Many questions often surround the launch of a new item, and getting the right answers has never been more critical. The pandemic has had an immense impact on the fashion retail industry, accelerating the shift to e-commerce, uprooting typical customer behavior, and halting delivery processes. Fashion brands are forced to adjust their strategies, and betting on a "maybe" item is no longer an option. They need to produce that which will perform well. This is where ML-powered new item evaluation can come in to save the day.
What is ML-powered new item evaluation?
In fashion, new items are added at least every season. In fast fashion, even quicker than that. Typically, companies forecast the future demand for any item based on historical data of its sales. However, when introducing a new item, brands don't have that data and base their decisions on personal opinion, historical data of similar items, or their gut. This doesn't usually provide highly accurate predictions, leading to overstocks, waste, increased costs or understocks, loss of customers, and missed revenue.
Wouldn't it be great to avoid these hurdles and know how any new item idea will perform? Especially in the post-covid world where customers are as selective as ever and will not wait around for you to stock up on their favorite item again. Artificial intelligence and machine learning have made that possible. ML-powered new item evaluation uses relevant data from the internet, retail, and runways to predict how customers will meet any new addition. How will it perform once it's added to the assortment? What is its potential? Will it be in demand or wither on the shelves?
What makes GFAIVE's new item evaluation special?
Once the pandemic hit, it was clear that fashion retailers would be faced with unprecedented changes in customer behavior and logistics issues. That's why our team focused on perfecting smart fashion forecasting tools and started offering accurate predictions to fashion brands. GFAIVE's models evaluate your designs for each geographic region and base their forecasts on external and internal data using product attributes.
How does it work?
We collect trends data from fashion influencers, Instagram images, trend reports, and designers at the forefront of the industry.
We analyze past sales performance and the past data from social media (by parsing previous Instagram posts).
Then, the process of extracting product attributes and categories occurs from post descriptions and/or hashtags.
Finally, we can see the correlation between sales of items with those product attributes and the number of engagements of a post with the same characteristics. Plus, we see the time delay from the moment of post publishing to the moment of peak sales and other critical moments in the item's lifecycle.
You may be thinking, "Alright, but this sounds complicated for me to use. I need clear and efficient forecasts!" We've thought of that too, and the reality is - our platform is straightforward to use. First, a designer or merchandiser uploads an image (or a selection of photos) to the platform. Our neural network then recognizes what is on the image, including colors, sleeve length, and patterns. It then places this image into a cluster. Finally, after mixing with external trends, it works like a scanner for the model and prepares a prediction on how this particular item will perform next season. Thus, allowing you to model scenarios of how variations of the same item may perform before launching full-scale production!
To conclude, we're living through an unprecedented time, and businesses from all over the world have had to adapt. Whether or not the pandemic is behind us soon, fashion retailers will always require highly accurate predictions of how future items may perform. Armed with such knowledge, they'll be able to increase revenue and brand loyalty and significantly step up their sustainability efforts and reduce the amount of waste.