- Access to accurate internal and market data is crucial for retail success, especially in the $2.5 trillion fashion industry, where understanding market dynamics is key.
- Softline retail lacks industry-standard product naming, making manual categorization time-consuming and prone to human error, posing challenges for effective market analysis.
- An AI-powered retail intelligence platform addresses categorization challenges, outperforming human professionals in accuracy, consistency, and speed.
- AI’s efficiency in processing vast data, maintaining consistent accuracy over time, and freeing up retail professionals for creative tasks highlight its potential to drive better business outcomes in the industry.
As a retail professional, you know that access to internal and market data is essential for success. Fashion, a $2.5 trillion industry, relies on you as the retailer to carry the products consumers want, when they want them, and where they can find them. This means you need to collect all of the relevant data to understand what is going on in the market and how your offering stacks up.
Access to data is the first step, but ensuring you have accurate data is crucial. When you are collecting data on a mass scale, categorization becomes incredibly important to ensure both accuracy and usability.
Having access to data and insights about your products is the first step, but in order to effectively utilize this data, it needs to be categorized in a way that makes sense. Manually conducting this research is time-consuming, and human error can lead to incorrect categorization, meaning you miss key opportunities.
By partnering with an AI-powered retail intelligence platform, you can make smarter decisions and get the most out of your data.
The Need for Categorization in Softline Retail
What makes competitive analysis so difficult in softline retail is that there is no industry standard for naming products. When you work in hardlines, it is easy to compare, say, 65-inch TVs across the market, because they are all classified as such. However, when you shift to apparel, for example, and want to classify a green sweater, things get complicated. What is meant by sweater? And how many shades of green are included but called something completely different?
Because word recognition software alone does not make data usable, categorization through artificial intelligence is necessary.
How Do AI Classifiers Compare to Retail Professionals?
So, what is the real difference between relying on human professionals or trusting AI to do this work? Our team was wondering the same thing. A few years ago, we ran an experiment where we put our AI classifiers to the test. Fifty retail professionals were challenged to classify 57 products. We then calculated comparable performance measures for both the AI and the professionals.
The results showed that the classifier outperformed human respondents in identifying garment types by around 2.5 percentage points and 9.3 percentage points for footwear.
At the time, these classifiers were processing over 16 million products daily. Now, as we have continued to develop and better this technology, these same classifiers are processing a total of 170+ million products daily (and over five billion SKUs). This number is over ten times what they were doing just a few years ago. With these advancements, the difference in the percentage points would be astronomical.
Better Results With AI-Powered Retail Intelligence
Not only can AI retail technology process significantly more data than even the most productive human simply due to hours in the day, but the errors it could make are more consistent. With human categorization, there were not only more errors in quantity, but there were more types of errors as well.
Also, insights from this study showed that human predictions decline in quality over time. After ten minutes, accuracy rates dropped about four percentage points on average.
Because AI does not have an attention span, it can continue to process these products, more quickly, at the same rate. Based on the results, it would have taken the respondents two and a half hours to classify a full sample of ~1,300 products, which AI does in seconds. To classify all 16 million products available at the time, it would take a retail professional working five day weeks, for seven and a half hours a day, a total of 18 years to complete the task.
This means that today, with 170 million products processed by the AI retail intelligence platform daily, it would take retailers 191.25 years to complete the task that this software does daily.
Without AI technology, humans physically cannot even scrape the surface of how much data is available.
See the rest of the data from the study here.
Conclusion – Embracing AI Retail Intelligence in Your Business
AI proves to be faster, more accurate, more consistent, and, overall, more reliable. This reality means we should rely on AI to take the strain of the data analysis, leaving retail professionals freer to do the creative parts of their jobs, like designing unique assortments customers want, discovering innovative ways to market products, and creating excellent shopping experiences.
Instead of fearing the onset of AI, we should embrace it as an opportunity to become more efficient and drive better business outcomes.
Discover how EDITED’s retail intelligence platform can save you time and increase accuracy in your business here.