How to knock a business pitch out of the park

Crrrack. The ball whistles through the air landing far over the fence, and the crowd goes wild… Well, it didn’t happen exactly like that, but I think a couple people at least clapped.  Last Thursday, I successfully pitched three months worth of brainstorming and work on Fantom ‘fit to a panel of entrepreneurs and investors as the final piece of the entrepreneurial journalism course. It was intimidating to sit up in front of strangers (especially ones who have all been very successful) and present to them an idea, my baby, delicately placed on the chopping block.

I knew my material well, so that part didn’t scare me. But these presentations are about so much more than your concept. They want you know know the market size, your target customer, and exactly how you plan to launch your product. In my case that meant answering questions like: Where am I going to get an initial stock of photos for people to tag and identify? How am I going to build an app? What will it look like and how will it be used? And maybe most importantly, how much will it cost?

I tackled these problems one by one and kept hearing the wise words of Gauri Manglik, the intelligent young co-founder behind Fondu, in my head: “the reason I succeeded was probably because I didn’t realize how much I didn’t know”. The more I plotted out exactly what I needed to do, the more I thought, “sure, I could do this!” Ignorance really is bliss.

When the time came to do the presentation, I really did find myself asking what I had been so worried about. It came together, I got some really good pointers and ideas on where to take Fantom ‘fit from here. The key thing I learned is that you don’t have to have all the answers, having the right questions can be just as valuable.

Introducing: Fantom ‘fit

Last week I launched Fantom ‘fit, a website for the project I’ve been working on all semester as part of an entrepreneurial journalism class with Adam Penenberg.

The idea is based on a reoccurring problem. I’m inspired by high fashion glossies just like the next design-obsessed woman, but I’m living on a Campbell’s-soup-grad-school-budget. When I see something I actually want to buy, it’s not in a magazine, it’s on the street.

The problem is there are only two ways to figure out what it is. You can ask. But you might be too embarrassed or (especially in New York) people won’t want to tell you where they scored their unique clothes because they don’t want you biting their style. The second option is to search online, but that’s usually a big time suck, personally I rarely find what I’m looking for.

It turns out I wasn’t the only one with this problem. The team from the Lean Start-up Machine had us test our customer assumptions out on the street and of the over 20 people we asked, every single one of them had this problem.

The solution I developed is now functioning as a Twitter account and a blog, both called Fantom ‘fit, where followers can tweet or email photos of items they see out on the streets that they want to purchase for themselves. I RT and post the photo on the blog to see if anyone else can identify it while I get to work doing my own research. If no one can find it in a week (users can also debate in the comments section and give tips on whether they think it’s vintage or one-of-a-kind) , the post will be updated with alternative and similar items.

Here’s how it works:

How Fantom Fit works on Twitter

See. It’s like magic minus the messy fairy dust.  The skirt, available at Urban Outfitters, was found thanks to a savvy Twitter follower. I had done my own searching, punching in every linguistic variation of “midi teal cheetah print skirt” the English language had to offer, only to turn up tens of thousands of results, none of them correct. Once again, the human eye triumphs over computers! This sort of image labeling is how Google Images improved their labeling system and now scientists are even using it to identify solar storm data.

I’m hard at work to expand this into a website with a forum and galleries. I’m going to build a web app to make the service convenient and portable. Eventually, users will get rewards for identifying objects in photos that can be redeemed in an online shop or at retailers worldwide. My hope is that users will be able to rack up points and build a reputation and connect with other stylists and fashion-savvy street-style spotters from around the globe.

Right now you can help me in several ways. Check out the website and let me know what you think. This baby is still, well, a baby. All feedback is welcome. I am a team of one right now, making it all the easier to pivot if need be. Also, please follow Fantom ‘fit on Twitter and send in your photos! The more followers it has, the better the chances the photos people are tweeting will be identified. I promise, this account WILL NOT spam you. Lastly, please tell anyone you think might be interested in this project. Tweet about it, post it on your Facebook. This site is especially geared toward fashion-lovers, photographers, bloggers, stylists, designers… anyone who has a keen eye for fashion and style.

Building a Lean Mean Startup Machine

Lean Startup Machine Customer Validation for Fantom Fit

Last week, the team from the Lean Startup Machine came to my entrepreneurial journalism class to share their tools and help us test out the riskiest assumptions about our customers and steer our business models accordingly. It was a great chance to learn about our customers and receive feedback outside the classroom.

My idea, is for a fashion app/website that allows users to upload street photos of people wearing things they want to buy and other users to help identify the products seen in the photos. Users will get points when they correctly identify items and score points redeemable for discounts and gift cards at retailers worldwide. [UPDATE: You can now visit Fantomfit.com and follow Fantom 'fit on Twitter to learn more!]

First we defined what all the assumptions about a Fantom ‘fit customer were. What do we believe is true about this customer? We assumed the following:

1. Under 40

2. Fashion-conscious

3. Has mobile phone

4. Want to BUY the product

5. Make purchases at least once a month

6. Visit Everyday

7. Will tag photos for 20 percent off

After we decided on this list we voted on what we each thought was the “riskiest assumption” meaning, the one most likely not to be true. The votes tallied, the riskiest assumption was that customers actually want to buy the products they see people wearing on the street. The next step was to develop a short list of questions we would ask strangers out on the street to test whether this was true. These were the questions we asked: (You can answer these poll questions too and see how online customers answers compared)

We polled 25 people in the area near Astor Place and St. Marks in Manhattan. Every single person responded yes to the first question. What was more surprising was that each of them could also recall a specific time that this happened and the majority were within the last several days.  I knew I was onto something, but we were all surprised at the huge response.

Survey-takers also told us that the only way they could figure out what the products were was to ask or do an exhaustive Google search. Sometimes that worked and sometimes it didn’t.

The best part was when the survey-takers were asked for a better solution, some of them actually said (unprovoked) an app that could upload photos might work better. A cluster of six stylish high school kids near St. Marks came up with this and when we told them that’s exactly what were working on they wanted to sign up. Brilliant!

Overall this was a great way to test out our ideas in the real world and it reinforced some of the assumptions I had about my customer. The next step is to assess the riskiest assumptions in order and test each of them the same way. If the assumption isn’t validated, it presents an opportunity to pivot the business model and reassess how the product can better meet customer needs.