Omnichannel Sales

Overview: The Omnichannel Sales explore provides a comprehensive view of weekly sales performance across all sales channels—both e-commerce and retail—including all markets and product lines, without filtering【26†Description】. It is an exhaustive dataset meant for in-depth analysis of omnichannel trends. This explore includes all sales data, even duplicative or competitive data, making it useful when you need the full picture of sales across channels and markets (including syndicated retail markets). In contrast, the Company Performance explore applies filters for a high-level snapshot; Omnichannel Sales is the unfiltered counterpart【26†Description】.

What’s Included in Omnichannel Sales

• All Sales Channels Combined: The explore aggregates data from every sales source:

  • DTC e-commerce platforms (Shopify, Magento, etc.)

  • Marketplaces (Amazon Seller/Vendor, etc.)

  • Retailer sales from syndicated data (e.g., SPINS, Nielsen, Circana) or portal data.

  • Wholesale shipments if applicable.

• No Owned-Brand Filter: Unlike Company Performance, this explore does not remove competitor product sales or duplicated channels. For example:

  • If syndicated data includes category sales or competitor products, those records are present here.

  • If the same sale is captured via two systems (maybe a retailer EDI and a third-party report), those might both appear unless deduped upstream.

• Time Grain: Weekly sales metrics (similar to Company Performance) – revenue and units by week for each combination of channel and product.

• Granularity: Because it’s comprehensive, you can likely break it down by product, by channel, by market, etc. It likely has dimensions for:

  • Channel (or source).

  • Product (SKU, category).

  • Market (if syndicated data includes multiple markets like Total US MULO, xAOC, etc., those could be dimension values).

• Measures: Weekly Net Sales, Units, maybe Orders (depending on data sources, retail POS typically doesn’t have “orders” concept, just sales).

• Possibly measure for Duplicate Sales Indicator: Not explicitly, but as a user you need to be aware some data might be overlapping. The documentation notes that “Omnichannel Sales - Total Company applies filters… for a high-level snapshot”【26†Description】, implying here we have the raw version.

Use Cases for Omnichannel Sales

• Deep Dive Analysis: When you need to analyze sales including external market data and do comparisons. For instance, calculating your brand’s share within a category — you’d use total category sales (including competitors) from syndicated sources in this explore versus your sales.

• Verifying Total Company Numbers: If you suspect the filters in Company Performance exclude something you actually want to consider, you can cross-check with Omnichannel Sales. It’s essentially a “truth source” of everything.

• Data QC and Reconciliation: Because it’s unfiltered, data teams might use this to ensure all sources are accounted for. If Company Performance shows X dollars and Omnichannel Sales shows X+Y, that Y might be known duplicates or competitor data.

• Category and Market Trends: Since competitor sales are in here, you can look at total market trend. For example, if Omnichannel Sales (for a category) is flat but your own sales are up, you’re gaining share.

Differences from Company Performance

• Filters: Company Performance excludes:

  • Duplicative channels (like syndicated market data that overlaps with direct retail data).

  • Competitor products/sales.

  • The description explicitly says Omnichannel Sales includes all markets and product lines, whereas Company Performance filters duplicates and competitor for a clean snapshot【26†Description】.

• Naming in Changelog: This explore might have previously been a backend or staging table; the CSV “Old Name (fka)” column mentions “A staging table used in Omni model sql”【26†Old Name】. Now it’s exposed as “Omnichannel Sales”. It suggests this is a raw combined table.

• When to use which:

  • Use Company Performance for reporting official internal numbers for your brand (since it’s cleaned of extraneous data)【26†Description, note】.

  • Use Omnichannel Sales for analysis, especially when context of market or duplicates is needed, or when developing new metrics that may require seeing all data before deciding to filter.

Practical Example:

Suppose your brand sells in two major retail channels that both report into Daasity:

• Retailer A provides store-level sales (which your system ingests).

• Nielsen provides a category report in which your brand’s sales at Retailer A might also appear as part of total market.

  In Omnichannel Sales, your Retailer A sales could appear twice (once from Retailer A source, once from Nielsen category). In Company Performance, one of those is removed to not double count. So, if doing a market share analysis, you might use Nielsen’s category total from Omnichannel Sales (which includes your brand and competitors), but use Company Performance for your own brand’s number to avoid double count, or carefully subtract if needed. It can get nuanced; this explore is where you’d see the raw figures to make those decisions.

Dimensions & Measures Likely Present

• Week (Week or date).

• Channel/Source (e.g., “Shopify”, “Amazon Seller Central”, “Nielsen – Total US MULO”, etc.).

• Product (Name, SKU, category).

• Sales Units.

• Net Sales.

• Possibly Gross Sales, Returns etc if included from different channels (though those might be unified in metrics).

• If syndicated data has fields like “Market” or “Competitive Flag”, those could be present (to filter competitor vs own brand, if needed).

• Owned vs Competitor: Perhaps a flag indicating if the product is your brand or not. If present, you could filter Owned = True to mimic Company Performance. If not present, then the separation is likely only by known product lists.

Best Practices:

• If using this explore for broad analysis, be cautious of double-counting. Know your data sources and overlaps. For example, if you aggregate all channels, know that some channels might already be subsets of others. Often, syndicated “market” is the superset that includes many retailers, whereas company direct data is granular. Combining without filtering essentially layers data.

• Use this explore when building custom models or advanced analytics that require everything first, then filter in logic rather than relying on pre-filtered explores.

• Document any assumptions. E.g., “Omnichannel Sales total minus competitor sales equals Company Performance” should theoretically hold true. You can validate that with this explore.

References:

• Daasity Metadata: “Weekly sales performance across all sales channels (ecommerce and retail)… includes all markets and product lines for in-depth analysis, whereas … Total Company applies filters to remove duplicate channels and competitor products…” – confirms scope.

• Daasity Changelog: Noting the standardized naming, likely referencing this as new name “Omnichannel Sales” (there may not have been a user-facing explore before; it was internal, now exposed).

• Possibly any internal documentation for Omni model (not directly given, but context implies this is core to Omni model SQL).