Retail Category Sales

**Overview**

CPG is an exciting space for analytics, but analysts, data engineers and data scientists at CPGs are often lacking easy-to-access and up-to-date data. The data needed does exist, but it's often locked away in clunky retailer reporting and file sharing systems that are hard to integrate with modern data stacks.

Crisp connects to over 40 leading U.S. retailers and distributors. Our self service data pipeline ingests a live-feed of data from retail vendor portals to bring you real-time data directly into Databricks that is normalized, reliable, and ready to use — so you never have to download a report again. Of course, Crisp also integrates modern retailer and distributor systems such as Walmart Luminate and UNFI Insights.

**Types of category sales data provided via Crisp to Retail Category Captains:**

- **Full Category Store.** Stores where the product is sold along with address and geographic information
- **Full Category Product.** Product description along with UPC (GTIN)
- **Full Category Quantity and Dollars.** Unit sales quantity and dollar amounts, often at the daily granularity

Not only does Crisp automate the ingestion of retailer and distributor data, we also can take care of cleaning and organizing the data so you can spend less time on data prep and more time on actual analysis.

**The Crisp Platform provides three different types of data, with different levels of pre-processing:**

**Source data.** For when you are developing a replacement for existing data pipelines and / or want to do your own data modeling. The source data schemata closely resembles what the upstream data source provides, so you can leverage any pre-existing modeling experience with this data.
**Normalized data.** For when you are developing retailer or distributor specific solutions and want to leverage source-specific attributes. The data model is a fully normalized schema and naming conventions are consistent across all data sources, making it easy to mix and match retailers in analyses.
**Harmonized data.** For when you are developing cross-retailer solutions, and it’s acceptable to use a narrower subset of data that is common across multiple data sources. The data model is built on the normalized model and combines common data elements, such as POS and inventory into a harmonized model across multiple retailers and distributors.

With Crisp, you can easily mix and match data from the models as you see fit.

**The applications of the data are vast, but some common use cases are:**

- **Cluster/segment stores.** Use sales performance across store locations to inform the creation of planogram clusters.
- **Optimize total category revenue.** Blend historic sales with forecasted sales for new products to optimize product placement throughout the retailer’s chain.
- **Optimize shelf placement and facing count.** Blend historic sales with planogram data to decide Which products will be a place where on the planogram and with how many facings.