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  --color-white: 0 0% 100%;
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  --color-dema-blue-700: 225 100% 37%;
  --color-dema-blue-600: 224 100% 46%;
  --color-dema-blue-500: 224 98% 55%;
  --color-dema-blue-100: 219 100% 91%;
  --color-dema-blue-50: 213 100% 96%;
  --color-dema-red: 7.42 81.58% 55.29%;
  --color-neutral-50: 0 0% 98%;
  --color-neutral-100: 0 0% 96.1%;
  --color-neutral-200: 0 0% 89.8%;
  --color-neutral-300: 0 0% 83.1%;
  --color-neutral-400: 0 0% 63.9%;
  --color-neutral-500: 0 0% 45.1%;
  --color-neutral-600: 0 0% 32.2%;
  --color-neutral-700: 0 0% 25.1%;
  --color-neutral-800: 0 0% 14.9%;
  --color-neutral-900: 0 0% 9%;
  --color-neutral-950: 0 0% 5.9%;
  --color-slate-50: 210 40% 98%;
  --color-slate-100: 210 40% 96.1%;
  --color-slate-200: 214.3 31.8% 91.4%;
  --color-slate-300: 212.7 26.8% 83.9%;
  --color-slate-400: 215 20.2% 65.1%;
  --color-slate-500: 215.4 16.3% 46.9%;
  --color-slate-600: 215.3 19.3% 34.5%;
  --color-slate-700: 215.3 25% 26.7%;
  --color-slate-800: 217.2 32.6% 17.5%;
  --color-slate-900: 222.2 47.4% 11.2%;
  --color-slate-950: 222.2 47.4% 7.2%;
  --color-zinc-50: 0 0% 98%;
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  --color-zinc-300: 240 4.9% 83.9%;
  --color-zinc-400: 240 5% 64.9%;
  --color-zinc-500: 240 3.8% 46.1%;
  --color-zinc-600: 240 5.2% 33.9%;
  --color-zinc-700: 240 5.3% 26.1%;
  --color-zinc-800: 240 3.7% 15.9%;
  --color-zinc-900: 240 5.9% 10%;
  --color-zinc-950: 240 10% 4%;
  --color-blue-50: 213.8 100% 96.9%;
  --color-blue-100: 214.3 94.6% 92.7%;
  --color-blue-200: 213.3 96.9% 87.3%;
  --color-blue-300: 211.7 96.4% 78.4%;
  --color-blue-400: 213.1 93.9% 67.8%;
  --color-blue-500: 217.2 91.2% 59.8%;
  --color-blue-600: 221.2 83.2% 53.3%;
  --color-blue-700: 224.3 76.3% 48%;
  --color-blue-800: 225.9 70.7% 40.2%;
  --color-blue-900: 224.4 64.3% 32.9%;
  --color-blue-950: 223.8 62% 20.8%;
  --color-blue-1000: 226 60% 12%;
  --color-red-50: 0 86% 97%;
  --color-red-100: 0 93% 94%;
  --color-red-200: 0 96% 89%;
  --color-red-300: 0 94% 82%;
  --color-red-400: 0 91% 71%;
  --color-red-500: 0 84% 60%;
  --color-red-600: 0 72% 51%;
  --color-red-700: 0 74% 42%;
  --color-red-800: 0 70% 35%;
  --color-red-900: 0 63% 31%;
  --color-red-950: 0 75% 15%;
  --color-green-50: 138.5 76.5% 96.7%;
  --color-green-100: 140.6 84.2% 92.5%;
  --color-green-200: 141 78.9% 85.1%;
  --color-green-300: 141.7 76.6% 73.1%;
  --color-green-400: 141.9 69.2% 58%;
  --color-green-500: 142.1 70.6% 45.3%;
  --color-green-600: 142.1 76.2% 36.3%;
  --color-green-700: 142.4 71.8% 29.2%;
  --color-green-800: 142.8 64.2% 24.1%;
  --color-green-900: 143.8 61.2% 20.2%;
  --color-green-950: 143.8 61.2% 10.2%;
  --color-yellow-50: 54.5 91.7% 95.3%;
  --color-yellow-100: 54.9 96.7% 88%;
  --color-yellow-200: 52.8 98.3% 76.9%;
  --color-yellow-300: 50.4 97.8% 63.5%;
  --color-yellow-400: 47.9 95.8% 53.1%;
  --color-yellow-500: 45.4 93.4% 47.5%;
  --color-yellow-600: 40.6 96.1% 40.4%;
  --color-yellow-700: 35.5 91.7% 32.9%;
  --color-yellow-800: 31.8 81% 28.8%;
  --color-yellow-900: 28.4 72.5% 25.7%;
  --color-yellow-950: 28.4 72.5% 15.7%;
  --color-orange-50: 33.3 100% 96.5%;
  --color-orange-100: 34.3 100% 91.8%;
  --color-orange-200: 32.1 97.7% 83.1%;
  --color-orange-300: 30.7 97.2% 72.4%;
  --color-orange-400: 27 96% 61%;
  --color-orange-500: 24.6 95% 53.1%;
  --color-orange-600: 20.5 90.2% 48.2%;
  --color-orange-700: 17.5 88.3% 40.4%;
  --color-orange-800: 15 79.1% 33.7%;
  --color-orange-900: 15.3 74.6% 27.8%;
  --color-orange-950: 15.3 74.6% 17.8%;
  --color-text-gradient-from: 209 92% 69%;
  --color-text-gradient-to: 245 97% 63%;
  --bg-color: 250 250 250;
  --rounded-xl: 8px;
  --rounded-xl: 4px;
}

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const dataFieldDefinitions = {
  account: {
    type: 'string',
    name: 'account',
    description: 'Identifies the account associated with the cost.',
    optional: false,
    dataWarehouseDefinition: 'ACCOUNT_ID',
    platformDefinition: 'Account identifier used in marketing costs.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  adId: {
    type: 'string',
    name: 'adId',
    description: 'Unique identifier for the advertisement.',
    optional: false,
    dataWarehouseDefinition: 'AD_ID',
    platformDefinition: 'Advertisement identifier for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  adName: {
    type: 'string',
    name: 'adName',
    description: 'Name of the advertisement.',
    optional: false,
    dataWarehouseDefinition: 'AD_NAME',
    platformDefinition: 'Name of the advertisement for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  adsetId: {
    type: 'string',
    name: 'adsetId',
    description: 'Identifier for the ad set.',
    optional: false,
    dataWarehouseDefinition: 'ADSET_ID',
    platformDefinition: 'Ad set identifier for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  adsetName: {
    type: 'string',
    name: 'adsetName',
    description: 'Name of the ad set.',
    optional: false,
    dataWarehouseDefinition: 'ADSET_NAME',
    platformDefinition: 'Ad set name for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  campaignId: {
    type: 'string',
    name: 'campaignId',
    description: 'Unique identifier for the campaign.',
    optional: true,
    dataWarehouseDefinition: 'CAMPAIGN_ID',
    platformDefinition: 'Campaign identifier for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  campaignName: {
    type: 'string',
    name: 'campaignName',
    description: 'Name of the campaign.',
    optional: true,
    dataWarehouseDefinition: 'CAMPAIGN_NAME',
    platformDefinition: 'Campaign name for marketing analysis.',
    inInventory: false,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: true,
  },
  // Repeating this pattern for all specified fields...

  // Inventory Fields
  brand: {
    type: 'string',
    name: 'brand',
    description: 'Product brand.',
    optional: false,
    dataWarehouseDefinition: 'BRAND',
    platformDefinition: 'Brand name of the product.',
    inInventory: true,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: false,
  },
  category: {
    type: 'string',
    name: 'category',
    description: 'Product category.',
    optional: false,
    dataWarehouseDefinition: 'CATEGORY',
    platformDefinition: 'Category classification of the product.',
    inInventory: true,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: false,
  },
  cogs: {
    type: 'number',
    name: 'cogs',
    description: 'Cost of goods sold.',
    optional: false,
    dataWarehouseDefinition: 'COGS',
    platformDefinition: 'Cost associated with the sale of a product.',
    inInventory: true,
    inOrder: false,
    inIndelivery: false,
    inMarketingCost: false,
  },
};

module.exports = { dataFieldDefinitions };

Bigquery

Dema.ai documentation

Discover how Dema collects, processes, and enriches your ecommerce data—from real-time events and orders to marketing costs, shipping rules, and returns. See how each data point comes together to power near real-time profitability insights, including Gross Profit 2 (GP2) costs like toll, pick & pack, and transaction fees.

Our core data model

Discover the key performance metrics that Dema tracks and calculates to empower data-driven decisions. This section explains what each metric represents, how it’s calculated, and why it matters. You can use these metrics to uncover growth opportunities, optimize spend, and monitor profitability in near real-time.

Metrics

Learn about the dimensions in Dema that let you slice, segment, and analyze your data across different categories (channel, country, product, etc.). These dimensions are automatically populated from your tracking script, ecommerce platform, or marketing integrations, giving you granular insights into user behavior and performance.

Dimensions

Understand how Dema attributes conversions to specific channels or campaigns, helping you pinpoint which marketing efforts drive results. Learn about our supported models—last-click, linear, ML-based multi-touch, and ad platform-reported—and discover how to interpret attribution data.

Attribution

Learn how Dema’s tracking script collects and processes your ecommerce data in near real-time. Understand which events are captured by default, how sessions are created (similar to GA Universal), and how to install Dema on Shopify, GTM, or via a server-side integration.

Tracking

Quickly invite team members to Dema and assign roles to streamline collaboration across your organization.

1. Invite your team

Learn how to install the Dema tracking script on various platforms like Shopify, custom sites, and Google Tag Manager. Verify your events are coming in and troubleshoot common issues.

2. Add pixel (Tracking script)

Learn how to setup UTM parameters effectively for seamless integration with Dema.

3. Setup UTM parameters

Learn how to map UTM parameters to the correct channels and channel groups in Dema to ensure accurate attribution and cost mapping.

4. Create UTM mapping

Mix and match ecommerce platforms, sFTP, and data warehouses to sync orders, inventory, and returns seamlessly with Dema.

5. Integrate orders and inventory

Connect Meta, Google Ads, TikTok, and more to track your marketing spend seamlessly in Dema.

6. Integrate marketing spend

Learn how to configure rules-based fulfillment and return costs for shipping, toll, pick & pack, and transaction fees, along with return thresholds and fallback margins.

7. Set up hierarchical costs and returns

Learn how to apply filters to your reports in Dema, refine your data, and focus on what matters most using dimension and metric filtering.

How filters work in reports

Learn how to customize and apply attribution models in Dema reports to better analyze and distribute your revenue data.

Setting your attribution model

Learn how to invite manage members to Dema, assign roles, and manage access levels for seamless collaboration across your organization.

Manage users

Learn how to set up and manage teams in Dema to streamline navigation, restrict data access, and simplify reporting.

Creating and managing teams

Learn how to set up and use labels in Dema to organize, find, and filter reports with ease.

Using labels to organize reports

Reset password

Learn how to set up and use Google SSO for secure and streamlined access to Dema.

Single Sign-On (SSO) with Google

Learn how Dema’s Marketing Mix Modeling uses profitability-focused models to optimize your marketing spend and maximize profit.

How MMM works in Dema

Learn how to interpret Dema’s MMM overview view.

Overview view

Learn how to interpret Dema’s MMM weekly analysis view based on historical data—and when to consider a future scenario for upcoming seasonality.

Weekly analysis view

Learn how to interpret Dema’s MMM yearly overview.

Yearly overview view

Learn how to use Dema’s Future scenario tool to plan upcoming budgets, optimize for profit targets, and test different marketing strategies.

Creating a future scenario

Learn how to create product sets in Dema to segment your catalog and export custom labels for Google and Meta feeds.

Create product sets

Learn how to manually override product sets directly from reports in Dema, giving you precise control over your feed segmentations.

Override product sets from reports

Learn how Dema's incrementality testing isolates the true impact of your marketing campaigns on both sales and profit.

How incrementality testing works in Dema

Learn how incrementality testing isolates true marketing impact and enhances MMM.

What is incrementality

Learn how to design and structure incrementality experiments in Dema to measure true business impact.

Structuring tests

Learn how to interpret the suggested experiment table in Dema and what each metric represents.

Analyze a suggested experiment

Learn how to interpret key metrics, graphs, and insights from Dema's incrementality test results.

Understand test results

Learn how to run multiple 2-cell experiments concurrently in Dema and understand the risks and best practices.

Concurrent tests

Discover how Dema uses a post-treatment window to capture delayed marketing effects and provide a complete picture of your campaign's performance.

Post-treatment window

Learn how Dema adjusts for seasonality in incrementality testing to ensure accurate measurement of marketing impact.

Seasonality effects

Understand the differences between platform conversion lift studies and Dema's geo-based incrementality tests, and why Dema provides a more reliable, neutral measurement.

Platform lift studies vs Dema

Key terms and concepts used in Dema's incrementality testing documentation.

Terminology

How to set up tracking with single page applications

Set up tracking with single page applications

How to do create custom metrics/dimensions

Custom metrics/dimensions

Upload old orders from CSV

Welcome

Documentation

Integrations

Blog

Changelog

Roadmap

Login

Support

Guide on integrating Shopify with our platform.

Shopify

Guide on integrating commercetools with our platform.

Commercetools

Guide on integrating Meta Ads data with our platform.

Meta Ads

Guide on integrating Google Ads data with our platform for enhanced advertising insights.