How do I handle columns with no direct equivalent?

Three options: 1) ignore the column if not useful, 2) map it to a Shopify metafield or PrestaShop feature to preserve it, 3) use it as a source for a transformation (e.g. concatenate 3 attribute columns into a single description).

How do I transform a value during mapping (e.g. convert ex-tax to inc-tax)?

In Excel/Sheets: a formula in an intermediate column (=B2*1.2 for 20% VAT). In Seegea: a visual transformation rule (e.g. Multiply by 1.2, Round to 2 decimals). In Python: a transformation function in the mapping script.

What is the hardest mapping case in practice?

Variants. A supplier file may have one row per product with Size1, Size2, Size3 and Price1, Price2, Price3 columns. Shopify expects one row per variant. This 'wide to long' mapping requires a structural transformation, not just column correspondence.

Does Seegea do the mapping automatically?

Seegea offers semi-automatic mapping: it detects probable columns by name similarity (Product name → Title, Reference → SKU) and pre-fills them. You validate and adjust errors. For complex transformations (wide to long, calculations), configurable rules are available without code.

Product CSV mapping: match the right columns to the right fields

CSV mapping is the forgotten step in e-commerce import guides. We talk about format, encoding, separator — but rarely about the correspondence between source file columns and CMS target fields. Yet that is where 60% of post-import errors originate.

Mappable data sources

The most common column correspondences

Typical supplier column	Shopify field	PrestaShop field
Product name / Label	Title	Name *
Long description	Body (HTML)	Description
Short description	Tags or metafield	Short description
Reference / Part number	Variant SKU	Reference
Barcode / EAN13	Variant Barcode	EAN13
Main category	Product Category	Default category (ID)
Supplier price excl. tax	Variant Price (× tax)	Price (excl. tax)
Stock / Available qty	Variant Inventory Qty	Quantity
Main image URL	Image Src	Image URL
Brand / Manufacturer	Vendor	Manufacturer (ID)

3 types of transformation during mapping

Value transformation

Format conversion (ex-tax → inc-tax), case normalization (ALL CAPS → Title Case), character cleanup (removing extra spaces, double quotes). Applied to an individual column.

Structural transformation

File reorganization: one product row with 3 size columns → 3 variant rows (wide to long). Or the reverse: multiple variant rows → one product row with option columns. More complex, often requires a script or Seegea.

Enrichment during mapping

Generate a Handle column from the Title (slugification), create an SEO Title by combining columns, or compute a compare-at price from supplier price + margin. Seegea does this with no-code rules.

Classic failed mapping: a supplier column contains both color and size in a single field (e.g. "Red-M"). Shopify expects two separate columns (Option1 Value = Red, Option2 Value = M). This split must happen before import, ideally via an Excel formula or a Seegea rule.

Documenting your mapping: an investment that pays off

If you manage multiple suppliers with different CSV schemas, document each mapping in a reference table. This avoids redoing the same work on every new supplier catalog delivery.

Seegea saves mappings per supplier. The next time you receive a CSV from the same supplier, Seegea automatically applies the previous mapping and only flags new columns or anomalous values.

Built in France between Annecy and Chantilly, Seegea makes CSV mapping accessible to the whole team — not just developers.

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FAQ

CSV mapping is the operation that makes columns from your source file (supplier, ERP, old CMS) correspond to the fields expected by Shopify or PrestaShop. For example: the supplier column 'Product name' → Shopify field 'Title', or 'Price excl. tax' → 'Variant Price' (with tax transformation).

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