Manual Product Tagging Vs AI Driven Product Tagging

Looking back it’s always inspiring to think of the trials our ancestors braved and the results they’ve achieved with the tools at their disposal. Without technology driving them they had to do everything the hard way. This begs the question, what could they accomplish with the advances of today? Fast forward to our time and think of the processes we run that feel like they belong in the past….what comes to mind? For us one of the primary thoughts that comes to mind concerns the practice of manually tagging products in the context of eCommerce, even when the solution to replace it plainly exists. In this post, we’ll explore the inherent problems of manual product tagging. Specifically, we’ll cover its lack of scalability, lack of learning and vulnerability to human error. We’ll also outline its successor, machine learning, as we highlight its ability to accomplish an improved output without manual product tagging’s inherent issues.

The first strike against manual product tagging revolves around the fact that it doesn’t scale. Regardless of the number of staff you hire to tag your products, it will continue to be a challenge to ensure every product is properly tagged in an effective and timely manner. If you can’t guarantee that each tag is up to standard, free of spelling errors, redundancies or omissions at scale then it may be time to consider an alternative.

The second issue concerning manual product tagging is that it doesn’t learn. Why? Consider the input method. Every time a person tags a product it’s a unique and isolated action in itself that must be repeated with every iteration upon the product in question. For example, if I were to tag product XYZ 2.0 and product XYZ 3.0 comes along next year then I’ll need to create brand new tags for it. Sounds cumbersome, right? Now multiply this same cycle numerous times over, not only for new versions of products but entirely new styles, brands, themes, etc. Long story short it gets old fast.

The third issue with manual product tagging is that it’s extremely vulnerable to human error. Considering the volume of tags created on any given day, week, month or year what are the odds they will contain copies or misspellings? Can you guarantee every tag is accurate and effective? Considering the simple fact that people think differently from one another it’s safe to assume that errors will occur, and based on our experience we’ve come to see that they do.

At any time you’ve found yourself dealing with one of these three issues I’ve just outlined then it may be a good time to consider leveraging Machine Learning. Imagine erasing every manually created tag attached to each of your products, and replacing them with uniform, relevant and accurate information based on user-generated content and unique attributes within each product description. Add these points to the fact that scalability and learning are inherent in machine learning’s design and you’ll have the basis of a strong argument as to why machine learning is worth considering.

Do you manually tag products for your eCommerce website? Why or Why Not?