Jason Schwartz – Robber Baron Blog

Matchbook Launches!

Posted on April 27th, 2011 at 14:00 by Jason Schwartz in Location Based Space, Matchbook | 6 Comments »

Today, Matchbook is available worldwide in the app store. You can download it here.

Matchbook is a dead simple bookmarking application for places like bars, restaurants, and shops. The idea is akin to taking a matchbook from a restaurant or bar so you remember to return to that spot.

Lets say that a friend recommends that you should stop by the Ace Hotel:

You search for Ace Hotel or click “I’m walking by it” if you’re nearby
Matchbook pulls up Ace Hotel and you simply click “bookmark this place”
At this point you can add a note or tags to describe the place
Your bookmarked places are automatically organized by neighborhood and viewable on map
You can run a search such as, “I’m looking for a place in the West Village that’s good for a date”. It will return the best places from your bookmarks, as well as the top saved places from all the other users.

That’s Matchbook in a nutshell, but there are a few other cool features we built in to make sure the experience is silky smooth.

There is a bookmarklet to instantly send places from your web browser to the app, similar to Instapaper.
Registration/Login is not required to use the app
All bookmarks are stored locally so the app works in the subway
You can share a place with a friend via text message. It includes the name and address in the body of the text, automatically creates a Matchbook account for the your friend, adds the place to their account, and provides a link where they can retrieve it.

We believe in building software that has its roots in the already occurring behavior of people outside the tech industry. Before we wrote a single line of code, we did a lot of user research. We knew that females have been slow to adopt location based services due to privacy concerns. We wanted to find out what women would be willing to do doing around location.

After speaking with hundreds of women we found a very pervasive pattern. A huge percentage of them already had some method of bookmarking places, such as emailing themselves or writing it in their notepad. Despite doing this work, they explained that the result wasn’t useful because the places weren’t centralized or organized on a map. Matchbook solves this problem.

Since privacy was the number one reason women shied away from other location services, we were very conservative with social features. We see this as a growing trend with apps like Path, that are socially cautious until there are better solutions for the elastic social network problem.

As we move forward, “where you are now” is only going to be one part of the location-based space. We are asking the question “where do you want to go in the future”. Ultimately it’s a different way to capture location data that will be used to tap the $140 billion dollar local ad market.

You can download Matchbook here

Lean Startup Guide to Building Software For Normals

Posted on March 22nd, 2011 at 11:14 by Jason Schwartz in Lean Startup, Matchbook, Normal People | 3 Comments »

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Cross posted from Matchbook

Next week we’ll be releasing an app called Matchbook. Signup to be notified when it’s out. We’re a proponent of the lean startup methodology, so we wanted to share the process we used to get this app out the door.

We like to build software that mimics real life. The goal of software should be to make already occurring behavior easier, not to create new behavior. So, if you’ve ever taken a matchbook from a restaurant to remember it later, then you have an understanding of what this app does. Matchbook is a dead simple bookmarking application for places. When someone gives you a recommendation about a bar, restaurant, or shop you can bookmark it. The app will organize those places so you can make a fast decision about where to go out. We’ve heard it described as Delicious or Instapaper for places.

Step 1: Problem Identification

I called up a buddy I often discuss tech with and said, “Something is nagging me about the location based space. It doesn’t feel like mainstream America is quite ready for the check-in.” The question became, “What type of location based activities are normal people ready for?”

Step 2: How We Answered That Question

Mobile location research should be preformed in real locations, outside of the office. To answer our question we sought out feedback from normal people instead of from the tech industry.

To achieve this we planted ourselves at a bar, approached groups of people, told them we were about to build an app, and asked some questions. We also used the dating site HowAboutWe.com to go on dates so we had the undivided attention of a female for market research. No judgment; we paid for dinner. This turned out to be a great place to do market research because:

There was a high concentration of normal people in our target market, which we identified as 20s-30s.
Groups of friends could more easily talk about how their real-life interactions work.
It was easier to motivate ourselves to do market research since it involved going to a bar, drinking, and getting girls phone numbers.

This is what we found:

A very large percentage of urban women have some method of bookmarking places. This includes emailing themselves, TXTing themselves, writing in the notepad app on their phone, adding an event in their calendar, and keeping a spreadsheet or google doc.
The percentage of women that did this increased when they were an iPhone owner.
Despite taking the time to remember places, most of them admitted that doing so was ultimately useless since the data was uncentralized and unorganized.
Restaurants, bars, and shops were the most commonly bookmarked places.
These same women were generally uncomfortable with the idea of broadcasting their location. Sharing and social media in general did not seem to be something they were thrilled with.

Step 3: Prototyping

We started wireframing the app in Omnigraffle. We spent most of our time removing features until we had what we thought might be the minimum viable product. We went back out to the bars and tested them. We rigged up a clickable prototype with a great app called Interface that allowed us to do our user testing. We would get a nights worth of feedback, re-do our wireframes, and then go back out. We iterated through this process about 30 times.

We kept going until:

People stopped getting confused
They could get through the app without any friction
They stopped asking questions about how to use it
They started saying “Wow, I would use this!” without prompting.

Step 4: Pivot 1

When we began, we thought that Matchbook would be a social app. We envisioned it helping people make plans, share tips, or share bookmarked places. As we talked to more women, we found that they were a little burned out on social and a more then a little concerned about sharing their location. The number of women that perfectly articulated the social circles problem was amazing. As a result, our wireframes pivoted away from social and became a personal app. We will probably add in social in the future, but we need to rethink exactly how that should work for this market.

Step 5: Minimum Viable Product

The MVP is a bookmarking application for places. The user can:

Search by text or click “I’m Walking By It” to find the place they want to bookmark.
The user can bookmark, add a note, and add tags.
Bookmarks are auto-organized by neighborhood and on a map
Users can run a search by tag. It will return places that match their search, as well as the top places from all the other users.
There is no social component at this time.

Step 6: Development

Once we had our MVP, we moved onto the development phase. We outsourced the entire thing, which involved a good chunk of time spent iterating through developers instead of code. That will be the subject of another post, but in the end we found a great team. My co-founder and I developed the entire thing for about $10,000, paid for out of our savings.

Step 7: Launch

A key problem with building an iPhone app is that Apple only allows 100 slots for beta testers. This was rough as we tried to test our assumptions. We needed to ASK all of our users to download it, which skews the data.

After some brainstorming we came up with an alternative. We are going to launch in the Canadian app store first. Since we can’t do a private beta, this will be our beta test. People in the US can’t see the Canadian app store so we will localize things there. We’ll use our Canadian launch to get feedback and gather metrics.

Once we’ve iterated based on that feedback we’ll launch a more polished product in the US app store. The idea is to couple the download traffic from launch PR, with the iTunes Recently Released app list. This concentration of downloads will hopefully bump us onto a Top Downloads list in our category.

These are the assumptions our lean process has yielded. We will be testing these in Canada next week:

There is a ‘need’ to capture a recommendation made for a place.
A large population of women in urban areas currently saves place recommendations by some method.
There is a ‘need’ to consult a repository of past recommendations when making a decision on where to go out.
The output of the work currently being done to capture recommendations is not useful for making a decision on where to go out.
That pain-point is acute enough for people to change their current behavior for a better solution.

Step 8: Customer Development

We started with this step at the same time as Step 3. We decided that offering local deals is the best bet for monetizing a location based startup. Since we don’t have the money for a sales force we began our customer development process by speaking with group buying sites. We found out that they:

Loved the idea of accessing users that have indicated where they want to go.
They want to send users deals for places that they have bookmarked.
They were willing to pay a fairly high cost-per lead or a reasonable flat fee for pushing a deal onto our platform.

To better understand the group buying market, we offered to help out a NY based group buying site with their metrics. This gave us enormous insight into the types of challenges our customers face, and we learned great tactics for optimizing daily deal sales.

That’s it for now. The app will be out in Canada in a week, and out in the US shortly after.

Thank for reading,
-Matchbook