A productized BI layer with cohort retention, channel P&L, LTV curves, and inventory velocity — built on a tested dbt metrics layer.
Most D2C brands have Looker Studio, Metabase, or Tableau connected to something. But the dashboards are either too generic to be useful or too fragile to be trusted. When someone needs the real answer, they pull a CSV from Shopify and build it in Sheets.
The problem is not the BI tool. It is the absence of a clean metrics layer underneath it — tested dbt models that define cohort, LTV, CAC, and contribution margin consistently, so the same question always returns the same answer regardless of who pulls it.
Analytics & Reporting builds that layer, and the dashboards on top of it — scoped to the views your team actually needs, in the tool you already use. In three to four weeks.
Most BI tools ship with generic templates. We build the views your team actually opens on Monday morning — cohort retention by acquisition week, channel P&L with contribution margin, and LTV curves segmented by channel.
Knowing your 90-day retention rate is useful. Knowing it broke down for the cohort you acquired through TikTok in March, and is strongest for customers who bought from your hero SKU first — that is actionable.
ROAS ignores fulfilment costs, returns, and the cost of goods. Channel P&L views built on your actual margin data show which channels are profitable, not just which drive attributed revenue.
Stock-outs are invisible in your BI tool until it is too late. Inventory velocity dashboards surface which SKUs are burning through stock faster than forecast — so you can reorder before you lose the sale.
Fragile dashboards break on Monday when the underlying data changes. We build on tested dbt models with defined metrics — so your analysts spend time answering questions, not fixing broken charts.
When finance, ops, and marketing all need different cuts of the same data, everyone ends up pulling their own CSVs. A well-modelled BI layer means each team gets their view without touching raw data or bothering the data team.