1. Automate data transfer to digital catalogs

Using advanced data capture tools like OCR, MICR, OMR etc. ensures accuracy in eCommerce product data entry.

Technologies that streamline data transfer across stages in eCommerce catalog management:

• Optical Character Recognition (OCR) allows you to accurately extract product prices, product features, manufacturer details etc.
• Barcodes help maintain product hierarchy & accurately update product inventory in real-time.

Custom built automated data entry solution unearth product details from intricate texts. Prioritize automated data entry for:

• Repetitive and bi-direction data entry between multiple sources and catalog.
• Reports associated with catalogs for respective eMarketplaces.
• Workflows that facilitate flows of crucial data between internal and external environment.

2.Conduct data profiling

Data profiling allows you to effectively review if your data is complete, correct, unique, reasonable and consistent, thereby reducing errors progressively.

Domino’s, the eCommerce company selling pizza, had a team of 35 data scientists but still struggled to manage 17TB of data from more than 14000, locations. Data profiling helped them integrate 85,000 structured and unstructured data sources and have that single view of customers and global operations. It improved their business performance from logistics to financial forecasting.

With soaring data size, automated data profiling workflows are critical for eCommerce players to flag incorrect data and send it for review without manual intervention.

Unique data profiling for eCommerce companies:

3. Build robust import/export rules

For moving your eCommerce catalog database across systems you need strong data import/export rules.

Steps to import/export catalog data:

• Begin by importing/exporting individual catalog contents like data source connections, database schemas, logical models, drill-through reports etc.
• For importing/exporting specific elements from your catalogs, ensure that descendant elements are also selected according to robust field-mapping rules. Automated data entry solutions prove extremely helpful conducting import/export tasks while adhering to pre-defined rules.

4. Normalize third-party ERP data

Suppliers aren’t obliged to ensure data standardization. Due diligence of format checks, authenticity checks, spell checks etc. becomes online retailer’s responsibility when product data comes into central database from multiple suppliers.

Leveraging manual data entry may ensure data standardization at the entry point through in-field validations, however, use of scripts and macros enriches third-party data in-real time. Include data consistency as a KPI to evaluate supplier performance and maintain relationship.

5. Ensure accuracy in field-level mapping

Advanced attribute mapping ensures in-depth customization. Collecting your eMarketplace platform’s API meta-field data enables you to link attribute fields across eMarketplaces & the platform.

Here is how you can accurately map catalog headers to requisite field names:

• Review all data fields for mapping accuracy.
• Identify fields with mismatch data.
• Ensure that catalog headings are unique for each field.
• Set rules detect and delete duplicates for previously mapped fields.
• Analyze fields that can accept multiple values and tagged fields.

Practical field-level mapping when building a good catalog:

Source: Salesforce.com

6. Investigate product-variant matrix accuracy

Maintaining a product-variant matrix might not sound challenging, but volume of product data and intricate product hierarchy adds complexity to it. Therefore product-variant matrix accuracy must be validated on a regular basis.

Best practices to ensure accuracy of a product-variant matrix:

• Have flexibility to configure matrices globally or product-wise.
• Allow admin rights to configure matrices only to important stakeholders.
• Product-variant figures must tally with inventory numbers.
• For data mismatch, reconciliation & identification of root cause of discrepancies must be done.

Source: Americommerce.com

Before allocating variants to products; create a variant group. It is one of the most important elements in product-variant matrices.

Product-variant matrix allows multiple ordering variant combinations:

Source: Americommerce.com

7. Have a strong error identification, prevention and correction framework

Discovering errors and correcting them is not enough. Identifying the error and capturing the source of error is required to address errors happening repeatedly in entering accurate product information.

Potential sources of data errors that can affect eCommerce catalog:

• Data acquisition error: Error caused by inexperienced/ill-trained data entry operators.
• Attribution error: Fields are attributed wrong values making data inconsistent or incomplete.
• Variations: Catalog data doesn’t correspond to actual inventory figures.
• Data manipulation errors: Errors don’t creep in during the capture phase only. To suit catalog visibility requirements, you may have to manipulate original data, and while doing so you create an entry for errors.

Two-fold approach to drive analysis:

• Error cluster analysis: Narrow down the error sources by analyzing inaccurate data sets.
• Monitor progress for error patterns: Make data surveillance and improvement a routine to make effective and accurate error identification and prevention a part of your data entry process.
• Data-quality checklist: Conduct a robust data quality check for product data entered in to any eMarketplace catalog. Strong data-quality checklists act as a harbinger of successful catalogs, weak checklists degrade product information quality, resulting in even removal of your products from eMarketplaces.

8. Follow eMarketplaces’ guidelines

Prime eMarketplaces like Amazon clearly mentions in their selling polices and seller code of conduct, and violation of any of those principles can take away your privileges to sell. Align your data entry procedures not just along a given eMarketplaces’ guidelines, but also along the changes that eMarketplaces regularly make in the guidelines.

Adhere to eMarketplaces’ guidelines strictly:

• Validate data authenticity and relevance to deliver trustworthy customer experience.
• Listings, images, product tags, product features in your catalog should not be misleading.
• Understand how you can list your products on an eMarketplace, I.e., to list your products on Amazon, you must use excel-based inventory files.
• Check what are listing regulations for specialized products that require additional information.
• Check if the eMarketplace aids in image capturing and creating digital catalogs through eCommerce data entry service-providers.