When the internet came into being, it changed our world as we see it today, with new possibilities opening up to us every single day. One such unique phenomenon is the emergence of eCommerce or online retail. At first, the idea seems absurd but fast forward to today its one biggest emerging spaces and the global economy.
Each day, experts break their head to think of new ways to provide a unique and fulfilling shopping experience to their users. We’re trying to apply newer technologies to provide a shopping experience that matches a real shopping experience, anything that makes online selling efficient is quickly integrated into the system. However, there are still grey areas where more the technology we employ is ‘archaic’ for the lack of a better word. We’re yet to find a definitive solution to those problems.
When it comes to eCommerce, an area that lacks the use of proper technology is ‘manual tagging of product attribute’, crucial yet left to archaic devices. With the help of this post, we’ll explore the inherent problems of manual product tagging. We’ll specifically cover its lack of scalability, lack of learning and the issue of human error. We’ll also outline its replacement, machine learning, as we throw light on its ability to accomplish an improved output without manual product tagging’s inherent issues.
The first concern against manual product tagging states that it doesn’t scale. Irrespective of the number of staff you hire to tag your products manually, accurate, useful and timely tagging of each product will continue to be a challenge. If you cannot guarantee that each tag is up to the mark, free of spelling mistakes, omissions at scale or redundancies or, then it probably time to consider an alternative.
The second issue with manual product tagging is that it doesn’t learn. Who so? Let’s consider the input method. Each time a person tags a product, it’s a unique and isolated action that must be repeated with every iteration on the product in question. Let’s look at an example; if I were to tag product X 2.0 and another product X 3.0 comes along next year, I’ll have to create brand new tags. Seems cumbersome, right? Now multiply the same cycle countless times over, not only for new versions of products but entirely new styles, themes, brands, etc. Long story short the tags get old fast.
It’s important to understand that the greater the volume, the more people, resources and costs required.
The third and possibly on the most significant issues with manual product tagging is that it’s incredibly prone to human error. If you consider the volume of tags created on any given day, week, month or even year, what are the odds they will have copies or spelling errors? Can you guarantee every product tag is accurate and useful? By just taking into account a simple fact that people think differently from one another, it’s safe to assume that errors will occur, and based on our experience at Okkular, we’ve come to see that they do.
So, if at any time, you’ve found yourself struggling with one of these three issues I’ve just outlined, then it may be an excellent time to consider leveraging AI and Machine Learning. Envision erasing every manually created tag attached to each of your products. AI-based tagging can replace them with relevant, uniform, and accurate, unique attributes within each product description that match your users’ preference.
Do you still manually tag products for your eCommerce website?
Tell us Why or Why Not?
Let us know your thoughts on the same, leave a comment or write to us at vkala@okkular.io
About Us:
Okkular is a Melbourne based Australian tech startup. We leverage AI and deep learning to create solutions for retailers to improve customer experience, reduce costs by automating labour-intensive task and drive sales with insightful automation and personalisation. A pioneer in our industry we have some of Australia’s biggest eCommerce and retail brands working with us the likes of Myers, Kookai, Blue Bungalow to name a few. To find out more about us and our offerings head to www.www.okkular.io
