Data unification + analytics platform purpose-built for DTC ecommerce brands.
Daasity is the data infrastructure layer for DTC brands - it pulls data from Shopify, ad platforms, email, fulfillment, and finance into a unified warehouse, then exposes it through dashboards and a metrics layer that other tools can build on.
What Daasity is, in one paragraph
Daasity sits one layer below attribution tools like Triple Whale and Northbeam. Where those tools focus on 'which ad got credit for the sale', Daasity focuses on 'where does ALL my data live and how do I query it consistently'. The platform pulls from Shopify, Klaviyo, ShipBob, Stripe, Meta Ads, Google Ads, and dozens of other DTC sources, normalizes them into a unified data model, and gives you SQL + dashboard access to the result.
The value proposition is the data layer, not the dashboards. Triple Whale gives you opinionated dashboards over their own attribution model. Daasity gives you the raw, unified data that lets your team build whatever dashboards or analyses they want. For brands with even minimal analytics resourcing, the flexibility matters more than the pre-built dashboards.
Heritage is DTC-specific. Daasity wasn't built as a generic data warehouse with DTC connectors added; it was built specifically for DTC brands' data models from day one. Customer cohorts, LTV calculations, repurchase windows, channel attribution windows - all pre-modeled in DTC-native ways the general-purpose warehouses (BigQuery, Snowflake) require you to build yourself.
Signature features
What stands out
Pre-modeled DTC data model
Customer cohorts, order lifecycle, channel attribution, fulfillment metrics - all pre-modeled in ways DTC analysts expect. Saves 100-300 hours of data-engineering work to set up the equivalent in BigQuery or Snowflake.
60+ DTC-specific connectors
Shopify (and Shopify+), Klaviyo, ShipBob, Yotpo, Recharge, Postscript, Loop, Stripe, Meta Ads, Google Ads, TikTok Ads, and dozens more. Each connector handles incremental sync, schema drift, and historical backfill.
Metrics layer (semantic layer)
Defines key DTC metrics (CAC, LTV, AOV, repeat rate, cohort retention) consistently across all reports. Teams can't accidentally calculate them three different ways - the metric definition lives in Daasity, every report inherits it.
Pre-built dashboards + customization
Dashboards for ad performance, customer cohorts, inventory, finance, executive summary. All editable; some teams use them out-of-box, others build their own on top of the data layer.
Data export to your warehouse
If you want the unified data in your own BigQuery or Snowflake (rather than Daasity's hosted warehouse), the export is supported. Useful for brands that already have data infrastructure investments.
SQL access for analysts
Direct SQL query interface for analyst-grade users. Skips the dashboard layer entirely for ad-hoc analysis. Important for serious analytics teams that want raw query power.
Pricing snapshot
Plans at a glance
Standard
From ~$799/mo
$2-5M DTC brands
Plus
From ~$1,499/mo
$5-20M DTC brands
Enterprise
Custom
$20M+ multi-brand groups
Shuttergen
Unified data is the foundation. Creative is what scales it.
Daasity gives you the data layer. Shuttergen gives you the creative output - ad variants tuned to what's winning in your category, ready to ship across the channels your Daasity dashboards measure.
Fit
Who this is - and isn't - for
Best for
- · DTC brands $2M+ with at least one analytics-literate stakeholder
- · Brands that want raw data access rather than pre-built dashboards only
- · Teams that have outgrown Triple Whale's opinionated dashboards but aren't ready to build their own data warehouse from scratch
- · Brands consolidating multiple SaaS analytics tools into one unified data layer
Skip if
- · Sub-$2M brands where the price floor dominates the value
- · Teams without analytics literacy - the value comes from using the SQL/data layer, not just the dashboards
- · Info-product / coaching businesses - Daasity is DTC-specific
- · Brands that already have a working data warehouse + analytics team - Daasity overlaps with what you've built
Where Daasity fits vs Triple Whale and Northbeam
These tools layer, not compete. Triple Whale is attribution + analytics with opinionated dashboards. Northbeam is attribution + incrementality with sophisticated methodology. Daasity is the underlying data layer that either can be built on top of - and that supports a much wider range of analysis than either provides natively.
The typical stack at $10M+ DTC scale: Daasity as the data + metrics layer, Triple Whale or Northbeam as the attribution + executive dashboard layer, plus tool-specific dashboards (Klaviyo for email, Shopify for ops). Daasity doesn't replace attribution tools; it provides the data infrastructure they sit on.
Where Daasity stands alone: brands that want raw data access without paying for an attribution tool's opinions. If you have a strong analyst on staff who'll build custom dashboards and analyses, Daasity gives them the unified data layer they need without forcing Triple Whale's specific point of view.
Unified data is the foundation. Creative is what scales it. Daasity gives you the data layer. Shuttergen gives you the creative output - ad variants tuned to what's winning in your category, ready to ship across the channels your Daasity dashboards measure.
When Daasity earns the subscription
Three personas where Daasity makes sense. First: DTC brands $5M+ with analyst resourcing. The data layer pays back through the analysis your analyst can do that's impossible in tool-specific dashboards. The classic 'I want to slice cohort LTV by acquisition channel by first-purchased product category' query is trivial in Daasity and impossible-without-engineering in Shopify + Triple Whale.
Second: brands consolidating multiple SaaS analytics tools. Teams paying for Shopify Analytics Plus + Klaviyo Analytics + Triple Whale + custom dashboards in 4 places can consolidate into Daasity + one attribution tool. The TCO math often favors Daasity.
Third: multi-brand portfolio groups. Daasity supports multi-brand data unification natively. For PE-backed DTC roll-ups or strategic groups with 3+ brands, the unified data layer across the portfolio is genuinely useful.
Where it fails: brands without analytics literacy. The platform's value comes from what you do with the data, not from staring at default dashboards. Without analyst capability, you're paying $799+/mo for what Triple Whale gives you for $349.
Daasity vs building it yourself
The honest counter-quote is 'just use BigQuery or Snowflake + Fivetran + dbt'. With a data engineer (or strong analytics-engineering contractor), you can build the equivalent setup for ~$500-2,000/mo in tooling + ~80-200 hours of build time upfront.
The DTC-specific data model is where Daasity wins the build-vs-buy. General-purpose warehouses + connectors give you raw tables; you have to build the data model yourself (customer cohorts, attribution windows, channel definitions). Daasity ships the DTC-native data model. That's ~100-300 hours of saved data-engineering work.
The decision tree: if you have a data engineer or analytics engineer with capacity, build it yourself - it's cheaper long-term and more flexible. If you don't have those resources but want unified DTC data, Daasity is the right tool. Don't try to learn data engineering through Daasity - learn it through BigQuery + dbt + Fivetran instead.
Internal: daasity-pricing.
FAQ
Frequently asked
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Related
Keep reading
Unified data is the foundation. Creative is what scales it.
Daasity gives you the data layer. Shuttergen gives you the creative output - ad variants tuned to what's winning in your category, ready to ship across the channels your Daasity dashboards measure.