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What's Inside

The problems we solve.

Sixteen questions operators actually ask, each answered with the exact thing inside Blufire that answers it - and the decision you walk away with. Grouped the way you run the business: the money, the customers, the marketing, the operation.

Prefer the section-by-section tour? See the product breakdown →
Group 01 - The money

What you actually kept.

Questions 01-04
01
How we solve it

The Executive section opens on the Portfolio Headline, the business read in CM1 terms - what you actually kept after product costs, shipping, fees, discounts and ad spend - and What Changed decomposes the period's move line by line, so revenue up, profit flat stops being a mystery. Each role lens - CEO, CFO, CMO, growth, ops - ranks the biggest problems for that seat, and a Data Health page sits behind every number so you know when a figure can be trusted.

Read the full answer → See it in the product breakdown S1 Executive →
02
How we solve it

Unit Economics is one pivotable Profitability Cube - contribution margin by product, channel or cohort - with a CM1 Waterfall showing what each cost line took on the way down. The Ranked Leak Queue orders every leak by the dollars recoverable, and the Recovery Tracker holds the fix accountable, because a leak you found and never re-checked is a leak you still have.

Read the full answer → See it in the product breakdown S2 Unit Economics →
03
How we solve it

The Promo Ledger scores every code on gross give versus incremental CM1, because a promo that lifts revenue can still destroy margin. Dependency bands show which customers are discount-dependent and which would have paid full price anyway, and the Promo-Restructure Simulator prices what a rebuilt promo calendar would return before you run it. You walk away knowing which codes to kill, keep or restructure.

Read the full answer → See it in the product breakdown S3 Discounting →
04
How we solve it

Cohort economics tracks NCAC and CM-payback per acquisition cohort in margin rather than revenue, because revenue payback flatters every channel. The Payback Waterfall shows the months to breakeven cohort by cohort, and Marginal CAC & Saturation shows where the next dollar of spend stops paying. You know the real payback window before you scale spend into it.

Read the full answer → See it in the product breakdown S2 Unit Economics →
Group 02 - The customers

What each customer is really worth.

Questions 05-08
05
How we solve it

The margin-true RFM cube re-scores every customer on the margin they leave, not the revenue they book, so the big-basket buyer who only ever buys discounted stops ranking as a VIP. Financial Buckets and the Discount-Dependency overlay separate the profitable loyalists from the expensive lookalikes, and every cell drills to the actual customer list, ready to act on.

Read the full answer → See it in the product breakdown S4 Customer Value →
06
How we solve it

The 7-State Lifecycle Board places every customer in a state, and State-to-State Migration shows who is drifting toward churn this period, before they are gone. Survival curves and BTYD-predicted CLV price what each drifting customer is still likely to be worth, so the win-back is sized in dollars, not sentiment. Any number drills to the customer list, and the audience is built from it.

Read the full answer → See it in the product breakdown S4 Customer Value →
07
How we solve it

The entry-category repeat scorecard and the first-order value to repeat curve show which first purchases create repeat customers and which create one-and-done buyers, with the ranked repeat drivers making the why explicit. The Entry Product x Channel matrix then shows where those first orders come from, so you know which products belong at the front door of which channel.

Read the full answer → See it in the product breakdown S4 Customer Value →
08
How we solve it

Persona Analytics names your base as audiences: the demographic and psychographic mosaics show who over- and under-indexes against the market, and Lookalike Headroom sizes how many more of each audience are out there. Each audience carries a grounded creative brief and its acquisition levers - first-order size and entry products - so find more of them becomes a brief you can hand to a media buyer.

Read the full answer → See it in the product breakdown S8 Persona Analytics →
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Group 03 - The marketing

Where spend actually earns.

Questions 09-12
09
How we solve it

The Model Comparison Matrix runs the attribution models side by side instead of trusting one, and Margin Triangulation checks their claims against the margin that actually arrived. What gets published is one Canonical Source Map - a frozen first-purchase source per customer that every other section reads - so the whole business argues from one answer instead of three.

Read the full answer → See it in the product breakdown S5 Acquisition →
10
How we solve it

The Channel Read prices every source in CM1-MER - contribution margin over spend, not platform ROAS - and the Channel Master Table holds channel x customer x product in one view. Per-source customer profiles show what each channel's buyers become after the first order, because a cheap acquisition that never repeats is not cheap. You decide where the next dollar goes, with the margin math in front of you.

Read the full answer → See it in the product breakdown S6 Marketing / Channels →
11
How we solve it

The Owned-Channel Overview reads flows versus campaigns versus total in CM1, so you can see which sends earn margin and which just move discounted volume. Margin-true segments built in the customer section dispatch to Klaviyo through the Activation Bridge, and the Audience Payback Ledger reads the round trip - whether the audience you pushed actually returned contribution margin - while sync health and the intervention log confirm it landed at all.

Read the full answer → See it in the product breakdown S9 Activation Bridge →
12
How we solve it

The Creative Leaderboard ranks every asset by the CM1 it earns rather than the ROAS the platform reports, and the Fatigue Board catches decay before it burns budget, with the Refresh Queue ordering what to replace next. Winning Attributes shows what the earners have in common, so the next brief starts from evidence instead of taste.

Read the full answer → See it in the product breakdown S10 Creative Analytics →
Group 04 - The operation

Run the quarter on proof.

Questions 13-16
13
How we solve it

Stockout Risk & Days-of-Cover flags what runs out and when, and Overstock & Dead Stock flags the capital going stale on the shelf. Reorder & Open-to-Buy turns both into a margin-weighted buy list, because the point of inventory analytics is a purchase order, not a report - and the ABC x XYZ portfolio matrix shows which SKUs deserve the working capital at all.

Read the full answer → See it in the product breakdown S7 Products / Inventory / Returns →
14
How we solve it

Refund economics reads returns as cohort timing and dollars, not a flat rate, and the Return Reasons view shows why, product by product, so the fix lands on the cause. Refund-Risk Scoring flags the orders and customers most likely to bounce back before that margin is counted as kept. You leave knowing which products, offers or customers need the returns fix first.

Read the full answer → See it in the product breakdown S7 Products / Inventory / Returns →
15
How we solve it

Planning & Forecasting projects revenue, CM1, demand and cash as forecast fans, and the Scenario Lab prices a COGS, AOV or discount change before you make it. Plan-vs-Actual then tracks the quarter with a CM1 variance waterfall, so a miss is explained, not just reported. Behind it, the Financial Models section runs the classic ecommerce models - cohort LTV, price elasticity, GMROI - each exposing its inputs, its formula and the records feeding it.

Read the full answer → See it in the product breakdown S11 Planning & Forecasting →
16
How we solve it

Experiments answers causally: geo-lift tests for paid media, email and SMS holdouts, and social ad-set holdouts, each returning a verdict with a confidence interval in CM1. Trustworthiness diagnostics sit beside every result, because an experiment you cannot trust is worse than no experiment. You walk away knowing what marketing caused, not what it correlated with.

Read the full answer → See it in the product breakdown S13 Experiments →

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