Andrew Blackmon of The Black Tux shares how the company is using machine-learning models to streamline the fitting process
When Patrick Coyne and I launched The Black Tux, we never expected such an incredible response. Following a positive piece in GQ magazine, demand for our rental garments was so high that we found ourselves with a waitlist of over 30,000 new customers. This obviously created cause for concern—while the initial demand was a great proof-of-concept (we were clearly solving a need), we didn’t want our potential new customers to seek an alternative tux rental given we were out of inventory. Pre-launch, we had envisioned steady growth that we could measure over time, but the quick (and overwhelming) interest created a new obstacle that we were unprepared for. We took the time to listen to our customers and cater to a larger group than we had planned. To our surprise, customers were willing to wait until our inventory was sorted, which bought us time to resolve the issue. We quickly realized that in order to retain a healthy amount of inventory and cut down on potential shortages in the future, we needed to raise more capital, which we eventually did.
As we continue to grow as a brand, we plan for our inventory by taking time to consider our customers’ satisfaction. We want to be mindful how we can give our customers their best fit, and realize reviewing customer satisfaction could help us to account for and reduce error in our sizing recommendations. When we originally debuted our fit algorithm technology, it only used a few specific types of measurements to conduct a recommendation. We learned over time that to achieve the best fit for our customers, we needed to use a more sophisticated process. By using machine-learning models to identify patterns from our customers’ fit preferences, such as customer sizes, shipped sizes and exchanged sizes separately for each type of garment, we could create better fit recommendations. Our new fit algorithm debuts in holiday 2016, using updated machine-learning data to provide our fit specialists with a percentage of probability for each predicted size—by garment—to help them better tailor their fit recommendations for harder-to-fit customers. That way, our fit specialists can give a better data-driven fit recommendation to our harder-to-fit customers or customers that may be between sizes.
While the 30,000 person wait list was a shock, we were able to find the silver lining. We realized from overcoming this obstacle that customer satisfaction needs to be fully integrated when thinking about a user’s experience on our site. The additional round of funding we raised allowed us to scale our inventory assortment, enabling us to offer more varied options to our already dedicated customer base. As we widened our assortment by introducing new style options, we realized that our fit technology needed to be refined to cater to a variation of the diverse customers visiting our site. The updated fit algorithm will help us to retain both new and old customers by building trust in our fit recommendations, enabling The Black Tux to continue to provide customers with high-quality formalwear.
The Black Tux was founded in 2013 by Andrew Blackmon and Patrick Coyne, longtime friends who saw a white space in the suit and tuxedo rental market while preparing for Blockmon’s wedding in 2011. They founded The Black Tux to offer a suit and tux rental that doesn’t feel like one, offering a higher quality product, an exceptional fit, a seamless experience, and at a better price. Prior to founding The Black Tux, Blackmon built his foundation in tech startups. He received his MBA at HEC Paris and graduated from Pepperdine University.
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