Best CPG Datasets, Databases & APIs

What is CPG Data?

CPG (Consumer Packaged Goods) data is a sub-category of commerce data. It is information about any sales marketing actions a marketing team takes in the field and also refers to insights and sales predictions which are generated by predetermined datasets. It takes into account both product information and consumer behavior data which is relevant to retailers and producers and helps them maxmize their industry profits.

How is CPG Data collected?

CPG data is collected by compiling different other datasets to give a comprehensive overview of a company’s business. It collects consumer data from their online presence as well as important information such as product rating and reviews.
A CPG dataset will also take your company’s information. It will look at product availability as well as prices and promotions to make sure your profits are at their maximum.

What are the typical attributes of CPG Data?

There are certain different types of CPG data which are recorded in the CPG dataset:
Observational data - This administrational data such as inventory or availability of products. This can also refer to consumer data, such as customer demographics, purchase frequency and store/brand loyalty. It can additionally highlight where there was sales success/failure so you can improve your profits.
Activity data - Activity data refers to the specific actions your representatives take in the field, such as customer service. It analyzes the actions they take in store and the jobs they undertake so you can increase performance efficiency.
Sales Data - Finally, sales data gives sales insights such as how much of each product is sold at each store location and can be used to make sure that your brand is as profitable as possible. CPG datasets can provide sales trends and analysis of similar product sales as well as distribution statistics.

What is CPG Data used for?

CPG data is used by all retail businesses. It highlights product availability and price information to ensure that the business is running at its most profitable. It also provides an important look into customer behaviour and trends to help with marketing campaigns and promotional offers. This information can be used to increase brand visibility and perception to make sure that it is as successful as possible.

How can a user assess the quality of CPG Data?

The best CPG datasets will contain regularly updated information on your brands performance and inventory details. It is key to have as dataset that provides a comprehensive look into your businesses details so that you can make it as profitable as possible. Before buying consumer marketing data, always check the data provider’s reviews. Lastly, ask for a data sample before you buy from a provider to ensure that their CPG data is of the quality your business needs.

Where can I buy CPG Data?

Data providers and vendors listed on Datarade sell CPG Data products and samples. Popular CPG Data products and datasets available on our platform are Facteus’ US Consumer Payments - CPG (synthetic) by Facteus, Brandbook Consumer Panel - South African Market (inc. Retail, CPG, Restaurant, Pharma Data) by Brandbook, and Edison Trends CPG - Transaction Data from E-Receipts for USA, UK, Canada, Australia, India & Singapore by Edison Trends.

How can I get CPG Data?

You can get CPG Data via a range of delivery methods - the right one for you depends on your use case. For example, historical CPG Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time CPG Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to CPG Data?

CPG Data is similar to Pricing Data, Stock Keeping Unit (SKU) Data, Product Review Data, Special Offer & Promotion Data, and Fashion & Apparel Data. These data categories are commonly used for Transaction Data Enrichment.

What are the most common use cases for CPG Data?

The top use cases for CPG Data are Transaction Data Enrichment.