Business intelligence
Stop running the weekly meeting on instinct.
Ecommerce sold business intelligence as a visibility problem. The market bought visualization, and the dashboards turned prettier while the guesswork underneath stayed put. It isn't a reporting layer. It's a capital allocation instrument.
Finite operator hours and finite ad dollars have to go where the next marginal dollar returns the most. Adverio's system connects fragmented data across Amazon, Walmart, and Target into one governed decision layer built to answer exactly that.
$56.2B
Amazon ad revenue in 2024, up 18% year over year. At that scale, governed vs ungoverned allocation is measured in seven figures. Amazon Q4 2024 earnings.
14.7%
of total revenue spent on ads by the average Amazon seller in 2025, up from 11.2% in 2022. Without incrementality, much of that defends organic sales. Marketplace Pulse, 2025.
Six lenses govern the decision. Each answers a single question and triggers a single instruction, at the item level, with the constraint layer cleared first.
Your dashboard says the quarter went fine. Your margin disagrees.
See Where My Next Dollar Wins15-minute call. No pitch deck.
The definition
Ecommerce business intelligence connects fragmented marketplace data, advertising metrics, catalog performance, and pricing signals into one decision layer. You already have the data. What you lack is a system that tells you what to do with it. If your reporting shows what happened last month but can't tell you which SKU to fix tomorrow, you don't have intelligence. You have a scoreboard.
The usual setup connects data sources, builds dashboards, and watches the metrics. Nothing about that is wrong. It just stops one step short of the only question that matters, which is what to do on Monday.
Branded ROAS can look healthy while generics quietly erode and margin compresses. Efficiency holds, growth doesn't, and the dashboard reports both as green. Without incrementality modelling, a meaningful share of ad spend is defending sales you'd win organically.
Govern on TACoS. Total ad cost against total revenue is the only view that shows whether advertising is building the brand or renting it.
Compare shares, not totals. Ad spend share against total sales share is the number that moves first.
Make every read produce an instruction. A do and a do-not, by SKU, by query, by channel. If a chart doesn't end in a decision, it's decoration.
Don't treat a healthy branded number as proof of growth. It's the easiest figure to fool yourself with.
Don't scale a channel you can't attribute. Spend that can't show what it influenced can't be defended in a budget meeting.
Don't add another dashboard to fix a decision problem. More visibility into the same guesswork changes nothing.
Each lens answers one question and triggers one decision.
| Lens | What it answers | The decision it triggers |
|---|---|---|
Incrementality | Which revenue would have arrived anyway | Reallocate protected branded spend into generics |
GEAR | Where the next dollar raises contribution margin | Fund only positive GEAR delta |
Query IQ | Rank and impression share by query class | Scale only where PDP readiness and rank velocity align |
CRO:SEO | Which of five conversion dials is broken | Fix the top blockers before spending |
Buy Box Stability | Whether ownership is stable enough to promote | Anchor spend to proven price bands |
Profit Pulse | True P&L by SKU after fees, returns, and freight | Kill or fix profit-leaking SKUs |
Don't run one lens on its own. GEAR tells you where to spend, and Profit Pulse tells you whether the SKU deserves it.
Don't score before the constraint layer is clear. See section 4.
Don't average the six into a single health number. The value is in which one is failing, and an average hides that.
All six lenses, one governed view across every marketplace.
Six lenses, and one of them is failing right now. An average would hide which.
Find My Profit-Leaking SKUs20-minute call. We bring the account read.
Free worksheet
Six lenses decide where your next dollar goes. Score how many of them you can answer today.
List your data sources, mark the lenses you can currently answer, and the sheet names the one you're blind on.
Takes 2 minutes.
The conventional playbook is strong on the ground next to this one. External traffic as a rank lever, the creator flywheel, DSP loyalty funnels, a direct-to-consumer layer for customer ownership, multi-marketplace expansion in a sensible sequence. All of it holds up.
On incrementality it gestures at the concept and stops. There's no conventional playbook for turning it into an instruction, which is worth saying plainly.
The break is operational. Compare ad spend share against total sales share. Then compare ad conversion rate against total conversion rate. The account starts separating spend that builds the brand from spend that buys sales the brand would have won anyway. That comparison is the whole argument. A dashboard can't make it for you, because a dashboard has no opinion.
Name the comparison you're running. If nobody can state which two numbers are being weighed, no incrementality work is happening.
Design the test where the account structure allows it. Some campaigns defend existing demand and some create it. They shouldn't share one performance target.
Don't accept "we measure incrementality" as an answer. Ask what decision changed last month because of it.
A score on a constrained item is a trap. Spending into a constraint buys a more expensive version of the same problem, so inventory health, pricing and unit economics, and listing readiness get cleared before any ad-level work happens. Structure comes before speed.
Prioritization then runs at the item level rather than the parent. Revenue concentration usually sits in one or two children inside a parent, so a parent-level view averages away both the item carrying the revenue and the item quietly bleeding.
An emergency room doesn't treat patients in arrival order. It triages by severity and treatability. The decision layer is the triage chart. Without one, teams work the loudest problem rather than the most recoverable one.
Clear the constraint gates in order. Stock sufficient to support spend, Buy Box stable enough to matter, pricing and margin healthy enough that the read isn't distorted by a promo.
Prioritize children, never parents. Then let time allocation mirror revenue allocation. A small share of items earns deep manual work and the long tail earns standardized treatment.
Give constraint clearing an owner. If the ad team can't see listing status and inventory cover, it can't explain margin. Full Amazon account management is where that ownership sits.
Don't treat every item equally. It's the most common way operator hours get spent on things that can't move.
Don't promote through Buy Box volatility. Swinging ownership makes the spend unmeasurable, not just inefficient.
Connect ad, retail, catalog, pricing, and review data into one governed dataset.
We normalize, score, and surface do and do-not plays with guardrails.
We launch structured tests across ads, listings, and pricing, then track lift against Incrementality and GEAR.
Decide who executes before you start. Keep your team, or hand it over. Both work, and the instrumentation is identical either way.
Don't instrument everything at once. Connect what the first three decisions need, then widen.
Signal
Branded spend at 62%. Generic rank is sliding.
Action
Reallocate 20% to top-five generics where CRO:SEO is 8+ and Buy Box stability is confirmed.
Guardrails
Pause if TACoS rises more than two points or CVR drops more than 10%.
Watch
Incremental sales, rank movement, and GEAR lift inside 21 to 30 days.
| Decision | Owner | What it protects |
|---|---|---|
| Reallocate branded budget into generics | Media lead | Incrementality of total spend |
| Fund or freeze a query by GEAR delta | Media lead | Contribution margin per query |
| Fix or hold a PDP before scaling | Catalog and creative | Cart-add and purchase share |
| Anchor or pull spend on Buy Box volatility | Ops and pricing | Measurability of every downstream read |
| Kill or repair a profit-leaking SKU | Finance and catalog | Margin at the item level |
| Widen the instrumented dataset | Analytics | Every decision made after it |
Every row gets a name attached before the first test runs. If you want the wider picture of how these seats work together, see our growth services.
The engagement
Six lenses, three marketplaces, and one P&L isn't a checklist problem. It's a governance problem.
Adverio owns the governance seat and the execution behind it. Every play routes into Amazon PPC management, content, and pricing. When the numbers say stop, we stop, and we name the number that said it.
Dashboards plus monthly playbook review. Built for teams that execute internally but want strategic oversight and prioritization clarity.
We implement ad reallocations, CRO changes, and pricing adjustments, then iterate weekly, reporting lift tied directly to Incrementality and margin impact.
The receipts
Questions
Dashboards visualize activity. We model incrementality, contribution margin, and capital allocation impact, then name the SKU, the query, and the spend line that changes.
Those are visualization tools and they do that job well. We layer intelligence consulting onto your existing infrastructure, so the output is a decision you can act on.
Ad platforms, retail analytics, catalog data, pricing history, and return data. We normalize and operationalize it.
Directional shifts appear within 15 to 30 days. Compounding lift builds across 60 to 90 days.
Yes. Amazon, Walmart, and Target all score on the same six lenses.
Depends on catalog complexity and scale. We model expected profit upside before engagement.
Quick answer
Score every marketplace decision against contribution margin, not revenue. That is what tells you which SKU to fix, which query to fund, and which spend to stop. Directional shifts appear within 15 to 30 days and compounding lift builds across 60 to 90 days.
If your team debates what to do next every week, the debate is the symptom. You're paying for a scoreboard and calling it intelligence.
Put My Data Behind One Decision LayerOne call, and you keep the read either way.
