud warehouses | Snowflake, BigQuery, Databricks, Redshift |
Modern ecommerce analytics has to respect consumer privacy across multiple regimes:
Practical implications: server-side tracking to survive ad-blockers and ATT, consent-aware data ingestion (drop or mask events from non-consenting users), regional data residency, audit logs on PII access, signed DPAs.
An 8-year-old DTC apparel brand running on Shopify Plus replaced a stack of GA4 + Looker Studio + spreadsheets with Snowflake + dbt + Analytify. Outcomes after 6 months:
Most retail teams underestimate the build cost. The honest comparison:
| Dimension | Build In-House | Buy (Analytify) |
|---|---|---|
| Time to first multi-source dashboard | 3-6 months | 1-2 weeks |
| Server-side tracking and attribution model | 4+ months engineering | included templates |
| Embedded merchant dashboards | 6+ months | SDK in days |
| Engineering team needed | 3-6 FTEs | 1-2 FTEs |
| Total 3-year cost | $1.5M-$5M | $150K-$600K typical |
| Risk | maintenance, attribution drift | vendor dependency |
Analytify is built for the way modern e-commerce teams work:
Ready to ship modern ecommerce analytics dashboards for your team and your customers?
Yes. Both have pre-built connectors. The platform unifies orders, products, customers, and events across storefronts and marketplaces in a single warehouse with consistent metric definitions.
Through server-side tracking via RudderStack, Segment, or direct integration. Server-side events bypass ATT for first-party data, and conversion APIs (Meta CAPI, Google Enhanced Conversions, TikTok Events API) restore much of the attribution signal.
Last-click, first-click, linear, time-decay, position-based, and data-driven (Markov / Shapley). All implemented as dbt models so the math is transparent and reproducible.
Yes. The embedded SDK supports per-seller dashboards with row-level security so each seller sees only their data, branded as your product. Common in marketplace and wholesale portals.
Analytify integrates with major CMP vendors (OneTrust, Cookiebot, Sourcepoint) and respects consent state at ingestion, non-consenting users’ events are dropped or masked. Regional data residency is supported.
It complements GA4. GA4 is great for aggregate web analytics but limited for stitching with order, customer, and ad data. Analytify pulls GA4 events into the warehouse and joins them with everything else for unified analytics.
Yes. Analytify integrates with Statsig, GrowthBook, and Eppo, surfacing experiment results in dashboards alongside business metrics. You can also run experiment analyses directly on warehouse data.
Typical phases: 1-2 weeks for connector setup and first multi-source dashboards; 4-6 weeks for governed semantic layer + 5-8 priority dashboards; 2-4 months for full stack including embedded analytics and attribution model. Faster than rebuilding because connectors and templates are productised.