Key Takeaways

✓4-layer dashboard architecture: Data collection → Data warehouse → Dashboard → Alerts

✓4 critical metrics: Daily orders + ASP, inventory turnover, fulfillment time, gross margin by channel

✓3 dashboard paths: Odoo built-in (<$1M/mo) → BI tools like Looker ($3M+/mo) → Custom build ($5M+/mo)

✓Beauty brand case study: +$87,400/month revenue recovery, 3-week payback period

✓Daily monitoring discipline required: 10-min daily check, 30-min weekly review, 60-min monthly strategy

Quick Answer

Real-time performance dashboards require 4 layers: (1) Data collection (all data flows to Odoo/ERP), (2) Data warehouse (for $2M+/month companies), (3) Dashboard visualization (Odoo built-in, Looker, or custom), (4) Alerts (Slack/email notifications). Focus on 4 critical metrics: daily order count + ASP (detect discounting), inventory turnover by SKU (identify dead stock), fulfillment time (customer retention driver), and gross margin by product/channel (profitability). Start with Odoo built-in dashboards (<$1M/month), migrate to BI tools like Looker/Tableau ($3M+/month). Beauty brand case study: $8,200 setup + $400/month = +$87,400/month revenue recovery in 3 months.

Why Most D2C Brands Are Flying Blind

Here's the pattern we see constantly: A founder checks their Shopify dashboard every Monday morning. Looks good. Revenue is "up." Then 3 weeks later, they realize inventory is empty on their best SKU, fulfillment time ballooned to 9 days, and customers are churning to faster competitors because they couldn't see the problem.

The difference between stale reporting and real-time dashboards:

The difference between reacting and controlling.

Part 1: The Dashboard Architecture That Actually Works

Don't build from scratch. We've implemented this 147 times.

A real-time dashboard has four layers:

Data Collection

The source of truth

All data flows to ONE place: your ERP (Odoo, NetSuite, whatever). Orders, inventory, cash, customer data. No exceptions. If you're using separate Excel files, stop. This is killing you.

Data Warehouse

Optional, but critical at scale

For companies doing $2M+/month, a single database struggles under query load. Use a data warehouse (PostgreSQL, BigQuery, Snowflake) to aggregate without slowing production. 5-minute refresh intervals are enough.

The Dashboard

What executives see

This is where you visualize. Charts, gauges, numbers. No "pretty" dashboards that look nice but tell you nothing. Real dashboards show: What's broken RIGHT NOW and what's trending wrong.

Alerts

The early warning system

A dashboard you check once a day is useless. Real dashboards push notifications when something goes wrong. Inventory below threshold? Slack alert. ASP dropping? Email alert. Churn spiking? Text to founder.

Part 2: The Four Metrics You Need to Watch

Here's what kills D2C brands: Watching 50 metrics and missing the 1 metric that matters.

Metric 1: Daily Order Count + ASP (Average Selling Price)

Why it matters:

Tells you if demand is real or if you're just discounting more.

Real scenario: A $2.1M brand showed "revenue growing 12%" month-over-month. In reality, orders dropped 23%; they were just discounting 40% more per order. Not sustainable.

Alert threshold: Drop more than 15% in 48 hours = investigate immediately

Metric 2: Inventory Turnover by SKU

Why it matters:

You have cash tied up in dead stock.

Real scenario: We found $430K in slow-moving inventory across a client's warehouse. They were paying $4,200/month in storage fees for products selling once per quarter.

Alert threshold: Any SKU not sold in 90 days = mark for clearance or liquidation

Metric 3: Fulfillment Time (Order to Shipped)

Why it matters:

This correlates directly with customer retention.

Real scenario: A $5.4M apparel brand was shipping 28 days average. Their competitor shipped in 3 days. They lost 34% of repeat customers.

Alert threshold: Any order pending > 5 days without explanation = escalate

Metric 4: Gross Margin by Product + Channel

Why it matters:

You'll discount your way into bankruptcy if you don't watch this.

Real scenario: A $3.2M brand was "making money" overall, but their Shopify channel was losing $0.34 per order after COGS + ad spend. They didn't know because they were averaging across channels.

Alert threshold: Any product or channel dropping below target margin by 5% = red flag

Skip the vanity metrics. Website traffic, social followers, email open rates—none of these matter if you're not turning them into orders with healthy margins.

Part 3: Building the Dashboard (The Technical Reality)

You have three paths:

Path 1: Use Built-in Odoo Dashboards

Pros

Free, native integration, no coding

Cons

Limited customization, slow if database isn't optimized

Reality

Works for companies doing <$1M/month

(Yes, we know the Odoo sales team will hate this statement. We don't care.)

Path 2: Use a BI Tool (Looker, Tableau, Power BI) — Recommended

Pros

Beautiful visuals, can handle large datasets, integrates with anything

Cons

Cost ($200-$2,000/month), requires data engineering

Reality

If you're doing $3M+/month, this is your move. Payback: 3-6 months

Path 3: Build Custom Dashboards with React/Node

Pros

Complete control, can optimize for your specific metrics

Cons

Need an engineer, takes 8-12 weeks, costs $25K-$80K

Reality

Only do this if you're $5M+/month with very specific needs

We recommend:

Start with Path 1 (built-in Odoo). When you hit $1.5M/month, migrate to Path 2 (Looker or Tableau). Skip Path 3 entirely unless you're $10M+/month.

Real D2C Example: The Setup That Works

Company: Beauty/skincare brand, $2.7M/month revenue

Problem: Decision-making was broken. CEO made calls based on "gut feel." Inventory team ordered based on spreadsheets updated Thursday afternoon. By Monday, data was stale.

Solution

Step 1: Connected Everything to Odoo

→ Shopify → Odoo (real-time order sync)

→ Amazon → Odoo (real-time order sync)

→ Stripe/PayPal → Odoo (real-time payment sync)

→ Inventory scanner → Odoo (real-time stock levels)

Result: Single source of truth. No more Excel.

Step 2: Built the Data Warehouse

Aggregated daily snapshots into PostgreSQL

5-minute refresh intervals

Cost: $400/month for AWS infrastructure

Step 3: Created Four Dashboards

Dashboard A: Operations

For warehouse/fulfillment team

• Pending orders count

• Average fulfillment time (today vs target)

• Inventory alerts (SKUs below reorder point)

• Returns rate (daily)

Refresh: Every 1 hour

Dashboard B: Sales

For marketing/sales team

• Orders by channel (Shopify, Amazon, DTC)

• ASP by channel

• Customer acquisition cost (CAC)

• Conversion rate

Refresh: Every 4 hours

Dashboard C: Finance

For CEO/CFO

• Daily revenue (cash in)

• Payouts (cash out)

• Margin by product

• Cash balance forecast (30-day outlook)

Refresh: Every 24 hours

Dashboard D: Alerts

For everyone

• Red: Inventory below safety stock

• Red: Orders pending > 7 days

• Red: Refund rate > 5%

Delivery: Slack notification + SMS to founder

Step 4: Set Alerts That Actually Matter

→ Inventory below threshold → Slack to operations manager

→ ASP drops > 15% → Email to CEO

→ Fulfillment time > 5 days → Escalate to logistics manager

→ Margin dips below 35% → Red flag to finance team

Result After 3 Months

Performance Improvements

→ Inventory decision-making: 3x faster

→ Fulfillment time: 6.2 days → 3.1 days

→ Slow SKU identification: Hours, not weeks

→ Cash visibility: Real-time vs Friday afternoon

Financial Impact

Revenue recovery: +$87,400/month

(mostly from fixing fulfillment issues customers left over)

Cost: $8,200 initial setup + $400/month infrastructure + $0 (open-source Metabase)

Payback: 3 weeks

Part 4: The Monitoring Discipline (Where Most Fail)

Here's what kills dashboards: You build one, executives glance at it once, then go back to gut feel.

Real story:

A $1.9M brand spent $18K building a beautiful Tableau dashboard. The CEO looked at it on launch day. By month 3, it was never checked. Why? Because the CEO had a "gut feeling" that things were good. (Spoiler: They weren't. Revenue dropped 23% the next quarter.)

To Actually Use Dashboards, You Need Discipline

Daily Stand-Up (10 minutes)

→ Operations lead: Check fulfillment dashboard

→ Marketing lead: Check sales by channel

→ CEO: Check alerts dashboard

→ If something is red, you assign an owner to investigate

Weekly Review (30 minutes)

→ Finance team reviews margin by product

→ Identify which products are pulling down average margin

→ Decide: Keep, discount, or kill

Monthly Strategy (60 minutes)

→ Look at 30-day trends, not daily noise

→ Identify patterns (seasonal, product lifecycle, channel shifts)

→ Adjust strategy based on real data

The rule:

If you're not willing to check your dashboard daily, don't build one. The cost of building > the value of ignoring it.

Your Action Items

This Month

List every system you use (Shopify, QuickBooks, Stripe, inventory system, etc.)

Identify what data is in each

Map which 4 metrics matter most to your business

Choose: Path 1 (Odoo built-in) or Path 2 (Looker/Tableau)?

Next Month

If Path 1: Build dashboards in Odoo

If Path 2: Hire a BI consultant (we recommend this)

Set up data warehouse if doing $2M+/month

Configure alerts in Slack/email

Ongoing

Daily dashboard review (10 minutes)

Weekly deep dive (30 minutes)

Monthly strategy reset (60 minutes)

Stop Flying Blind

Look, real-time visibility isn't nice-to-have anymore. It's survival. Your competitors have it. Your best customers expect it (they want order tracking). Your team needs it to do their jobs.

The companies we work with that nail this? They spot problems in hours instead of weeks. They fix fulfillment bottlenecks before customers churn. They kill underperforming products before wasting another $40K on ad spend.

The question isn't whether you can afford dashboards.

The question is whether you can afford not to have them.

Ready to See What's Actually Happening in Your Business?

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