Automating Product Naming: From Coded SKUs to Descriptive Titles in Google Sheets

In the fast-paced world of ecommerce, efficient catalog management is paramount. Businesses often rely on concise, coded item numbers (SKUs) to track products, but these codes are rarely customer-friendly. The challenge arises when needing to convert these internal codes into descriptive, unique product names for listings, marketing, and inventory management. Manually generating these names for hundreds or thousands of SKUs is a time-consuming and error-prone process. Fortunately, Google Sheets offers powerful tools to automate this transformation, turning complex item numbers into rich, descriptive product titles.

The Challenge: Deconstructing Coded Item Numbers

Many item numbering systems follow a structured format, combining various product attributes into a single string. For instance, an item number like 12PEX90 might represent a 1/2" PEX 90° Fitting. The core difficulty in automating name generation lies in parsing these composite codes. Unlike data fields separated by clear delimiters (like commas or hyphens), these codes often concatenate attributes directly, with varying lengths for each component. This makes it challenging to programmatically identify where one attribute ends and the next begins.

Consider an item number structured as (connection size)(material)(fitting type). The 'connection size' could be 12 for 1/2 inch or 2 for 2 inches. The 'material' might be PVC (3 characters) or CP (2 characters). The 'fitting type' could be 90 for a 90-degree elbow. The lack of consistent delimiters and the variable length of each segment (e.g., '12' vs. '2' for size, 'CP' vs. 'PVC' for material) are the primary hurdles. This variability makes simple text splitting insufficient and calls for more sophisticated parsing techniques.

The Strategy: Deconstruction, Lookup, and Reconstruction

An effective automation strategy involves three key steps:

1. Deconstruction: Breaking down the composite item number into its individual attribute components. This is often the most complex step due to the lack of consistent delimiters and variable segment lengths. Google Sheets' REGEXEXTRACT function becomes invaluable here, allowing you to define patterns to pull out specific parts of the string. For example, a pattern might first extract a numeric size, then a sequence of non-numeric characters for the material, and finally the remaining characters for the fitting type. The challenge is in crafting a robust regex that accounts for all variations.
2. Lookup: Once the individual components are extracted, each coded segment needs to be translated into its descriptive equivalent. This is achieved using lookup tables. A dedicated 'Decoder' sheet or named ranges within your Google Sheet can house these tables, mapping codes like 12 to 1/2", PEX to PEX, and 90 to 90° Fitting. Functions like XLOOKUP or VLOOKUP are perfect for this, efficiently retrieving the descriptive name for each code component. For optimal organization, it's best to create separate lookup tables for each attribute type (e.g., 'SizeTable', 'MaterialTable', 'FittingTable').
3. Reconstruction: Finally, the descriptive components are reassembled into a coherent, customer-friendly product name. Functions such as TEXTJOIN or CONCATENATE allow you to combine the translated parts, along with any necessary spacing or punctuation, to form the final product title. For instance, combining 1/2", PEX, and 90° Fitting with spaces results in 1/2" PEX 90° Fitting.

Advanced Google Sheets Techniques for Dynamic Naming

To handle the intricacies of variable-length codes and apply the transformation across an entire catalog, advanced Google Sheets functions are essential:

• REGEXEXTRACT and REGEXREPLACE: These are your primary tools for deconstruction. Crafting the right regular expressions is crucial. When segment lengths vary (e.g., 2-character vs. 3-character material codes), you might need to build conditional logic or nested regex patterns that try different lengths or look for specific character sets. For example, one might attempt to extract a 3-character material code, and if that fails, try a 2-character one.
• XLOOKUP and Structured Tables: Beyond simple lookups, structuring your decoder data into named tables (e.g., SizeTable[Code], SizeTable[Name]) makes formulas cleaner and more robust. XLOOKUP offers more flexibility than VLOOKUP, especially when lookup columns are not the leftmost.
• LAMBDA, MAP, and BYROW: For applying these complex deconstruction, lookup, and reconstruction steps to an entire column of item numbers, LAMBDA functions combined with MAP or BYROW provide a powerful, dynamic array solution. Instead of dragging formulas down, a single formula in the header row can process all item numbers, automatically expanding as new data is added. This significantly improves sheet performance and maintainability.
• Error Handling: Given the complexity, errors are inevitable. Functions like IFERROR, IFNA, and even ERROR.TYPE (as seen in advanced solutions) are critical for gracefully handling cases where a code component might not be found or a regex pattern fails to extract a part. This ensures your automated naming system doesn't break down when encountering unexpected data.

=let(
  getsplit, lambda(x, mc, let(
    s, regexextract(x, "^([\d\.]+)([^\d]{"&mc&"})(.+)"),
    size, textjoin(" x ", true, index(xlookup(split(index(s,1,1), ".")&"", SizeTable[Code], SizeTable[Name]))),
    hstack(
      size,
      xlookup(index(s,1,2)&"", MaterialTable[Code], MaterialTable[Name]),
      xlookup(index(s,1,3)&"", FittingTable[Code], FittingTable[Name])
    )
  )),
  map(K4:K, lambda(code, if(code="",, let(
    r, torow(getsplit(code, 3), 3),
    e, ifna(error.type(index(r,1,1)), 0),
    textjoin(" ", true, if(e<>0, getsplit(code, 2), r))
  ))))
)

(Example of a complex Google Sheets formula using LET, LAMBDA, REGEXEXTRACT, XLOOKUP, TEXTJOIN, and error handling to deconstruct and reconstruct item names dynamically.)

Best Practices for Your Decoding System

• Standardize Your Codes: While automation can handle some variability, the more consistent your internal item numbering system, the simpler and more robust your Google Sheets solution will be. Define clear rules for each segment.
• Comprehensive Decoder Tables: Ensure your lookup tables cover every possible code component. Missing entries will lead to errors in your automated names. Regularly audit and update these tables as new products or variations are introduced.
• Test Thoroughly: Before deploying your automated naming system across your entire catalog, test it rigorously with a diverse set of item numbers, including edge cases, new formats, and potential ambiguities.
• Document Your Logic: For complex formulas, add comments or a separate documentation tab explaining the regex patterns and lookup logic. This is invaluable for future maintenance and troubleshooting.

Beyond Naming: The Broader Impact on Ecommerce Operations

Automating product naming in Google Sheets extends far beyond just generating titles. It forms a critical backbone for several ecommerce operations:

• Improved Customer Experience: Clear, descriptive product names enhance searchability and help customers quickly understand what they are buying.
• Enhanced SEO: Rich product titles containing relevant keywords are crucial for search engine optimization, driving organic traffic to your product pages.
• Reduced Data Entry Errors: Eliminating manual input for product names drastically reduces the risk of typos and inconsistencies, leading to a more accurate and reliable catalog.
• Faster Product Onboarding: New products can be added to your catalog and online store much faster when naming is automated, accelerating time to market.
• Streamlined Inventory Management: Consistent naming conventions make it easier to track inventory, reconcile stock, and manage product variants across different systems.

Mastering these Google Sheet workflows for product naming is a powerful step towards a more efficient and error-free ecommerce operation. By automating this crucial catalog management task, you free up valuable time and resources, allowing you to focus on growth. Sheet2Cart can then take these meticulously crafted product names and seamlessly sync them with your Shopify or WooCommerce store, ensuring your online catalog is always up-to-date and accurate.

Related reading

• Streamlining Ecommerce Inventory: Mastering Google Sheets Data Synchronization
• Automating Product Catalogs: From Spreadsheet Nightmare to Seamless Sync