Shopee is one of Southeast Asia’s most dynamic e-commerce marketplaces — but its data isn’t always easy to use right out of the box. Product listings can vary across regions, sellers often upload inconsistent data, and attributes such as price, rating, or stock can fluctuate daily.
If you’ve ever tried analyzing Shopee data for competitive insights, you’ve probably faced the same problem: messy, inconsistent datasets. This guide walks you through how to clean and normalize Shopee data for accurate insights that actually drive decisions.
Why Cleaning Shopee Data Matters
Shopee data comes from millions of sellers using different naming conventions, languages, and formats. Without cleaning and normalization, you risk:
- Duplicate or outdated product records
- Misaligned price and stock data
- Inconsistent category or brand naming
- Missing product attributes (e.g. color, size, origin) These inconsistencies distort analytics — making it impossible to trust your insights on market share, pricing, or content performance.
Step 1: Identify Key Data Attributes
Before you start cleaning, define your core attributes. For most Shopee analyses, this includes:
- Product ID, title, and URL
- Shop name and ID
- Category and subcategory
- Price, discount, and stock
- Ratings, reviews, and sales volume Once you’ve set a data schema, every cleaning step will align around it.
Step 2: Handle Missing and Duplicate Values
Shopee data often includes missing fields, especially for new or inactive listings.
- Missing data: Fill values with nulls or default values for analysis stability.
- Duplicates: Drop records with identical product IDs or URLs.
- Outdated listings: Filter based on last_updated timestamps to ensure relevance. If you collect data over time, schedule automated jobs to refresh or remove obsolete listings weekly.
Step 3: Normalize Product Categories and Attributes
Shopee category trees vary across countries — and sellers may mislabel their items.
For example, “Beauty & Personal Care” might appear as “Health & Beauty” or even “Cosmetic Item” in your raw dataset.
To normalize categories:
- Map categories to a master taxonomy that fits your analytical needs.
- Use string matching or NLP to merge similar labels.
- Standardize attributes like brand, size, and color using controlled vocabularies. This step ensures your dashboard reflects true category-level performance — not fragmented silos of mislabeled data.
Step 4: Standardize Numeric and Text Fields
In Shopee’s multilingual environment, you’ll encounter various price formats, currencies, and text encodings.
- Convert all currencies to a base unit (e.g., USD or local currency).
- Normalize rating scales (e.g., 1–5) and convert text-based ratings to numeric.
- Clean text fields with regex or language detection tools to handle multi-language product titles.
Step 5: Validate and Enrich the Dataset
After cleaning, run validation checks:
- Are product IDs unique?
- Are category mappings complete?
- Do timestamps follow a consistent format? You can also enrich Shopee data with external attributes like seller tier, historical prices, or keyword ranking, turning your dataset into a high-value asset for competitive benchmarking.
Getting a Reliable Shopee Dataset
If you need large-scale, ready-to-analyze Shopee datasets — organized by category, keyword, or region, and updated continuously — consider using Easy Data’s services.
Easy Data provides:
- Structured, cleaned Shopee datasets across Southeast Asia
- Custom Shopee data scraping tailored to your analysis needs Whether you’re tracking price trends, brand performance, or market share, Easy Data helps you get the foundation right — so your insights are always built on accurate, normalized data.
Conclusion
Cleaning and normalizing Shopee datasets is not just a technical step — it’s a strategic one.
When your data is consistent, complete, and well-structured, your analytics become more accurate, your reports more credible, and your decisions more confident.
The process takes effort, but the payoff is clear: trustworthy insights that truly reflect the market.
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