We build your data stack so your team can ask AI real questions and trust what comes back — customer intelligence, marketing, revenue, and assortment. For a fraction of an in-house team.
CSV exports on Monday mornings. A spreadsheet with 47 tabs. Every question takes a week to answer. You know there's signal in your data — you just can't get to it.
You need a data engineer, an analyst, and someone who understands retail. That's three hires, $300k+ in salary, and six months before anyone ships a dashboard.
Six weeks of "discovery," a strategy deck, and a Jira board. Six months later, still no dashboard. They assigned a junior with six months of experience.
Your platform's analytics show you what happened. They don't tell you why, what's next, or where you're bleeding margin. You need answers built-in dashboards were never designed to give.
There's a better way.
3 hires to cover the stack
$300k+ fully loaded
6+ months to recruit & ramp
No retail domain expertise
6+ weeks of discovery
Strategy decks, not dashboards
Junior staff, constant handoffs
Generic, not retail-native
Ships week 1
Senior team, full stack
Built for DTC brands
A fraction of the cost
You give us access to your data sources. We assess your current state and tell you exactly what to build first.
Full data stack audit
Tool recommendations
Prioritized build plan
AI-readiness assessment
Pipelines, models, dashboards — built and documented from day one. Our platform turns your raw data into customer intelligence, marketing performance, and revenue clarity you can act on.
Data pipelines across all sources
Propensity & cross-sell models
Production dashboards
Marketing & revenue analytics
This isn't a one-time project. We manage your analytics as your business evolves — new questions, new data sources, new models.
Anomaly detection & alerting
New data sources as you grow
Dashboards that evolve with your business
Fractional analytics team, always on
Real results from DTC brands using Clicar's analytics. Not projections — outcomes.
Retail-native
Built by people who've spent years inside retail data. We know customer behavior, marketing performance, and DTC economics.
Fractional, not freelance
Dedicated hours each week. Same team, every time. The person on the call is the person building your pipeline.
Enterprise analytics, startup speed
Propensity models, demand forecasting, GMROI analysis — shipped in weeks, not quarters.
0%
$0.0M
Targeted customers based on actual purchase propensity instead of demographics. Focused ad spend on segments most likely to convert. 3x return on ad spend. 67% cross-sell prediction accuracy.
Built a bottom-up demand forecasting model that computed exact PO quantities. 30% reduction in inventory in six months. Lower holding costs. Better working capital.
We'll ask about your data problems, show you what we'd build first, and tell you honestly if we're the right team for it.