Beta Byte is a new series for the early stage tech that lies somewhere between idea and traction. Exciting. Awkward. Raw. Are you in this phase?
What is the name of the company and/or product?
What is the customer value proposition? What business problem is being solved?
Fitparel.com takes the guessing-game out of size selection when purchasing clothes online. You enter the size of an item that you know fits well and we recommend similar items based on its dimensions. We indicate the size of the apparel you need to purchase and also the quality of fit between the fits-me-well item and the similar item.
What is the vision?
Buying books and electronics online is very easy. We want to bring this ease to shopping for clothes and other apparel. Trying an item in a store will always be the best method for determining fit. But it is time consuming and expensive. There should be some middle ground between that and the game of chance that is buying clothing online currently. That’s the niche we’d like to fill.
When did development start?
Data collection has been ongoing for more than a year. Development of the product’s core technologies has been moving forward for about 6 months.
Who is on the team?
Ravi Chityala and Nick Labello.
What is the target “launch” date?
We’ve soft launched a beta; please check it out and give us your feedback!
What is your greatest strength?
We are very well versed with programming math and science. This was handy for analyzing the sizing data from hundreds of different companies and distilling it into straightforward recommendations.
What is your biggest weakness?
We had little experience with marketing to the general public before this project.
What is the specific technology or combination of technologies, framework, languages, etc. that makes Fitparel run? Is there any IP?
Through a lot of laborious effort we collected sizing information, for more than 200 hundred brands. We performed statistical analysis to gain insight on the sizing characteristics of specific brands, and further developed what we call FitFunction. It is a qualitative assessment of how well an item you may be interested in buying will fit based on how close its measurements are to some similar item that you know fits well. The site itself is coded in Python/Django. Most of our ancillary code and analysis was also written in Python. We also have a patent pending for our method.
What is the a revenue model? How big is the market?
At this time, revenue will be generated by commission earned from affiliate marketing programs. FitParel allows you to take what you know about a companies product that fits well, and convert that knowledge to information about another brand. As such an inherent trait of FitParel is that it allows vendors to attract new customers away from their competitors. We hope to leverage that aspect to build deeper mutually beneficial partnerships with some companies.
What is one resource you could use that would take this to the next level?
Development time and marketing techniques. We have more ideas than we can implement on our own around our day jobs. We are interested in discussing the needs of the consumer. We are also interested in hearing from you about interesting marketing methods.
Is there anything else you would like to add?
Try fitparel.com and keep us posted with comments. If you have any interesting suggestions for marketing or features, we would like to hear from you. Send us an email to firstname.lastname@example.org or email@example.com.