Custom Attributes

Introduction to Creating and Managing Data Customizations

Summary: Every brand has unique data and needs, and Daasity recognizes that one size doesn’t fit all. This article introduces the ways you can customize your data in Daasity – from configuring attributes like channel mappings and product hierarchies, to incorporating your own data (via Brand Supplied Data or custom integrations), to more advanced transformations available to managed customers. We’ll explore what Brand Supplied Data (BSD) is and how to use it, how to map and organize your data attributes to fit your business, and how Daasity supports deeper customizations through code for those who need it. By leveraging these customization features, you can ensure the Daasity platform mirrors your business reality and provides accurate, business-specific insights.

What Are Data Customizations in Daasity?

“Data customizations” refer to adjustments or additions you make to the standard Daasity data model and configurations in order to better represent your business. Out of the box, Daasity will pull data from your sources and use default schemas and mappings (for example, default channel names from Shopify or default product categories from your catalog). But many companies want to customize:

• Attributes/Dimensions: e.g., grouping marketing channels into broader categories, defining product categories or hierarchies, labeling certain orders as “VIP” or “B2B” if they meet criteria, etc.

• Supplemental Data: e.g., adding your product cost data for margin analysis, uploading retail sales targets, or adding a list of orders that were wholesale (so they shouldn’t count as DTC revenue).

• Business Plans/Targets: e.g., uploading your sales and marketing forecasts (which we discuss in the forecasting article) to compare against actuals.

• Custom Transformations: e.g., maybe you need to adjust how returns are handled in the data or implement a custom customer segmentation that isn’t standard.

Daasity provides multiple tools for these needs. For most users, the primary method is via the Brand Supplied Data (BSD) sheet and related interfaces in the app. For advanced needs, Enterprise clients with managed services can have custom SQL code or transformations added.

Brand Supplied Data (BSD) Overview

Brand Supplied Data (BSD) is one of the key mechanisms for data customization. The BSD is essentially a Google Sheets workbook (or similar online sheet) connected to Daasity where you, the merchant, can input various types of reference data, mappings, and plan values. When you update the BSD sheet, those values flow into your Daasity data warehouse and get integrated into the dashboards.

To access your BSD sheet:

1. In the Daasity app, navigate to the Data section (or a section labeled Brand Supplied Data).

2. You’ll see tiles or tabs for different BSD categories (like Revenue Plan, Marketing Budget, Marketing Spend, Channel Mapping, SKU Mapping, etc., depending on your configuration).

3. Clicking a tile (e.g., Revenue Plan) will open the connected Google Sheet or interface where you can input data . You might need to have Google account access granted to Daasity for this.

Typical tabs included in BSD:

• Revenue Plan – for entering sales projections (daily/monthly by channel) .

• Marketing Budget – for entering planned marketing spend and efficiency targets by channel/vendor .

• Marketing Spend – for entering actual marketing spend data for any channels that are not automatically integrated (or overriding/adding to integrated data) .

• Channel Mapping – to customize how marketing channels or sources are grouped (e.g., mapping a list of campaign source values into standardized channels like Paid Search, Email, Organic Social, etc.).

• Other Order Sources – to classify orders that come from outside typical channels (for example, phone orders or wholesale orders that you still load into Shopify – you can mark those so they’re not counted as e-commerce).

• Discount Code Mapping – to categorize or label certain discount codes (e.g., flag all “Friends & Family” discounts).

• Products & Hierarchy – often includes tabs like SKU Mapping (to map raw SKU codes to product names or categories), SKU Hierarchy (to define category trees or collections), SKU Cost (to input cost of goods per SKU for margin calculations).

• Configuration – some sheets have a config tab where you list out allowed values for dropdowns (like defining what channels or vendors exist for use in other tabs).

Using BSD is straightforward: each tab will have instructions and required columns. For example, the Channel Mapping tab might have a column for “GA Source/Medium” and a column for “Channel Category” – you’d list out how each source/medium should roll up (e.g., facebook / cpc -> Paid Social). The key is not to alter the structure (don’t remove columns or change headers unless instructions say so) . Daasity’s system periodically reads this sheet (or you can trigger a refresh) to pull in the latest mappings or data. After updating BSD, you typically should see the effects in your dashboards after the next data load.

If you don’t have access to the BSD sheet or need it re-shared, contact [email protected] – since it’s often a Google Sheet, access issues can happen if someone was removed or if permissions need to be updated.

Custom Attributes and Mappings

Let’s dive a bit deeper into common attribute customizations and how to manage them:

• Marketing Channel Attribution (Channel Mapping): Digital marketing data often comes with sources, mediums, campaign names, etc., that might not be cleanly categorized by default. Daasity’s Channel Mapping BSD tab allows you to remap how sessions and orders are attributed to channels . For example, Google Analytics might classify some traffic as “(Other)” or mix paid social into referral. With channel mapping, you define rules such as “UTM Medium = cpc from facebook = Paid Social” or “Source contains newsletter = Email”. By maintaining this mapping, all your reports in Daasity (sales by channel, CAC by channel) will adhere to your channel definitions rather than raw source data. This is a powerful customization to get accurate marketing attribution.

• Discount Code Mapping: If you run many promotions, you might use naming conventions in coupon codes to infer campaign types (e.g., codes starting with “BF” are Black Friday promotions). In the Discount Code Mapping tab, you can classify promo codes into buckets like “Black Friday Sale”, “Welcome Offer”, etc. Daasity can then roll up metrics like discount usage or promotional sales by those categories. This helps you analyze performance by promo type, not just individual codes.

• Product and SKU Hierarchy: Many brands maintain their own product taxonomy (e.g., grouping SKUs into product families, categories, collections, etc.). Out-of-the-box, Daasity pulls product data from sources like Shopify which have their own categories or none at all. The SKU Mapping tab lets you map each SKU or product to your defined hierarchy. For instance, SKU 123 -> “Protein Powder” as Product Name, Category = “Supplements”, Subcategory = “Protein”. You might also map variant-level details or bundle relationships. With this in place, all reports can use your product categories for filtering and aggregation (like sales by category, etc.). Similarly, SKU Cost tab is where you put the cost for each SKU, enabling margin calculations. If these costs change over time, you update them here so that gross profit metrics remain accurate.

• Other Order Sources: If you occasionally import orders from sources outside your primary e-commerce (for example, you bulk-import wholesale orders into Shopify for record-keeping), those might inflate your DTC metrics. The “Other Order Sources” tab allows you to list identifiers (maybe a specific tag or source) for orders that should be considered “Other” (non-DTC). Daasity’s model can then exclude those from core DTC metrics or report them separately. Essentially, you’re customizing the definition of what counts as an e-commerce order vs an “Other” order.

All these mapping and attribute customizations are usually available to standard users via the BSD sheet or the Daasity UI under Data > Custom Attributes . They do not require coding – just data entry and understanding of your own business keys. Properly managing these ensures your dashboards speak your language (e.g., correct channel names, correct product groupings, etc.).

Incorporating Your Own Data (Custom Data Sources)

Beyond mappings and small reference data, you might have entirely new datasets you want to bring into Daasity. Examples:

• You have in-store retail sales data from a POS system that isn’t yet an official Daasity integration.

• You have a list of customer loyalty scores from another tool.

• You run a unique promotion and have an Excel sheet of the results to combine with sales.

Daasity allows custom data sources to be integrated usually through the Data File integration approach. You can upload or connect CSV/XLS files into Daasity (via SFTP, S3, email, or manual upload) . When set up, Daasity will ingest that file regularly and merge it into the data model.

How to add a data file integration:

• In the app, go to Data > Integrations > New Integration.

• Choose the “Data File (CSV, XLS)” option (it’s often listed as a source type alongside the APIs).

• Configure the data source (where will the file come from?). Supported sources include Amazon S3 buckets, SFTP servers, an Email inbox (Daasity can generate a unique email to send files to), or Daasity’s own storage if you want to upload via UI .

• Configure the file format and schedule: define the path or name of the file, how frequently to expect new files, and if there’s a date pattern in filenames (for incremental loads) .

• Field Mapping: You’ll map the columns of your CSV to the fields in Daasity’s table or a custom table. Daasity might provide templates or guidance for how to structure the data. For example, if uploading “store sales”, you’d map columns like Date, Store ID, Sales Amount, etc., to a Daasity schema or a custom schema you define.

• Save and run – Daasity will start pulling that data in.

Once in, your custom data can be used in Explores and dashboards. In many cases, bringing a custom data file might require Daasity team to create a custom explore or integrate it with existing ones. For instance, if you add a Loyalty Score per Customer, you might then ask Daasity to join that to the customer table so you can see it in customer analytics explores. Or if you upload Retail POS data, Daasity might enable a “Retail Sales” explore for you.

It’s clearly a more advanced customization, but it’s a crucial one – it means even if Daasity doesn’t natively have an integration for something, you can still include that data in your single source of truth.

Advanced Customizations (Managed Services & Code Repository)

For truly advanced needs, Daasity offers a Managed Data Service or Enterprise customizations that involve altering the code of the data model or adding new transformations:

• Custom SQL Transformations: Daasity’s pipelines transform raw data into analytics-ready tables. If you need a transformation that is unique (beyond what the standard pipeline does), the data team can implement custom SQL script. For example, maybe you have to allocate a certain cost across orders in a special way, or you maintain a special list of “House Accounts” that should be treated differently in customer metrics. Custom scripts can be inserted into the workflow to handle these cases. Daasity’s team would write and maintain this code for you under a services agreement.

• LookML Code Repository Access: Enterprise clients often get access to a Git repository containing the LookML (the semantic model for Looker). With this, your technical analysts or engineers can actually create Custom LookML – defining new measures, dimensions, explores, or even adjusting existing ones. This is the most technical level of customization and requires understanding of Looker’s modeling language. It offers ultimate flexibility: you could create entirely new data tables in Looker, write new metrics, or change labels. Daasity typically encourages using extension frameworks (so you’re adding on top of their base model rather than editing core files) to ensure you can still get updates to the base model without overwriting your changes . For example, you might extend the Orders explore to include a new dimension “Is VIP Order” based on some logic you have in a custom view file.

• Custom Data Marts or Schemas: In some cases, brands want to build totally custom schemas on the data – like a specialized set of tables for a very specific analysis (say, a machine learning scoring pipeline). Under managed services, Daasity can help set up additional schemas or data marts in your warehouse that feed from the core data. These would be unique to you and would likely come with custom explores.

All these advanced customizations are typically scoped and delivered by Daasity’s technical team. The key benefit is you get exactly what you need in the data model, at the cost of complexity and potentially additional service fees. For most day-to-day needs, the earlier discussed BSD and attribute mapping cover a lot of ground without needing to code.

Best Practices for Data Customization

• Use BSD for Configurable Data: If it’s something that can be handled with a simple mapping or a list (like channel mapping, or adding a missing value), BSD is your friend. It’s quick, and you can update it anytime when things change (e.g., new marketing channel emerges, just add a row for it in Channel Mapping).

• Document Your Customizations: Keep a log of what customizations you’ve done. For example, if you uploaded product costs in SKU Cost tab as of a certain date, note that so you know if margins change after that date, it might be because costs were updated. If you have a custom metric or mapping, write down the definition and reasoning so future team members understand the context.

• Validate After Changes: Whenever you change a mapping or upload new BSD data, check the next day’s data or run an Explore to ensure it had the intended effect. Did your “Facebook” channel now correctly group under “Paid Social” in the dashboard? If not, maybe the mapping rule needs tweaking. By validating, you catch issues early and ensure data accuracy.

• Work with Daasity Support: Don’t hesitate to reach out to Daasity’s support when doing a customization. They can confirm if what you want is achievable through BSD or if it requires a custom transform. They might also have best practices from other customers (for example, how to map channels effectively, or how to structure a custom data file for ingestion). Use their expertise so you’re not reinventing the wheel.

• Segregate Test Data: If you want to test a custom data import, perhaps do it with a small sample file first. Daasity can ingest that and you verify the structure, then proceed to regular full files. This avoids large-scale mistakes like mis-mapping columns or duplicating data.

In summary, Daasity’s platform is quite flexible. Out of the box, it works well for standard use cases, but the combination of BSD and custom integrations allows you to tailor it to your business. With proper data customizations, you ensure that your reports reflect reality – whether that’s aligning with how your internal teams categorize things, or including every bit of relevant data in the analysis. This is how you turn Daasity from a generic analytics tool into your company’s bespoke analytics hub.

Related Resources:

• Brand Supplied Data (BSD) Overview – detailed instructions for each BSD tab and how it feeds the model.

• Channel Mapping Guide – how to set up and troubleshoot channel attribution adjustments.

• Daasity Code Repository – overview of how the LookML code repository works for custom development (Enterprise).

• Data Flow Diagrams – see where in the pipeline certain BSD inputs influence the data (e.g., Channel Mapping affects the Channel Attribution Data Mart ).