Thirteen sections. One number.
Blufire reads your true contribution margin - what is actually left after product costs, shipping, fees, discounts and ad spend. Thirteen sections turn that one number into decisions: what to sell, what to stop discounting, where spend actually pays back, and which customers to win back.
See the margin you actually kept, and what moved it.
The money group reads the business in CM1 terms: the executive read at the top, the unit economics underneath it, and the discounts quietly giving margin away.
One read of the business in CM1 terms: what you kept, what changed it, and your biggest problems ranked for your seat - CEO, CFO, CMO, growth or ops - with a data health page behind every number.
- You can trace exactly what changed this period with the delta decomposition
- You can open the read built for your seat, down to a CFO scorecard over time
- You can verify any figure against data health and connector status
Pivot profit by product, channel or cohort to see exactly where margin is made and where it leaks, what each channel really pays to acquire a customer, and how long that customer takes to pay it back.
- You can pivot the profitability cube by any dimension you sell across
- You can trace any period's margin move through the CM waterfall & bridge
- You can work a ranked queue of margin leaks and track what you recover
The promo ledger shows which codes earn incremental margin and which give it away, which customers would have paid full price anyway, and what a restructured promo plan would return.
- You can read a margin verdict on any code without leaving the ledger
- You can find full-price win-back targets among discount-dependent buyers
- You can simulate one promo or the whole promo calendar before it runs
The same CM1 math every other section reads
Product screens from a live store. Client name withheld.
Know who your customers are, and what they are worth.
Not orders - people. Every buyer valued in margin terms, and a base you can finally name, segment and act on.
Every customer valued over their whole life in margin terms: the lifecycle state they sit in today, who is drifting toward churn, what they are likely to be worth next - and any number drills to the actual customer list.
- You can watch customers move state to state and open any one's full profile
- You can rank what actually drives repeat purchase, from entry category to first-order value
- You can build cohorts and segments, or start from the ones it recommends
Your customers as nameable audiences - who they are demographically and psychographically, what to sell each one, and the acquisition levers that grow your best - each with a grounded creative brief.
- You can see where each audience over- and under-indexes, and the lookalike headroom left
- You can open the creative read behind every audience before you brief it
- You can drill the persona x product matrix down to real products
Margin-true value, not revenue value
Spend where profitable customers actually come from.
Attribution you can interrogate, channels read in CM1, audiences that land in the platforms and report back, and creative ranked by what it earns.
Attribution models compared side by side instead of trusted blindly: the journeys customers actually take, where the funnel leaks, and one published source-of-truth mapping that every other section reads.
- You can reconcile models in one comparison matrix, then publish the canonical source map
- You can grade each channel's arrival quality and catch drift early
- You can clean up UTM hygiene and read your top conversion paths
Channel x customer x product in one CM1 view - whether each channel is acquiring profitable customers, what those customers become, and how email and SMS flows really stack up against campaigns.
- You can compare per-source customer profiles side by side
- You can decompose blended CM1-MER into each channel's contribution
- You can read the SKU x channel matrix to see which products travel through which channels
Push an audience to Klaviyo, Google or Meta and know it actually landed - then read the round trip in CM1 to see whether the activation paid for itself.
- You can schedule dispatches on the activation calendar and watch audience pressure
- You can keep one canonical audience registry with full lineage
- You can read each audience's payback ledger, treatment against holdout
Every ad asset ranked by the margin it earns, fatigue caught before it bleeds spend, and the attributes that win surfaced - margin-true, not platform ROAS.
- You can work the refresh queue straight off the fatigue board
- You can read format x placement performance and clean test readouts
- You can reconcile platform spend against what the data honestly shows
Audiences go out, margin comes back
Run the catalog, the plan and the proof from one place.
The unglamorous work that decides the quarter: stock, plans, models and proof - all reading the same margin math.
Which SKUs earn and which bleed: what to reorder before it stocks out, what to clear before it goes dead, and what returns really cost - drillable straight to a buy list.
- You can place every SKU on the ABC x XYZ matrix and spot bundle affinities
- You can watch days-of-cover and reorder before the stockout, not after
- You can score refund risk and see what each return reason really costs
Revenue, margin, demand and cash projected ahead - test a scenario before you commit to it, then track the plan against actuals with a variance waterfall.
- You can project operating cash flow and working capital, not just revenue
- You can stress-test COGS, AOV or discount moves in the what-if lab
- You can map each channel's payback and breakeven on the CM1 efficiency frontier
The classic ecommerce financial models - cohort LTV, price elasticity, marketing mix, GMROI - each run on your data, each showing its inputs, its formula and the records behind the answer.
- You can run marketing mix and price elasticity on your own data
- You can value the whole base as customer equity, not just one cohort
- You can track GMROI, open-to-buy and recurring revenue with the same rigor
Proof that marketing caused incremental margin rather than just correlating with it: geo-lift tests, email holdouts and ad holdouts, each returning a verdict and a confidence interval in CM1.
- You can set up a geo-lift test for paid media inside the section itself
- You can hold out email, SMS or an ad set and read the true lift
- You can keep every test's history and trustworthiness diagnostics on record
Decisions with receipts, not dashboards
Shopify
Klaviyo
Google Ads
Meta
30 seconds to connect.
The rest of the rail, on real data.
Persona briefs, creative that converts, discount dependency, bundles and holdout tests - the same margin-true engine, everywhere you make a decision.
