Your Roadmap to Instant Insights · 3 of your 5 dashboards are buildable today
The analytics Summit Gear Co could be running on
This roadmap reads top down: the dashboards and AI analyst you'd work in, then the layers beneath that feed them. The arrows show how your data flows up the stack.
This is your permanent roadmap. Bookmark it, or forward the link to your technical lead.
Dashboards as StoriesSurface · what you use
Every dashboard is a story: a named audience with a problem worth solving.
Which Channels Actually PayStart hereInitial build
Owner / marketing lead
Money goes into ads and channels; customers come out. What happens in between is attribution theater. You can't see the true cost of a real customer, channel by channel.
- · What does a new customer really cost, channel by channel?
- · Which channels bring customers who come back?
- · Where should the next dollar of spend go?
Metrics included: Spend by channel · Cost per new customer · Revenue per channel
Data sources: Online store / marketplace · Advertising
Where the Money Actually IsInitial build
Owner / CEO / finance lead
You can see revenue, but not what any of it costs, so nobody truly knows which products, services, or customers make money and which quietly lose it.
- · Which products or services actually make us money?
- · Which customers or jobs are we losing money on?
- · Where would a price change matter most?
Metrics included: Gross margin by segment · Contribution margin · Cost breakdown
Data sources: Online store / marketplace · Accounting
Inventory & Sell-throughInitial build
Owner / ops / buying lead
Cash sits on shelves in the wrong SKUs while the winners stock out. The buy decisions that caused it were made on gut.
- · Which products are overstocked, and which are about to stock out?
- · How fast does each SKU actually sell through?
- · Where is cash tied up in inventory right now?
Metrics included: Sell-through rate · Weeks of cover · Stockout rate · Inventory value by age
Data sources: Online store / marketplace · Accounting · Inventory & fulfillment
Customers Who Come BackAfter the build
Owner / growth lead
New customers get all the attention, but the business runs on the ones who return. You can't see who comes back, how often, or what changed when they stopped.
- · What share of customers buy again, and how fast?
- · Which first purchases predict a loyal customer?
- · Where are repeat rates slipping?
Metrics included: Repeat purchase rate · Customer retention curve · Revenue from returning customers
Data sources: Online store / marketplace
Location P&L & LaborAfter the build
Owner / director of ops
Each location tells its own story at month-end, and labor creep hides in the averages until the P&L lands.
- · How is each location doing today, not last month?
- · Where is labor out of line with sales?
- · Which locations are drifting, and on what?
Metrics included: Location contribution margin · Labor % of sales · Prime cost · Sales per labor hour
Data sources: Online store / marketplace · Accounting · Inventory & fulfillment
Your company's institutional memorySurface · intelligence
On top of the modeled data sits an AI analyst that reads Summit Gear Co's governed numbers. Ask it anything your dashboards don't answer, and tell it what you never want to miss again.
Ask (pull)
Ad-hoc questions in plain English, answered from your modeled data.
Alerts (push)
Custom watchdogs you set by describing the incident once.
How it's built: your data flows up
How raw data becomes trusted numbersModeling · trusted numbers
A layered modeling environment (built on dbt), with every metric traceable back to its raw source.
Want to see it model by model? The full tree is in the buildable spec below.
Where your data livesFoundation · where data lives
These flow into a central warehouse, the foundation layer of your roadmap.
The buildable specPreview
The dbt project behind your stack: every source staged, modeled, and traced to the dashboard it powers. This is the summary; the full spec, with DAGs, model descriptions, and the metric catalog, is yours to download.
↓ Download the full spec (Markdown)analytics/
models/
staging/
commerce/
accounting/
ads/
email_sms/
inventory/
intermediate/
marts/
finance/
growth/
operations/
retention/
kpi/
_docs/
.github/
workflows/
seeds/
snapshots/
macros/
tests/
skills/
Source → model → dashboard
How each dashboard traces back to your raw sources.
Metric definitions
Every metric on the roadmap, defined, with its grain and the dimensions you can slice it by.
Spend by channel
Marketing and ad spend, normalized across platforms.
grain weekly · by channel, campaign
Cost per new customer
Spend divided by genuinely new customers, per channel.
grain monthly · by channel
Revenue per channel
Revenue attributed to the channel that acquired the customer.
grain monthly · by channel, cohort
Gross margin by segment
Revenue minus direct costs, per product, service, or segment.
grain monthly · by product, service, segment
Contribution margin
What each sale contributes after all variable costs.
grain monthly · by product, channel
Cost breakdown
Where each dollar of revenue goes: COGS, delivery, overhead.
grain monthly · by category, segment
Sell-through rate
Units sold as a share of units available, per period.
grain weekly · by sku, category, channel
Weeks of cover
How long current stock lasts at the recent sales rate.
grain weekly · by sku, location
Stockout rate
Share of items (or days) out of stock when demanded.
grain weekly · by sku, location
Inventory value by age
On-hand value bucketed by how long it has sat.
grain weekly · by age_bucket, category
Repeat purchase rate
Share of customers who buy again within the period.
grain monthly · by cohort, segment
Customer retention curve
Share of each cohort still buying at N periods after their first purchase.
grain monthly · by cohort
Revenue from returning customers
Share of revenue produced by repeat customers vs. first-timers.
grain monthly · by segment
Location contribution margin
Location revenue minus its direct costs.
grain weekly · by location
Labor % of sales
Labor cost as a share of sales, by location and daypart.
grain daily · by location, daypart
Prime cost
Cost of goods plus labor, the operator's headline cost.
grain weekly · by location
Sales per labor hour
Revenue generated per scheduled labor hour.
grain daily · by location, daypart
What's possible
AI + Summit Gear Co's data
Your roadmap above is the foundation. This is the agentic layer that comes with an engagement: previews below use patterns typical for your industry, not your data. The custom version is built from your live, governed numbers and tuned to how you run.
The Morning Read
A standing analyst reviews the business every morning
What Summit Gear Co's first one might say:
A best seller quietly eroding margin. Your highest-revenue category is trending toward your thinnest margin as discounts deepen.
Ad spend outrunning return. One channel's return on ad spend slipped two weeks running while budget held flat. Recoverable if caught now.
Inventory aging. Stock that hasn't moved in 60 days. Cash you could free.
First-timers not returning. Last month's new customers are repeating below your trailing rate.
The alarm it didn't raise. Last week's revenue dip is a holiday-shifted comparison, not a real decline. Restraint is the feature: a system that cries wolf gets ignored by Friday.
Every item in a real Morning Read is traced to the exact query behind it and reviewed by a human before it reaches you.
Ask in Slack
Included@analyst which products are dragging our margin, and by how much?
A dashboard from a prompt
In preview“Returns by product and reason, last 90 days” becomes a live dashboard. No ticket, no wait.
A delivery from a prompt
On our roadmap“Email me channel margin every Monday at 7” and the schedule sets itself up.
The full AI-enabled build
IncludedPipelines, models, tests, dashboards, and the analyst itself, built by AI in weeks, not quarters. A human signs every metric.
Your custom Morning Read and the rest of the agentic layer come with an engagement. See everything that's possible →
How Strata BI builds this
This roadmap is the foundation. We take what's here and keep digging, enriching it with everything specific to your business, then feed it into our purpose-built engine. The result: your full analytics stack, built in roughly a fifth of the time a traditional team would take, even one using AI.
And we bake in efficiency, cost, and security protocols most engineers never get to, so what you end up with is fast, lean, and built to last. Your team gets trained for full autonomy, so nothing in the build is ever a black box.
What it takes to make this real
Ready · done ✓
Your roadmap
You're holding it: the dashboards, the AI analyst, and the foundation they need, mapped to your business.
Set
The build: weeks, not quarters
We make this roadmap real: one fixed price, scoped together on a 30-minute call.
Go
Your team, empowered
Trained to run it all yourselves, with us one message away when you want more.
Your roadmap ends with a conversation.
This roadmap is your plan, permanent and shareable, so forward it to anyone. When you're ready, book a 30-minute consultation: we'll walk through it together and scope exactly what it takes to make it real.
Book a consultation →