Skip to content
DataNexx

Data category

Retail & E-commerce Data

Catalogs, prices and promotions, inventory, returns with reasons and forecasts compared with actual sales.

Evaluate Your Data Request this type of data

Retail data for AI is the record of how products, prices and promotions actually performed: catalogs, aggregated sales, stock-outs, returns with reasons and forecasts compared with real demand. AI teams use it for demand forecasting, product understanding and shopping assistants. The misses matter as much as the hits. DataNexx helps retailers and brands license these records with customer identities removed.

Why it matters for AI

Recorded, judged, resolved.

Retail records show how products, prices and promotions actually performed — including the forecasts that missed and the items that came back. Customer identities are removed and transactions are aggregated before licensing.

Typical data

  • Product catalogs & attributes
  • Product images
  • Aggregated sales transactions
  • Prices & promotion results
  • Inventory & stock-outs
  • Returns with reasons
  • Reviews & support conversations
  • Forecast vs actual sales

Representative workflow

Illustrative

  1. 01Product listing
  2. 02Price or promotion
  3. 03Demand forecast
  4. 04Orders
  5. 05Fulfillment
  6. 06Return or review
  7. 07Actual sales vs forecast

Who typically holds it

  • Retailers and online stores
  • Marketplace sellers and brands
  • Wholesalers and distributors
  • Fulfillment providers

Potential AI applications

  • Demand forecasting
  • Product-attribute extraction
  • Pricing and promotion models
  • Shopping and support assistants

Which systems hold retail and e-commerce data?

The product information system (PIM) holds catalogs, attributes and images. The order management system (OMS) holds orders, fulfilment and returns. The point-of-sale and e-commerce platforms hold transactions. Planning tools hold forecasts, prices and promotion calendars.

The useful link runs from plan to result. A forecast, the promotion that ran and the sales that followed form one example. Store-level data adds local context, such as weather, events and nearby competition.

  • Product attributes and images from the PIM
  • Aggregated sales by product, store and week
  • Promotion calendars with the measured lift
  • Returns with the stated reason

What do AI teams build with retail data?

Retail AI datasets built from these records serve several uses. Teams train demand forecasting on years of sales, including stock-outs and missed forecasts. They train attribute extraction on clean catalog data and images. They build pricing and promotion models from promotions with known results.

Shopping and support assistants also learn from returns. A return reason tells a model what customers expected and did not get.

Wholesalers and distributors add a different view. Their order books show demand from shops, not shoppers, and they often reach further back in time.

How is customer data protected in retail datasets?

Retail systems hold a lot of personal data: names, addresses, order histories and loyalty profiles. These are removed, and transactions are aggregated before licensing. Card numbers fall under the PCI Data Security Standard and are never included.

Reviews and support messages need care too. They can contain names and personal stories, so only scoped, de-identified excerpts are considered. Product images may belong to a brand or a photographer, so rights are checked first.

How does a retailer prepare a first description?

A short description is enough to start. No sales files are shared.

  • Product categories and channels: stores, web or marketplaces
  • Number of years of sales and forecasts, roughly
  • Which systems hold catalogs, orders and returns
  • Who owns the product images

Example record (illustrative)

Illustrative record: a fictional, aggregated promotion result. Not a real retailer.
FieldWhat it holds
Product groupKitchen storage, mid-price range
PromotionTwo-week discount, featured on the home page
Forecast unitsPlanned weekly units, banded
Actual unitsSold weekly units, banded; stock-out on day nine
Return reasonsTop reasons: lid does not fit, smaller than expected
TransformationsNo customer data; sales aggregated by week and region

Frequently asked questions

What retail data is most useful for AI?

Records with outcomes: forecasts against actual sales, promotions with measured results and returns with reasons.

Is customer data included?

No. Customer identities are removed and transactions are aggregated. Card data is never included.

Can marketplace sellers take part?

Yes, for data they hold the rights to. Marketplace terms can limit how some reports are reused, so these are checked first.

Are product reviews licensable?

Sometimes, in scoped and de-identified form, if your terms allow it. Bulk review exports are not considered.

Does it cost anything to take part?

No. There is no fee to the data owner. Compensation comes from the licence with the AI company.

How quickly do you reply?

We reply within 24 hours. The first step is a short description of your catalog, channels and history.

Sources

Trust & compliance

Data licensing without losing control.

Your data stays yours until you decide otherwise. We assess before anything is shared, and we treat rights and privacy as conditions of a transaction — not afterthoughts.

No raw data for an initial evaluation

Nothing moves without authorization

Ownership is not the same as licensing rights

Specialist review where it is needed

Scoped records, never bulk exports

How we handle rights, privacy and confidentiality

Related data categories

Own valuable data?

Check your data's licensing potential.

A confidential, no-raw-data assessment of your company's records — from lab results and production logs to cases, claims and transactions.

Evaluate Your Data Book a call

Building AI?

You specify it. We source it.

Tell us the proprietary data your model needs — domain, structure, formats and rights. We approach the organizations that produce it.

Request a Dataset