Direct-to-Consumer Brands: D23.io as the Single Source of Truth

Table of Contents

1. Why DTC Brands Need a Single Source of Truth
2. The Data Fragmentation Problem
3. Understanding D23.io and Apache Superset
4. Consolidating Your DTC Data Stack
5. Building Contribution-Margin Reporting
6. Real-World DTC Implementation
7. Security, Compliance, and Governance
8. Measuring ROI and Business Impact
9. Getting Started with D23.io

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Why DTC Brands Need a Single Source of Truth

Direct-to-consumer brands operate in an environment of relentless data creation. Every customer interaction—from ad click to checkout to post-purchase email—generates a data point. Yet most DTC operators still spend their mornings jumping between Shopify analytics, Klaviyo dashboards, Meta Ads Manager, and Google Analytics, stitching together fragmented truths in spreadsheets.

This fragmentation costs time, decision quality, and money. A founder we worked with recently spent 12 hours per week manually reconciling data across platforms, only to discover discrepancies that delayed product decisions by two weeks. When you’re competing on speed and margin, that’s a luxury you can’t afford.

The solution isn’t another dashboard tool or yet another analytics platform. It’s consolidation: bringing all your customer, transaction, and marketing data into a single, governed, real-time semantic layer where every stakeholder speaks the same language. That’s what D23.io delivers—a modern data warehouse and analytics foundation built on Apache Superset that turns fragmented data into unified, actionable intelligence.

For DTC brands, this shift is transformative. You move from reactive reporting (“Why did revenue dip last week?”) to proactive insight (“Which customer cohort is most profitable, and how do we acquire more like them?”). You reduce decision latency from days to hours. You cut operational overhead by automating data pipelines that previously required manual intervention. And you build the data foundation that scales with your business from $1M to $100M+ in annual revenue.

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The Data Fragmentation Problem

Why DTC Brands Accumulate Siloed Data

DTC brands don’t set out to create data chaos. It happens organically. You launch with Shopify because it’s fast and affordable. You add Klaviyo for email because it integrates seamlessly. You run paid ads on Meta and Google because that’s where your customers are. Each tool solves a real problem—but none of them talk to each other in a way that serves your business.

According to research on direct-to-consumer strategy, successful DTC brands must balance channel architecture, customer acquisition, commerce infrastructure, and loyalty programs. But without unified data, you’re flying blind across all of these dimensions. You can’t answer basic questions like:

• Which acquisition channel delivers the highest lifetime-value customers?
• What’s the true contribution margin by product, cohort, or geographic market?
• How many days does it take from first touch to repeat purchase?
• Which email segments generate the best ROI relative to cost?

Each platform gives you partial answers. Shopify tells you what sold. Klaviyo tells you who opened emails. Meta tells you how much you spent. But no single platform connects these threads into a coherent narrative about your business.

The Hidden Costs of Data Silos

Data fragmentation imposes real costs. First, there’s the operational tax: analysts and founders spend 10–15 hours per week extracting, cleaning, and reconciling data across platforms. That’s a full-time job that adds no strategic value.

Second, there’s the decision cost. When reporting is slow or unreliable, you either delay decisions or make them with incomplete information. A DTC brand that can’t answer “What’s our contribution margin by customer cohort?” in real time will inevitably overspend on acquisition or underspend on retention.

Third, there’s the opportunity cost. Your team is so busy pulling reports that they don’t have time to ask better questions. They can’t explore “What if we adjusted our email frequency?” or “How would a 10% price increase affect our unit economics?” because building those analyses takes weeks.

According to comprehensive DTC guides, brands like Allbirds and Warby Parker succeeded partly because they had direct access to customer data and could iterate rapidly. But that advantage only exists if you can actually access and act on that data quickly.

The Case for Consolidation

Consolidation solves these problems at the root. Instead of maintaining separate data sources, you build a single semantic layer—a governed, documented, real-time reflection of your business. Every stakeholder (marketing, finance, product, operations) queries the same definitions and sees the same numbers.

This approach is proven. Harvard Business School research on DTC brands documents how Dollar Shave Club, Harry’s, Glossier, and Allbirds used data-driven decision-making to disrupt mature markets. The common thread: they had unified visibility into customer economics and could iterate faster than incumbents.

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Understanding D23.io and Apache Superset

What Is D23.io?

D23.io is a modern data consolidation platform purpose-built for DTC brands and mid-market operators. It’s built on Apache Superset—an open-source data visualization and business intelligence tool trusted by enterprises and startups alike. But D23.io isn’t just Superset; it’s a curated, pre-configured, governance-first implementation that handles the operational complexity most teams don’t want to manage.

When you deploy D23.io, you get:

• Data ingestion: Pre-built connectors for Shopify, Klaviyo, Meta Ads, Google Analytics, Stripe, and 100+ other sources
• Semantic layer: A governed data model that defines how revenue, cost, margin, and cohorts are calculated—once, consistently, across all reports
• Real-time dashboards: Interactive visualizations that update as data flows in, not on a nightly schedule
• Access control: Role-based permissions so finance sees revenue, marketing sees CAC, and executives see contribution margin
• Audit trails: Complete lineage and governance so you can pass SOC 2 and ISO 27001 audits

For DTC brands, this is transformative because it moves you from “I have lots of data” to “I understand my data and can act on it.”

Why Apache Superset Matters

Apache Superset is the foundation. It’s not a SaaS platform that holds your data hostage or charges per user. It’s open-source software you own and control. You can host it on your own infrastructure (AWS, GCP, Azure) or with a managed provider. You’re not paying per query or per dashboard. You’re not locked into a vendor’s roadmap.

This matters for DTC brands because it means your analytics infrastructure scales with your revenue, not against it. At $1M revenue, you might run a small Superset instance on a $200/month server. At $50M revenue, you might run a larger cluster, but you’re still in control of the cost structure.

Superset also integrates with your existing data infrastructure. If you have data in Postgres, Redshift, BigQuery, Snowflake, or any major warehouse, Superset can query it directly. You’re not forced to move data or adopt a proprietary storage format.

D23.io as Your Implementation Partner

D23.io handles the complexity. Instead of hiring a data engineer to build and maintain Superset, you work with D23.io to:

1. Design your semantic layer: Define how revenue, cost, margin, and cohorts are calculated
2. Build your connectors: Connect Shopify, Klaviyo, Meta, Google, and other sources
3. Create your dashboards: Build interactive reports for marketing, finance, and operations
4. Implement governance: Set up access control, audit trails, and compliance frameworks
5. Train your team: Ensure your operators can query and explore data independently

This is not a “set it and forget it” engagement. D23.io provides ongoing support, handles schema changes when your platforms evolve, and helps you iterate as your business questions change.

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Consolidating Your DTC Data Stack

The Architecture: From Silos to Unified Source

Consolidation follows a proven architecture. Your source systems (Shopify, Klaviyo, Meta, Google) push data into a central warehouse. That warehouse contains raw data—tables for transactions, customers, email opens, ad spend, and so on. On top of that warehouse sits your semantic layer: a set of curated, governed tables and metrics that represent your business.

Your dashboards, reports, and analyses query the semantic layer, not the raw data. This separation is crucial. It means your business logic lives in one place. When you need to change how “contribution margin” is calculated, you update the semantic layer once, and every report automatically reflects the new definition.

Here’s a concrete example for a DTC fashion brand:

Raw data (from Shopify): order_id, customer_id, sku, quantity, gross_revenue, timestamp

Raw data (from Klaviyo): customer_id, email_open, email_click, segment, timestamp

Raw data (from Meta): campaign_id, spend, impressions, clicks, timestamp

Semantic layer (unified truth):

• customer: customer_id, acquisition_channel, acquisition_cost, lifetime_revenue, repeat_purchase_rate
• order: order_id, customer_id, gross_revenue, cogs, shipping_cost, contribution_margin
• cohort: cohort_id, acquisition_month, size, revenue_per_customer, margin_per_customer
• campaign: campaign_id, channel, spend, attributed_revenue, attributed_margin, roas

Every stakeholder queries the semantic layer. Finance calculates profit using contribution_margin. Marketing calculates ROAS using attributed_revenue and spend. Product calculates repeat purchase using repeat_purchase_rate. Everyone is working from the same definitions.

Connecting Your Sources

D23.io provides pre-built connectors for the platforms most DTC brands use:

Shopify: Orders, customers, products, inventory, refunds—everything you need to understand revenue and unit economics.

Klaviyo: Email list membership, opens, clicks, revenue attributed to email. This lets you measure email ROI and segment performance.

Meta Ads: Campaign spend, impressions, clicks, conversions. Critical for understanding acquisition cost and channel attribution.

Google Analytics: Traffic, sessions, conversion funnels, behaviour flow. Helps you understand on-site experience and conversion efficiency.

Google Ads: Search campaign spend, clicks, conversions. Often a significant acquisition channel for DTC brands.

Stripe: Payment transactions, failed charges, subscription revenue. Essential for understanding cash flow and churn.

These connectors run on a schedule (typically hourly or daily) and automatically sync new data into your warehouse. You don’t need to maintain ETL pipelines or write custom extraction code. The connectors handle schema changes when platforms update their APIs.

Building Your Data Model

Once data flows into your warehouse, you need a data model—a set of tables and metrics that represent your business. This is where strategy matters.

Most DTC brands need these core tables:

customers: One row per customer, with acquisition channel, acquisition cost, first purchase date, last purchase date, total lifetime revenue, and repeat purchase indicator.

orders: One row per order, with customer_id, order date, gross revenue, COGS (cost of goods sold), shipping cost, refunds, and calculated contribution margin.

products: One row per SKU, with name, category, price, COGS, and margin.

email_segments: Membership in Klaviyo segments, with segment name, join date, and performance metrics (open rate, click rate, revenue per send).

campaigns: One row per paid campaign, with channel, spend, attributed conversions, attributed revenue, and calculated ROAS.

cohorts: Aggregated view of customers grouped by acquisition month, with cohort size, revenue per customer, and margin per customer.

Building this model requires understanding your business. What metrics matter most? How do you attribute revenue to channels? What’s your definition of a “repeat customer”? These aren’t technical questions—they’re business strategy questions. D23.io helps you translate strategy into a data model.

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Building Contribution-Margin Reporting

Why Contribution Margin Matters for DTC

Revenue is a vanity metric. A DTC brand can grow revenue 100% year-over-year and still be unprofitable if it’s acquiring customers at a loss.

Contribution margin—revenue minus variable costs (COGS, payment processing, shipping, acquisition cost)—is the metric that actually matters. It tells you whether each customer generates profit or loss after direct costs.

For DTC brands, contribution margin reporting should answer:

• What’s our total contribution margin by month?
• Which products have the highest margin?
• Which customer cohorts (acquired in which month, from which channel) are most profitable?
• How does margin change as we scale acquisition?
• What’s the payback period for acquisition spend?

Without unified data, calculating contribution margin is painful. You need to export order data from Shopify, cost data from your accounting system, acquisition spend from Meta and Google, and then manually match them up. By the time you have an answer, the data is stale.

With D23.io, contribution margin is a real-time metric available to everyone.

Designing Your Contribution-Margin Model

Here’s how to structure contribution-margin reporting:

Step 1: Define gross margin

Gross margin = (Revenue – COGS – Payment Processing) / Revenue

For a DTC brand selling $100 product with $30 COGS and 2.9% + $0.30 payment processing:

• Revenue: $100
• COGS: $30
• Payment processing: $3.20
• Gross margin: ($100 – $30 – $3.20) / $100 = 66.8%

Step 2: Add customer acquisition cost (CAC)

CAC by cohort = Total acquisition spend / New customers acquired

If you spent $50,000 on Meta ads in January and acquired 500 customers, CAC = $100 per customer.

Step 3: Calculate contribution margin after CAC

Contribution margin = (Revenue – COGS – Payment Processing – CAC) / Revenue

Using the example above:

• Contribution margin = ($100 – $30 – $3.20 – $100) / $100 = -33.2%

This tells you that customers acquired in January, on average, don’t break even in their first purchase. You need repeat purchases to achieve profitability.

Step 4: Add repeat-purchase economics

If January cohort customers make an average of 2.5 purchases before churning:

• Total revenue per customer: $250
• Total COGS: $75
• Total payment processing: $8
• Total CAC: $100
• Contribution margin: ($250 – $75 – $8 – $100) / $250 = 51.2%

Now you can see the true economics: customers are profitable, but only if they repeat. This insight changes everything about how you think about acquisition and retention.

Building Interactive Dashboards

Once your semantic layer calculates contribution margin, you build dashboards that let stakeholders explore it:

Executive dashboard: Total revenue, total contribution margin, contribution margin %, trend over time, contribution margin by channel.

Marketing dashboard: CAC by channel, ROAS by campaign, contribution margin by acquisition channel, payback period by cohort.

Product dashboard: Revenue and margin by product category, margin trend, top and bottom performers, repeat purchase rate by product.

Finance dashboard: Contribution margin by cohort, cohort retention and repeat purchase rates, LTV:CAC ratio, payback period.

Each dashboard is interactive. You can filter by date range, channel, product category, or cohort. You can drill down from a summary number to underlying transactions. This interactivity is crucial because it lets operators answer their own questions without waiting for analysts.

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Real-World DTC Implementation

A Case Study: Fashion DTC Brand

Let’s walk through a real implementation. A Sydney-based fashion DTC brand (let’s call them “StyleCo”) was growing rapidly but had a data problem:

• Revenue: $8M annually, growing 40% YoY
• Channels: Shopify store, Meta ads, Google ads, organic
• Email: Klaviyo for segmentation and campaigns
• Problem: No unified view of customer economics. Marketing couldn’t measure true ROAS. Finance couldn’t calculate contribution margin by cohort. Founders spent 10+ hours per week in spreadsheets.

PADISO worked with StyleCo to implement D23.io. Here’s what we did:

Week 1-2: Data discovery and model design

We audited StyleCo’s data sources: Shopify (orders, customers, products), Klaviyo (email list, segments, revenue attribution), Meta Ads (campaign spend, conversions), Google Ads (search spend, conversions), Stripe (payments), and their accounting system (COGS by SKU).

We designed a semantic layer with tables for customers, orders, products, campaigns, and cohorts. We defined how contribution margin would be calculated: revenue minus COGS minus payment processing minus CAC, with repeat-purchase adjustments.

Week 3-4: Data ingestion and transformation

We built connectors from each source into a Postgres warehouse. We wrote SQL transformations to build the semantic layer tables. We implemented data quality checks to catch issues (e.g., orders with missing COGS, campaigns with zero conversions).

Week 5-6: Dashboard development and training

We built four dashboards: executive (revenue, margin, trend), marketing (CAC, ROAS, payback), product (revenue and margin by category), and finance (contribution margin by cohort, LTV:CAC). We trained StyleCo’s team to use Superset and explore data independently.

Results after 3 months:

• Decision speed: Founders could answer key questions in minutes instead of days
• Insight quality: StyleCo discovered that their January cohort had 40% higher LTV than February cohort (due to a product mix shift), which informed Q2 product strategy
• Operational efficiency: Analysts spent 2 hours per week on reporting instead of 10, freeing time for deeper analysis
• Cost reduction: By understanding true CAC and payback period, StyleCo optimised ad spend and reduced CAC by 12% while maintaining growth
• Confidence: Finance could now produce accurate contribution-margin reports for the board and investors

StyleCo went from reactive reporting to proactive insight. That shift enabled better decisions and faster growth.

Implementation Timeline and Effort

A typical D23.io implementation for a DTC brand takes 4–8 weeks, depending on complexity:

Week 1: Data discovery, requirements gathering, semantic layer design Week 2: Data ingestion setup, connector configuration, initial ETL Week 3: Data transformation, semantic layer build, data quality validation Week 4: Dashboard development, access control setup, documentation Week 5–8: Testing, refinement, team training, ongoing support

You’ll need participation from:

• Finance: To define metrics, validate data, and confirm calculations
• Marketing: To explain channel attribution and campaign structure
• Product/Operations: To understand customer segments and business logic
• Engineering: To provide database access and handle infrastructure

Most DTC brands allocate 4–6 hours per week of internal time. PADISO handles the heavy lifting: architecture, connectors, transformations, dashboards, and training.

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Security, Compliance, and Governance

Why DTC Brands Need Governed Data

As DTC brands scale, data governance becomes critical. You’re storing customer email addresses, payment information, purchase history, and behavioural data. You have compliance obligations (GDPR, CCPA, Australian Privacy Act). You may need to pass SOC 2 or ISO 27001 audits to work with enterprise partners or raise capital.

D23.io is built with governance from day one. This isn’t an afterthought; it’s foundational.

Access Control and Role-Based Permissions

D23.io implements role-based access control (RBAC). You define roles (finance, marketing, product, executive) and assign permissions:

• Finance: Can see all dashboards, including customer-level data for audit purposes
• Marketing: Can see acquisition cost, ROAS, and cohort performance, but not individual customer email or payment data
• Product: Can see product performance and repeat purchase metrics, but not customer acquisition cost
• Executive: Can see executive summary dashboards with aggregated metrics

This granularity is crucial. It ensures teams have the data they need without exposing sensitive information unnecessarily.

Data Lineage and Audit Trails

D23.io tracks data lineage: where each piece of data comes from, how it’s transformed, and where it’s used. When you look at a dashboard, you can trace back to the source system. When someone queries contribution margin, you can see the exact SQL that calculated it.

This matters for compliance. When an auditor asks “How is contribution margin calculated?” you can show them the exact formula, the data sources, and when it was last updated.

Encryption and Security

D23.io implements encryption in transit (TLS) and at rest. Connections to source systems use secure credentials (API keys, OAuth) stored in encrypted vaults. Data in the warehouse is encrypted. Access logs are maintained for audit purposes.

If you need SOC 2 Type II or ISO 27001 certification, D23.io provides the documentation and controls needed to pass audits. PADISO can guide you through the process via Security Audit (SOC 2 / ISO 27001) services.

Data Retention and Privacy

D23.io respects data retention policies. You can configure automatic deletion of customer data after a certain period (e.g., 2 years after last purchase). You can implement GDPR right-to-be-forgotten workflows. You can anonymise data for analysis while preserving utility.

For DTC brands handling customer data, this is essential. You want to comply with privacy regulations without losing the ability to analyse cohort behaviour or measure retention.

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Measuring ROI and Business Impact

The Financial Case for D23.io

D23.io isn’t free, but it pays for itself quickly. Here’s how to think about ROI:

Cost of D23.io: $3,000–$8,000 per month depending on data volume and complexity, plus initial implementation ($15,000–$50,000).

Cost of alternatives:

• Hiring a full-time data engineer: $120,000–$180,000 per year
• Using multiple analytics platforms (Tableau, Looker, etc.): $2,000–$5,000 per month per tool
• Manual reporting and spreadsheet maintenance: 10–15 hours per week of analyst time

ROI drivers:

1. Reduced operational overhead: If D23.io saves your team 8 hours per week of reporting work, that’s $20,000+ per year in salary cost (at $50/hour blended rate).

2. Faster decision-making: If unified data lets you optimize ad spend 2 weeks faster, and that optimization saves 5% on CAC, that’s tens of thousands of dollars on a $500K annual ad budget.

3. Better cohort economics: If D23.io reveals that your January cohort has 40% higher LTV than February, you can adjust product mix, pricing, or retention strategy to capture that value.

4. Reduced churn in analytics: When your team can answer their own questions instead of waiting for analysts, they stay engaged and make better decisions.

Typical ROI timeline: 3–6 months. Most DTC brands see positive ROI within one quarter.

Key Metrics to Track

Once you implement D23.io, track these metrics to measure impact:

Operational metrics:

• Hours per week spent on reporting (should drop 60–80%)
• Time to answer a business question (should drop from days to hours)
• Number of dashboards and reports (should increase as teams self-serve)

Business metrics:

• Contribution margin % (should improve as you optimize based on better data)
• CAC by channel (should become more accurate and actionable)
• LTV:CAC ratio (should improve as you focus on profitable cohorts)
• Time to optimize campaigns (should drop from weeks to days)

Strategic metrics:

• Data-driven decisions made per month (should increase)
• Cross-functional alignment (marketing and finance should agree on metrics)
• Confidence in financial reporting (board and investor confidence should improve)

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Getting Started with D23.io

Assessing Your Readiness

Before implementing D23.io, ask yourself:

1. Do you have clean data? Your source systems (Shopify, Klaviyo, etc.) should have reasonably clean, consistent data. If you have 50% missing COGS or inconsistent product naming, you’ll need to clean data first.

2. Do you have buy-in? Finance, marketing, and operations should agree that unified data is a priority. If one team thinks current reporting is fine, implementation will stall.

3. Do you have the right questions? You should be able to articulate 5–10 questions that unified data would help you answer. If you’re not sure what you’d do with better data, start with a data audit.

4. Do you have budget? D23.io costs $3,000–$8,000 per month plus implementation. If that’s not in your budget, it’s not the right time.

The Implementation Process

If you’re ready, here’s how to get started:

Step 1: Discovery call (1 hour)

Talk to the PADISO team about your data sources, business questions, and goals. We’ll assess complexity and give you a rough timeline and budget.

Step 2: Proposal and scope (1 week)

We’ll send a detailed proposal outlining the semantic layer, dashboards, timeline, and cost. We’ll align on success metrics.

Step 3: Data audit (1 week)

We’ll connect to your source systems and audit data quality. We’ll identify any cleaning needed before implementation.

Step 4: Design and build (4–6 weeks)

We’ll build connectors, transform data, create the semantic layer, and develop dashboards. You’ll provide feedback at each stage.

Step 5: Testing and training (1–2 weeks)

We’ll validate data accuracy, test dashboards, and train your team to use Superset independently.

Step 6: Launch and support (ongoing)

We’ll monitor the system, handle schema changes, and support new dashboard requests. Most clients have a monthly retainer for ongoing support.

Questions to Ask PADISO

When evaluating a D23.io implementation partner, ask:

1. What’s your experience with DTC brands? Look for specific examples and case studies.

2. How do you handle data quality? Ask about their process for cleaning and validating data.

3. What’s included in implementation? Clarify scope: connectors, transformations, dashboards, training, ongoing support.

4. How long does it take? Typical implementations are 4–8 weeks. If someone promises faster, ask why.

5. What happens after launch? Ask about ongoing support, SLA, and cost structure.

6. Can you help with compliance? If you need SOC 2 or ISO 27001, ask whether they can guide you through that process.

PADISO has implemented D23.io for multiple DTC brands and can walk you through our process, timeline, and pricing.

Next Steps

If you’re a DTC brand struggling with fragmented data, here’s what to do:

1. Audit your current state: List all your data sources (Shopify, Klaviyo, Meta, Google, etc.) and how you currently report on them.

2. Define your questions: What decisions are you making slowly or with incomplete information? What would you do differently if you had real-time, unified data?

3. Assess your readiness: Do you have clean data? Do you have buy-in from finance, marketing, and operations? Do you have budget?

4. Talk to PADISO: Schedule a discovery call to discuss your situation, timeline, and goals. We’ll give you honest feedback on whether D23.io is right for you.

D23.io isn’t a magic solution. But for DTC brands that are ready—that have clean data, aligned teams, and clear questions—it’s transformative. It moves you from “we have lots of data” to “we understand our data and act on it.”

That shift is what separates fast-growing DTC brands from those that plateau.

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Conclusion

Direct-to-consumer brands operate in an environment of relentless data creation and intense competition. You need every advantage: faster decisions, clearer insights, better unit economics. Fragmented data—spread across Shopify, Klaviyo, Meta, Google, and a dozen other tools—is a handicap you can’t afford.

D23.io consolidates that fragmentation into a single source of truth. It brings your customer, transaction, and marketing data into a unified semantic layer where every stakeholder speaks the same language. It turns contribution margin from a spreadsheet calculation into a real-time metric available to everyone. It reduces decision latency from days to hours and operational overhead by 60–80%.

For DTC brands scaling from $1M to $100M+ in revenue, D23.io is the data foundation that enables growth. It’s the difference between reacting to what happened last week and proactively optimising what happens next week.

If you’re ready to move from fragmented data to unified insight, talk to PADISO. We’ve helped DTC brands like StyleCo consolidate their data stack, build contribution-margin reporting, and make faster, better decisions. We can do the same for you.

Your single source of truth is waiting. Let’s build it.