By Samantha Broekert, PIM Specialist
Supplier onboarding: supplier data in production without copy-paste.
The sooner a new supplier becomes productive, the faster their range generates revenue. Yet, for many retailers and wholesalers, supplier onboarding remains an obstacle course of Excel sheets, emails and manual corrections. There is a better way: let your product data flow from the very first interaction.
Three hallmarks of modern supplier onboarding.
- Self-service portal or API: the supplier delivers data directly into your environment. 2) Templates that mirror your data model, providing validation at the source. 3) AI enrichment and mapping to ensure inconsistent input still maps correctly to your structure. By focusing on these areas, you ensure that good product data starts before your PIM.
How quickly can a new supplier go live?
By using standardised templates and a dedicated portal, onboarding can often be completed within days rather than weeks, helping you launch products faster.
From Excel to structured input.
Avoid requesting free-form Excel sheets. Instead, offer templates with predefined dropdowns, mandatory fields and specific units of measurement. This leads to fewer supplier errors and significantly reduces the correction work for your team. This approach ensures you maintain Control over product data from the first touchpoint, rather than fixing errors later.
Does this scale for hundreds of suppliers?
Yes: that is where the value truly lies. Automation ensures that the marginal cost and effort of adding a new supplier is significantly reduced, allowing you to expand your assortment without a proportional increase in manual work.
Automated validation and feedback.
Validation at the source provides suppliers with immediate feedback on missing attributes or incorrect values. Within the ConnectingTheDots Import & Onboarding component, this happens before the data ever touches your production environment, keeping your core database clean and governed.
AI enrichment to fill the gaps.
When a supplier fails to provide specific details, Workflows & AI can often derive the missing information from spec sheets or existing descriptions, all while adhering to your established data model and governance rules. This results in Less manual work for your content team, allowing them to focus on quality rather than repetitive tasks.