Did you read what I simply wrote? The lounge in Fukuoka was nice amazingly, given that it’s a local airport which is a brief 2½ hour airline flight. There’s the right souvenir shopping in the terminal, including Royce chocolate-covered potato chips and all kinds of various Kit-Kats. I believe we were the only Business Class passengers who were actually worked up about being aboard a Sanrio airplane – most everyone else seemed to notice as a minor curiosity/annoyance.

The airline flight attendants seemed genuinely happy that we were enjoying it a lot. On both our Sanrio plane tickets they packed us down with all types of fun swag to take home with us. Even the barf luggage and the safety credit cards are custom! Service begins with sparkling wine and hot towels. As I stated in the intro, they provide Din Tai Fung onboard, so while there have been other options on the menu, I wasn’t going to pick them. I had fashioned the Taiwanese Beef Noodle Soup, and it came with crab and asparagus in XO sauce, rooster marinated in Shaoxing wines, pickles, and steamed taro buns for dessert. My hubby opted for the Japanese menu that was nearly the same as a bento box – a dozen little interesting seasonal things all on the dish. Both our meals were delicious… but not “I’d happily pay to eat this on the ground” tasty.

If you do not, put those metrics aside to check out better ones to monitor right now. Quantitative data is simple to understand. It’s the numbers we monitor and measure–for example, sports activities ratings and movie ratings. As something is positioned soon, counted, or placed on a range, it’s quantified. Quantitative data are technological and nice, and (presuming you do the mathematics right) you can aggregate it, extrapolate it, and put it into a spreadsheet. Quantitative data doesn’t rest, although it can be misinterpreted certainly.

It’s also not enough for starting a business. To start out something, to discover a problem well worth solving sincerely, you will need qualitative input. Qualitative data is messy, subjective, and imprecise. It is the stuff of interviews and debates. It’s hard to quantify. You can’t measure qualitative data easily. If quantitative data answers “what” and “how much,” qualitative data answers “why.” Quantitative data abhors emotion; qualitative data marinate in it.

When you first get started with an idea, supposing you’re following core principles around Lean Startup, you’ll be looking for qualitative data through problem interviews. You’re talking with people–specifically, to people you think are prospective customers in the right marketplace. You’re exploring. You’re getting from the building. Collecting good qualitative data takes preparation. You will need to ask specific questions without leading prospective customers or skewing their answers. You must avoid letting your enthusiasm and fact distortion rub off on your interview subjects.

1. Who helped you
2. I. Mudayris, Friday Sermon, PA TV, 7 January 2005
3. Life Coach
4. The office at home is utilized as a location of business to meet or confer with clients

Unprepared interviews produce misleading or meaningless results. We cover how to interview people in Lean Analytics, but there have been many others that have done so as well. Ash Maurya’s publication Running Lean provides a great, prescriptive approach to interviewing. I recommend Laura Klein’s writing on the subject also. Sidebar: In writing Lean Analytics, we proposed the idea of scoring problem interviews. The basic concept is to take the qualitative data you gather during interviews and codify it enough to offer (hopefully!) new understanding into the total results. The purpose of scoring problem interviews is to lessen your own bias and ensure a wholesome dose of intellectual honesty in your time and efforts.

Not everyone agrees with the approach, but I am hoping you’ll take a peek and give it a try for yourself. I won’t spent lots of time on vanity metrics, because I think most people reading OnStartups understand these. As mentioned above, if you have a bit of data that can not be applied (you don’t understand how movement in the metric changes your behavior) then it’s a vanity metric and you should ignore it.