Have you ever spent hours building a product catalog, only to find that your products are having a lot of bulk import issues? You might be having formatting issues or your file size is exceeding the maximum limit while uploading. It is too much stress, isn’t it?
In this blog, we will be discussing 8 common bulk product import issues and their solutions for Shopify users. Well then, let’s sort it out together.
Problem 1: Duplicates
The most common issue that every eCommerce marketer and Shopify user face is duplication. When you import products to your eCommerce store in bulk, there is a chance of duplicates. The duplicated product might have the same SKU or name with different variations. Now, how do you avoid duplicate products when importing products via CSV files to Shopify?
Find and Remove Duplicates using Unique Identifiers
There are many ways to solve the issues of duplications in bulk import. Here are some simple steps that you can do to solve this issue.
The first thing that you should do is to check if you have already imported the product. To check it, just navigate to Products > Products and find your product. If you see the product listed, click edit product and check if the details match what you entered in your CSV file. If they don’t match, then you need to edit the product manually and save it.
The second step is to create a unique identifier for each product. For example, if you are importing products by id, then change the id of each product to something unique. If product id is id, then change it to id_1, id_2, etc. This will solve the problem of duplicate product import issues.
Problem 2: Blank Fields
Another very common CSV import problem is missing data.
Incomplete data, such as bills with month and day information but no year information, is an example of incomplete data that a user can correct. Users can fill in other gaps in data with the help of the system they’re using.
Missing city/state data can usually be automatically appended if zip codes and contact role information are supplied. New contact role information must be manually rectified in the system, including suggestions from the systems based on matching relevant contacts or data.
In some circumstances, a person or another system must pull in incomplete data. For example, you can use public records to obtain real estate sales data that is lacking the original list date or comparable property information.
Find and Remove Blank Feilds
There are two ways to solve this issue:
1. Purchase matching products from other stores and import them into your eCommerce store.
2. Sign up to Apimio for free, Connect to your eCommerce store, and import these items automatically.
Problem 3: Products with different SKUs but the same name
Product names are critical components of any marketing communications strategy. A name can have a significant impact on a product’s perceived value, how well it sells, and the impact it has on the company’s brand.
In many cases, a company will have multiple products with the same name, but different SKUs. When this occurs, does it make sense to have multiple names for each? Should a company consolidate names? Or should the company keep the names as they are?
Products with different names but the same SKU is a problem when you move from a single product to a multi-product store. How to solve it properly?
Find and solve issue of products with different SKUs but the same name
There are many ways to solve this problem. Your chosen solution should depend on your product catalog structure and the way you want to treat it in the future, as well as the flexibility of your platform and any limitations that might exist in your e-commerce software.
To solve this issue, you can either set up different filters for different SKUs or display the product price based on the SKU. For example, if you install a filter for each SKU, then when a user searches for “Red T-shirt”, the product with SKU-A will be displayed and when the user clicks on that product, the product with SKU-B will be displayed.
Problem 4: Missing product variants in bulk import spreadsheet
A common problem in importing bulk products using a CSV file is missing product variants. When you try to upload the file to your Shopify store, you find that the product variants are missing. This problem occurs when there is more than one variant of a product. The bulk product importer cannot handle this situation, and only imports the first variant.
This problem is very common because most Shopify vendors support multiple variants. For example, an iPhone 6 has 16GB, 64GB and 128GB options. You can have all three at once, but the bulk importer will only upload the first one it encounters in the CSV file.
Find and refill missing product variants in bulk importing
For each variant, copy the following columns from your spreadsheet: SKU, name, price, color, weight, and inventory. As a result, you will have 5 additional columns for each variant: sku2, name2, price2, color2, and weight2.
Problem 5: Missing Data
Another very common Shopify bulk import issue is missing data. Incomplete data, such as bills with month and day information but no year information, is an example of incomplete data that a user can correct. Users can fill other gaps in data with the help of the system they’re using.
Missing city/state data can usually be automatically appended if zip codes and contact role information are supplied. New contact role information must be manually rectified in the system, including suggestions from the systems based on matching relevant contacts or data.
In some circumstances, a person or another system must pull in incomplete data. For example, public records could be used to obtain real estate sales data that is lacking the original list date or comparable property information.
Find and extract missing data
When dealing with missing data, a simple solution is to discard all of the data for every sample that is missing one or more data items. One disadvantage of this strategy is that the sample size will be lowered. This is especially important when the sample size is too small to produce meaningful results in the analysis. You may require additional sample data pieces in this scenario.
This issue is far more serious than it appears at first. For example, if 10% of the data on a 5-item questionnaire is absent at random, around 41% of the sample will have at least one question missing.
You can extract missing data by using the following functions:
DELBLANK(R1, s) – suffices the underlined range with the data in range R1 (by columns) missing any empty cells.
DELROWBLANK(R1, head, s) – fills the highlighted range with the data in range R1 skipping any row which has one or more empty cells; if the head is TRUE then the first row of R1 (presumably containing column headings) is always copied (even if it contains an empty cell); this argument is optional and defaults to head = FALSE.
DELROWNonNum(R1, head, s) – fills the highlighted range with data from range R1, removing any rows with non-numeric cells; If head is TRUE, the first row of R1 (probably containing column headings) is always copied (even if it contains a non-numeric cell); otherwise, head = FALSE is used.
Problem 6: Formatting Issues
When importing our products using excel, we faced a formatting issue. The content in the file did not have the right format. So we had to make some changes.
Formatting issue: When you uploaded the products from an excel sheet, some products were imported with different date formats. E.g. The entered date changes to a text, number, or another format of the date (for example, MM/DD/YYYY may change to DD/MM/YYYY).
Find and solve formatting issues
Choose a Date format by right-clicking the cell holding the Date, selecting ‘Format Cells,’ clicking ‘Date’ under Number à Category, and lastly choosing a Date format. (Example: DD/MM/YYYY format).
Problem 7: File Size
The file is simply too huge, which is one of the most typical CSV import problems. Too many fields or records in the file, too many columns, or too many rows can cause this. Limits set by the program that uses the file or the amount of accessible memory on the machine might both cause the import problem.
Divide the file up in smaller size
If your import fails due to file size concerns, you’ll need to go back and divide the file up into smaller bits. After that, you can upload them easily.
Problem 8: Non-digestible formats
One of the most common bulk import issues is the non-digestible format on Shopify. Simple format mismatches that require format normalizations, such as phone numbers or social security numbers, are examples of non-digestible formats. You require format normalization AND data normalization for complex format mismatches. For example, the date format is different than expected, inconsistent, or incorporates text rather than being normalized, as in “July 31st, 2020” or “June 20th 19.”
Convert non-digestable formats into digestable CSV format
It might be a time-consuming and hard task to reduce CSV import mistakes. There’s a better way: use an out-of-the-box CSV data importer to avoid these common blunders and speed up the procedure.
Apimio’s data importer assists users with varying levels of technical expertise. It instructs you on the kind of data that users can upload. Moreover, it also lets you know about the fields that users have to fill in. Configuration flags expand the Flatfile data importer by allowing users to add custom columns on the fly. This gives your clients complete control right away.
Integrating the Apimio data importer allows you to concentrate on differentiating essential aspects specific to your product’s experience, safe in the knowledge that the CSV import component is being handled properly. Using technology like Flatfile allows you to import data faster and more seamlessly for your customers, partners, and providers.
Easiest solution to all your bulk import issues: Use a PIM
Most databases when dealing with large quantities of data, you’ll need to import the data in bulk. The only problem is when you have to do this, you’re usually restrained in how many records you can import at one time.
Most database software will allow you to import the data in batches of 100 or 1,000 at a time. I’ve run into issues where we needed to import over 10 million records into a database and we had no choice but to do it one at a time.
Using PIM (Product Information Management) system, like Apimio, is a powerful solution for any company that wants to do better business. It helps you to manage your data and information in a systematic manner and gives you peace of mind by reducing the human error factor.
