Operator
Founders, CXOs, heads of ecommerce, GMs
Marketer
Growth leads, performance marketers, brand & creative
Data & analytics
Analysts, data engineers, BI leads
Operator [#operator]
Founder, CXO, head of ecommerce, GMYou spend your time on
The P&L, channel-level profitability, inventory, pricing strategy, vendor and marketplace negotiations.
Your typical questions
“What’s my true contribution margin this month?” “Which channel is actually profitable?” “Are returns eating my margin?” “Is our discount strategy working?”
What is Ocular
Start with the question → Profitability & Margins
Every question an operator typically asks, mapped to the right module.
P&L
The waterfall view and channel comparison are the operator’s home base. (in Use Your Data → P&L)
Sales Performance
Top products, discount depth, holiday performance, new vs. repeat mix. (in Use Your Data → Sales Performance)
Purchase Retention Cohort
When leadership asks about LTV, CAC payback, or acquisition vs. retention. (in Use Your Data → Cohorts)
FAQs → Reconciliation
For the moments when a number doesn’t look right.
Skip for now: the breakdown-level data models and the metric/dimension dictionary. You don’t need them.
Marketer [#marketer]
Growth lead, performance marketer, brand & creativeYou spend your time on
Ad spend allocation, creative briefs, attribution, campaign performance, channel mix, retargeting.
Your typical questions
“Is my ad spend actually working?” “Which creatives should I brief next?” “Which campaigns are wasting budget?” “Why doesn’t my Meta ROAS match Shopify?”
What is Ocular
Start with the question → Marketing & Creative
Your most common workflows mapped to modules.
Marketing report
Blended ROAS, MER, per-platform performance, demographic and dayparting heatmaps.
Ad & Campaign Performance
Storefront Net ROAS vs. Ad Platform ROAS, attribution model selection, bubble chart for budget decisions.
Creative Deep Dive
For creative brief prep.
Ad Tracking Setup Guide
UTM, fbclid, gclid configuration. Do this once at the start of every new ad account.
Ad & Campaign Naming Convention
Make downstream reporting useful by getting names right at the source.
Attribution philosophy
When you need to defend the numbers, this is why Ocular says what it says.
Skip for now: the data-model deep dives. The Marketing, Ad & Campaign Performance, and Creative Deep Dive modules surface everything you need without going to Chart Builder.
Data & analytics team [#data-team]
Analyst, data engineer, BI leadYou spend your time on
Custom analyses, building dashboards for the org, validating numbers against source systems, owning the team’s understanding of how Ocular’s semantic layer is built.
Your typical questions
“How does Ocular define contribution margin?” “How do I build a custom retention cohort in Chart Builder?” “Why doesn’t Shopify’s revenue match Ocular’s net revenue?”
What is Ocular + Salient features
Context. What is Ocular · Salient features.
Data modeling philosophy
How raw data becomes the unified semantic layer; facts, dimensions, derived metrics.
How your data flows
Ingestion → cleaning → enrichment → semantic layer.
Data Dictionary
The metric and dimension reference, A–Z. The single most-linked page in the docs.
Data Model deep dives
Sales, Customer Activity, Fulfilment, Meta Ads, Google Ads. One page per model.
Common analyses
Recipes for ROAS, retention, LTV, contribution margin, return rate — built in Chart Builder.
Chart Builder
For ad-hoc analyses and sharing the result with non-technical stakeholders.
Reconciliation FAQs
The patterns you’ll get asked to explain.
Skip for now: nothing. The data team typically needs the deepest read of the docs.
Not sure where to start?
Evaluating Ocular
Read What is Ocular, Salient features, then skim Start with the question to see if your real-world questions are covered.
Implementing for the first time
Go to Setting up your workspace, then Implementation checklist.
Stuck on a specific issue
Start with the question is the fastest path. If a number doesn’t match a source, jump to FAQs → Reconciliation.